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Arginine methylation of histone and non-histone proteins is involved in transcription regulation and many other cellular processes . Nevertheless , whether such protein modification plays a regulatory role during apoptosis remains largely unknown . Here we report that the Caenorhabditis elegans homolog of mammalian type II arginine methyltransferase PRMT5 negatively regulates DNA damage-induced apoptosis . We show that inactivation of C . elegans prmt-5 leads to excessive apoptosis in germline following ionizing irradiation , which is due to a CEP-1/p53–dependent up-regulation of the cell death initiator EGL-1 . Moreover , we provide evidence that CBP-1 , the worm ortholog of human p300/CBP , functions as a cofactor of CEP-1 . PRMT-5 forms a complex with both CEP-1 and CBP-1 and can methylate the latter . Importantly , down-regulation of cbp-1 significantly suppresses DNA damage-induced egl-1 expression and apoptosis in prmt-5 mutant worms . These findings suggest that PRMT-5 likely represses CEP-1 transcriptional activity through CBP-1 , which represents a novel regulatory mechanism of p53-dependent apoptosis .
Appropriate cellular response to DNA damage is critical for the maintenance of genome stability that is fundamental to the survival and development of organisms . In response to DNA damage , eukaryotic cells can activate checkpoint signaling pathways that are orchestrated by DNA damage sensors , mediators , transducers and effectors . In mammals , the damaged DNA is cooperatively recognized by the phosphoinositide 3-kinases ATM or ATR and the protein complexes Rad9-Rad1-Hus1 ( 9-1-1 ) and RFC-Rad17 [1] . ATM and ATR initiate a phosphorylation cascade that eventually leads to the stabilization of the tumor suppressor p53 . By selectively activating cell cycle controlling genes such as p21 and proapoptotic genes such as Bax , Puma and Noxa , p53 can induce either cell cycle arrest that allows the repair of damaged DNA , or apoptosis that eliminates those over-damaged cells in which DNA lesions are irreparable [1] , [2] . In addition to phosphorylation , the activation of p53 also involves other protein modifications including methylation and acetylation that are implicated in increasing p53 protein stability [3] . Moreover , the activity of p53 is also regulated by transcription coactivators such as histone acetyltransferases p300 and CBP which are recruited by p53 to form transcription initiation complex to facilitate the transcription of p53 target genes [4]–[6] . DNA damage sensing and signaling pathways are evolutionarily conserved across diverse species ranging from C . elegans to humans . In C . elegans , the p53 homolog CEP-1 acts as a key effector to mediate germ cell apoptosis triggered by ionizing irradiation [7] , [8] . Following DNA damage , CEP-1/p53 transcriptionally activates the cell death initiator EGL-1 , a C . elegans BH3-only protein analogous to the mammalian p53 targets Puma and Noxa , leading to the activation of the core cell death pathway that is essentially controlled by several evolutionarily conserved apoptotic factors , including the Bcl-2-like antiapoptotic protein CED-9 , the C . elegans Apaf-1 homolog CED-4 , and the caspase CED-3 [9] , [10] . In addition , inactivation of components in the checkpoint signaling pathways also gives rise to abnormal apoptosis of germ cells following DNA damage . For example , mutations in mrt-2 , hus-1 and clk-2 , which encode the C . elegans homologs of mammalian Rad1 , Hus1 and Rad5 , respectively , suppress cell cycle arrest and germ cell apoptosis induced by γ-irradiation [9] , [11] , [12] . Similarly , inactivation of atm-1 and atl-1 , the C . elegans homologs of mammalian ATM and ATR , respectively , suppresses both DNA damage-induced cell cycle arrest and apoptosis in C . elegans germline [13] . These facts indicate that the DNA damage signaling pathway leading to apoptosis in C . elegans is essentially similar to that in mammals , which makes C . elegans an excellent model organism for identifying novel components involved in cellular response to DNA damage . Protein arginine methyltransferases ( PRMTs ) are a family of proteins that catalyze the addition of one or two methyl groups to the guanidine nitrogen atoms of arginine , a process of posttranslational modification termed protein arginine methylation [14] , [15] . PRMTs have been found in diverse species and 11 members are identified in mammals [14] , [15] . Depending on the methylated forms of arginine residues of their substrates , mammalian PRMTs are classified into two types . The type I PRMTs , including PRMT1 , 3 , 4 , 6 and 8 , catalyze asymmetric dimethylation of arginine residues ( aDMA ) . In comparison , the type II PRMTs , including PRMT5 , 7 and 9 , catalyze symmetric dimethylation of arginine residues ( sDMA ) [15] . In recent years , a growing body of evidence indicates that protein arginine methylation plays important roles in regulating multiple cellular processes such as transcriptional regulation , signal transduction , DNA repair as well as RNA processing [14] . The regulation of transcription by PRMTs generally involves the recruitment of these proteins to promoters by transcription factors and methylation of histone tails [14] , or methylation of transcription coactivators such as p300/CBP [16] , [17] and transcription elongation factors such as SPT5 [18] . Recent studies have also shown that protein arginine methylation is likely involved in cellular response to DNA damage . For example , it has been reported that PRMT1 , CARM1/PRMT4 and p300 function cooperatively to promote p53 transcriptional activity on the cell cycle controlling gene GADD45 [19] . In addition , it was found that PRMT2 promotes apoptosis by negatively regulating NF-κB independently of its methylation activity , which is also implicated in DNA damage-induced apoptosis [20] . Nevertheless , whether other PRMTs are involved in cellular response to DNA damage , especially in p53-dependent apoptosis , still remains largely unknown . Moreover , aberrant expression of PRMTs are found to associate with a wide variety of human diseases including many cancers , but the underlying mechanisms still need to be further elucidated [14] . To better understand the signaling mechanisms underlying cellular response to DNA damage , we took advantage of the genetic tractable model organism C . elegans to determine whether PRMTs and protein arginine methylation are involved in p53-dependent apoptosis . We found that inactivation of prmt-5 , which encodes the C . elegans homolog of mammalian PRMT5 , significantly increased germ cell apoptosis following ionizing radiation . Our genetic analyses indicate that prmt-5-mediated apoptosis is dependent on cep-1/p53 and requires the core cell death pathway . Furthermore , we provide evidence that CBP-1 , the C . elegans homolog of mammalian p300/CBP , acts as a transcription coactivator of CEP-1 and can be methylated by PRMT-5 . The formation of a tripartite complex among PRMT-5 , CEP-1 and CBP-1 and the methylation of CBP-1 by PRMT-5 likely repress the transcriptional activation of the cell death initiator EGL-1 in response to DNA damage . Our findings not only demonstrate that PRMT-5 is a novel component critical for DNA damage-induced apoptosis in C . elegans , but also suggest a negative regulatory mechanism underlying p53-dependent apoptosis .
To identify putative protein arginine methyltransferases in C . elegans , we used the sequences of individual mammalian PRMTs to search the C . elegans genome database . 6 putative open reading frames either containing conserved motifs of arginine methyltransferase or sharing other homology with mammalian PRMTs were obtained . Based on their sequence similarity to mammalian PRMTs , we designated these genes prmt-1 , -2 , -3 , -4 , -5 , and -6 , respectively ( prmt represents protein arginine methyl transferase ) ( Figure S1A , S1B ) . Next , we examined whether these prmt genes are involved in C . elegans programmed cell death using RNA interference ( RNAi ) to knock down their expressions . Our time-course analyses of both embryonic and germ cell corpses indicated that inactivation of these genes did not obviously affect the cell death profiles , which suggests that protein arginine methylation may not be involved in developmental cell deaths in C . elegans ( data not shown ) . However , when irradiated with γ-ray , animals pre-treated with prmt-5 RNAi showed an obvious increase of germ cell corpses compared with control RNAi-treated worms , suggesting that prmt-5 is likely involved in DNA damage-induced apoptosis ( Figure S1C ) . C . elegans prmt-5 gene is defined by the open reading frame C34E10 . 5 located on the linkage group III , which encodes a protein of 734 amino acids . The predicted worm PRMT-5 protein shows the highest sequence similarity to human type II protein arginine methyltransferase PRMT5 ( 34% sequence identity and 48% similarity , respectively ) . The sequence similarity is particularly strong between the residues 105 to 730 of C . elegans PRMT-5 and residues 58 to 633 of human PRMT5 . C . elegans PRMT-5 also shares homology with yeast Skb1 and Drosophila Dart1 ( Figure S2 ) . Previously , a genome-wide RNAi screen showed that inactivation of prmt-5/C34E10 . 5 could cause increased level of spontaneous mutation in C . elegans , suggesting that prmt-5 is important for genome stability [21] . However , it is not known whether prmt-5 also plays a role in DNA damage-induced apoptosis . To further determine this , we analyzed a mutant strain prmt-5 ( gk357 ) containing a deletion of 522 bp that removes a small region of the exon 1 and the whole exons 2 and 3 of prmt-5 genomic locus ( Figure 1A ) . Using an antibody generated against recombinant PRMT-5 , we detected the expression of PRMT-5 in wild type but not in prmt-5 ( gk357 ) mutants , indicating that prmt-5 ( gk357 ) is likely a strong loss-of-function allele ( Figure 1A ) . prmt-5 ( gk357 ) animals display no obvious developmental defects except that the growth rate is slightly lower than that of wild-type animals . Similar to prmt-5 ( RNAi ) worms , prmt-5 ( gk357 ) animals do not show discernible defects in developmental cell deaths ( data not shown ) . However , when exposed to γ-irradiation ( IR ) , prmt-5 ( gk357 ) mutants exhibited a strong increase of germ cell apoptosis compared with that in wild-type animals . The IR-induced apoptosis in prmt-5 ( gk357 ) animals occurred mostly in the germline meiotic region containing pachytene-stage cells and the apoptotic cells displayed disc-like structures which were morphologically indistinguishable from those in wild-type worms ( Figure 1B ( a–b ) ) . A further staining of irradiated animals with acridine orange ( AO ) , a fluorescence dye that preferentially stains cell corpses internalized in engulfing cells , also indicated that prmt-5 ( gk357 ) worms contained significantly more AO-positive germ cell corpses than wild-type animals ( Figure 1B ( c–f ) ) . Collectively , these data indicate that prmt-5 loss-of-function mutation leads to excessive germ cell apoptosis following γ-irradiation . We evaluated the dosage effect of IR on germ cell apoptosis in prmt-5 ( gk357 ) animals by exposing them to different doses of γ-irradiation . In both wild-type and prmt-5 ( gk357 ) animals , IR induced an increase of germ cell apoptosis in a dose-dependent manner , but the number of germ cell corpses in prmt-5 ( gk357 ) worms was significantly higher than in wild-type animals at all tested irradiation doses ( Figure 1C ) . The appearance of germ cell corpses in prmt-5 ( gk357 ) mutants reached a peak 36 h post irradiation of 120 Gy , which was about 2 times of that in wild-type animals ( Figure 1C and 1D ) . To determine whether other DNA-damage agents can also induce excessive germ cell apoptosis in prmt-5 ( gk357 ) mutants , we treated prmt-5 ( gk357 ) animals with ethylnitrosourea ( ENU ) , a DNA–alkylating agent that can cause a broad spectrum of DNA lesions . Our results indicate that ENU induced elevated germline apoptosis in both wild-type and prmt-5 ( gk357 ) animals in a concentration-dependent manner . Moreover , significantly more germ cell corpses were observed in the prmt-5 ( gk357 ) mutants than in wild-type animals at all tested ENU concentrations ( Figure 1E ) . Importantly , the excessive germ cell apoptosis observed in the prmt-5 ( gk357 ) mutants following γ-irradiation was strongly reduced when a GFP::PRMT-5 fusion protein was overexpressed under the control of the pie-1 promoter ( Ppie-1 ) which specifically drives gene expression in germ cells [22] , indicating that germline-specific expression of PRMT-5 rescued the germ cell apoptosis phenotype in the prmt-5 ( gk357 ) mutants ( Figure 1F ) . Taken together , these findings suggest that prmt-5 likely antagonizes DNA damage-induced apoptosis in C . elegans . Several lines of evidence have shown that germ cell apoptosis induced by DNA damage requires the core cell death pathway because mutations of genes essential for programmed cell death , including ced-3 , ced-4 , ced-9 and egl-1 , block such cell death . To determine whether the strong increase of IR-induced germ cell apoptosis in prmt-5 ( gk357 ) mutants is dependent on the core cell death pathway , we generated prmt-5 ( gk357 ) ;ced-3 ( n717 ) and prmt-5 ( gk357 ) ;egl-1 ( n1084 n3082 ) double mutants and found that germ cell apoptosis was barely induced by IR in these worms ( Figure 2A ) . Moreover , in prmt-5 RNAi-treated ced-4 ( n1162 ) loss-of-function and ced-9 ( n1950 ) gain-of-function mutants , IR-induced germ cell apoptosis was either abrogated or strongly suppressed as compared with that in prmt-5 RNAi-treated wild-type animals ( Figure 2B ) . These results suggest that prmt-5 acts through the core cell death pathway to regulate DNA damage-induced apoptosis . It was reported previously that mutations in mrt-2 , hus-1 and clk-2 , which encode C . elegans homologs of mammalian checkpoint signaling components Rad1 , Hus1 and Rad5 , respectively , inhibit both DNA damage-induced cell cycle arrest and apoptosis in C . elegans [9] , [11] , [12] . The progeny of checkpoint mutants are also hypersensitive to IR treatment owing to defects in DNA repair [12] , [23] . Although prmt-5 ( gk357 ) animals laid fewer eggs than wild-type worms after irradiation , which was potentially resulted from excessive germline apoptosis , the survival of prmt-5 ( gk357 ) progeny was comparable to that of wild-type animals ( Text S1 , Table S1 ) . In addition , prmt-5 ( gk357 ) worms displayed similar cell cycle arrest in germline mitotic region to that in wild type following IR treatment ( data not shown ) . Together , these data suggest that prmt-5 does not act as a checkpoint gene to affect cell cycle progression; instead , its effect is likely specific to apoptosis in response to DNA damage . We thus asked whether checkpoint signaling affects prmt-5-mediated apoptosis upon DNA damage . To test that , we used RNAi to inactivate prmt-5 in hus-1 ( op241 ) , mrt-2 ( e2663 ) and clk-2 ( mn159 ) mutants and induced germ cell apoptosis with γ- irradiation of 120 Gy . Our data indicate that mutations in hus-1 , mrt-2 and clk-2 significantly inhibited IR-induced germline apoptosis in prmt-5 ( RNAi ) worms ( Figure 2C ) . Furthermore , the number of germ cell corpses was strongly reduced , but not entirely suppressed , in hus-1 ( op241 ) ;prmt-5 ( gk357 ) double mutants compared with that in prmt-5 ( gk357 ) single mutants after IR treatment ( Figure 2D ) . These findings suggest that checkpoint signaling is important for prmt-5-mediated apoptosis , and prmt-5 likely acts in parallel to , or downstream of , checkpoint genes to regulate apoptosis in response to DNA damage . Previous studies have shown that CEP-1 transcriptionally activates egl-1 in response to DNA damage [9] , [10] , [24] . Because egl-1 loss of function blocked IR-induced apoptosis in prmt-5 ( gk357 ) animals ( Figure 2A ) , we asked further whether CEP-1 activity is required for IR-induced excessive apoptosis in prmt-5 ( gk357 ) worms . To answer this , we constructed double mutants of prmt-5 ( gk357 ) with the cep-1 deletion allele gk138 . In cep-1 ( gk138 ) ;prmt-5 ( gk357 ) double mutants , no germ cell apoptosis was observed after γ-irradiation of 120 Gy ( Figure 3A ) , suggesting that prmt-5 functions upstream or at the level of cep-1 . Further analyses did not reveal any obvious changes in mRNA or protein levels of cep-1 in prmt-5 ( gk357 ) animals after irradiation ( data not shown ) , indicating that prmt-5 mutation likely does not affect either the transcription or mRNA stability or protein stability of cep-1 . To understand how cep-1 and egl-1 might be involved in IR-induced excessive apoptosis observed in prmt-5 ( gk357 ) animals , we examined whether the mRNA level of egl-1 was affected in prmt-5 ( gk357 ) animals after being exposed to γ-irradiation . Using Northern blot analysis , we found that IR-induced egl-1 mRNA level was significantly enhanced in prmt-5 ( gk357 ) mutants compared with that in wild type ( Figure 3B ) . For example , in three independent Northern blot analyses , the average egl-1 mRNA level was induced by about 4-fold in wild-type animals 24 h after irradiation ( Figure 3C ) . In prmt-5 ( gk357 ) animals , the basal level of egl-1 mRNA appeared slightly higher than in wild-type worms though it might not be sufficient for triggering excessive apoptosis under physiological condition , and egl-1 expression was further increased by about 8-fold after irradiation as compared with that in non-irradiated wild-type animals ( Figure 3B and 3C ) . When comparison was made between IR-treated wild-type and prmt-5 ( gk357 ) animals , we constantly observed a further increase of egl-1 mRNA by 1 . 5- to 2 . 5-fold in prmt-5 ( gk357 ) mutants ( Figure 3B and 3C ) . In contrast , in cep-1 ( gk138 ) ;prmt-5 ( gk357 ) double mutants , IR-induced egl-1 expression was completely abrogated ( Figure 3B and 3C ) , indicating that cep-1 activity is absolutely required for IR-induced over up-regulation of egl-1 mRNA caused by loss of prmt-5 function . ced-13 , which encodes another BH3-only protein in C . elegans , was also reported to be transcriptionally activated by CEP-1 following DNA damage [25] . However , we found that ced-13 ( tm536 ) deletion , which likely represents a strong loss-of-function mutation of ced-13 [25] , did not suppress the IR-induced excessive germ cell apoptosis in prmt-5 ( gk357 ) animals ( Figure 3D ) . By Northern blot analysis , we were not able to detect obvious expression of ced-13 following γ-irradiation in either wild type or prmt-5 ( gk357 ) animals , which is likely due to a very low expression level of ced-13 . Thus we used the more sensitive semi-quantitative RT-PCR assay to examine ced-13 expression in response to DNA damage . As reported before [25] , our results indicate that ced-13 expression was increased in both wild type and prmt-5 ( gk357 ) animals after γ-irradiation . However , ced-13 mRNA level was not significantly increased in prmt-5 ( gk357 ) mutants compared with that in wild-type animals ( Figure 3E ) , which is consistent with that ced-13 ( tm536 ) deletion did not suppress IR-induced excessive germ cell apoptosis in prmt-5 ( gk357 ) animals ( Figure 3D ) . In addition , our Northern blot analysis showed that the transcripts of ced-3 , ced-4 and ced-9 were not changed in prmt-5 ( gk357 ) mutants after DNA damage ( data not shown ) . Taken together , these findings suggest that prmt-5 mutation results in a specific over up-regulation of egl-1 which leads to excessive germ cell apoptosis following DNA damage . Since our genetic data indicate that cep-1 is required for the IR-induced over up-regulation of egl-1 and the excessive germ cell apoptosis caused by loss of prmt-5 function , we next investigated how PRMT-5 may regulate CEP-1 transcriptional activity . In mammals , PRMT5 was found to regulate transcription by methylating symmetrically the arginine 3 residue of histone H4 ( H4R3 ) and the arginine 8 residue of histone H3 ( H3R8 ) [26] . We wondered whether C . elegans PRMT-5 could also methylate histone H4 or H3 . Thus we prepared recombinant PRMT-5 and incubated it with core histones and the donor of methyl group , 3H-S-AdoMet ( 3H-SAM ) . Our results indicate that PRMT-5 methylated histone H4 but not H3 in vitro ( Figure S3A ) , suggesting that histone H3 is less likely a substrate of PRMT-5 in C . elegans . We therefore examined whether PRMT-5 is important for H4R3 symmetric dimethylation ( H4R3sMe2 ) in vivo as is mammalian PRMT5 . Using an antibody that specifically recognizes H4R3sMe2 , however , we did not detect significant difference in H4R3 symmetric dimethylation in germline between wild-type and prmt-5 ( gk357 ) animals ( Figure S3B ) . Thus , worm PRMT-5 seems not to control the general status of H4R3sMe2 to regulate CEP-1 transcriptional activity . We then determined whether PRMT-5 was able to enter nucleus by examining the subcellular localization of a translational fusion protein GFP::PRMT-5 driven by the promoter of the germline-specific gene pie-1 ( Ppie-1gfp::rmt-5 ) . About 20% worms from an integrated transgenic line carrying Ppie-1gfp::prmt-5 displayed GFP signals , which were enriched in germline nuclei , suggesting that GFP::PRMT-5 was able to localize to nucleus ( Figure 4A ) . Therefore we tested whether PRMT-5 could interact with CEP-1 by co-expressing Flag-tagged CEP-1 and Myc-tagged PRMT-5 in HEK293 cells and performing immunoprecipitation . Our results indicate that Myc-PRMT-5 was co-immunoprecipitated with Flag-CEP-1 ( Figure 4B ) , suggesting that these two proteins can interact in mammalian cells . Furthermore , we used in vitro GST pull-down assay to determine if they could directly interact with one another . We found that the GST-CEP-1 fusion protein immobilized on glutathione sepharose beads , but not GST , interacted with purified PRMT-5His6 protein ( Figure 4C ) . Reciprocally , GST-PRMT-5 interacted with the full-length CEP-1 ( Figure 4D and 4E ) . Furthermore , 35S-labeled CEP-1 ( 421–644 ) , but not CEP-1 ( 1–420 ) or CEP-1 ( 221–420 ) , interacted with GST-PRMT-5 , indicating that the C-terminal region of CEP-1 was necessary and sufficient for its binding to PRMT-5 ( Figure 4D and 4E ) . These data indicate that PRMT-5 and CEP-1 can directly and specifically interact with each other . The direct interaction between PRMT-5 and CEP-1 promoted us to examine whether PRMT-5 could methylate CEP-1 . In vitro , when incubated with the full-length GST-CEP-1 fusion protein or various truncations of GST-CEP-1 in the presence of 3H-SAM , PRMT-5 did not methylate the full-length CEP-1 or its truncated fragments ( Figure S3C ) , suggesting that PRMT-5 may act by other mechanisms to regulate CEP-1 transcriptional activity rather than directly methylates CEP-1 . For example , PRMT-5 might function through other factors in complex with CEP-1 to affect its transcriptional activity . To determine whether PRMT-5 acts through other CEP-1 cofactors , we sought to identify proteins that likely function together with CEP-1 . In mammals , p300/CBP was found to act as a transcription coactivator of p53 , and the acetylation of p53 by p300 plays an important role in p53 stabilization in response to DNA damage [4]–[6] . In addition , p300-mediated histone acetylation also contributes to the transcription of p53 target genes [27] . In C . elegans , the p300/CBP homolog CBP-1 was shown to regulate the differentiation of some embryonic cell types [28] , [29] , and CBP-1 may function in concert with the transcription factor LIN-1 to negatively regulate vulva cell specification [30] . However , it is not known whether CBP-1 could act together with CEP-1 to control gene expression in response to DNA damage . We explored this possibility first by checking if CEP-1 could interact with CBP-1 . Using GST pull-down assay , we found that GST-CEP-1 fusion protein interacted with 35S-labeled CBP-1 ( 771–1285 ) and CBP-1 ( 1286–1770 ) , which are within the HAT domain of CBP-1 ( Figure 5A and 5B ) , indicating that CBP-1 and CEP-1 directly interact with one another . Moreover , partial inactivation of cbp-1 by RNAi significantly suppressed DNA damage-induced apoptosis and egl-1 expression in wild-type animals ( Figure 6 , see below and Materials and Methods ) , suggesting that CBP-1 is important for CEP-1 transcriptional activity . All together , these findings suggest that CBP-1 likely acts as a cofactor of CEP-1 in C . elegans . Because both PRMT-5 and CBP-1 can interact with CEP-1 , we wondered whether PRMT-5 and CBP-1 could directly interact with one another . Using GST pull-down assay , we found that GST-PRMT-5 interacted with 35S-labeled N-terminal fragment ( 1–320 ) and a fragment within the HAT domain ( 1286–1770 ) of CBP-1 ( Figure 5C ) , indicating that PRMT-5 binds to at least two sites in CBP-1 . These findings suggest that PRMT-5 , CEP-1 and CBP-1 likely form a complex . To further prove this , we tested whether these proteins could interact in HEK293 cells by co-expressing Myc-PRMT-5 , Myc-CEP-1 and Flag-CBP-1 ( 1–1620 ) in different combinations and performing immunoprecipitation . As shown in Figure 5D , PRMT-5 and CEP-1 were associated with CBP-1 in HEK293 cells when they were individually co-expressed with CBP-1 ( 1–1620 ) . Furthermore , when these three proteins were co-expressed , both PRMT-5 and CEP-1 were pulled down by CBP-1 as well ( Figure 5D ) , providing further evidence that these three proteins can form a complex . We next examined whether CBP-1 could be modified by PRMT-5 . When incubated with the recombinant PRMT-5 in the presence of 3H-SAM , the CBP-1 N-terminal fragment containing amino acids 1–320 was strongly methylated . Another fragment ( amino acids 1603–1770 ) in the HAT domain was weakly methylated ( Figure 5E ( a ) ) . Moreover , we found that a point mutation , R234A , completely abolished the N-terminal methylation of CBP-1 by PRMT-5 ( Figure 5E ( b ) ) , indicating that R234 is the major residue for PRMT-5-mediated CBP-1 methylation . Importantly , the residue R234 is located in a GRG motif which is also present in the N-termini of human p300 and CBP ( Figure 5E ( c ) ) , implying that mammalian PRMT5 can also modify CBP/p300 to affect their functions . Our biochemical data suggest that PRMT-5 likely regulates CEP-1 transcriptional activity through the CEP-1 cofactor CBP-1 . To further determine this , we investigated the cellular effect of cbp-1 on prmt-5-mediated apoptosis in response to DNA damage by performing cbp-1 RNAi and examining whether it affects IR-induced apoptosis in prmt-5 ( gk357 ) animals . Because cbp-1 RNAi treatment of early larvae ( L1–L2 stage ) gave rise to cell cycle arrest in adult germlines , we performed cbp-1 RNAi in L4-stage animals to partially inactivate cbp-1 ( Figure 6A , Text S1 , Figure S4A ) . Such cbp-1 RNAi treatment did not obviously change the numbers of mitotic nuclei in germlines of wild-type , prmt-5 ( gk357 ) or ced-1 ( e1735 ) animals as compared with control RNAi ( Figure S4B ) . In addition , cbp-1 RNAi performed in L4-stage animals did not affect the number of germ cell corpses in ced-1 ( e1735 ) worms ( Figure S4C ) . These data indicate that cbp-1 RNAi treatment of L4-stage animals does not affect either germline development or physiological germ cell death . Thus cbp-1 RNAi was carried out in L4-stage animals in our following experiments . Compared with control RNAi treatment , cbp-1 RNAi caused a strong reduction of germ cell corpses in wild type when exposed to different doses of γ-irradiation ( Figure 6C ) . Similarly , at different time points post γ-irradiation of 120 Gy , cbp-1 ( RNAi ) animals also displayed significantly fewer cell corpses than control RNAi-treated wild-type animals ( Figure 6D ) . In agreement with this , the induction of egl-1 expression by γ-irradiation of 120 Gy in cbp-1 ( RNAi ) animals was decreased by about 40% compared to that in control RNAi-treated wild-type worms ( Figure 6E ( lanes 1–2 ) and Figure 6F ) , suggesting that CBP-1 functions to promote CEP-1 transcriptional activity . In prmt-5 ( gk357 ) animals , γ-irradiation induced excessive germ cell apoptosis in a dose-dependent manner , but it was strongly suppressed by cbp-1 RNAi ( Figure 6B–6D ) . Consistently , the IR-induced over up-regulation of egl-1 in prmt-5 ( gk357 ) animals was significantly decreased by cbp-1 RNAi treatment ( Figure 6E ( lanes 3–4 ) and Figure 6F ) . These findings suggest that cbp-1 functions downstream of prmt-5 , providing further evidence that PRMT-5 likely acts through CBP-1 to regulate CEP-1 transcriptional activity ( Figure 7 ) .
Aberrant expression of PRMT5 is associated with many cancer types such as lymphoma , leukemia , gastric carcinoma and testicular tumors [26] , [31] , [32] . It has been shown that overexpression of PRMT5 in lymphoma is correlated to a global increase of H4R3 and H3R8 symmetric dimethylation , which likely suppresses the expression of the tumor suppressor gene ST7 to affect tumorigenesis [26] . However , whether and how PRMT5 controls the expression of apoptosis-related genes to affect cell proliferation , apoptosis , as well as tumorigenesis remains poorly understood . Here we have shown that the C . elegans arginine methyltransferase PRMT-5 plays an important role in DNA damage-induced apoptosis . prmt-5 loss-of-function mutation does not affect developmental cell deaths but leads to excessive germ cell apoptosis in response to DNA damage , suggesting that prmt-5 negatively regulates DNA damage-induced apoptosis in C . elegans . Our further genetic analyses indicate that mutations of cep-1/p53 and genes in the core cell death pathway suppress IR-induced excessive germ cell apoptosis in prmt-5 ( gk357 ) mutants , indicating that prmt-5-mediated apoptosis following DNA damage is dependent on cep-1/p53 and requires the core cell death pathway ( Figure 7A ) . Meanwhile , we found that mutations in checkpoint genes significantly inhibited IR-induced excessive germ cell death in prmt-5 ( gk357 ) mutants , suggesting that checkpoint signaling pathways are important for prmt-5-mediated apoptosis ( Figure 7A ) . Importantly , loss of prmt-5 function causes a specific over up-regulation of the cell death initiator EGL-1 in response to DNA damage , which is directly responsible for the excessive germ cell apoptosis observed in prmt-5 ( gk357 ) mutants . Collectively , these findings demonstrate that PRMT-5 is a negative regulator of CEP-1/p53-dependent cell death pathway in C . elegans . Our results have suggested a novel mechanism underlying cep-1/p53-dependent cellular response to DNA damage in C . elegans . In C . elegans germ cells , it seems that PRMT-5 does not affect the symmetric dimethylation of H4R3 although mammalian and plant PRMT5 has been reported to do so [26] , [33] . Presently we can not exclude the possibility that PRMT-5 methylates other arginine residues on histone H4 to regulate CEP-1/p53-dependent gene expression , but it seems less likely that PRMT-5 modifies H3R8 to affect transcription in C . elegans since PRMT-5 does not methylate histone H3 in vitro ( Figure S3A ) . Regardless of histone arginine methylation , our results have revealed a novel regulatory mechanism underlying DNA damage-induced egl-1 expression . The fact that PRMT-5 interacts with but does not methylate CEP-1 implies that PRMT-5 likely regulates CEP-1 transcriptional activity by affecting a CEP-1 cofactor ( s ) . In support of this , we have identified CBP-1 as a cofactor of CEP-1/p53 , which is supported by two lines of evidence: firstly , CBP-1 and CEP-1 physically interact with one another both in vitro and in mammalian cells; secondly , reducing cbp-1 expression significantly suppressed cep-1-dependent germ cell apoptosis and egl-1 transcription in response to DNA damage . Moreover , we found that PRMT-5 can form complex with CEP-1 and CBP-1 , and PRMT-5 can methylate the residue R234 in the N-terminus of CBP-1 . Therefore PRMT-5 likely affects CEP-1 transcriptional activity through its effect on CBP-1 despite that it may not be the sole target of PRMT-5 . In agreement with this notion , we found that partial inactivation of cbp-1 by RNAi significantly suppressed the excessive germ cell apoptosis in prmt-5 ( gk357 ) animals following DNA damage . Consistently , cbp-1 RNAi significantly reduced egl-1 expression in prmt-5 ( gk357 ) mutants . Based on these experimental findings , we propose a possible model for PRMT-5 functioning through CBP-1 and CEP-1 to negatively regulate DNA damage-induced apoptosis in C . elegans: In wild-type animals , PRMT-5 and CBP-1 are likely recruited by CEP-1 to form a complex so that CBP-1 is methylated by PRMT-5 , which represses the capacity of CBP-1 for enhancing CEP-1-dependent transcription of egl-1 . In this case , egl-1 expression is maintained at a proper level to avoid excessive germ cell apoptosis after DNA damage ( Figure 7B ) . In prmt-5 ( gk357 ) animals , the repression on CBP-1 by PRMT-5 is removed , thus CBP-1 promotes the transcriptional activity of CEP-1 by mechanisms yet to be uncovered , leading to high expression level of egl-1 following DNA damage , which in turn causes excessive germ cell apoptosis ( Figure 7B ) . Although ced-13 was reported to be a transcription target of CEP-1 , we found that IR-induced ced-13 expression is indistinguishable between wild type and prmt-5 ( gk357 ) mutants and ced-13 loss of function does not suppress IR-induced apoptosis in prmt-5 ( gk357 ) animals . These results suggest that the regulatory effect of PRMT-5 on IR-induced CEP-1 transcriptional activity is likely specific to egl-1 transcription . It was found recently that IR-induced ced-13 expression is only detected in somatic tissues but not in germline where DNA damage-induced apoptosis takes place [24] , suggesting that ced-13 may play a role in DNA damage response in soma while egl-1 is the major CEP-1 target responsible for initiating germ cell apoptosis after DNA damage . In addition , although PRMT-5 is implicated in maintaining genome stability [21] , our results indicate PRMT-5 likely acts mainly in germline to regulate DNA damage response since the survival of prmt-5 ( gk357 ) embryos was comparable to that of wild-type animals after IR treatment ( Table S1 ) , which also suggests that prmt-5 probably does not obviously affect the repair of DNA lesions caused by γ-irradiation . As prmt-5 ( gk357 ) worms also displayed similar cell cycle arrest in germline mitotic region to that in wild-type animals upon DNA damage ( data not shown ) , it seems that prmt-5 acts differently in sensing DNA damage from checkpoint genes . Thus , apart from a role in regulating DNA damage-induced germ cell apoptosis , it remains to be elucidated how prmt-5 inactivation causes increased accumulation of mutation observed previously [21] . Because strong loss of function of cbp-1 causes lethality [29] , we could only analyze the role of cbp-1 in prmt-5-mediated germ cell apoptosis in response to DNA damage by using the partial loss-of-function mutation of cbp-1 ( cbp-1 RNAi ) , which suppressed IR-induced germ cell apoptosis in prmt-5 ( gk357 ) mutant worms to a less extent than suppressed by the strong loss of function of cep-1 . Therefore we can not conclude that PRMT-5 acts solely through CBP-1 based on the cbp-1 RNAi results . Nevertheless , in agreement with our findings that PRMT-5 likely regulates CEP-1 activity through CBP-1 in C . elegans , a recent study also identified human PRMT5 as a negative regulator of p53-mediated apoptosis involving p300/CBP in mammalian cells . Jansson et al . reported that PRMT5 is associated with the CBP-binding protein Strap . As a result , PRMT5 is recruited to p53 to methylate the latter on the arginine residues in an RGRER motif [34] . Similar to that in C . elegans , inactivation of PRMT5 by siRNA significantly enhanced DNA damage-induced apoptosis of mammalian cells [34] . Unlike human PRMT5 , however , we did not find that C . elegans PRMT-5 modify CEP-1 , which is consistent with that CEP-1 does not contain an RGRER motif as human p53 [34] . Thus our findings suggest a possibility that human PRMT5 may affect p53 activity through additional mechanisms except for p53 arginine methylation . For example , it is likely that human PRMT5 can function similarly to its C . elegans counterpart to regulate p300/CBP activity by arginine methylation . Previously , it has been shown that the coactivator-associated arginine methyltransferase 1 ( CARM1/PRMT4 ) can methylate p300/CBP in the KIX domain to disable the interaction between the p300/CBP KIX domain and the kinase inducible domain ( KID ) of CREB , which blocks CREB-dependent transcription of genes such as Bcl-2 [16] . On the other hand , CARM1-mediated methylation of p300/CBP enhances nuclear hormone receptor ( NR ) -dependent gene transcription [16] , [17] . These findings indicate that the arginine methylation of p300/CBP is one of the mechanisms underlying transcription regulation . In C . elegans , PRMT-5 methylates an arginine residue located in the GRG motif in the N-terminus of CBP-1 . Interestingly , this GRG motif also exists in the N-termini of mammalian p300 and CBP . Furthermore , it has been found that PRMT5 is present in the p53 co-activator complex containing p300/CBP in mammalian cells [34] . Thus , it will be very important to determine whether human PRMT5 can methylate p300/CBP on the same arginine residue to regulate its coactivator activity in promoting apoptosis-related gene transcription . Further in-depth mechanistic studies in both C . elegans and mammalian cells will be needed to establish the role of the site-specific methylation of p300/CBP by PRMT5 in regulating p53-dependent apoptosis .
prmt-5 ( gk357 ) and cep-1 ( gk138 ) deletion strains were generated by Dr . Donald Moerman ( C . elegans Reverse Genetics Core Facility , Vancouver , B . C . , Canada ) and provided by C . elegans Genetic Center ( CGC ) . ced-13 ( tm536 ) deletion strain was provided by Dr . Shohei Mitani . Worms were cultured and maintained by using standard procedures [35] . The Bristol N2 strain was used as wild type . Deletion strains were outcrossed with N2 strain for 6 times . Double mutants were constructed with standard protocol [35] . To make RNAi constructs of prmt genes , the exons 1 and 2 of prmt-1 ( nucleotide +10–954 ) , exon 3 of prmt-2 ( nucleotide +372–755 ) , exon 8 of prmt-3 ( nucleotide +5135–5487 ) and exons 4 and 5 of prmt-5 ( nucleotide +805–1536 ) were amplified by PCR and cloned into pPD129 . 36 , respectively . RNAi constructs for prmt-4 , -6 and cbp-1 were obtained from an RNAi library ( Geneservice Ltd ) . For bacterial and mammalian expression of PRMT-5 and CEP-1 , the cDNAs of prmt-5 and cep-1 were cloned into the bacterial expression vectors pET21a and pGEX4-T-2 and the mammalian expression vectors pCMV-myc and pCMV-tag2B , respectively . The full-length cDNA of cbp-1 was obtained by ligating the cDNA fragments from the following yk cDNA clones: yk1426d05 , yk838d03 , yk822d08 , yk1753c05 and yk1403a01 , and verified by sequencing . Different cbp-1 cDNA fragments were amplified from the full-length cDNA by PCR and cloned into bacterial and mammalian expression vectors as above . His6-tagged and GST-fusion proteins were expressed and purified as described previously [36] . RNA interference was performed by using the feeding assay . Briefly , The L1 larvae were grown on RNAi plates seeded with bacteria HT115 ( DE3 ) expressing dsRNA of individual prmt genes . The progeny maintained on the RNAi plates were synchronized to young adult stage and treated with different doses of γ-irradiation as described below . For cbp-1 RNAi treatment , worms at early L4 stage were cultured on RNAi plates seeded with bacteria HT115 ( DE3 ) expressing dsRNA of cbp-1 . After reaching young adult stage , cbp-1 ( RNAi ) worms were irradiated with γ-ray and germ cell corpse phenotype and Northern blot assays were analyzed 36 h or 24 h post irradiation , respectively . Synchronized young adult animals were irradiated with γ-ray by using a 60Co source located in the Peking University Health Science Center . Irradiated animals were put back to culture at 20°C to different time points . Worms with normal germline morphology were scored for germ cell corpses by using Nomarski optics . For acridine orange ( AO ) staining of germ cell corpses , irradiated worms were incubated in M9 medium containing AO ( 50 µg/ml ) and bacteria OP50 in dark for 2 h . Worms were recovered on NGM plates for another 2 h and examined with epifluorescence microscopy . To induce germline apoptosis with ENU , young adult animals were incubated in M9 medium containing OP50 bacteria and ENU of different concentrations ( 0 , 1 . 0 , 2 . 5 and 5 . 0 mM ) for 4 h . Worms were then recovered on NGM plates for 24 h and germ cell corpses were scored with DIC microscopy . Worms synchronized to young adult stage were irradiated with γ-ray of 120 Gy as described above . 24 h later , irradiated worms were harvested and total RNA was extracted using the Trizol reagent ( Invitrogen ) . 20 µg of total RNA was denatured and resolved on 1 . 2% agarose-formaldehyde gel in MOPS buffer ( 20 mM 3-Morpholinopropam sulfonsaure , 2 mM Sodium acetate , 1 mM EDTA ) and further blotted onto a nylon membrane . For hybridization , the membrane was incubated with 32P-labeled probes prepared from egl-1 cDNA in a buffer containing 7% SDS , 1% BSA , 1 mM EDTA , 250 mM NanPO4 ( pH7 . 2 ) . The membrane was extensively washed and exposed to a phosphor imager screen ( Amersham ) . To examine the equal loading of total RNA samples , the membrane was striped and re-probed with 32P-labeled probes of α-actin . Relative fold induction of egl-1 mRNA was quantified with the ImageQuant 5 . 2 software and normalized with α-actin mRNA . For expression of GFP::PRMT-5 fusion protein in germline , we cloned the cDNA of prmt-5 into the germline expression vector pTE5 in frame with GFP ( Ppie-1 gfp::prmt-5 ) . This PRMT-5-expression vector was linearized with SacII and co-injected at the concentration of 1 µg/ml into worm germline with the SacI-digested N2 genomic DNA ( 60 µg/ml ) and the SalI-linearized injection marker pTG96 ( 1 µg/ml ) which expresses Psur-5gfp only in somatic tissues . The transgenic extrachromasomal arrays from F2 and F3 generations were integrated into worm genome by γ-irradiation . Gonads of integrated transgenic worms were dissected out and stained with Hoechst 33342 ( 4 µM ) to examine the localization of GFP::PRMT-5 with epifluorescence scope , and the germline expression of GFP::PRMT-5 was further confirmed by using Western blot assay . Human embryonic kidney cells ( HEK293 ) grown in Dulbecco's modified Eagle's medium ( HyClone ) supplemented with 10% fetal bovine serum ( HyClone ) were transfected with 2 . 0 µg of mammalian vectors expressing worm proteins with different tags ( i . e . , pCMV-myc-prmt-5 , pCMV-tag2B-cep-1 , pCMV-myc-cep-1 , pCMV-tag2B-cbp-1 ( 1–1620 ) ) by using the calcium phosphate-mediated transfection assay . 36 h after transfection , cells were harvested and lysed in a buffer containing 50 mM Tris ( pH 8 . 0 ) , 150 mM NaCl , 0 . 5% sodium deoxycholate , 1% Triton X-100 , 1 mM phenylmethylsulfonyl fluoride ( PMSF ) . The lysate was incubated with anti-Flag antibody ( M2 ) -conjugated agarose beads ( Sigma ) for more than 2 h at 4°C . The beads were washed extensively in a buffer containing 50 mM Tris ( pH 8 . 0 ) , 150 mM NaCl , 1 mM PMSF and 1% NP-40 and bound proteins were eluted with protein sample buffer . The eluted proteins were resolved on SDS-PAGE and detected with Western blot assay . For GST pull-down assay , purified GST or GST fusion proteins were immobilized on glutathione-Sepharose beads and incubated with [35S]methionine-labeled proteins at 4°C for more than 2 h . The beads were washed extensively and bound proteins were eluted and separated on 12% SDS-PAGE and exposed to X-ray film or phosphor imager screen ( Amersham ) for autoradiography . PRMT-5His6 or GST-PRMT-5 was respectively incubated with proteins including core histones , Myelin basic protein ( MBP ) , full-length and truncated GST-CEP-1 proteins , wild-type and mutant CBP-1 ( 1–320 ) His6 and CBP-1 ( 1603–1770 ) His6 in the presence of 0 . 55 µCi of 3H-S-AdoMet in PRMT assay buffer ( 25 mM Tris , pH 7 . 5 , 1 mM EDTA , 1 mM EGTA , and 1 mM PMSF ) for 1 h at 30°C in a final volume of 20 µl . Reactions were stopped by adding SDS sample buffer and heated at 100°C for 10 min . Samples were resolved on 12% SDS-PAGE and further stained with Coomassie blue and dried to expose to X-ray film for autoradiography . | Protein arginine methylation is an important posttranslational modification . Aberrant expression of protein arginine methyltransferases ( PRMTs ) are found in a wide variety of human diseases , especially in many cancers . Given that deregulation of apoptosis is usually related to tumorigenesis , it is not known whether PRMT–mediated protein arginine methylation plays a role in apoptosis . Here we employ the genetic tractable model organism C . elegans to explore the potential regulatory roles of PRMTs in apoptosis . We find that C . elegans PRMTs do not affect developmental cell deaths . However , genetic inactivation of the C . elegans homolog of the mammalian type II protein arginine methyltransferase PRMT5 causes excessive germ cell apoptosis in response to DNA damage . Our genetic analyses indicate that prmt-5–mediated apoptosis is dependent on the C . elegans p53 homolog CEP-1 and requires the core cell death pathway . We further demonstrate that loss of prmt-5 leads to a specific up-regulation of the cell death initiator EGL-1 following DNA damage . Finally , we identify CBP-1 , the C . elegans homolog of human p300/CBP , as a CEP-1 cofactor in C . elegans , and we provide genetic and biochemical evidence that PRMT-5 likely functions through CBP-1 to affect CEP-1/p53 transcriptional activity , thereby negatively regulating CEP-1/p53-dependent apoptosis . | [
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] | 2009 | Caenorhabditis elegans Protein Arginine Methyltransferase PRMT-5 Negatively Regulates DNA Damage-Induced Apoptosis |
Variations in cell migration and morphology are consequences of changes in underlying cytoskeletal organization and dynamics . We investigated how these large-scale cellular events emerge as direct consequences of small-scale cytoskeletal molecular activities . Because the properties of the actin cytoskeleton can be modulated by actin-remodeling proteins , we quantitatively examined how one such family of proteins , enabled/vasodilator-stimulated phosphoprotein ( Ena/VASP ) , affects the migration and morphology of epithelial fish keratocytes . Keratocytes generally migrate persistently while exhibiting a characteristic smooth-edged “canoe” shape , but may also exhibit less regular morphologies and less persistent movement . When we observed that the smooth-edged canoe keratocyte morphology correlated with enrichment of Ena/VASP at the leading edge , we mislocalized and overexpressed Ena/VASP proteins and found that this led to changes in the morphology and movement persistence of cells within a population . Thus , local changes in actin filament dynamics due to Ena/VASP activity directly caused changes in cell morphology , which is coupled to the motile behavior of keratocytes . We also characterized the range of natural cell-to-cell variation within a population by using measurable morphological and behavioral features—cell shape , leading-edge shape , filamentous actin ( F-actin ) distribution , cell speed , and directional persistence—that we have found to correlate with each other to describe a spectrum of coordinated phenotypes based on Ena/VASP enrichment at the leading edge . This spectrum stretched from smooth-edged , canoe-shaped keratocytes—which had VASP highly enriched at their leading edges and migrated fast with straight trajectories—to more irregular , rounder cells migrating slower with less directional persistence and low levels of VASP at their leading edges . We developed a mathematical model that accounts for these coordinated cell-shape and behavior phenotypes as large-scale consequences of kinetic contributions of VASP to actin filament growth and protection from capping at the leading edge . This work shows that the local effects of actin-remodeling proteins on cytoskeletal dynamics and organization can manifest as global modifications of the shape and behavior of migrating cells and that mathematical modeling can elucidate these large-scale cell behaviors from knowledge of detailed multiscale protein interactions .
The spatiotemporal coordination of the assembly , disassembly , and organization of the actin cytoskeleton is essential for efficient cell migration . The underlying mechanisms by which the actin cytoskeleton is organized and remodeled into specific architectures , which are then conveyed over large scales into observable cell morphologies , remain unclear . However , careful observation of large-scale morphology and behavior can shed light on these mechanisms . The heterogeneity of wild-type populations [1] can be used as a “natural experiment” in which potentially meaningful correlations between observations at molecular and global scales are determined . Because of the complex relationships between the underlying molecular interactions and observable parameters , physical and mathematical modeling is often necessary to interpret such quantitative data in terms of fundamental molecular mechanisms [2] . To achieve a mechanistic understanding of how the global shape and migratory behavior of cells are generated , we used a combination of quantitative analysis of natural cell-to-cell variation and mathematical modeling to help us grasp how the large-scale organization and function of the actin meshwork emerges and propagates from the dynamics of its molecular components . The actin cytoskeleton can be remodeled by many different families of proteins , including the enabled/vasodilator-stimulated phosphoprotein ( Ena/VASP ) family , which affects dynamic processes such as growth , capping , and bundling of actin filaments [3] , thereby regulating the local spatial organization of the actin cytoskeleton in cells [4–7] . Members of this family—represented by VASP , Mena ( mammalian Ena ) , and EVL ( Ena/VASP-like protein ) in mammals—are largely functionally interchangeable [8] and have been recognized as important regulators of the actin cytoskeleton during cell migration and axon growth , as well as during filopodia formation , platelet aggregation , and phagocytosis [4 , 5 , 7 , 9–13] . Ena/VASP proteins have been of special interest in the field of cell migration , because they have been found to be both positive and negative regulators of cell speed in diverse motile cell types ranging from the actin-based movement of the intracellular pathogen Listeria monocytogenes to overall amoeboid migration of eukaryotic cells . The central proline-rich region of the Listeria surface protein ActA binds Ena/VASP proteins [14 , 15] , which in turn recruit profilin–actin complexes [9 , 16] that are necessary for efficient actin monomer addition to growing filaments supporting bacterial propulsion . This mechanism accounts for the dramatic decrease in speed observed in Listeria when Ena/VASP proteins are depleted [8 , 17] and the speed increase observed when VASP is added to a reconstituted motility system [18] . Analogously , suppression of Ena/VASP protein function has been shown to decrease the speed of migrating neutrophils [10] and chemotaxis efficiency by Dictyostelium discoideum[11] . Conversely , Ena/VASP protein depletion resulted in faster moving fibroblasts due to the reorganization of the actin network , which became highly branched with short actin filaments , leading to more persistent lamellipodial protrusion [5 , 6] . A functional mechanism for these proteins has emerged , suggesting that Ena/VASP proteins remodel actin networks by enhancing the formation of long actin filaments , competing with capping protein , and potentially decreasing the branching activity of the actin-related protein 2/3 ( Arp2/3 ) complex [4 , 6 , 7 , 19 , 20] . However , additional studies found no evidence for the latter two activities of VASP [21 , 22] , and its exact molecular functions remain controversial . Cell morphology represents the global manifestation of the cell's structural organization of the cytoskeleton and thus reflects the specific migratory behavior of different cell types . For example , epithelial fish keratocytes , which are among the fastest locomoting cells , exhibit flat lamellipodia as they glide along two-dimensional surfaces , whereas neutrophils have thicker , more amorphous pseudopodia that allow them to crawl through three-dimensional tissues with speeds comparable to that of keratocytes [23] . Keratocytes have been described as a “fan-” or “canoe-” shaped , exhibiting minor variations in shape and direction during migration [24–26] . With their simple stereotyped shape , keratocytes have been regarded as a good model system to study shape in migrating cells [26–29] . However , not all migrating keratocytes in culture are perfectly stereotyped; a certain fraction naturally exhibits more irregular morphologies [30–32] that have not been studied as well . Following our initial observation that these keratocyte morphologies were correlated with the presence or absence of VASP at the leading edge of the lamellipodium , we investigated how Ena/VASP activity influenced cell morphology as well as motile behavior . We hypothesized that the specific actin filament dynamics produced by actin remodeling proteins , such as Ena/VASP , organize the actin network and contribute to global cell morphology and migration . Quantitative analytical approaches were necessary to discern relationships between numerous perceptible morphological phenotypes and also to detect subtle changes caused by molecular manipulations . To confirm our initial observation , we measured cell shape , leading-edge shape , filamentous actin ( F-actin ) distribution , cell speed , directional persistence , and VASP enrichment at the leading edge in a population of keratocytes . Systematic quantitative analysis revealed that these parameters correlated with VASP enrichment at the leading edge , spanning a clear continuum of coordinated phenotypes . Moreover , we have developed a mathematical model that explains the properties of this continuum—in particular , the quantitative correlations observed between the observable , large-scale parameters—in terms of small-scale molecular interactions between VASP and the growing actin architecture . Specifically , our model suggests that the role of Ena/VASP in protecting growing filaments allows for larger-scale cohesion in the actin meshwork , promoting smooth canoe shapes and faster migration . By experimentally manipulating Ena/VASP availability at the leading edge and thus local actin filament growth kinetics due to Ena/VASP activity , we were able to alter the prevailing morphology and trajectory of keratocytes within a population in a way that was accurately predicted by our model . Together , our results suggest that Ena/VASP proteins play a major role in cell morphology and motility by modulating the organization and thus promoting the large-scale coherence of the actin network . Our general approach of using detailed mathematical modeling to connect quantitative measurements of large-scale cell morphological and behavioral features to specific protein biochemical activities should be broadly applicable to many cytoskeleton-associated proteins involved in cell migration .
Populations of primary migrating epithelial fish keratocytes are heterogeneous in cellular morphologies , sizes , and motile behaviors . Most descriptions of keratocytes focus on a subpopulation of cells with stereotyped canoe-like shapes [24–26] and smooth lamellipodial leading edges; however , many have more irregular shapes and rough leading edges [31 , 32] ( Figure 1A and 1B ) . We initially examined cichlid keratocytes with these extreme morphologies and focused on their leading-edge morphology , which we classified by eye as smooth or rough . Differences in morphology became more evident when we observed by immunofluorescence that VASP was localized as a uniform thin line at the leading edge of keratocytes with smooth leading edges and did not appear at the edge of cells with rough margins ( Figure 1A and 1B ) . VASP was especially evident at focal adhesions at the rear sides of the cell body of rough polarized keratocytes ( Figure 1B ) and in keratocytes found in epithelial sheets ( unpublished data ) . When we examined enhanced green fluorescent protein ( EGFP ) -VASP expression in live migrating keratocytes , we observed a similar localization , with VASP more strongly localized at smooth leading edges ( Figure S1 ) . Similar results were observed when the localization of EVL , a different member of the Ena/VASP family , was examined ( unpublished data ) ; however , we decided to focus on VASP because its function has been more thoroughly characterized . When individual migrating keratocytes expressing EGFP-VASP spontaneously switched from rough to smooth morphologies , an increase in VASP fluorescence at the leading edge and a decrease at focal adhesions was observed when keratocytes achieved the smooth morphology ( Figure S1 ) . Morphology switching was generally an uncommon event on the time scales over which time-lapse sequences were collected ( tens of minutes ) , suggesting that the correlation between VASP localization and cell morphology is stable over the time scale of actin filament turnover in these cells ( <30 s ) [33] . Our observations , which suggested a relationship between Ena/VASP localization at the leading edge and large-scale cell morphology , prompted us to investigate whether VASP redistribution caused these morphological changes . To test whether Ena/VASP proteins directly modulated leading-edge shape , we manipulated their availability at the leading edge of keratocytes . To decrease Ena/VASP availability , we used a construct ( FP4-mito ) derived from the Listeria ActA protein , which localizes to mitochondria when expressed in eukaryotic cells [15 , 34] and has four proline-rich repeats ( P4 ) that efficiently bind Ena/VASP proteins [14 , 15 , 35] . FP4-mito was previously shown to function as an Ena/VASP dominant-negative construct by sequestering and mislocalizing Ena/VASP proteins at the surface of mitochondria thus preventing their function at their normal sites of activity , such as the leading edge , tips of filopodia , and cell–cell contacts in tissue culture cells as well as in developing embryos [4 , 5 , 36–38] . As a control , we used a similar construct ( AP4-mito ) that has been previously used as negative control [4 , 5 , 36–38] because it contains point mutations that dramatically reduce binding to Ena/VASP proteins [39] while retaining the ability to localize to mitochondria [5] . When we expressed EGFP tagged FP4-mito in keratocytes , VASP ( Figure 2A and 2B ) and EVL ( unpublished data ) were efficiently mislocalized to mitochondria , and a higher percentage of migrating keratocytes , which were observed with time-lapse video-microscopy , exhibited the rough morphology ( 70% ) compared with controls ( Figure 2C ) . Conversely , when EGFP-VASP was overexpressed , a significantly lower percentage of keratocytes ( 43% ) exhibited the rough phenotype ( compared to cells expressing EGFP-FP4-mito , p = 0 . 03 , Figure 2C ) . These results suggest that VASP enrichment at the leading edge can tilt the balance of morphology toward the smooth phenotype . We also used these time-lapse sequences to examine differences in motile behavior between cells with smooth and rough morphologies . When we measured migration speed , we found that smooth cells were significantly faster than rough cells ( Figure 2D , p < 0 . 01 ) suggesting that lamellipodial morphology , which can be influenced by VASP availability at the leading edge , is tightly coupled to the migration speed of these cells . Because fish keratocytes have been observed to generally migrate with persistent straight trajectories over long distances in vitro [40] , we examined whether directional persistence was related to morphology . We found that smooth cells expressing control constructs ( EGFP and EGFP-AP4-mito ) had significantly straighter trajectories compared with those of rough cells ( p < 0 . 001 , Figure 2E ) . Since Ena/VASP availability influenced the fraction of smooth , straight-moving keratocytes within a population , we next examined whether manipulating VASP availability at the leading edge would alter cell trajectories . When Ena/VASP proteins were mislocalized ( EGFP-FP4-mito ) , smooth cells moved in more curved trajectories that were similar to those of rough cells and significantly different from smooth cells expressing control constructs ( p < 0 . 001 , Figure 2E ) . In contrast , when EGFP-VASP was overexpressed , rough keratocytes , which had curved trajectories in controls , maintained straighter trajectories similar to those from smooth cells expressing control constructs and EGFP-VASP . These results suggest that directional persistence was more sensitive to VASP availability at the leading edge than was leading-edge morphology . Taken as a whole , our results show that VASP localization at the leading edge correlates with smooth , fast , and straight-moving keratocytes , and that manipulating Ena/VASP availability alters the morphology and trajectory curvature of keratocytes within a population . Thus far , we had observed that VASP localization was related to broad classes of keratocyte leading-edge morphologies and that we could manipulate morphology by mislocalizing or overexpressing VASP . We wondered whether morphological variation among wild-type keratocytes might be related to VASP levels at the leading edge , and therefore we performed a detailed , quantitative characterization of a keratocyte population . Instead of using a binary and subjective classification of smooth versus rough , we characterized the natural morphological heterogeneity of keratocytes along several measurable and objective phenotypic continua . To measure cell morphology rigorously , we determined mathematically the major modes of shape variation by applying the principal components analysis ( PCA ) to a population of keratocyte shapes represented as aligned , polygonal contours [41] . We found that three primary modes of shape variability accounted for over 95% of all morphological variation: one mode corresponding approximately to cell size , one corresponding to aspect ratio ( i . e . , whether cells were shaped more like a wide canoe or a rounded “D” ) , and one corresponding to the position of the cell body along the front–rear direction ( see Materials and Methods , Figure S2 ) [41] . Since we wanted to test whether cell morphology was related to VASP levels , we quantified VASP enrichment at the leading edge of cells by dividing the highest mean VASP fluorescence intensity across the leading edge ( “peak” ) by the lowest VASP mean intensity ( “base” ) found interior to the leading edge . This measure of “VASP peak-to-base ratio” is illustrated in Figure 3A and 3B . We found that the population of keratocytes examined ( n = 43 ) displayed a wide and apparently continuous range of VASP peak-to-base ratios ( Figure 3C ) . When we compared cell morphology to VASP enrichment at the leading edge , we found that only the shape mode that correlated with VASP levels described the canoe-to-rounder-D–shape transition . Keratocytes with VASP enriched at the leading edge ( high VASP peak-to-base ratios ) had a tendency to resemble canoe shapes , whereas cells with low VASP at the edge were more likely to have rounder D shapes ( p = 0 . 0002 , n = 43 , Figure 3D ) . To evaluate the shapes of leading edges quantitatively instead of qualitatively classifying them as smooth or rough , we measured the degree of roughness of the leading edges by calculating the sum of the local curvature at each of 90 points along front of the cell contours ( see Materials and Methods ) . Since curvature at a point is defined as the reciprocal of the radius of the osculating circle , sharply bending curves that are present in rough leading edges osculate small circles and thus have large local curvatures . Our results confirmed our qualitative observation ( from Figure 1 ) that strong VASP localization at the leading edge correlated with smooth edges ( p = 0 . 0003 , n = 43 , Figure 3E ) . Additionally , canoe-shaped keratocytes had decreased local curvature and thus smooth leading edges ( p = 0 . 0003 , n = 43 , Figure 3F ) . In summary , enrichment of VASP at the leading edge correlated with canoe shape and smooth leading edges , strongly suggesting a morphological continuum related to VASP activity at the lamellipodial edge . To examine the behavior of live keratocytes with smooth or rough leading edges , we followed their contours , which were generated from each frame of time-lapse sequences of keratocytes overexpressing EGFP-VASP . The shape of the leading edge in rough cells varied widely , whereas smooth cells maintained a constant shape with minor fluctuations ( Figure 4 Video S1 ) . In the particular example shown ( Figure 4 ) , we also observed that the smooth keratocyte migrated at approximately twice the speed of the rough one . Our results indicated that the five parameters considered thus far—VASP peak-to-base ratio , cell shape , local leading edge curvature , speed , and directional persistence—all correlated with each other , creating a continuum of keratocyte phenotypic morphologies . One extreme of this continuum contained fast , straight-moving cells with VASP enriched at the leading edge , canoe-like shapes , and smooth leading edges ( Figure 2E 2F , and S3 ) . We refer to cells in this end of the continuum as “coherent” to convey their stable morphology and directed movement . The opposite extreme in the continuum of keratocyte morphologies encompassed slow , meandering cells with low VASP at the leading edge , rounder D shape , and rough leading edges , which we denote as “decoherent . ” Because previous studies have indicated that keratocyte leading-edge shape may be related to actin filament ( F-actin ) density [28] , we compared the distribution of F-actin to keratocyte morphology and VASP levels at the leading edge . Keratocytes with high VASP and a coherent morphology had F-actin distributions along the leading edge that peaked in the middle at the front of the cell ( Figure 5A ) , whereas cells with low VASP and a decoherent morphology had uniform F-actin distributions ( Figure 5B ) . We also found that the F-actin density along the leading edge of coherent cells was increased compared with decoherent cells ( Figure 5C ) . To compare VASP enrichment to the enhancement of F-actin in the middle of the leading edge of different cells , we calculated a ratio ( referred to as “F-actin peak ratio” ) of the mean F-actin intensity values from the middle half of the leading edge ( 1/4 to 3/4 position along the edge ) to the mean of the F-actin values from the rest of the leading edge ( positions 0 to 1/4 and 3/4 to 1 along the edge ) of each cell ( Figure 5C ) . We found a significant correlation between F-actin enhancement in the middle of the leading edge ( F-actin peak ratios ) and VASP enrichment ( VASP peak-to-base ratios ) , suggesting that VASP accumulation at the leading edge is associated with the peaked or graded accumulation of F-actin in coherent cells ( p < 0 . 0001 , n = 43 , Figure 5D ) . When we examined the relationship of the Arp2/3 complex to F-actin and cell morphology , we found that Arp3 distribution , as measured by immunofluorescence , corresponded to that of F-actin in both coherent and decoherent cells , which had peaked and flat distributions , respectively ( Figure 5E and 5F ) . When we compared the spatial distribution of the ratio of Arp3 to F-actin to infer the degree of filament branching , no consistent differences in Arp3–to–F-actin ratios were observed between different keratocyte morphologies ( unpublished data ) , suggesting that VASP activity does not significantly affect branching in keratocyte lamellipodia , consistent with previous findings using purified protein systems [21 , 22] , but in contrast to other studies employing cells or purified proteins [4 , 6 , 19] . To unify our observations into a functional context , we developed a mathematical model that accounted for self-organization of keratocyte leading edge and VASP-mediated F-actin growth dynamics . This model allowed us to make predictions about keratocyte shape and was based on the following assumptions about actin dynamics and protrusion at the leading edge: ( 1 ) The F-actin network is organized in a dendritic array such that actin filaments are oriented at ±35° relative to the locally normal direction of protrusion [42] . Filaments are distributed over a wide range of angles , but this distribution is doubly-enhanced and peaked at ±35° due to optimal growth conditions for both mother and daughter filaments , the angle between which is 70° . Since mother and daughter filaments are oriented at the same angle with respect to the leading edge [42] , we lump all filaments growing to the left and to the right into two groups , and do not explicitly keep track of individual angles . ( 2 ) Growing barbed ends at the leading edge elongate with a rate limited by membrane resistance and local concentration of actin monomers ( G-actin ) [43] . ( 3 ) Arp2/3-mediated filament branching takes place with equal rate per each existent leading-edge filament [28] ( Text S1 ) . This per filament rate is equal to the total number of filaments nucleated over the whole leading edge per second divided by the total number of the uncapped leading-edge filaments . The molecular pathway determining this rate is unknown; a plausible mechanism could be based on rapidly diffusive molecules , the total number of which is conserved , controlling the total number of branching events per cell . Assuming that the branching takes place only along the leading edge , each filament has equal probability to become a mother filament . Then , as the total number of growing filament ends increases , the branching rate per filament inversely decreases . A filament at +35° branches off filaments oriented at −35° , and vice versa [42] . We define the leading-edge filament as the filament whose growing barbed end is in physical contact with the membrane . ( 4 ) VASP associates with/dissociates from barbed ends with constant rates and remains associated with elongating barbed ends until it dissociates [6 , 20 , 44] . ( 5 ) VASP protects barbed ends from capping; unprotected barbed ends are capped at a constant rate [6 , 20] . ( 6 ) The barbed ends of elongating actin filaments undergo lateral flow along the leading edge with a rate proportional to local protrusion [28 , 45] . ( 7 ) The shape of the leading edge is determined by the graded radial extension model [29] , according to which the local slope of the leading edge is determined by the ratio of the local normal protrusion rate to that in middle front of the cell . ( 8 ) The length of the leading edge is a constant parameter . At the sides of the leading edge , boundary densities of the uncapped ( VASP-free and VASP-associated ) barbed ends are constant parameters in the model . These parameters are crucial for the model predictions ( discussed below ) . These assumptions , which are expressed mathematically in Text S1 , lead to equations governing VASP activity , F-actin density , protrusion rate , and leading-edge stability and shape . The analytical and numerical solutions qualitatively explain our experimental observations as follows . In coherent cells , which have high VASP activity at the leading edge and low effective capping rate , the average density of actin filament barbed ends at the leading edge is increased , as well as the proportional VASP density associated with these ends ( see Figure 5A–5D; F-actin density , measured along the curve very close to the leading edge in this figure , is proportional to the number of actin filaments per micrometer intersecting with the curve parallel and just behind the leading edge , and therefore is also proportional to the density of barbed ends , assuming that all filaments abutting the leading edge are growing [42] ) . A simple explanation for this increase in F-actin density in the presence of VASP is that VASP skews the balance between branching and capping . Without VASP , nascent barbed ends emerge at a constant rate , whereas a constant capping rate maintains an average number of growing filaments . VASP protects a fraction of the growing barbed ends from capping , so the effective capping rate per total number of growing ends decreases , increasing the average number of growing filaments ( see Text S1 for quantitative details ) . In addition , when VASP is enriched at the leading edge , actin filaments , which grow for longer time periods before capping , undergo significant lateral flow ( illustrated in Figure 6A ) . When we investigated the stability of the leading edge of coherent keratocytes mathematically , we found that high VASP activity maintains greater density of barbed ends abutting the membrane at the front , leading to low membrane resistance per filament . This low resistance allows the protrusion rate to become insensitive to F-actin density , and instead limited by G-actin concentration . The even distribution of G-actin along the leading edge , together with the lateral flow of actin filaments , leads to the smooth leading edge of coherent cells ( Text S1 ) . In this coherent regime , significant fluctuations of the F-actin density do not cause respective fluctuations of the local protrusion rate , and the leading edge remains smooth . In decoherent cells , which have low VASP activity at the leading edge and a high effective capping rate , elongating filaments are rapidly removed from the leading edge by capping and the density of barbed ends decreases ( Figure 5C ) . Barbed ends , which grow for shorter time periods before capping , undergo slow lateral flow and are not redistributed along the leading edge . In this decoherent regime , fluctuations of F-actin density cause respective fluctuations of the local protrusion rate: high local branching density due to stochastic fluctuations at random locations increases the number of filaments pushing the membrane at the front creating a local protrusive “lobe” ( Figure 6B and Text S1 ) . Barbed ends slide faster into and slower out of the lobe , creating a positive feedback between actin filament local focusing and protrusion that causes short-scale instabilities of the leading edge , thus making its shape rough . When we modeled the F-actin profiles along the leading edge of cells , we found that they depended crucially on the boundary conditions at the sides of the leading edge and on the total branching rate . We assumed that at the sides of the leading edge the cell , where the large adhesions are located , there are specific local conditions generating and maintaining a constant density of uncapped barbed ends . If this fixed boundary density is equal to the average density being maintained along the leading edge by the dynamic balance between branching and capping , then the F-actin density along the leading edge is constant ( Text S1 ) . However , if the boundary density is less than this threshold , more nascent filaments branch out closer to the center of the cell . This , in turn , increases the net branching rate at the center , because more nascent filaments branch off the higher number of the existent filaments at the center . The existent growing barbed ends start to effectively compete for resources ( because the total number of branching events per second is conserved ) , and if the F-actin density at the cell sides is kept lower , the center “wins . ” This positive feedback leads to the characteristic inverted parabolic profile of the F-actin distribution along the leading edge with maximum at the center ( Figure 6C ) that matches our observations ( see Figure 5A and 5C ) . The lateral flow is crucial for maintaining the coherent inverted parabolic profile of the F-actin distribution along the leading edge; without it , the barbed ends would cluster irregularly at random locations . The flat F-actin distribution at the leading edge of decoherent cells is due , in part , to the slow and irregular lateral flow along the leading edge . The characteristic canoe shape of coherent cells is achieved through a graded distribution of extension along the leading edge . Experimentally , we observed that coherent cells with high VASP at the leading edge have increased F-actin density at the leading edge ( Figure 5C ) , which according to our model , leads to increased rates of actin growth and protrusion ( Figure E of Text S1 ) . With this high F-actin density peaking in the middle of the leading edge , the rate of protrusion , which is insensitive to the density of barbed ends , decreases very slowly along the leading edge , so the leading edge remains flat and extends far from side to side creating the characteristic wide canoe shape ( Figure 6D ) . At the sides , where the F-actin density decreases significantly , membrane resistance starts to limit protrusion , and the rapidly decreasing protrusion rate leads to high curvature at the sides of the leading edge . In decoherent cells , the overall shape of the leading edge remains parabolic , although with sharper transitions from the center to the curved sides , which are apparent as a rounder D keratocyte leading edge shape ( Figure 6D ) . Because these cells are characterized by lower F-actin densities , which correspond to a qualitatively different region in the density–velocity relation compared with coherent cells ( Figure E of Text S1 ) , the protrusion rate in decoherent cells decreases faster from the center to the sides , where protrusion drops to levels that cannot overcome membrane resistance . Consequently , the distance from the center to the sides is less than that in coherent cells , so decoherent cells are narrower from side to side . Since our model predicted that VASP was responsible for the morphological phenotypes observed , we tested our model by acutely delocalizing Ena/VASP proteins from the leading edge of keratocytes . VASP was delocalized by competition with the pharmacological barbed end capper , cytochalasin D [6 , 46] . VASP delocalization was often accompanied by a decrease in cell width , suggesting that these two parameters were functionally connected ( Figure 7A , B ) . This result also supported our model , which proposed that low VASP activity at the leading edge resulted in narrow D shaped keratocytes ( see Figure 6C , D ) . In a population of keratocytes , cytochalasin treatment eliminated cells with highest enrichment of VASP at the leading edge ( Figure 7C ) . Our quantitative comparison of shape showed that cytochalasin treatment eliminated keratocytes with extreme canoe shapes ( Figure 7D ) . Moreover , the observed correlations that established a relationship between cell shape , local leading edge curvature , F-actin distribution , and VASP enrichment at the leading edge of wild-type cells were absent in cells treated with cytochalasin ( Figure 7D–7G ) . Our results show that cytochalasin D , acting as a barbed end capper , antagonized VASP localization at the leading edge and altered the shape of keratocytes and the F-actin network towards the decoherent side of the phenotypic continuum . During extensive observation of different keratocyte morphologies , we hypothesized that coherent keratocytes with high VASP at the leading edge represented a mature state of cellular organization and migration . We evaluated the contribution of VASP in the generation of smooth lamellipodia in coherent cells by obstructing lamellipodial protrusion and examining its subsequent emergence and recovery . When we placed a barrier in the path of movement of a coherent keratocyte with EGFP-VASP enriched at the leading edge , the front edge of the lamellipodium that reached the barrier became temporarily stalled and the levels of VASP at the leading edge dramatically decreased ( Figure 8 and Video S2 ) . When the barrier was removed , the leading edge instantly resumed protrusion and appeared rough with protruding microregions enriched in EGFP-VASP . EGFP-VASP quickly became uniform as the cell continued to regain the original smooth leading edge shape . This rapid redistribution of VASP and thus barbed ends along the leading edge confirms the previously described phenomenon of lateral flow , which is important for the maintenance of coherence , as suggested by our model ( Figure 6 ) .
Ena/VASP proteins have not only been implicated in the global determination of migration speeds in different cell types [5 , 6 , 10 , 11 , 17] , but also affect the spatial organization of local actin-related cellular structures , such as lamellipodia that contain a branched dendritic network or filopodia , which possess long actin filaments . The ultrastructure of wild-type lamellipodia and growth cones has revealed long actin filaments , whereas those with depleted Ena/VASP revealed shorter , more branched filaments [4 , 6] . Lamellipodial structure may also be reorganized to give rise to filopodia by altering actin filament length , through changes in the activities of cappers and antagonizing factors that facilitate filament growth [7 , 44 , 47] . Therefore , the balance between the activity of Ena/VASP proteins and capping proteins may determine the type of actin network architecture present in different cell types , which may be observed as changes in cell morphology . Our initial observations of epithelial fish keratocytes revolved around cell shape and leading-edge morphology . Keratocytes have broad , flat lamellipodia that lack filopodia and have been generally described as having a characteristic fan or canoe shape [24–26] despite the fact that morphological variation is part of the natural heterogeneity of keratocytes obtained from primary cultures [31 , 32] . Particularly , very little attention has been devoted to less-regular morphologies and to understanding how “coherent , ” smooth keratocytes differ from “decoherent , ” rough ones . In this study , we have shown molecular differences between these two extreme morphologies and established a strongly correlated suite of morphological phenotypes related to Ena/VASP accumulation at the leading edge . Coherent keratocytes have VASP that is enriched at the leading edge and peaked F-actin distributions along the edge , whereas decoherent cells have sparse VASP and flat F-actin distributions , suggesting that VASP activity at the leading edge modulates the architecture of the actin network , which then becomes evident as the morphological and motile phenotypes observed . EGFP-VASP delocalization from the leading edge of keratocytes after cytochalasin D treatment showed that Ena/VASP proteins might be binding at or near the barbed end of actin filaments , in agreement with a previous study in fibroblasts , which proposed that this mechanism protects actin filament barbed ends from capping [6] . This proposed anticapping activity of Ena/VASP has been controversial: biochemical studies have demonstrated that Ena/VASP proteins can inhibit actin filament capping by several different barbed binding proteins [20] , whereas other in vitro studies showed no evidence of such competition by VASP [21 , 22 , 48] . Even though the net result of Ena/VASP activity appears to result in increased actin filament length , the in vivo molecular mechanism of this effect is still unclear . Increased actin filament length by Ena/VASP proteins may stem from direct competition with capping protein for barbed end binding , increased actin filament growth rate , reduced filament branch formation , or a combination of any of these activities [6 , 19–21] . Our results are more consistent with the hypothesis that a primary function of VASP at the leading edge is to oppose the activity of capping proteins . A mathematical model helped us understand how the underlying actin network organization and dynamics were influenced by these VASP activities and how that could lead to distinct cellular morphologies . This model pointed to a specific molecular mechanism by which VASP activity increases the length of filaments within the actin network: VASP prevents filaments from being capped , thus allowing them to grow for a longer time . We also experimentally tested the prediction that VASP was needed for the maintenance of the coherent phenotype based on the mechanistic assumption that VASP competes with capping . We treated cells with cytochalasin D to antagonize barbed-end binding by VASP and thus increase filament capping . We observed a drop in VASP density at the leading edge after cytochalasin D treatment and , in agreement with our model , the side-to-side lamellipodial width decreased linearly with a rate of ∼0 . 1 μm/s , similar to that of the inward actin flow ( C . A . W . , P . T . Yam , L . Ji , K . Keren , G . Danuser , and J . A . T . , unpublished data ) ] . The decrease in VASP levels at the edge continued for a few tens of seconds during which the keratocyte width shrank by 20%–30% , and then stabilized . Moreover , VASP displacement from the leading edge not only decreased cell width , but also eliminated cells with extreme coherent canoe-shaped morphologies . Together with the altered fraction of keratocyte morphologies observed after VASP mislocalization or overexpression , these data support the idea that VASP activity is important for the maintenance of the coherent morphology . When keratocyte migration was examined as a function of cell morphology , we found that coherent , smooth cells migrated significantly faster than decoherent , rough cells , which demonstrates that cell morphology is tightly coupled to the speed of migrating keratocytes . These results are consistent with previous descriptions of keratocytes with fast protrusion rates as fan-shaped , whereas cells with slower protrusion rates were described as irregular or fibroblast-like in shape [30] . Our mathematical model suggests that the increased F-actin density at the leading edge , which is observed in coherent cells , creates less resistance per filament as the filament elongates , so the rates of F-actin growth and protrusion accelerate ( Figure E of Text S1 ) , leading to the observed faster migration speed . In decoherent cells , which have low F-actin density resulting from low VASP activity at the leading edge , the membrane resistance per filament is large and becomes the limiting factor in the protrusion rate , which becomes very sensitive to the F-actin density and thus cells migrate slower . We observed a strong relationship between keratocyte speed and morphology , which depended on VASP localization at the leading edge . A positive correlation between VASP localization and cell speed or protrusion has also been observed in Dictyostelium [11] and melanoma cells [49] , contrary to observations in fibroblasts [5 , 6] . These conflicting observations suggest that different cell types may distinctly coordinate protrusion with overall cell migration and may have different rate limiting parameters of actin dynamics and cell motility . When we examined the directional component of velocity in keratocytes , we observed that rough , decoherent , wild-type keratocytes had increased curvature of trajectory compared to smooth , coherent , wild-type keratocytes . Unlike the smooth and regular leading edge of coherent keratocytes , the leading edge of decoherent cells can fluctuate widely during protrusion . In other words , different regions of the leading edge may protrude at different rates in an uncoordinated fashion . This phenomenon may be associated with greater frequency of cell turning , because either the whole left or right half of the lamellipodium would advance faster than the other half , effectively changing the average orientation of the leading edge and consequently changing the direction of migration . Thus , morphological variations manifest themselves during cell migration creating different behavioral patterns . We also found that Ena/VASP protein mislocalization led to increased trajectory curvature . This result is consistent with previous studies showing that intracellular Listeria that were deficient in Ena/VASP recruitment exhibited increased trajectory curvature [50] and VASP-null Dictyostelium displayed decreased cell directionality during chemotaxis [11] . Note , however , that directional control may be mechanistically quite different in these cell types . Epithelial fish keratocytes can rapidly migrate in a graceful gliding motion , all the while maintaining a relatively uniform and persistent shape . This migratory behavior requires the exquisite coordination of the intricate cellular migration machinery composed of three processes—protrusion , adhesion , and retraction—which are typically dissected separately . This work , in which we focused on the lamellipodial protrusive actin-based machinery resulting in the elongation and capping of actin filaments , is no exception . Future work , armed with broader and more detailed mathematical models , should strive to integrate our increasing understanding of these individual parts of the machinery and to understand how they interact to generate spatiotemporally coordinated cell migration in different cell types . We believe that this work , though limited in scope and susceptible to hidden variables and as-yet unknown molecular players , provides an example of how information from multiple spatial and organizational scales can be successfully brought together to explain part of a complex phenomenon . Within the reductionist context of this work , quantitative analysis and mathematical modeling were crucial to the understanding of cell shape and motile behavior in terms of the molecular activity of Ena/VASP proteins . In view of the strong correlation between VASP enrichment at the leading edge and the quantitative morphological parameters analyzed in fixed cells , a more quantitative characterization of the morphology ( shape , leading-edge curvature ) of live migrating cells may be warranted in the future to provide more detailed insights about the dynamics and activity of Ena/VASP . It is important to note that even though our mathematical model was able to recapitulate and provide a self-consistent explanation of our quantitative observations of cell morphology , F-actin organization , and motile behavior , it was only able to do so in a qualitative manner . Ideally , future modeling will be able quantitatively bridge experimental data and theory . Some steps in this direction are discussed in Text S1 . Nevertheless , our current model served as an important tool to generate a testable prediction and to interpret the cell morphologies observed . Overall , cell morphology represents a large-scale manifestation of underlying cytoskeletal organization and dynamics . Regulation and modulation of the actin cytoskeleton are likely to be major biological mechanisms affecting cell migration . Actin-remodeling proteins localize to propulsive structures in morphologically diverse cell types—neutrophils , fibroblasts , neurons , and intracellular bacterial pathogens—where they play crucial roles in the morphogenesis and maintenance of pseudopods , lamellipodia , filopodia , or bacterial comet tails , all of which inherently have different actin network organizations . Ena/VASP proteins , which are capable of enhancing the elongation of actin filaments by competing with capping protein for barbed-end binding , have emerged as important actin-remodeling proteins and strong candidates for the modulation of the underlying actin cytoskeleton that dictates cell morphology and migration .
Keratocytes were cultured from the scales of the Central American cichlid Hypsophrys nicaraguensis as described [51] , but the isolated scales were sandwiched between two acid-washed glass 25-mm coverslips and cultured at room temperature in the dark using Leibovitz's L-15 medium ( Gibco BRL; http://www . invitrogen . com ) supplemented with 14 . 2 mM HEPES pH 7 . 4 , 10% FBS , and 1% antibiotic-antimycotic ( Gibco BRL ) before transfection or processing for immunofluorescence the day after isolation . Keratocytes were transfected using a small-volume electroporator for adherent cells as previously described [52 , 53] . Coverslips containing keratocytes were placed in fish Hank's Balanced Salt Solution ( HBSS ) [54] containing 85% NaCl , and 20 μl of plasmid DNA ( 1 μg/μl ) in water were placed directly onto the cells . Keratocytes were immediately electroporated with three pulses of 185 V and allowed to recover for ∼24 h in culture media to allow for expression . Before live cell imaging or immunofluorescence , sheets of keratocytes that had migrated off the scales were washed for ∼5 min in 85% PBS , 2 . 5 mM EGTA , pH 7 . 4 to separate individual migrating cells . Indirect immunofluorescence was performed using rabbit polyclonal anti-murine VASP ( 2010 ) and anti-murine EVL ( 1404 ) antibodies [4 , 5] . Keratocytes were fixed in ice cold 2 . 5% glutaraldehyde , 0 . 025% Triton X-100 in PBS for 1 min . Autofluorescence was quenched by incubation in 0 . 1% sodium borohydride in PBS twice for 5 min . Cells were blocked and permeabilized using PBS-BT ( 3% BSA , 0 . 1% Triton , 0 . 02% sodium azide in PBS ) before incubation with antibodies diluted in PBS-BT . F-actin was labeled by incubation with fluorescently labeled phalloidin ( Invitrogen; http://www . invitrogen . com ) . Indirect Arp2/3 immunofluorescence was performed using rabbit polyclonal anti-human Arp3 antibodies as described previously [55 , 56] , except that cells were simultaneously fixed and permeabilized in cytoskeleton buffer containing 0 . 32 M sucrose ( CBS ) [57] , 4% formaldehyde , 0 . 1% Triton X-100 , and 0 . 5 μg/ml FITC-phalloidin ( Invitrogen ) for 15 min . Images were acquired using an Axioplan microscope ( Carl Zeiss Microimaging; http://www . zeiss . com ) equipped with a CCD camera ( MicroMAX 512BFT; Princeton Instruments; http://www . piacton . com ) . FP4-mito , AP4-mito , and mouse VASP in pMSCV [5 , 17] were subcloned into pEGFP-C1 ( Clontech Laboratories; http://www . clontech . com ) using standard molecular biology techniques . BglII and HindIII restriction sites were used to subclone FP4-mito , AP4-mito , and murine EVL . HindIII and BspEI were used to subclone VASP . Because individual keratocytes are heterogeneous in their responses to pharmacological agents , they were treated with 0 . 5 μM for 5 min; 0 . 8 μM for 2 , 3 , and 5 min; or 1 . 0 μM cytochalasin D ( Sigma; http://www . sigmaaldrich . com ) for 2 min in culture media . Time-lapse images were collected at 10-s intervals using a Nikon Diaphot-300 inverted microscope with a CCD camera ( MicroMAX 512BFT; Princeton Instruments; http://www . piacton . com ) . The rear of keratocytes was tracked using the “Track Points” option of MetaMorph software ( Molecular Devices; http://www . moleculardevices . com ) to measure speed and direction as previously described [31 , 50 , 58] . For population speed analysis , tracks were truncated to correspond to the same time ( 150 s ) . In this study , a minority of cells imaged using a different objective ( n = 11 ) and persistent circlers ( n = 4 ) were excluded from trajectory analysis . The population used for trajectory analysis included: EGFP smooth , n = 20; EGFP rough , n = 23; AP4-mito smooth , n = 9; AP4-mito rough , n = 16; FP4-mito smooth , n = 11; FP4-mito rough , n = 26; VASP smooth , n = 9; and VASP rough , n = 11 . Keratocyte leading-edge morphology was classified as smooth or rough by eye by determining whether each cell was more similar to the smooth or rough reference cells depicted in Figure 1A and 1B . Long trajectories were collected using a low-magnification air objective , which had a resolution suboptimal for detailed cell shape measurements . To compare immunolocalized Arp3 and F-actin along the leading edge and the enrichment of immunolocalized VASP ( VASP peak-to-base ratios ) across the leading edge of keratocytes , measurements were obtained using the “linescan” option in MetaMorph and background subtracted . F-actin and VASP distributions along the leading edge were calculated using the cell outline polygons as guides ( see “cell shape analysis” section below ) . For each vertex point along the leading edge of a given cell , intensities were sampled at 20 points ( ∼2 μm ) for F-actin and ten points for VASP ( ∼1 μm ranging from ∼0 . 3 μm outside to ∼0 . 7 μm inside the outlines ) spaced one pixel apart along the inward normal and averaged . Micropipettes were pulled using a P-92 Flaming-Brown micropipette puller ( Sutter Instruments; http://www . sutter . com ) from 0 . 5 mm inner diameter ( ID ) /1 . 0mm outer diameter ( OD ) glass capillaries , and positioned with a Narishige MMO-202ND micromanipulator . Keratocytes were transfected overnight using FuGENE6 ( Roche Diagnostics; http://www . roche . com ) and allowed to recover and express EGFP-VASP for one day . Time-lapse images were acquired using a Zeiss Axiovert 200 inverted microscope with Nomarski differential interference contrast optics and a Cascade II 512B CCD camera ( Photometrics; http://www . photomet . com ) . Cell morphology was measured by representing cell shapes as polygonal outlines and comparing those outlines with the PCA , as described [41] . Briefly , cell shapes were manually determined by using the “magnetic lasso” tool in Adobe Photoshop to trace the edge of each cell , based on images of fluorescent phalloidin . Each lasso selection was converted into a binary mask and outlines were extracted from those masks to derive a series of ( x , y ) points corresponding to the cell boundary . Each series was resampled to 150 points , evenly spaced along the cell boundary . Finally , the outlines were mutually aligned to bring the shapes into a common reference frame . The remaining variability in the point positions was then characterized with PCA to derive a small number of highly explanatory modes of shape variation . This analysis determined that three principal “shape modes , ” which are illustrated in Figure S2 , are sufficient to explain over 95% of the variability in shape of the 43 untreated cells and 27 cytochalasin D–treated cells . Of these modes , only the second—describing shapes ranging from canoe to D—correlated with VASP distribution . To quantify the shape of an individual cell , we measured its position along this mode in terms of standard deviations from the mean shape . To calculate the roughness of each cell's leading edge , we used a measure that we refer to as “local leading-edge curvature . ” Mathematically , the curvature of a function at a particular point is defined as the reciprocal of the radius of the circle that has the same tangent as the function at that point . A sharply bending curve will share a tangent with a small circle , and thus have a large curvature; in the limit , a straight line is tangent to an infinitely large circle and has zero curvature . The curvature of a parametric plane curve [x ( p ) , y ( p ) ] at a point p can be calculated as ( x′·y ′′ – y′·x′′ ) / ( x′2+y′2 ) 3/2 , where prime signifies the first derivative at point p and double prime the second derivative . We calculated the curvature at each of 90 points along the leading edge of the keratocyte outlines , using central-difference approximations to the derivatives . To determine the values of “local leading-edge curvature , ” we summed the absolute values of the curvatures along the leading edge , and multiplied this by the length of the leading edge to account for the fact that smaller keratocytes will have higher total curvature due to their size alone . ( Under this measure , a perfectly smooth semicircle sampled at 90 points would have a value of 90π [≈283] ) . Overall , rough leading edges have high local leading-edge curvature values and smooth leading edges have low values . To examine the contours of migrating keratocytes ( Figure 4 ) , cell outlines were calculated as described in P . T . Yam , et al . ( unpublished data ) . The mean speeds per cell for each pair of transfected keratocyte populations ( e . g . , EGFP versus AP4-mito , EGFP versus FP4-mito , etc . ) and for rough and smooth cells ( e . g . , EGFP rough versus EGFP smooth ) were statistically compared using the Mann-Whitney test . Trajectories were evaluated by comparing mean angles between 2 and 45 μm ( distance traveled ) using the same test . The proportions of smooth and rough cells present in all combinations of populations of transfected keratocytes were compared using the two-sample test for binomial proportions [59] . Linear regression was used to compare the relationship between VASP peak-to-base ratios , cell shape , F-actin distribution ( F-actin ratio ) , and local leading-edge curvature . Briefly , we modeled the densities of right- ( left- ) oriented growing barbed ends along the leading edge with functions b+ ( x , t ) ( b− ( x , t ) ) for ends not associated with VASP and with functions b̃+ ( x , t ) ( b̃− ( x , t ) ) for ends associated with VASP . According to the model assumptions , the following equations govern these densities: We considered these equations on the leading edge: −L ≤ x ≤ L . We choose the boundary conditions at x = ±L as follows: The meaning of these conditions , choice of the model parameters , and methods of solution of equations are thoroughly explained in Text S1 . We described the leading-edge profile with the function y = f ( x ) . The overall steady shape is derived from the Graded Radial Extension model [28 , 29] according to the formula: where is the local protrusion rate , which is a function of the local density of barbed ends . To investigate the local stability of the leading edge , we solved the system: where b̄ is the average density of barbed ends and bl is the local density of barbed ends . | The shape of animal cells is largely determined by the organization of their internal structural elements , including the filamentous structures of their cytoskeleton . Motile cells that crawl across solid substrates must assemble their cytoskeletal actin filaments in a spatially organized way , such that net filament growth and cell protrusion occur at the front of the cell . Actin filament dynamics , in turn , influence the overall shape of the cell by pushing on the plasma membrane . In this work , we have explored the ways that variations in small-scale actin filament growth dynamics are coupled to large-scale changes in cell shape and behavior . By manipulating the availability of a family of actin-binding proteins ( Ena/VASP ) that regulate actin filament growth , we can alter the overall cell shape and motile behavior of epithelial fish keratocytes—unusually fast-moving and regularly shaped cells . We have also found that unperturbed keratocytes in a population exhibit a continuum of shape and behavioral variations that can be correlated with differences in Ena/VASP levels . We have developed a mathematical model that allows us to explain our observations of intrinsic cell-to-cell shape variation , motile behavior , and cell responses to molecular perturbations as a function of actin filament growth dynamics in motile cells . | [
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"biophysics"
] | 2007 | Emergence of Large-Scale Cell Morphology and Movement from Local Actin Filament Growth Dynamics |
Schistosomiasis , one of the most prevalent neglected parasitic diseases affecting humans and animals , is caused by the Platyhelminthes of the genus Schistosoma . Schistosomes are the only trematodes to have evolved sexual dimorphism and the constant pairing with a male is essential for the sexual maturation of the female . Pairing is required for the full development of the two major female organs , ovary and vitellarium that are involved in the production of different cell types such as oocytes and vitellocytes , which represent the core elements of the whole egg machinery . Sexually mature females can produce a large number of eggs each day . Due to the importance of egg production for both life cycle and pathogenesis , there is significant interest in the search for new strategies and compounds not only affecting parasite viability but also egg production . Here we use a recently developed high-throughput organism-based approach , based on ATP quantitation in the schistosomula larval stage of Schistosoma mansoni for the screening of a large compound library , and describe a pharmacophore-based drug selection approach and phenotypic analyses to identify novel multi-stage schistosomicidal compounds . Interestingly , worm pairs treated with seven of the eight compounds identified show a phenotype characterized by defects in eggshell assemblage within the ootype and egg formation with degenerated oocytes and vitelline cells engulfment in the uterus and/or oviduct . We describe promising new molecules that not only impair the schistosomula larval stage but also impact juvenile and adult worm viability and egg formation and production in vitro .
Parasitic trematodes of the genus Schistosoma cause schistosomiasis , a life-threatening infectious disease affecting both humans and animals . Schistosomiasis , one of the world’s greatest neglected tropical diseases , contributes to the global morbidity with 4 , 026 , 000 DALYs ( disability-adjusted life years ) [1] . Among human parasitic diseases , schistosomiasis ranks second behind malaria in socio-economic terms , public health importance and prevalence in the developing world . More than 780 million people are at risk of infection and it is estimated that there are approximately 261 million infected people in 78 countries , of whom 85% reside in sub-Saharan Africa [2] . Three major species ( S . mansoni , S . haematobium and S . japonicum ) account for the majority of human infections . Similar to other trematodes , schistosomes have a complex life cycle consisting of both free-living and parasitic forms with several developmental stages [3] . Moreover , whereas most of the trematodes are hermaphrodites , schistosomes have evolved two separate sexes and the sexual maturation of female worms as well as the subsequent production of eggs are both dependent on the pairing status with males [4–6] . Sexually mature adult females can produce , a large number of eggs each day , formed within the ootype by one oocyte and 30–40 vitelline cells . The eggs are responsible for both parasite transmission and disease pathogenesis as part of the egg produced can be trapped into host’s tissues causing granuloma formation and inflammatory processes which interfere with organs function [7] . To date praziquantel ( PZQ ) is the only drug recommended for the treatment of schistosomiasis being very effective against adult worms of all the medically important Schistosoma species ( S . mansoni , S . haematobium and S . japonicum ) [8 , 9]; however , PZQ is relatively ineffective against the juvenile and schistosomula larval stages both in vivo and in vitro [10–13] and it does not prevent re-infection [14 , 15] . Moreover its increasingly widespread use in mass chemotherapy campaigns and the identification of field [16–19] and laboratory isolates that exhibit significantly reduced susceptibility to PZQ [20–24] represents a serious concern for the development of drug-resistance strains . Consequently the search for novel schistosomicidal compounds is currently viewed as an urgent goal and there is great interest in new chemical compounds with demonstrated ( i ) ability to kill the parasites , possibly targeting different developmental stages , in order to lower worms burden and ( ii ) capacity to impair egg production , so that pathological effects are minimized , or even completely abolished . In this work we show how we succeeded in meeting both these requirements , and identified a number of new molecules having effects on both worm viability and egg production . To meet our aims , a recently developed high-throughput assay based on ATP quantitation in the larval stage schistosomula of S . mansoni [25] was used to screen a large compound library . In the follow up step a pharmacophore-based drug selection approach and phenotypic analyses were employed to identify novel multi-stage schistosomicidal compounds . We herein describe promising new hits for further chemical optimization . These hits are active on the schistosomula larval stage as well as on juvenile and adults worms . Interestingly , treatments of worm pairs with the majority of the new hits identified , induce a phenotype characterized by defects in eggshell assembly in the ootype and egg formation with degenerate oocytes and vitelline cells engulfment in the uterus and/or oviduct . Moreover , some compounds induce also gut dilation with detachment of the gastrodermis in adult worms .
Gambogic acid ( GA ) , perhexiline maleate ( 1:1 racemic mixture of ( R ) and ( S ) enantiomers ) ( PHX ) , dimethylsulphoxide ( DMSO ) , percoll , fetal bovine serum ( FBS ) , thimerosal were purchased from Sigma-Aldrich . CellTiter-Glo ( CTG ) reagent , used in the schistosomula viability luminescence-based assay was from Promega . Biowhittaker Dulbecco-Modified Eagle’s Medium ( DMEM ) with or without phenol red , HEPES , L-glutamine were from Lonza . Antibiotic-antimycotic reagent ( 100x ) was from ThermoFisher scientific; carmine-red and canada balsam were purchased from Merck . At the time the high throughput screening ( HTS ) was performed the compound collection consisted of around 40 , 000 small molecules from both commercial and non-commercial suppliers . In addition to FDA- and/or EMA-approved drugs the collection contained a structurally diverse range of chemotypes with average molecular weight 370 Da . Analysis of the collection revealed an attractive distribution of physicochemical properties ( e . g . logP , sp3 character and hydrogen bond donor/acceptors ) and good structural diversity ( average Tanimoto [26] distance from the nearest neighbour of 0 . 38 ) . Female ICR ( CD-1 ) 4–7 week-old mice ( Harlan Laboratories or EMMA ) were housed under controlled conditions ( 22°C; 65% relative humidity; 12/12 hours light/dark cycle; standard food and water ad libitum ) . Animals were subjected to experimental protocols ( Authorization N . 25/2014-PR ) approved by the National Research Council , Institute of Cell Biology and Neurobiology animal care and use committee and the public Veterinary Department of the Italian Ministry of Health , and experiments were conducted according to the ethical and safety rules and guidelines for the use of animals in biomedical research provided by the relevant Italian law and European Union Directive ( Italian Legislative Decree 26/2014 and 2010/63/EU ) and the International Guiding Principles for Biomedical Research involving animals ( Council for the International Organizations of Medical Sciences , Geneva , CH ) . All adequate measures were taken to minimize animal pain or discomfort . A Puerto Rican strain of S . mansoni was maintained by passage through albino Biomphalaria glabrata , as the intermediate host , and ICR ( CD-1 ) outbred female mice as definitive host as previously described [25] . Mice infected 7–8 weeks previously with single sex or double sex cercariae were euthanized with intraperitoneal injections of Tiletamine/Zolazepam ( 800 mg/kg ) + Xylazine ( 100 mg/kg ) and adult parasites were harvested by reversed perfusion of the hepatic portal system and mesenteric veins [27] . Juvenile worms were obtained from mice 28 days after infection . Cercariae were converted to newly transformed schistosomula by mechanical transformation using an optimized version of the protocol of Brink et al . , 1977 [28] , previously described by Protasio et al . [29] and adapted in our laboratory [25] . The schistosomula viability assay was carried out as previously described [25] . Briefly , compounds dissolved in DMSO were transferred to 384-well , black , tissue culture plates using the acoustic droplet ejection technology ( ATS-100 , EDC Biosystems , USA ) . DMSO alone and gambogic acid ( 10 μM ) were used as high and low control in each plate . A suspension of schistosomula in complete DMEM medium ( without phenol red ) was transferred to assay plates by a multidrop dispenser ( Thermo Fisher , USA ) in the number of about 100 schistosomula per well in a final volume of 30 μl . After 24 hours incubation at 37°C and 5% CO2 , a volume of 30 μl of CellTiter-GLO reagent ( CTG ) ( Promega , USA ) was added resulting in the generation of a luminescence signal proportional to the amount of ATP present in the well . Sample luminescence levels ( proportional to ATP levels ) were detected 30 minutes after CTG addition and quantified as RLU ( Relative Luminescence Unit ) by a charge-coupled device ( CCD ) -based detector ( ViewLux , PerkinElmer USA ) . Screening and potency results were evaluated by GraphPad ( Prism , USA ) . Worms recovered from infected mice at 28 days ( juvenile worms ) or 7–8 weeks ( adult worms ) post-infection were cultured in DMEM ( with phenol red ) complete tissue culture medium at 37°C in 5% CO2 atmosphere . For all treatments , 5–10 males or couples were incubated with selected compounds at the indicated concentrations and cultured in 3–5 ml of complete tissue culture medium for up to 7 days , unless otherwise stated as previously described [30] . The compound was given to parasites in vitro only once without medium addition and/or replacement . During the culture time , survival was monitored daily under a Leica MZ12 stereomicroscope and viability scored as previously described , based on phenotypical changes like motility and general appearance ( tegumental damage and darkness , gut peristalsis and morphology , plate attachment ) Briefly , the type and number of phenotypic responses were recorded into a ‘severity score’ ranging from 0 ( severely compromised ) to 3 ( no effect ) as previously described [25 , 30] . The following phenotype scoring criteria were used: 3 = plate-attached , good movements , clear; 2 = slower or diminished movements , darkening , minor tegumental damages; 1 = movements are heavily slowered , dark , tegument is heavily damaged; 0 = dead , lack of any movements . For each sample the following formula was used: Σ ( wormscores ) numberofworms Data were expressed as % severity score ( viability ) relative to DMSO . All tests were repeated at least three times . Images were recorded using with an Olympus BX41 microscope served by an Olympus SP-350 camera ( for adult worms ) or an Olympus AX70 fluorescence microscope supported by an Olympus XM10 camera with the Olympus CellSens Standard 1 . 8 . 1 software ( for eggs , spermatozoa , oocytes and vitelline cells in the worm culture media ) . The number of eggs produced by all worm couples was counted at day 3 using an inverted LEICA DM IL microscope . Carmine-red staining was performed essentially as previously described [30] . Briefly , adult worms were fixed for at least 24 hours in AFA ( 95% ethanol at 70% , 3% formaldehyde at 37% , 2% glacial acetic acid ) at room temperature , stained for 30 minutes with 2 . 5% hydrochloric carmine-red and then de-stained by several washes in acidic alcohol ( 70% EtOH at 100% , 2 . 5% HCl at 37% and 27 . 5% double-distilled H2O ) until no more color was released by the samples . Next , samples were dehydrated for 2 min in 90% , 30 sec in 100% ethanol and worms preserved in Canada balsam on glass slides . Images were taken on an Olympus FV1200 confocal laser scanning microscope using an UPlanFLN 40X immersion oil objective ( NA = 1 . 30 ) with optical pinhole at 1AU and a multiline argon laser at 488 nm as excitation source . The images were collected as a single stack . All statistical tests were performed using GraphPad Prism version 6 . 0c software ( San Diego , CA , USA ) . The data are shown as mean ± standard error of the mean ( SEM ) or ± standard deviation ( SD ) as indicated . Differences observed in the in vitro assays were analyzed by Student’s t-test . For all experiments , p-values < 0 . 05 were considered to be statistically significant .
A compound collection comprising 38 , 811 molecules was screened at a single concentration of 10 μM using the schistosomula viability assay previously described [25] . The screening was carried out in multiple batches , according to the production of schistosomula , with the average number of plates per run being 10 . In order to determine the quality of the results , the Z’ value [31] was calculated for each tested plate using the vehicle ( DMSO ) dispensed wells as negative control and gambogic acid dispensed wells as positive control . All plate Z’ was found to be above the 0 . 5 threshold which is commonly considered the lowest acceptable value for a robust assay ( Fig 1A ) . The activity of each tested compound was calculated as percentage of ATP reduction against the vehicle ( 0% ) and gambogic acid ( 100% ) , thus representing the percentage of dead schistosomula . This normalization allowed compensation for the schistosomula batch-to-batch variations . The compound activity distribution was found to be Gaussian , as a consequence the positivity threshold was set to 60% , which is the average sample activity plus three times the standard deviation ( Fig 1B ) . By using this threshold , 275 active compounds were identified ( 0 . 7% hit rate ) . It has to be noted that the average compound activity is greater than 0 and the activity distribution is rather broad . These aspects will be commented on in the discussion section . The 275 hits from the HTS were subjected to quality control by LC-MS in order to check compound identity and purity ( acceptable purity criteria set to be > 90% peak area in the diode array trace ) . Sixteen compounds were discarded at this stage as they failed to pass the QC . Hit selection was performed using a clustering approach based on the Taylor Butina algorithm [32] , a non-hierarchical clustering method that ensures that each cluster contains molecules with a certain cut-off ( or threshold ) distance from a central compound . Circular fingerprints with radius 2 and 2048 bits were generated using the RDKit software [33] , with the purpose of generating a similarity matrix based on a Tanimoto index [26 , 33] . The effective number of neighbours for each molecule was calculated based on the Tanimoto level ( 0 . 8 ) used for clustering . This procedure gave a collection of 80 clusters of which 30 were singletons . Eighty centroids were selected together with 30 compounds picked randomly from the most populated clusters . The final set of 110 compounds was tested again in an independent schistosomula dose response curve assay ( 40 nM-50 μM ) and from this screen 22 compounds were confirmed to be active with LD50 ranging from 5 to 40 μM . After visual inspection eight compounds were selected to be further profiled . Fresh powders of these eight compounds were repurchased/resynthesized and retested on schistosomula ( Fig 2 ) . The previously found LD50’s were confirmed for all eight compounds . In order to investigate the efficacy of the eight selected compounds on other S . mansoni developmental stages , survival assays and bright-field microscopy analyses were performed on adult male worms . All compounds but SmI-8 and SmI-82 negatively influenced adult worms viability; SmI-1 , SmI-10 ( perhexiline maleate ) , and SmI-11 showed the highest effect being the most active compounds at 10 μM ( Fig 3 ) . Indeed SmI-1 and SmI-11 were able to induce complete parasites death 7 days after a single compound treatment , an effect similar to that previously reported for perhexiline maleate ( SmI-10 ) [30] ( Fig 3 ) . Importantly , SmI-1 and SmI-11 were highly effective also on juvenile S . mansoni worms ( 4 weeks old ) ( Fig 4A ) and mature paired parasites ( 7–8 weeks old ) ( Fig 4B ) . In particular at the concentration of 10 μM , SmI-1 and SmI-11 were able to induce parasites death in juvenile worms respectively 24 hours and 5 days post treatment ( Fig 4A ) and in mature couples at 5 and 7 days after treatment using 5 μM concentration ( Fig 4B ) . Such impact on all parasites stages viability has been previously reported also for the compound perhexiline maleate ( SmI-10 ) [30] . Importantly at sub-lethal concentrations ( 1 and 2 . 5 μM ) , even though not significantly affecting viability or parasites pairing , both compounds caused marked reduction in the number of eggs laid by worm pairs 3 days upon treatment ( Fig 4C ) . Ligand-based molecular modelling approaches are generally used when a reliable target structure is not available from which to derive the structural requirements important for activity . Pharmacophore modeling belongs to this kind of methodology and has been widely used in drug discovery [34] . To identify novel scaffolds by using the three identified hits , SmI-11 , SmI-10 ( perhexiline maleate ) , and SmI-1 , 3D pharmacophore models were developed . A 3D structure for each compound was generated using energy minimization and the resultant minimized structures were used as starting points for subsequent conformational analysis using a multi-objective genetic algorithm implemented in BALLOON software [35 , 36] . Unique low energy conformations within 10 kcal/mole of the corresponding global energy minimum were collected for each molecule . Pharmacophore models were then generated using Align-it software [37] . On the basis of the structural characteristics of compounds SmI-11 , SmI-10 , and SmI-1 aromatic and lipophilic features , hydrogen bond donor , hydrogen bond acceptor and positive charge were considered for model development . The generated pharmacophore models were visually evaluated through the generation of molecular maps as described in Fig 5 . A database search to find compounds matching the pharmacophores was conducted using an expanded version of IRBM’s internal library consisting of 40 , 000 molecules . Results were ranked on the basis of the Tversky similarity index [38] and the number of features in the corresponding phamacophoric model . Top 130 candidates were selected and assayed on schistosomula: 11 resulted with a good LD50 and were further profiled . The resulted compounds consisted of seven hits derived from the SmI-10 ( perhexiline maleate ) model , one from the SmI-11 model ( sulphonamide ) , and three based on the pharmacophoric model of SmI-1 ( Imidazo ( 1 , 2-a ) pyridine ) ( Fig 6 ) . Next , to test their ability to impact worms survival the new 11 compounds were all assayed on adult male worms at the concentrations of 10 and 20 μM with five of them ( SmI-233 , SmI-251 , SmI-290 , SmI-291 , and SmI-308 ) being active . In particular at the concentration of 20 μM we found less than 50% survival rate in worms 72 h upon treatment and almost complete death occurring at day 7 ( Fig 7 ) . Importantly all the selected compounds were also very active on both juvenile worms ( 4 weeks old ) ( Fig 8A ) and mature pairs ( 7–8 weeks old ) ( Fig 8B ) . In particular at the concentration of 20 μM all five compounds induced death of all juvenile parasites in 24 h and at the concentration of 10 μM less than 30% survival rate 72 h upon treatment with almost complete death occurring at day 5 ( SmI-233 , SmI-290 , SmI-291 , and SmI-308 ) or day 7 ( SmI-251 ) . The SmI-308 compound was also very effective on juvenile worms even at lower concentrations ( 2 . 5–5 μM ) . With respect to adult worm pairs , at the concentration of 20 μM all five compounds induced almost complete death at day 5 ( SmI-233 , SmI-290 , SmI-291 and SmI-308 ) or day 7 ( SmI-251 ) . The compounds also demonstrated to be very active on pairs treated at the concentration of 10 μM at day 7 . Moreover using sub-lethal doses ( 5 or 2 . 5 μM ) all the compounds , with the only exception of compound SmI-308 , had a strong impact on egg production during 72 hours of treatment ( Fig 9A ) . Intriguingly , the compounds causing egg production impairment also induced the release of a number of oocytes , spermatozoa , vitelline cells , eggshell fragments , and abnormal eggs in the tissue culture medium while no effect was recorded for the compound SmI-308 . An example of the tissue culture media observed 24 and 72 hours after treatment with compounds SmI-290 and SmI-308 is shown in Fig 9B . Compared to the other compounds a quite peculiar phenotype was observed in parasites treated with the SmI-308 compound . Despite the fact that parasites did not seem to be affected in terms of viability , pairing status , egg production , and germ cells release during the first 72 hours at sub-lethal doses , bright-field microscopy showed a general swelling of both male and female worms with a marked gut dilatation ( S1 Fig ) . In order to characterize the phenotypic alterations induced by compound treatment in more detail , carmine-red staining and confocal laser scanning microscopy analysis were performed . Remarkably , with the unique exception of compound SmI-308 , all selected compound- treated worms showed impairment in egg formation ( Fig 10A and 10B ) . Indeed , in the ootype of female parasites treated for 72 hours with SmI-1 , SmI-11 , SmI-233 , SmI-251 , SmI-290 ( 5 μM ) , and SmI-291 ( 2 . 5 μM ) we did not detect any mature eggs . Moreover , the ootype and often also the uterus showed the presence of disorganized oocytes , vitelline cells and intense carmine-red positive elements thought likely to be eggshell components . A similar ootype phenotype was observed in worms treated with perhexiline maleate ( 5 μM ) 72 hours after treatment ( S2c Fig ) . On the contrary the ootype of worms treated with the SmI-308 compound or DMSO ( vehicle ) contained healthy eggs ready to be laid ( Fig 10A and 10B ) . The overall structure of the ovaries seemed not to be affected thus retaining the normal organization with small immature oocytes ( IO ) in the anterior part and large mature oocytes ( MO ) in the posterior part . Also the vitellarium seemed to preserve its structure . However the ovaries of females treated with compounds SmI-233 , SmI-251 , SmI-290 , and SmI-291 ( 5 μM ) contained oocytes with alterations . In particular the ovary of worms treated with compounds SmI-233 , SmI-290 and , to a lesser extent , with SmI-291 showed black spots in MO likely due to degeneration . In SmI-251-treated females aggregates of degenerate cells in the anterior part containing MO and a marked degeneration of IO were observed in the ovary . A strong degeneration of IO was also observed in parasites treated with SmI-233 and SmI-291 compounds treatment ( Fig 10A and 10B ) . Moreover oviducts of parasites treated with compounds SmI-11 , SmI-251 , and SmI-290 showed a remarkable engulfment and defects of trafficking of MO towards the ootype . This phenotype was also sporadically observed in some worms treated with SmI-1 , SmI-233 , and SmI-291 compounds ( Fig 10A and 10B ) and in perhexiline maleate-treated samples ( S2 Fig ) . On the contrary we did not detect oocytes in the oviduct of female parasites treated with compound SmI-308 or DMSO ( vehicle ) ( Fig 10A and 10B ) . Finally female worms of pairs treated for 72 hours with SmI-233 , SmI-290 ( 5 μM ) and SmI-291 ( 2 . 5 μM ) also showed an increased number of degenerated cells in vitelline follicles ( Fig 10A and 10B ) . In addition , although phenotype consistency was not always seen for all worms within a test sample , we observed that all compounds with the only exception of SmI-1 led to gut dilation with detachment of the gastrodermis and accumulation of carmine-red positive particle aggregates in the lumen ( Fig 11 ) . This alteration was especially notable for compounds SmI-290 , SmI-291 and SmI-308 ( Fig 11 ) . The morphology and cellularity of the testicular lobes were similar in males of pairs treated with all selected compounds or DMSO with the exception of those treated with SmI-308 that showed cavities in the testicular lobes ( S3 Fig ) , cavities also present in the ovary of females . Mature sperm was present in all seminal vesicles and receptacles of male and female treated parasites .
In modern drug discovery , the process of lead identification involves both the screening of a compound collection against a target or a complete organism and the validation of a set of hits having acceptable activity to fight the disease . To this aim small organisms such as schistosomula can be employed using in vitro assays to screen large set of compounds in an automated , objective and high-throughput manner [25] . The initial screening using schistosomula can offer both advantages and disadvantages: the use of such a small and handy stage represent an useful tool for increased throughput and improved automation but the screen workflow with schistosomula need to be validated in the other stages of the parasite . Further phenotypic screen in the juvenile and adult parasite developmental stages as well as egg formation and production assessment are required in order to identify novel multi-stage schistosomicidal compounds with dual effects on both parasites viability and egg production impairment . In an attempt to search for new chemical compounds for the treatment of schistosomiasis having these features , combination of high throughput library screening on schistosomula , a pharmacophore approach and phenotypic analyses on both juvenile , known to be less sensitive to PZQ compared to mature egg-laying adults [10–13] , and adult parasites was successfully used for the identification of eight novel multi-stage schistosomicidal compounds , including perhexiline maleate that we previously described [30] . As reported in the results section , the HTS campaign was carried out in multiple batches , with compound throughput depending on the yield of each lot of the schistosomula production . On average ten 384-well plates were carried out per run . As a consequence we found extremely important to ensure consistent parasite production and minimize potential sources of contaminations . Once all HTS data were analysed they were found to be Gaussian distributed confirming that there is no bias in the compound collection or in the assay itself . The distribution of the results was rather broad likely due to random errors arising from both the influence of multiple assay runs and the limited number of parasites per well . In addition , the average of all HTS values was greater than zero suggesting that DMSO controls , which are located in a corner of the plate , may have slightly suffered from evaporation . In follow up to the HTS screen our approach to profile 300 hit compounds was supported by computational chemistry . A combination of clustering and retesting identified 22 bona fide inhibitors from which eight hits were selected for further work . In macroscopic terms these eight compounds can be grouped into 3 categories: i ) biphenyl analogs ( SmI-1 , SmI-8 , SmI-12 , SmI-82 ) ; ii ) biaryl analogs/compounds containing two linked aryl rings ( SmI-5 , SmI-11 , SmI-85 ) ; iii ) non-aryl analogs ( SmI-10 ) . One compound from each of these categories ( SmI-1 , SmI-11 and SmI-10 ) was selected taking into account its effect on viability reduction of both juvenile and adult parasites as wells as egg production impairment . Computational analysis was used to generate more detailed pharmacophore models to identify the key elements of the structures responsible for biological activity . Use of these pharmacophores to identify further potential schistosomicidal compounds proved successful . From the 130 compounds that based on the pharmacophores were selected for assay on schistosomula , 11 confirmed hits were obtained ( Fig 6 ) . These included five analogs that were of interest and that retained activity against juvenile and adult worms . Importantly , all selected compounds , with the only exception of SmI-308 , were also able to interfere with the egg production process when used at sub-lethal doses . Along with survival data , the egg impairment caused by the selected compounds represents an outstanding aspect in fighting the spreading of schistosomisiasis as the egg production represents a key component for the transmission and immunopathology of the disease . In addition , morphological studies by confocal microscopy analyses highlighted strong characteristic egg-associated phenotypes for seven out of eight hits . A lack of eggshell formation and an absence of mature eggs in the ootype associated to degeneration of immature and/or mature oocytes and vitelline cells and a remarkable engulfment of MO and vitelline cells , appeared to be some common features among these seven compounds ( Fig 10 ) . Defects in the egg machinery with production of abnormal eggs were observed for all compound-treatments with the only exception of compound SmI-308 . On the other hand , parasites treated with SmI-308 showed a completely different phenotype characterized by a remarkable gut enlargement with tegumental invaginations and oedema-like swellings of the body ( S1 Fig ) also detectable in the reproductive organs ( ovary and testis ) ( S3 Fig ) similar to the one recently described by others with arylmethylamino steroids compounds [39] . Defects in the ootype , with formation of dysplastic eggs have been shown before as consequences of knocking-down the activity of protein kinases [40–43] as well as treatment with derivatives of biarylalkyl carboxylic acid [44] . Alteration of the gastrodermis , that we describe here , has been detected with several other schistosomicidal compounds including mefloquine [45 , 46] or artemether [47] or derivatives of biarylalkyl carboxylic acid [44] or arylmethylamino steroids compounds [39] . Eggshell assemblage and egg formation occurring in schistosomes [48] is a complex process also epigenetically regulated [49] . Further understanding of their regulatory molecular mechanisms is valuable since disruption of these processes may provide leads for intervention with drugs for controlling schistosomiasis . We can conclude that the pharmacophore approach can represent an important tool in new hits identification process . It is encouraging that we have been able to identify a number of compounds with IC50 <10 μM against all developmental stages of the parasite definitive host and with potency against juvenile worms higher than PZQ . It is interesting that some morphological alterations associated to the treatment seem to be related to defects in the egg machinery and even thought the potential targets are still unknown , the phenotypes observed with these novel schistosomicidal compounds could drive investigation toward some common mechanisms , such as on genes and signaling pathways involved in eggshell assemblage and eggs formation . | Schistosomiasis is a neglected disease caused by parasitic flatworms called schistosomes . The disease affects hundreds of millions of people in developing countries in the poorest tropical and subtropical regions of the world and it represents a major public health and socio-economical problem in several countries . In humans , these blood flukes reside in the mesenteric and vesicle venules . They have a life span of many years and produce hundreds of eggs daily , which are able to pass through the gut lumen or the bladder to be finally excreted into the environment for maintaining the life cycle . Part of the eggs can be trapped in host tissues inducing immunologically mediated granulomatous inflammation and fibrosis leading eventually to severe sequelae such as hepatosplenomegaly and even death . Importantly , schistosome infections increase susceptibility to other parasitic , bacterial and viral diseases . To date , essentially a single drug , praziquantel , is available to treat this parasitic disease . Despite its high tolerability and efficacy against adult parasites it has an incomplete efficacy across all stages of the S . mansoni life cycle and it does not prevent reinfection . Moreover the potential risk of drug resistance is an increasing concern . In search of novel schistosomicidal molecules we screened a large compound collection using the schistosomula , larval stage of the parasite . We identified eight novel molecules able to impair viability of schistosomula , juvenile and adult worms and also egg formation and production , two important features required for both disease transmission and progression . | [
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"n... | 2017 | Discovery by organism based high-throughput screening of new multi-stage compounds affecting Schistosoma mansoni viability, egg formation and production |
Integrative analysis of gene dosage , expression , and ontology ( GO ) data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers . Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques . Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results . An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome . Statistical analysis identified 29 recurrent gains and losses and 3 losses ( on 3p , 13q , 21q ) associated with poor outcome after chemoradiotherapy . The intratumor heterogeneity , assessed from the gene dosage profiles , was low for these alterations , showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis . Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis , metabolism , macromolecule localization , translation , and transcription . Four genes on 3p ( RYBP , GBE1 ) and 13q ( FAM48A , MED4 ) correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort . These integrated analyses yielded 57 candidate drivers of 24 genetic events , including novel loci responsible for chemoradioresistance . Further mapping of the connections among genetic events , drivers , and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes . The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers .
Cervical cancer is one of the most common malignancies affecting women worldwide and a major cause of cancer death for women globally [1] . Radiotherapy combined with cisplatin is the treatment of choice at the locally advanced stages [2] . Improved therapy is needed , since more than 30% of the patients show progressive disease within 5 years after diagnosis and treatment related side effects to organs within the pelvis are frequent . Tumor stage , size , and lymph node involvement are the most powerful markers of aggressive disease , but do not fully account for the observed variability in outcome and are not biologically founded . A better handling of the disease may be provided by the discovery of efficient biomarkers for therapeutic planning and intervention , but requires more insight into the mechanisms underlying cervical carcinogenesis and treatment relapse . During carcinogenesis , genetic and epigenetic alterations drive the evolution of tumor towards increased malignancy and treatment resistance . The changes enable tumor cells to overcome microenvironmental constraints , sustain proliferation , and invade adjacent tissues and distinct organs [3]–[5] . Gene dosage alterations like gains and losses regulate the expression of genes and are motive forces for this evolution [6] , [7] . Tumor cells bearing an increasing number of gains and losses successively emerge and are selected for based on the growth advantage caused by the genetic changes . Discovery and functional assessment of gene dosage alterations involved in carcinogenesis are therefore essential for understanding the biology of the disease . At the locally advanced stages of cervical cancer , numerous gene dosage alterations and severe aneuploidy are frequently seen [8]–[10] . Moreover , pronounced intratumor heterogeneity in the gains and losses exists within the tumors , reflecting a high genetic instability [9] . The consequences of these alterations for the tumor phenotype are difficult to predict , since large chromosomal regions involving multiple genes are generally affected and some aberrations may be random events without biological significance [11] . Genome wide screening of DNA copy numbers in a decent number of patients enables identification of recurrent gene dosage alterations; i . e . , alterations characteristic of the disease , and alterations associated with the clinical outcome [12] , which are likely to be important in carcinogenesis and treatment resistance . Combining the data with expression profiles of the same tumors reveals the genes that are regulated primarily by the genetic events . The potential of this integrative strategy was recently demonstrated in a study on 15 early stage cervical cancers , where genes affected by aberrations on 1q , 3q , 11q , and 20q were reported [13] . Genetic events promoting tumor evolution and treatment resistance have , however , not been explored on a genome wide scale , and their biological meaning has not been addressed . The present work was conducted to discover candidate driver genes and assess their function in the carcinogenesis and chemoradioresistance of cervical cancers . Genome wide screening of DNA copy numbers and expressions was performed in 102 patients with locally advanced disease . Of these , pairwise data were available for 95 patients . Reliable comparison of gains and losses across the patients was ensured by using the tumor ploidy , as determined by flow cytometry , and the GeneCount method to correct for the normal cell content of the samples and extract the absolute copy numbers and thereby the gene dosages [14] . The use of GeneCount also enabled mapping of the intratumor heterogeneity in the gene dosage alterations , providing information of the chronological order in which they had occurred during tumor evolution [14] . The recurrent gene dosage alterations , the alterations associated with outcome after chemoradiotherapy , and the genes that were regulated by these alterations were identified . Further analysis of gene ontology ( GO ) categories [15] was performed to identify biological processes that were overrepresented among the affected genes and therefore probably regulated by the gene dosage alterations . Such large scale and combined genomic , transcriptional , and functional analysis is powerful in detection of driver genes and their biological meaning , but has not been presented before . We demonstrate the potential of this approach by the identification of five biological processes in carcinogenesis that were associated with recurrent and predictive gains and losses of a set of genes . The set included four genes within the predictive losses for which repressed expression was related to poor outcome in the patient group and in an independent cohort of 41 patients . The genes are candidate drivers of the genetic events and novel biomarkers of cervical cancers .
Cervical cancer patients subjected to curative chemoradiotherapy were included in the study ( Table 1 ) . Most cases were squamous cell carcinoma and human papillomavirus ( HPV ) positive . Aneuploidy was seen in about half of the tumors , including some of the adenosquamous carcinomas and HPV negative cases ( Figure S1A , S1B ) . Based on 97 patients , we generated an absolute gene dosage profile of the cancer genome by the use of array comparative genomic hybridization ( aCGH ) and the GeneCount analysis tool ( Figure 1A ) . All chromosomes were affected with gains and losses , however , some regions were more frequently found to be aberrant than others ( Figure 1B ) . Clustering of the patients based on gene dosages revealed no clear groups with characteristic aberrations . The recurrent gains and losses were identified by considering both the amplitude and frequency of each alteration in Figure 1B [16] . Hence , a larger weight was given to high-amplitude events that are less likely to be random aberrations without biological significance . The recurrent alterations comprised more than 42% of the genome , and consisted of 14 regions ( 528 Mb ) with gain and 15 ( 734 Mb ) with loss ( Figure 1C ) . Most of these alterations were also seen in the adenosquamous carcinomas and the HPV negative tumors ( Figure S1C , S1D ) . The most common alterations were gain on 1q , 3q , 5p , 20q , and Xq and loss on 2q , 3p , 4p , 11q , and 13q , each involving 44–76% of the patients ( Figure 1C , Table 2 ) . High level amplification ( seven regions ) and homozygote deletion ( six regions ) helped to depict the peak of five recurrent gains and two recurrent losses ( Table 2 , Table S1 ) . The frequency of the homozygote deletions was low ( 1–3% , Table S1 ) , and none of the tumors had more than one of them . Homozygote alteration is therefore probably not a common mechanism of gene regulation in cervical cancers , in contrast to the highly frequent heterozygote deletion . The highest gene dosage of 36 was found in a diploid tumor with a copy number of 72 on 11q22 . 1-2 ( Table 2 ) . Gene dosage alterations responsible for poor clinical outcome may not be as common as the recurrent ones . All alterations in Figure 1B were therefore included in the survival analysis . The LASSO method identified three regions with loss , 3p11 . 2-p14 . 1 , 13q13 . 1-q21 . 1 , and 21q22 . 2-3 , which jointly showed the strongest association to progression free survival ( Table 2 ) . The 3p11 . 2-p14 . 1 and 13q13 . 1-q21 . 1 regions overlapped with the recurrent 3p12 . 3-p14 . 2 and 13q12 . 2-q21 . 32 losses , whereas the predictive loss of 21q22 . 2-3 was distal of the recurrent loss of 21q21 . 1-3 . The predictive losses were not correlated and were related to poor outcome also when analyzed separately ( Figure 2A–2C ) . The intratumor heterogeneity of the losses was low and similar to that of the recurrent losses ( Figure 1D ) . Most patients had more than one of the predictive 3p , 13q , and 21q losses . We therefore investigated whether there was an increased risk of relapse in cases of two or three losses . Kaplan-Meier plots for patients with different combinations of the predictive losses revealed three major groups with different outcome ( Figure S3 ) . Patients without any of the losses had a low risk of relapse and a survival probability of 91% ( Figure 2D ) . Patients with 3p and/or 13q loss , without 21q loss , had an intermediate survival probability of 68% , whereas those with 21q loss had the lowest survival probability of 44% . The risk of relapse therefore seemed to be particularly high when loss of 21q22 . 2-3 was involved . The predictive impact of the 3p , 13q , and 21q losses were assessed by multivariate analysis together with tumor size , stage , and lymph node status . Histological type , HPV status , and heterogeneity status showed no correlation to outcome in univariate analysis and were therefore not included . The losses and tumor size had independent predictive value ( Table 3 ) , showing that the gene data contained information of the progression free survival that was not covered by tumor size . Since tumor size is a strong predictor ( Figure 3A ) , we also investigated the predictive impact of the three losses for small and large tumors separately . About 20% of the patients with tumor size less than the median had relapse and all of them had one or more of the losses ( Figure 3B ) . In the cases of tumors larger than the median , about 47% of the patients progressed and all except two of them had one or more of the losses ( Figure 3C ) . None of the patients with loss involving 21q were disease free after 28 months , suggesting a particularly high risk of relapse in cases of a large tumor bearing loss of 21q22 . 2-3 . There was no difference in tumor size for patients with and without loss in Figure 3B or in Figure 3C ( data not shown ) . The gene data therefore enabled identification of high and low risk patients both in cases of a small and a large tumor . To find genes regulated by the recurrent and predictive gene dosage alterations , we used cDNA microarrays and generated a cancer gene expression profile . The profile was based on 100 patients , including 95 of those analyzed with aCGH . Expression data were available for 1357 of the about 4000 known genes within the altered regions , and a significant correlation to gene dosage was found for 191 of them ( Table 2 ) . Several correlating genes were identified for each region , except for 8q24 . 13-22 , 10q23 . 31 , and 11p12 , where no genes were found . Typical examples of correlation plots are shown in Figure S4 . The results were confirmed with the Illumina gene expression assay on 52 patients . Although the Illumina analysis was based on a lower number of patients , an excellent correlation between the Illumina and cDNA data and between the Illumina and gene dosage data was found for almost all of the genes , as demonstrated in Table S2 . We also performed a second cDNA analysis , including only tumors with more than 70% tumor cells in hematoxylin and eosin ( HE ) stained sections . Totally 179 of the genes ( 94% ) were identified , suggesting few false positive results due to normal cells in the samples . The observations supported our conclusion that the genes in Table 2 were gene dosage regulated . The latter analysis identified 26 genes that were not depicted when all patients were considered . These genes were not considered further , since the results were based on only half of the data set . Expression of known oncogenes and tumor suppressor genes within the depicted regions , like MYC ( 8q24 . 21 ) , BRCA2 ( 13q13 . 1 ) , RB1 ( 13q14 . 2 ) , and TP53 ( 17p13 . 1 ) , was not significantly correlated to gene dosage . These genes are therefore probably not regulated primarily by gains and losses . The TP53 and RB1 results were consistent with the high frequency of HPV positive tumors ( Table 1 ) . The predictive losses on 3p and 13q involved the same correlating genes as the corresponding recurrent ones , whereas PCP4 , RIPK4 , and PDXK were correlating genes within the predictive 21q region ( Table 2 ) . To depict the correlating genes that most probably were involved in development of chemoradioresistance , we required that the gene was significantly associated with clinical outcome both at the gene dosage and expression level . Moreover , a clear difference in the survival curves should also be seen in an independent cohort of 41 patients when based on the Illumina gene expression data . The criteria were fulfilled for four genes; RYBP and GBE1 on 3p and MED4 and FAM48A on 13q , which were termed predictive genes ( Figure 4 ) . Two more genes , GTF2F2 and RNASEH2B on 13q , were correlated to outcome based on the cDNA data , but were not considered further since the tendency based on the Illumina data was weak ( p>0 . 15 ) . The relationship to outcome was not strong enough for PCP4 , RIPK4 , and PDXK on 21q to be included among the predictive genes either . Biological processes associated with the recurrent and predictive gene dosage alterations were found by comparing the GO categories of the affected genes with those of all genes in the data set [15] . One or more biological processes were annotated to 155 of the correlating and predictive genes and to 5824 of all genes . The categories apoptosis , carbohydrate metabolism , translation , and RNA-protein complex biogenesis and assembly were significantly overrepresented among the correlating genes within the recurrent gains , whereas macromolecule localization , generation of precursor metabolites and energy , transcription from RNA polymerase II promoter , and establishment or maintenance of chromatin architecture were overrepresented among those within the recurrent and predictive losses ( Table 4 ) . Fifty-six genes were included in the significant categories and were candidate drivers of the biological processes . In addition , we included the predictive gene FAM48A , which was not associated to any process in the GO database , as a potential driver of chemoradioresistance together with RYBP and MED4 ( transcription ) and GBE1 ( generation of precursor metabolites and energy ) . We generated a map to visualize the connections between genetic events , affected genes , and biological processes ( Figure 5 ) . The processes carbohydrate metabolism and generation of precursor metabolites and energy were combined in metabolism , translation and RNA-protein complex biogenesis and assembly were combined in translation , and transcription from RNA polymerase II promoter was combined with establishment or maintenance of chromatin architecture in transcription . The combined categories were closely related , justifying this strategy . All but six of the recurrent alterations were associated with a process and represented in the map . The predictive 3p and 13q losses were merged with the corresponding recurrent losses , since the regions overlapped , and linked to metabolism ( GBE1 ) and transcription ( RYBP , MED4 ) in addition to chemoradioresistance . The predictive 21q loss was not connected to any known gene , but associated with chemoradioresistance . The map revealed features that seemed to be characteristic of recurrent and predictive alterations in cervical cancer . First , many of the genetic events were associated with clusters of genes in the same biological process . For example , gain on 3q affected three genes in apoptosis and three in translation , gain on 5p was linked to tree apoptosis genes , and loss on 6q was associated with four genes in transcription . Second , several events , like gain on 3q , 19q , 20q and loss on 2q , 6 , and 11q , were connected to more than one biological process , either through the regulation of several genes or because some genes had multiple functions .
This work presents the first coupling of gene dosage and expression profiles in a large sample set of cervical cancers . We based our study on absolute gene dosages , which are more sensitive than the commonly used aCGH ratios in detecting gains and losses and enable comparisons across tumors with differences in ploidy and normal cell content [14] . This strategy and the large number of patients ensured reliable identification of recurrent gene dosage alterations , events associated with clinical outcome , and their intratumor heterogeneity . Further analysis based on GO categories provided an objective way of organizing the numerous correlating genes into biological meaningful information . We demonstrate a large potential of the integrative approach by the discovery and functional assessment of candidate driver genes that represent novel biomarkers of the disease . In particular , novel loci associated with clinical outcome were identified , providing the first evidence that gene dosage can be responsible for developing chemoradioresistance in cervical cancers . The recurrent gene dosage alterations were consistent with earlier reports on advanced stage cervical cancer based on conventional CGH [8] , [9] , [17] . However , a more precise definition of the altered regions was achieved here due to the improved resolution of the array technique . The high frequency of the alterations suggests that they play a causative role in carcinogenesis . Hence , many of the alterations are common also in other squamous cell carcinomas , like head and neck cancers [18] , [19] . Moreover , the recurrent loss on 3p and 13q overlapped with the losses associated with poor clinical outcome , strengthening the hypothesis of a central role in tumor evolution . Less frequent alterations can , however , also be crucial for tumor evolution , as was demonstrated by the recurrent gain on 11q22 in 14 patients and predictive loss on 21q in 23 patients . The low intratumor heterogeneity of the recurrent and predictive gene dosage alterations indicated that they had occurred prior to many of the other alterations . The result was consistent with our previous cervical cancer study based on conventional CGH [9] , showing a homogeneous intratumor distribution of the frequent gains on 3q , 5p , and 20q and losses on 3p and 11q14-qter . Moreover , regions overlapping with the 1p , 1q , 3q , 8q , 9q , and 20q recurrent gains and 2q , 3p , 4p , 11q , and 17p losses have been found to be altered in precancerous cervical intraepithelial lesions [17] , [20]–[23] , suggesting that the events had occurred at an early stage . It is therefore likely that the alterations identified here , and the consequently control of biological processes and development of chemoradioresistance , emerge early during carcinogenesis . It should be noted that a low heterogeneity was seen for some of the less common alterations as well , implying that they had occurred early . The affected genes in these regions may also be crucial for tumor evolution , however , other mechanisms than gene dosage alterations , such as epigenetic events or mutations , probably play the major role in their regulation . Moreover , some of the highly heterogeneous alterations may be important for disease progression a later stage , being a result of the continuing tumor evolution towards increased aggressiveness . The gene dosage alterations were associated with specific biological processes that are closely related to known cancer hallmarks [3]–[5] , indicating that the genes involved are drivers of carcinogenesis . Hence , gain of the genes in apoptosis , including the anti-apoptosis genes BIRC2 , BIRC3 , and ATF5 , can help carcinoma cells to evade apoptosis [3] . Aberrations of the genes in metabolism , like gain of ARNT and IDH3G in carbohydrate metabolism , and loss of COX7C and ATP5J in oxidative phosphorylation , can be part of a metabolic reprogramming towards increased glycolysis and decreased mitochondrial function to meet the high energy demand linked to tumor growth [4] . In particular , gain of ARNT may increase hypoxia and hypoglycemia tolerance by signaling through the HIF1A pathway [24] . Loss of the genes in molecular localization , including HRB and TSG101 , can lead to abnormal protein internalization and recycling and thereby abrogated degradation of proteins like growth factor receptors [25] , [26] . Finally , aberrations of the genes in translation and transcription , such as gain of the translation initiation factors EIF4A2 , EIF4G1 , EIF2S2 , and EIF2S3 and loss of the transcriptional repressors HDAC2 and HDAC4 , can be a way to control the formation and activity of essential proteins . The EIF-proteins are central in adaptation to hypoxia and can stimulate MYC translation and thereby oncogenic processes like cell proliferation [27] , [28] . Improper function of HDAC2 and HDAC4 may also increase proliferation [29] . Many of the genes , including BIRC2 , BIRC3 , ATF5 , NUP62 , FASTKD3 , IDH3G , and POFUTI , have been found to be regulated by gains or losses in previous cervical cancer studies [30]–[33] . Our findings link each gene to one or more specific biological processes , and thereby indicate the functional meaning of the genetic events in carcinogenesis . Loss and down regulation of GBE1 and RYBP on 3p and MED4 and FAM48A on 13q were associated with poor clinical outcome , suggesting that the genes are drivers of chemoradioresistance . The mechanisms underlying these findings and possible associations to known aggressive phenotypes like hypoxia and rapid proliferation [34]–[36] are not clear , but a tumor suppressor function of the genes has been indicated . GBE1 , which plays a role in carbohydrate metabolism , has been found to be down regulated in ovarian cancers [37] . Loss of the transcriptional repressor RYBP may impair death receptor-mediated apoptosis [38] , [39] , and the encoded protein has been shown to be down regulated in many tumor types , including cervical cancer [40] . Loss of the transcriptional activators MED4 may impair transcription of genes with anti-cancer effect , like the vitamin D receptor [41] , [42] . The function of FAM48A is less clear , but some studies indicate that loss of this gene can promote aggressiveness . Hence , FAM48A is required for activation of the MAPK p38 pathway [43] , which represses cell proliferation [44] . We found no candidate driver gene of chemoradioresistance within the predictive loss on 21q . Only a few tumor suppressor genes have been identified in this region . One candidate is the transcriptional regulator PRDM15 , which was not included in our cDNA data set [45] . Our data showed , however , no correlation between PRDM15 expression , assessed with the Illumina method in 52 patients , and gene dosage ( data not shown ) , suggesting that the gene is not regulated by genetic loss . Further investigation with denser microarrays or possibly microRNA screening would be needed to find the drivers in this region . The connection between genetic events , genes , and biological processes may provide insight into more general aspects of cervical carcinogenesis . Several genes were often associated with a single genetic event , supporting the hypothesis that there can be multiple drivers of an event that coordinately promote tumor evolution [11] . In cases of genes in the same biological process , like the anti-apoptosis genes BIRC2 and BIRC3 on 11q22 , a broad and therefore efficient control of the process may be obtained . Hence , BIRC2 and BIRC3 may play complementary roles in apoptosis evasion , since upregulation of BIRC3 , but probably not BIRC2 , may impair hypoxia induced apoptosis [46] , [47] . In cases of genes in different biological processes , such as metabolism ( NDUFS1 ) , macromolecule transport ( HRB ) , and transcription ( SMARCAL1 , HDAC4 ) on 2q , the collective control of these processes through a single event is likely to give a growth advantage that is selected for in carcinogenesis . One or more genes in all biological processes were affected in most tumors due to the high frequency of the recurrent gene dosage alterations . All processes were therefore probably important , and the control of them through gains and losses seems to be a common feature of the disease . The candidate driver genes represent novel biomarkers that may be utilized in the handling of cervical cancers . Diagnostic assessment of the biomarkers may help to understand the evolutionary status and therefore the biology of the cancer in individual patients . In particular , the predictive biomarkers may be used in addition to tumor size for classification of patients into risk groups in a personalized treatment regime . The biomarkers also open for the possibility to specifically repress biological processes in carcinogenesis by molecular targeting , and thereby interfere with tumor evolution . The use of drugs to inhibit translation by interaction with EIF-proteins has shown promising results [48] and been suggested as a tool to target tumor hypoxia [49] . The approach may be applied at all stages of the disease , since the genetic events probably emerge early . Moreover , improved outcome after chemoradiotherapy might be achieved by targeting the predictive biomarkers . Hence , viral-mediated delivery of RYBP has been shown to induce apoptosis in a number of cancer cell lines [38] , and could be a useful strategy for the patients with loss of this gene .
A cohort of 102 patients was included for basic analyses to identify gene dosage alterations with aCGH ( 97 patients ) , affected transcripts with cDNA microarrays ( 100 patients ) , and to confirm the affected transcripts with the Illumina method ( 52 patients ) ( Table 1 ) . An independent cohort of 41 patients was used to validate relationships between gene expression and outcome with the Illumina method ( Table 1 ) . All patients received external irradiation and brachytherapy combined with adjuvant cisplatin and were followed up as described previously [50] . Eighteen patients received extended radiation field due to enlarged common iliac and para-aortal lymph nodes . Progression free survival , defined as the time between diagnosis and the first event of locoregional and/or distant relapse , was used as end point . Six patients died of causes not related to cancer and were therefore censored . Tumor samples were collected at the time of diagnosis . One – four biopsies , approximately 5×5×5 mm in size , were taken at different locations of the tumor , immediately snap-frozen in liquid nitrogen and stored at −80°C until used for analyses . The study was approved by the regional committee of medical research ethics in southern Norway , and written informed-consent was achieved from all patients . The aCGH experiments and generation of absolute gene dosage profiles have been described previously for all 97 patients ( ArrayExpress accession no . E-TABM-398 ) [14] . The array slides were produced at the Microarray Facility at the Norwegian Radium Hospital and contained 4549 unique genomic BAC and PAC clones that covered the whole genome with a resolution of approximately 1 Mb . Genomic DNA was isolated from the biopsies , labeled , and co-hybridized with normal female DNA to the array slides . DNA from different biopsies of the same tumor was pooled . The biopsies of all except two patients had more than 50% tumor cells in HE stained sections from the middle part of the sample . Median tumor cell fraction was 70% ( range 30–90% ) . After array scanning , image analysis , spot filtering , and ratio normalization , the GLAD algorithm was applied for ratio smoothing and breakpoint detection [51] . The cDNA microarray experiments have been presented previously for 48 of the 100 patients [50] . The array slides were produced at the Microarray Facility at the Norwegian Radium Hospital and contained more than 12000 unique cDNA clones , including most known oncogenes and tumor suppressor genes . Total RNA was isolated from the biopsies , labeled , and co-hybridized with reference RNA ( Universal Human Reference RNA , Stratagene , La Jolla , CA ) to the array slides . RNA from different biopsies of the same tumor was pooled . Only biopsies with more than 50% tumor cells in HE stained sections were utilized . Median tumor cell fraction was 70% ( range 50–90% ) . All hybridizations were performed twice in a dye-swap design ( ArrayExpress accession no . E-TABM-817 ) . After array scanning , image analysis , spot filtering , and ratio normalization , the average expression ratios were calculated from the two data sets and used in the further analyses . The gene expressions were mapped to the gene dosages based on the exact chromosomal position of the cDNA and genomic clones , as derived from Ensembl ( http://www . ensembl . org/Homo_sapiens/searchview ) . Results based on cDNA data were validated with Illumina gene expression beadarrays in 52 of the patients subjected to aCGH and in the independent cohort of 41 patients . HumanWG-6 v3 beadchips ( Illumina Inc . , San Diego , CA ) with 48000 transcripts were used . RNA was isolated from the biopsies as described above and amplified using the Illumina TotalPrep RNA amplification kit ( Ambion Inc . , Austin , TX ) with 500 ng of total RNA as input material . cRNA was synthesized overnight ( 14 hr ) , labelled , and hybridized to the chips at 58°C overnight , according to the standard protocol . The hybridized chip was stained with streptavidin-Cy3 ( AmershamTM , PA43001 , Buckinghampshire , UK ) and scanned with an Illumina beadarray reader . The scanned images were imported into BeadStudio 3 . 1 . 3 . 0 ( Illumina Inc . ) for extraction , quality control , and quintile normalization . The annotation file HumanWG-6_V3_0_R0_11282955_A was used . The recurrent gene dosage alterations were identified based on a score that was calculated for each genomic clone by multiplying the average gene dosage amplitude with its frequency [16] . Gains and losses were considered in two separate procedures . Semi-discrete data were used , for which amplitudes lower than 1 . 1 were set to 1 when searching for gains and amplitudes higher than 0 . 9 were set to 1 when searching for losses . The score significance was assessed by comparison to similar scores obtained after data permutation [16] , adjusting the p-value by a multiple testing procedure to control the false discovery rate ( FDR ) [52] . Recurrent alterations with an FDR q-value <5% were reported . Gene dosage alterations associated with clinical outcome were identified with the LASSO method in the Cox proportional hazards model [53] , as implemented in [54] . The LASSO is a method for variable selection and shrinkage in regression models when the number of covariates is larger than the number of individuals . By performing shrinkage in addition to selection , the LASSO is more stable than stepwise procedures where variables are either retained or discarded from the model sequentially , one at a time . In groups of highly correlated variables the LASSO tends to select only one variable in the group [55] , and reported therefore one representative of each DNA region that jointly explained the outcome . Each region was thereafter found by selecting neighbouring genomic clones with strong correlation ( r>0 . 9 ) to the one reported . Survival curves were generated by Kaplan-Meier analysis and compared by using log-rank test . Spearman's rank correlation analysis with an FDR q-value <5% was used to search for significant correlations between gene dosage and expression . The analysis was based on semi-discrete data , retrieved as described above . To identify biological processes that were overrepresented among the correlating genes , the GO categories of the genes were compared with those of all genes on the array by using the master-target procedure with the Fisher's exact test in the eGOn software [15] . The GO categories were found in eGOn from public databases , based on the gene reporter EntrezGeneID . | Genetic gains and losses , i . e . changes in gene dosages , are common abnormalities of human cancers . Discovering these defects and understanding the biological meaning can lead to improved therapeutic opportunities . This paper reports a large scale screening of gene dosage alterations in cervical cancer and gives a broader exploration of the expression and function of genes with gains or losses . We have focused on the most frequent gene dosage alterations and the alterations associated with survival after chemoradiotherapy , since these defects are likely to be of major importance for developing disease . The most notable finding was the discovery of a set of biological processes that are known hallmarks of cancer and were associated with gains and losses of specific genes . Moreover , novel loci associated with chemoradioresistance independent of existing clinical markers were found , and the genes involved were depicted . Our results indicated that gene dosage alterations play a causative role in the carcinogenesis and chemoradioresistance of cervical cancer and pinpointed candidate biomarkers of the disease . | [
"Abstract",
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"Methods"
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] | 2009 | Gene Dosage, Expression, and Ontology Analysis Identifies Driver Genes in the Carcinogenesis and Chemoradioresistance of Cervical Cancer |
The onset of anthelmintic treatment of neurocysticercosis ( NCC ) provokes an acute immune response of the host , which in human cases is associated with exacerbation of neurological symptoms . This inflammation can occur at the first days of therapy . So , changes in the brain cysts appearance may be detected by medical imaging . We evaluated radiological changes in the appearance of brain cysts ( enhancement and size ) on days two and five after the onset of antiparasitic treatment using naturally infected pigs as a model for human NCC . Contrast T1-weighted magnetic resonance imaging with gadolinium was performed before and after antiparasitic treatment . Eight NCC-infected pigs were treated with praziquantel plus albendazole and euthanized two ( n = 4 ) and five ( n = 4 ) days after treatment; another group of four infected pigs served as untreated controls . For each lesion , gadolinium enhancement intensity ( GEI ) and cyst volume were measured at baseline and after antiparasitic treatment . Volume and GEI quantification ratios ( post/pre-treatment measures ) were used to appraise the effect of treatment . Cysts from untreated pigs showed little variations between their basal and post treatment measures . At days 2 and 5 there were significant increases in GEI ratio compared with the untreated group ( 1 . 32 and 1 . 47 vs 1 . 01 , p = 0 . 021 and p = 0 . 021 ) . Cyst volume ratios were significantly lower at days 2 and 5 compared with the untreated group ( 0 . 60 and 0 . 22 vs 0 . 95 , p = 0 . 04 and p = 0 . 02 ) . Cysts with lower cyst volume ratios showed more marked post-treatment inflammation , loss of vesicular fluid and cyst wall wrinkling . A significant and drastic reduction of cyst size and increased pericystic enhancement occur in the initial days after antiparasitic treatment as an effect of acute perilesional immune response . These significant changes showed that early anthelmintic efficacy ( day two ) can be detected using magnetic resonance imaging .
Neurocysticercosis ( NCC ) is a neurological parasitic disease caused by the infection of the brain by the larval stage of Taenia solium [1] . NCC represents a serious and persisting public health problem because it is the most frequent cause of late-onset seizures in developing countries [1 , 2] . Treatment with anthelmintic drugs such as praziquantel and/or albendazole has been associated with increased severity of symptoms within the first days of therapy [3–7] . Even though praziquantel and albendazole have different mechanisms of action [8 , 9] , both drugs cause the destruction of cysts and subsequent release of antigens , triggering the host immune response [7 , 10–13] . Using the porcine NCC model and the antihelmintic drug praziquantel , this acute post-treatment inflammatory response was associated with pericystic inflammation [14] accompanied by an increase of vascular permeability , pro-inflammatory and regulatory cytokine profiles [15] during the second and fifth day . Using the same model , radiological changes in the appearance of brain cysts have been reported after two weeks of praziquantel treatment [16–18] . Similarly , the use of albendazole in the porcine model resulted in an increase of pro-inflammatory cytokines [14] . Medical imaging has been a useful tool in the diagnosis and medical follow-up of NCC patients [7] . Cyst appearance , size , perilesional enhancement and edema are imaging criteria to determine the radiological resolution of NCC after treatment [19] . The earliest radiological changes related to the size and appearances of brain cysts after conventional anthelmintic treatment has been reported during the first week of treatment in humans [20 , 21] and in pigs after two weeks [16–18] . However , the radiological evolution of brain cysts during the first days of treatment , when perilesional inflammation establishes and symptoms increase in treated patients , has been scarcely explored . In the present study , we evaluated the early radiological changes on MRI following the onset of antiparasitic treatment ( days two and five ) in pigs naturally infected with T . solium as a model for human NCC and confirmed the radiological findings with an ex-vivo histopathological examination .
A total of twelve pigs naturally infected with Taenia solium cysticercosis were obtained in endemic villages , transported to our facilities in Lima , and randomly divided in three groups , control or untreated , PZQ+ABZ 2d and PZQ+ABZ 5d , as follows: Four pigs remained untreated as a control group and 8 pigs were treated with the same combination of anthelmintic drugs and sacrificed at two ( n = 4 ) and five ( n = 4 ) days after treatment . The treatment consisted of combined therapy with praziquantel ( Helmiben , Farmindustria , Peru ) given for only the first day at 75 mg/kg/day , divided into three doses of 25 mg/kg administered every two hours [10] , and albendazole ( Zentel , GlaxoSmithKline , Peru ) given daily until sacrifice at 15 mg/kg/day [22] . All pigs had pre and post-contrast MRI before treatment ( Pre-treatment MRI ) and on the day of sacrifice ( Post-treatment MRI ) . Two hours before sacrifice , an Evans blue solution was infused as previously reported [15] . For all interventions , pigs were anesthetized with an intramuscular injection of a mixture of ketamine ( Ket-A-100 50 mg/kg , Agrovet Market SA , Peru ) and xylazine ( Dormi-Xyl 2mg/kg , Agrovet Market SA , Peru ) [15] . After euthanasia , the pig brains were placed on dry ice slabs and cut in 1-cm sections . Cysts with pericystic capsules were collected from both hemispheres for histopathology and RNA studies . Specimens from the right hemisphere were fixed in 10% neutral buffered formalin , embedded in paraffin and then sectioned coronally at 4 μm thickness . Conventional hematoxylin-eosin was performed on every slide and two sections were examined with conventional light microscopy . Microphotographs were taken at 15X magnification with a Carl Zeiss stereoscope with AxioVision software to obtain a single large image ( “cyst map” ) [22] . Pre- and post-treatment GEI , pre- and post-treatment cyst volume , cyst volume ratio , GEI ratio , Inflammatory Score Composite ( ISC ) and cyst damage score composite ( CDSC ) were all continuous parameters . Treatments groups were used as a categorical variable ( untreated , been treated at 2d and 5d ) . Mann Whitney test was used to compare pre-GEI and pre Cyst volume between the different treatment groups . Pre-post treatment differences for GEI and cyst volume were analyzed by the Wilcoxon test in each treatment group , individually . To evaluate if the mean change in GEI and cyst volume from pre to post-treatment measures differed in the three groups , we used a generalized estimating equation ( GEE ) analysis . To verify those post-treatment differences ( cyst volume and GEI ) truly result from treatment rather than from left-over effects of ( usually random ) pre-test differences between groups , we used an analysis of covariance ( ANCOVA ) with pre-treatment measures as covariates . Finally , we used the Mann-Whitney U test to compare ratios ( changes between pre- and post-treatment measures ) of GEI and cyst volume between treatment groups . Since ratio analysis results were highly correlated with unstandardized group analyses , we used ratios for the correlations with histopathology . Spearman correlation was used to assess the relation between each radiological ( GEI and cyst volume ) and histopathological ( ISC and CDSC ) parameters . All statistical analyses were performed using software R program for Windows , version 3 . 2 . 2 . Graphs were performed using the ggplot2 package [25] . Values of p under 0 . 05 were considered to be statistically significant . The study was conducted in accordance with the National Institutes of Health/AALC guidelines , and was reviewed and approved by the Institutional Ethics Committee for Animal Use at Universidad Peruana Cayetano Heredia ( assurance number A5146-0 ) .
The study animals were seven male and five female pigs . Their weight range was variable ( mean: 69 . 8 kg; range: 15–120 kg ) . A total of 328 brain cysts were obtained from the 12 pigs . The parasite cyst burden in each pig brain was also variable ( mean: 27 . 3; range: 1–152 ) ( Table 1 ) [22] . A number of estimates of GEI showed increases around cysts in treated pigs compared to cysts in control untreated pigs . At baseline ( before antiparasitic treatment ) , cysts in pigs from the Control and PZQ+ABZ 2d groups had higher GEI than cysts from the PZQ+ABZ 5d group ( 30 . 22 and 28 . 32 vs . 24 . 1 , p<0 . 05 ) . Post-treatment GEI values were higher in both treated groups compared with control pigs ( PZQ+ABZ 2d: 36 . 04 and PZQ+ABZ 5d: 35 . 8 vs . Control: 33 . 31 , p<0 . 001 ) . When comparing pre- and post-treatment GEI in each group , there were marginal differences in cysts from control animals ( 30 . 22 vs . 33 . 31 , p = 0 . 048 ) , while GEI around cysts in treated groups increased markedly ( PZQ+ABZ 2d: 28 . 32 vs . 36 . 04 , p<0 . 001 and PZQ+ABZ 5d: 24 . 1 vs . 35 . 8 , p<0 . 001 ) ( S1 Table ) . GEE analysis confirmed that the effect of treatment in increasing the enhancement around cysts changed from basal to days 2 and 5 ( RC for interaction term between pre-post GEI measures and groups: 4 . 996 , <0 . 001 ) ( S2 Table ) . Additionally , after adjusting for pre-treatment differences , GEI increased significantly in both treated groups ( PZQ+ABZ 2d: 7 . 324 , p-value = 0 . 001 and PZQ+ABZ 5d: 9 . 442 , p-value<0 . 001 ) compared with the control group ( S2 Table ) . Ratio analysis was also used to assess the increases in enhancement between groups ( across time ) . Individual cyst GEI ratio ( post-/pre-treatment GEI ) demonstrated a similar effect ( mean ratios were 1 . 01 for cysts of control pigs , 1 . 32 for cysts of pigs in PZQ+ABZ 2d group , and 1 . 47 in PZQ+ABZ 5d group; p = 0 . 021 between groups and p = 0 . 387 comparing both treatment groups ) ( Table 2 , Fig 1 ) . On baseline MRI ( before antiparasitic treatment ) , cysts from the control and PZQ+ABZ 5d groups had larger volumes ( 106 . 16 mm3 and 114 . 18 mm3 , respectively ) than those from PZQ+ABZ 2d pigs ( 74 . 56 mm3 ) ( p<0 . 05 ) . On post-treatment MRI , cysts from PZQ+ABZ 2d and PZQ+ABZ 5d groups had lower cyst volume than cysts from the control group ( 48 . 64 mm3 vs . 97 . 92 mm3 , <0 . 001 and 24 . 36 mm3 vs . 97 . 92 mm3 , p = 0 . 03 ) . Cyst volume also decreased in the 5-d treated cysts compared to the 2-d treated cysts ( 48 . 64 mm3 vs . 24 . 36 mm3 , p<0 . 001 ) ( S1 Table ) . Similar to GEI , pre- and post-treatment cyst volumes in control pigs were similar ( 106 . 16 vs . 97 . 92 , p = 0 . 045 ) , while post-treatment cyst volumes were significantly smaller in treated animals ( PZQ+ABZ 2d: 74 . 56 vs . 48 . 64 , p<0 . 001 and PZQ+ABZ 5d: 114 . 18 vs . 24 . 36 , p<0 . 001 ) ( S1 Table ) . Adjustment for pre-treatment measures in ANCOVA confirmed that cysts from both treated groups had smaller volumes than cysts from the control group ( PZQ+ABZ 2d: -62 . 117 , p-value = 0 . 014 and PZQ+ABZ 5d: -95 . 032 , p-value<0 . 001 ) . Similar to enhancement , GEE analysis confirmed that the effect of treatment on cyst volume was more marked at day 5 ( RC for interaction term between pre-post measures and groups: -48 . 201 , <0 . 001 ) ( S2 Table ) . A similar effect was also seen when individual cyst volume ratios ( post-/pre-treatment ) were compared between groups . Cyst volume ratio was lower ( more reduction ) in cysts from both treated groups than in those from the control group ( 0 . 60 for ABZ+PZQ 2d and 0 . 22 for ABZ+PZQ 5d vs . 0 . 95 for controls , <0 . 05 ) ( Fig 1 ) , demonstrating cyst volume reduction after treatment . However , cysts from pigs in PZQ+ABZ 5d group had similar volume reduction than did cysts in the PZQ+ABZ 2d group ( 0 . 22 vs . 0 . 60 , p = 0 . 248 ) ( Table 3 ) . Further analysis demonstrated a significant negative relationship between cyst volume ratio with GEI ratio after 5 days of treatment ( r = -0 . 412 , p<0 . 001 ) ( S3 Table , Fig 2 ) suggesting that cysts with more enhancement ( GEI ) experience greater reduction in volume . To confirm the radiological changes , we performed an ex-vivo examination to measure inflammation and the cyst damage using the ISC ( inflammatory score-composite ) and the CDSC ( cyst damage score-composite ) , then we correlated those histological parameters with GEI and cyst volume ratios ( radiological parameters ) . Cysts from right brain hemispheres ( n = 165 ) were selected for histopathological studies . Of these , only 105 cysts had a complete cyst structure and capsule and were therefore evaluable . Both treated groups had higher ISC and CDSC than the control group ( p<0 . 001 , Mann Whitney test ) . Both scores were higher at 5d compared to 2d , but there were no significant differences in these variables between both treated groups ( ISC: 352 vs 304 , p = 0 . 364; CDSC: 388 vs 336 , p = 0 . 405 for CDSC ) ( S4 Table ) . Higher ISCs were significant and positively associated with GEI ratio ( r = 0 . 002 , p = 0 . 028 ) , meaning that cysts with higher increases in enhancement have more post-treatment pericystic inflammation . However , there was no significant correlation between GEI and CDSC ( r = -0 . 001 , p = 0 . 286 ) ( S3 Table ) . Interestingly , there was a significant negative relationship between cyst volume ratio ( post-/pre-treatment measure ) and post-treatment inflammation ( ISC ) at day 5 ( RC = -0 . 002 , p = 0 . 004 ) , suggesting that cysts with increased inflammation showed increased reduction in volume . Slides of cysts with high volume ratio ( higher reduction of cyst volume ) showed loss of vesicular area and excess cyst wall folding upon themselves or wrinkling accompanied by granulomatous reaction ( Fig 3A and 3C ) . In both cases , eosinophils have invaded the parasite’s wall as an effect of treatment ( Fig 3B and 3D ) . This eosinophilic invasion has been observed before at points of high inflammation [26] and it is a demonstration of an acute response . However , there was no significant relationship between volume ratio and cyst damage ( CDSC ) in any group ( S3 Table ) .
Combined treatment of parenchymal NCC with praziquantel and albendazole destroys brain cysts in humans and pigs [1 , 10] , which is associated with a better clinical evolution in cases of human NCC [10] . However , after anthelmintic treatment humans are not usually reimaged until six or 12 months after treatment so early effects are not measured . Despite the efficacy of combined treatment , in humans therapy causes an exacerbation of symptoms , usually seizures , due to acute inflammatory response to degenerating or dying cysts [27] . To assess early radiological changes , we examined MRI parameters of enhancement and cyst size and confirmed those findings with an ex-vivo histopathology ( tissue-based semi-quantitative estimates of inflammation , and cyst damage ) in naturally T . solium-infected pigs treated with albendazole and praziquantel at 2 and 5 days post initiation of treatment , compared to untreated control animals . Enhancement has been associated to the disruption of the BBB in porcine NCC [22 , 28 , 29] as it happens in other diseases such as multiple sclerosis [30–32] , gliomas , metastases and abscesses [33] . Earlier studies in NCC employing contrast-enhanced computed tomography ( CT ) in pigs [16–18] described the appearance of pericystic enhancement two weeks following praziquantel treatment . In humans , anthelmintic treatment also exacerbates gadolinium ( Gd ) enhancement during the first days of therapy [7] , causing a change from an initial ring pattern of enhancement to a disc pattern , as seen using Gd T1-MRI [34] . These results are coherent with the post-treatment increase of enhancement reported in this study . We observed that the effect of treatment on enhancement increases with time already on day 2 and is further increased on day 5 . Also , there was a positive correlation between increase of enhancement and inflammation . As enhancement is associated with BBB disruption , the following or parallel process that occurs is the extravasation of immune cells into the injured area and the increase of the inflammatory response . This agrees with previous studies where pro-inflammatory cytokines [14] and eosinophils where more abundant in pericystic tissues where the BBB had been disrupted [26] . Unexpectedly , we found that cyst volume was reduced very early after the onset of antiparasitic treatment . Reductions in cyst volume were evident in both treated groups on day two and were more pronounced five days after treatment , when the median of cyst volume loss was almost 78% ( [1–0 . 22]*100; pre vs . post-treatment ) . Changes in the size of brain cysts in pigs had previously been reported after two weeks of praziquantel treatment [16]; however our findings suggest that sizable changes in the cyst size occur already by the second day of treatment . These results might have been more marked because we used combined therapy , and are consistent with early cyst size decrease observed on day 3 [21] and after one week [20] of antiparasitic treatment in humans . The reduction of the size of the parasite likely results from treatment-induced cyst damage and associated increased permeability of the cyst membrane , with a consequent increase in density of the cyst contents due to the influx of host albumin , protein coagulation , and loss of water [35] . The reduction of the size of the cyst was also accompanied with increased enhancement and inflammation . A previous study from our group reported that enhancement was associated with granuloma formation [22]; in this study we found similar results but additionally accompanied with cyst reduction . However , there was no association with cyst damage score ( extension of the damage ) . A possible explanation could be that the combined treatment damages the scolex first , before damage is histologically noticeable and extended at the cyst wall level . Only afterwards would the cyst shrink and release fluid through the most heavily damaged regions of its wall . Similarly , a previous study concluded that the scolex is the primary target and its dissolution carries the complete resolution of the cyst [36] . As for cysts with little or no enhancement with a negligible change in size , they would represent those cysts in patients that do not respond to drug therapy , although our suggestion is valid only up to five days . Despite these significant findings , our study has some limitations . We used a small number of animals and the parasite load per pig brain was very variable , making it difficult to compare groups . However , we used three statistical analyses to handle baseline differences to truly measure the effect of treatment . The variable thickness of MRI scans introduced some noise in the measurements of enhancement and volume; nevertheless , cyst volume and enhancement were significantly different in the treated groups . Minor drawbacks include use of only one hemisphere for histopathological assessments; however , differences in cyst load between hemispheres were not discernable [37] . Also , we used only two representative slides to assess the immune response of the entire cysts , which , nevertheless , sufficed to show differences in inflammation and cyst damage with treatment and over time . Despite these limitations , the changes observed after treatment corroborate the increase of inflammation seen in post mortem histological studies in pigs treated with antiparasitic drugs compared to untreated animals [14 , 15] and were also confirmed by pre- and post-treatment MRI observations of gadolinium enhancement made in the same pigs . Finally , our study found that combined albendazole plus praziquantel treatment produces a rapid and pronounced reduction of the cyst size in the initial days of the treatment and an acute inflammatory response characterized by an increase of Gd enhancement . This may lead to a release of cyst contains by the extreme cyst damage and a subsequent reduction of cyst size . These results define the pathophysiology of the early exacerbation of symptoms induced by treatment of human NCC , which may lead to earlier monitoring of NCC treatment and thus improved and safer interventions . | Neurocysticercosis ( NCC ) is a frequent parasitic infection of the human brain and the most common cause of adult onset epilepsy in developing countries . Acute inflammatory response in NCC plays an important role in the pathogenesis of symptoms by anthelminitic therapies . The anthelmintic recommended therapy for NCC has drawbacks as the exacerbation of inflammation around degenerating cysts provokes the appearance of symptoms at the first days of treatment . Radiological changes in the appearance of cysts usually are seen after months of therapy . To evaluate if significant radiological changes ( enhancement and size ) occur in the first days of therapy , we used a porcine NCC model and magnetic resonance imaging ( MRI ) with contrast solution . The major radiological changes observed after treatment with albendazole and praziquantel were an increase in enhancement and the significant reduction in cyst size by day 2 and more evident on day 5 . Cysts with greater changes also experienced exacerbated inflammation , loss of vesicular fluid and wrinkling of the cyst wall . These results show an early therapeutic effect and the possible utility of repeat MRI imaging within a few days after starting treatment . Finally , these findings contribute to our understanding of the treatment induced early exacerbation of symptoms . | [
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"signs... | 2017 | Radiological evolution of porcine neurocysticercosis after combined antiparasitic treatment with praziquantel and albendazole |
There is strong evidence that hotspots of meiotic recombination in humans are transient features of the genome . For example , hotspot locations are not shared between human and chimpanzee . Biased gene conversion in favor of alleles that locally disrupt hotspots is a possible explanation of the short lifespan of hotspots . We investigate the implications of such a bias on human hotspots and their evolution . Our results demonstrate that gene conversion bias is a sufficiently strong force to produce the observed lack of sharing of intense hotspots between species , although sharing may be much more common for weaker hotspots . We investigate models of how hotspots arise , and find that only models in which hotspot alleles do not initially experience drive are consistent with observations of rather hot hotspots in the human genome . Mutations acting against drive cannot successfully introduce such hotspots into the population , even if there is direct selection for higher recombination rates , such as to ensure correct segregation during meiosis . We explore the impact of hotspot alleles on patterns of haplotype variation , and show that such alleles mask their presence in population genetic data , making them difficult to detect .
There is now compelling evidence , from sperm studies and from the analysis of patterns of genetic variation , that the general pattern of recombination in humans is highly nonuniform throughout the genome [1–4] . Both of these approaches have shown that a large proportion of crossing over is restricted to small regions , so-called recombination hotspots ( typically 1–2kb wide ) , where crossing over occurs much more frequently than in the surrounding region . This heterogeneity of rate is clearly an important factor in determining the association between alleles along the genome , and therefore an understanding of the forces controlling hotspot occurrence and evolution would greatly benefit many analyses that employ variation data . A number of studies have found that fine-scale patterns of recombination are poorly conserved between humans and our nearest relative , the chimpanzee [5–8] . That is , hotspots are present in both species , but largely in different genomic locations . Consistent with the idea of rapid evolution of hotspots through time , historical estimates of the rate of recombination at a number of hotspots were found to be inconsistent with their present day intensity in sperm by Jeffreys et al . ( 2005 ) [9] . Many hotspots must be transient features of the genome , which suggests that hotspots currently present within the population might frequently be polymorphic , a possibility not incorporated in most current models of evolution . A possible explanation of hotspot transience is the phenomenon of biased gene conversion . In essence , the idea is that an allele that locally disrupts a hotspot may have an unequal probability of transmission in individuals heterozygous for the disrupting allele . Both current models and empirical observations of recombination ( both discussed further below ) strongly suggest that typically we expect this transmission bias to favor transmission of the hotspot-disrupting allele [10] . The result is an increase in the probability of this allele being driven to fixation in the population , resulting in the elimination , or strong reduction in intensity , of the hotspot . Boulton et al . ( 1997 ) [11] observed that biased transmission should lead to the elimination of hotspots from the genome over time . The reasons for the survival of hotspots in the face of this drive are unclear , and Boulton et al . ( 1997 ) [11] described this problem as the “recombination hotspot paradox . ” Boulton et al . ( 1997 ) [11] and Pineda-Krch and Redfield ( 2005 ) [12] investigated possible resolutions of the hotspot paradox via simulation . Both studies found that the proposed benefits of recombination , e . g . , breakup of deleterious combinations of mutations or ensuring correct segregation , are insufficient to maintain a hotspot in the presence of driven disrupting alleles . There is already direct evidence that biased gene conversion in favor of hotspot-disrupting alleles occurs at several specific human hotspots . A number of authors have investigated particular hotspots in male meiosis using sperm studies ( see Carrington and Cullen ( 2004 ) [13] for a review ) . Jeffreys and Neumann ( 2005 ) [14] and Jeffreys and Neumann ( 2002 ) [15] showed that in two well-characterized human hotspots , DNA2 and NID1 , respectively , variation at particular SNPs appeared to affect hotspot activity . In each case , one of the two alleles strongly suppressed hotspot activity , and was overtransmitted in heterozygotes . An earlier study also found a signal in the data that strongly suggested a similar phenomenon operating at the human MS32 hotspot [16] . Given the necessarily low number of individuals analyzed in sperm studies at any one hotspot , and the very small number of human hotspots that have been investigated in this manner , it seems likely that such “hotspot alleles” segregating in the population at large are common . This phenomenon has also been observed at a hotspot in mice [17] and has been studied with artificial alleles in yeast ( e . g . , see [10 , 18 , 19] ) . The study of the phenomenon of hotspot evolution in humans is of particular interest . Many features of recombination are highly conserved across eukaryotes and so it is likely that much of the work of Boulton et al . ( 1997 ) [11] and Pineda-Krch and Redfield ( 2005 ) [12] on the hotspot paradox will hold true in many organisms . There are , however , some known features that may be more specific to our species . First , the fact that hotspots are visible from patterns of linkage disequilibrium ( LD ) implies that they must exist in particular locations for tens of thousands of generations , contrasting with the lack of sharing between humans and chimpanzees [5–8] . Second , humans have a relatively small effective population size . This means that genetic drift will play a key role in the fate of alleles that alter our local recombination landscape . Motivated by the above observations , our aim here is to study the effect of transmission bias at hotspot-influencing mutations on human hotspot evolution . Throughout , we consider realistic human parameters , and the human-specific features described above . In particular , we address , in separate sections , three specific and important questions regarding the properties of recombination hotspots . First , how long do we expect a typical hotspot to persist in the population ? This quantity is key to understanding the proportion of hotspots that should be shared between humans and chimpanzees . It also enables us to ask whether all , or nearly all , hotspots should be shared between human populations . Second , for plausible human population dynamics , which models of how hotspots arise are compatible with the observed spectrum of human hotspot intensities ? Third , what is the effect of alleles that disrupt or enhance a hotspot on diversity patterns within a population ? A signal for disrupting or enhancing alleles would allow these alleles to be identified , helping to reveal more about the mechanisms of double-strand break ( DSB ) initiation . Boulton et al . ( 1997 ) [11] and Pineda-Krch and Redfield ( 2005 ) [12] use fully simulation-based approaches to consider the effect of biased transmission on the fate of hotspots under a range of models and parameters . Our work builds on this , but differs in several key respects . First , we develop an analytical framework that fully allows for the effect of drift and biased transmission on hotspots . This permits intuition regarding the effect of changes in model parameters , and allows rapid calculation of results . Second , there has been a rapid accumulation of data on the human recombination landscape . This enables us to focus strongly on realistic parameter values . We present a dynamic picture of evolving hotspots in humans and suggest solutions to the hotspot paradox in humans . In this article , we consider a general setting in which primary sequence changes can disrupt , or introduce , hotspots . As described above , such hotspot-influencing mutations have been found at an appreciable fraction of studied human hotspots [14–16] . The factors that control the location and heat of hotspots remain far from completely understood . Work in yeast suggests that both local nucleotide sequence and larger features of chromosome structure are involved [20] , and we are beginning to learn more about the control of human hotspots . Recent work [4] has found strong evidence that particular sequence motifs are overrepresented in hotspot locations . Further , the two hotspot SNPs whose alleles suppress hotspot activity [14 , 15] disrupt two of these motifs , providing compelling evidence that the motifs directly influence these hotspots in cis [4] . Although the mechanism of hotspot disruption or introduction is not the focus of our study , the mutation ( or creation ) of recombination-promoting motifs demonstrates one way in which this can occur .
We consider biased gene conversion in terms of the DSB repair model [21] . This is the working model of recombination in yeast [20] , and is likely to apply similarly to mammals [1] . We stress that our results are not dependent on this particular model , only on the empirical observation of biased transmission in hotspots in the species of interest . However , the DSB model does offer a natural biological explanation for biased gene conversion at hotspots , and so , for the sake of completeness , we offer a brief description here of this model and its implications . Under the DSB model , recombination occurs during meiosis as a result of a DSB at a site . The break occurs on either the maternal or paternal chromosomes . During the repair process , information at sites immediately flanking the break site is lost . The other chromosome remains intact , and must supply the missing information via gene conversion to repair the break , so that whenever a DSB occurs , the offspring carries the genetic material of the unbroken copy in a region immediately surrounding the break . DSBs are processed by specific repair machinery to produce one of two outcomes . The first possibility is gene conversion . Here , one of the parental chromosomes is present for all but a short tract flanking the DSB site , where the information is copied from the intact chromosome . The second possibility , gene conversion accompanied by crossing over , results in the chromosomal material in the offspring on one side of the crossover being derived from the maternal parental chromosome and the material on the other side being from the paternal parental chromosome . Note that even when crossing over occurs , it is accompanied by gene conversion repair of the DSB . Imagine two alleles , A and B , at a particular locus where allele B reduces the rate of DSBs in cis , so that the haplotypes containing the A allele are more often subject to DSBs . In an AB heterozygote , gene-conversion repair of DSBs will cause transmission to be biased in favor of allele B . Therefore , any segregating site or allele able to prevent the local occurrence of DSBs on the chromosome is automatically favored by biased gene conversion ( see Figure 1 ) . We start by constructing a model to describe the frequency through time ( the frequency trajectory ) of a segregating allele that influences the heat of a hotspot . In this model , the frequency of the allele will change through time due to random genetic drift , as well as both biased gene conversion and mutation . The population genetic behavior of models of biased gene conversion has previously been studied by a number of authors [22 , 23] . We present an analogous model changing the parameters to describe a recombination hotspot . At a locus L , two allelic classes , A and B , are present . During meiosis in an AB heterozygote , a chromosome in the A class initiates a DSB with probability rA and a chromosome in the B class initiates a DSB with probability rB . Note that the frequency trajectory of the alleles A and B does not depend upon the DSB rate in the homozygotes , as biased transmission does not occur in these individuals . Thus , our model can apply to the case in which rates in heterozygotes are a nonadditive function of the homozygote rates . Without loss of generality we assume that rA > rB , and accordingly sometimes refer to A as the “hotspot” allele and B as the “disrupting” allele . The difference in the rate of initiation of DSBs between the two alleles is denoted by rH , i . e . , rH = rA − rB . Using just a two-allele system simplifies reality , since there may in practice be multiple alleles at the locus corresponding to differing DSB initiation rates , but this assumption helps us build a simple model that nevertheless provides insight into the evolution of hotspots . It is important to note that the rate of DSB formation at the hotspot is distinct from the crossover rate , since many DSBs may result in gene conversion that is not accompanied by crossover . Thus , our parameters reflect the rate of DSB initiation at the hotspot , which can be several times greater than the rate of crossing over in some human hotspots [24] . When a DSB is initiated then with probability p , the allele that initiated the DSB is transmitted . Any value of p between 0 and 1/2 is biologically possible , with p = 0 corresponding to initiation always occurring very close to L , and p = 1/2 to no bias . Potentially , a mutation at any of a number of sites could disrupt the hotspot , and we assume that the chance of a particular site mutating again to allow the hotspot to recover is negligible . We assume also that any such mutation results in a change of rate to rB , so that mutant chromosomes always become members of the B class . This leads to a simple model of one-way mutation in which only mutation out of the hotspot allele ( i . e . , from the A class to the B class ) is possible . When the A allele is transmitted to the offspring chromosome , with probability μD , the allele mutates to the non-hotspot allelic class B . We are deliberately vague here about the exact relationship of the sites that control the hotspot to the hotspot itself . This stems from our wish to retain generality , particularly given the current incomplete knowledge about the exact mechanism of hotspot disruption . To simplify the analysis , we assume a constant-size random-mating population with discrete generations ( i . e . , the standard Wright-Fisher model ) and without selection , although these assumptions could be relaxed . The census size of the population is N , and the effective size of the population is Ne . The effective population size quantifies the magnitude of genetic drift in a population; the larger the effective population size , the smaller ( or slower ) the effect of genetic drift . We make use of various methods employed to study similar population genetics models; more specifically , we use the diffusion limit of the Wright-Fisher Model , in which time is rescaled in units of 2Ne . For large Ne , a model describing the frequency of an allele experiencing biased gene conversion is equivalent to a model of selection with no dominance [23] ( genic selection; see Text S1 section 1 for details ) . If the hotspot allele is undertransmitted ( p < 1/2 ) , then the hotspot allele effectively acts as a deleterious mutation ( although it is not maladaptive ) . Thus , it is clear that just as the properties of selected alleles in a population are in part governed by the effective size of that population , the effective population size will also affect the properties of alleles that locally influence the heat of recombination hotspots . In this model , the distortion away from non-Mendelian segregation in heterozygotes is 2rH ( 1/2 − p ) in favor of the hotspot-disrupting allele . We refer to 2rH ( 1/2 − p ) as the drive coefficient . The drive coefficient is equivalent to the selection coefficient in a model of genic selection . As in many population genetics models , we are interested in the relative strength of the drive compared to genetic drift . This is quantified by the product of the effective population size and the drive parameter: This population scaled drive parameter is equivalent to the population scaled selection parameter 4Nes in a model of genic selection . Where we do not explicitly consider variation in rH , the drive parameter 2rH ( 1/2 − p ) will hereafter be denoted by g , and the population scaled drive parameter by 4Neg . Having developed a model of biased transmission in hotspots , we can estimate relevant parameters for humans . Although relatively little is known about the general fine-scale properties of hotspots in humans , a number of sperm-based studies have investigated crossover and gene conversion rates in particular hotspots . The rate of crossover in ( male ) human hotspots so far characterized by sperm studies varies by over two orders of magnitude , ranging on autosomes from the DNA1 hotspot , which has crossing over activity of 0 . 5 × 10−5 Morgans ( crossover events per male meiosis ) , to the DNA3 hotspot , which has 130 × 10−5 Morgans [24] , and as high as 370 × 10−5 for the SHOX pseudoautosomal hotspot [24] . The strength of drive for or against an allele is actually determined by the rate of gene conversion repair of DSBs rather than simply the rate of crossover at the site of the allele . This rate is much more difficult to measure than the crossover rate , since detecting highly localized gene conversion products is more difficult when crossover does not occur . However , conversion without crossing over was estimated by Jeffreys and May ( 2004 ) [24] to be four to 15 times more likely to result from a DSB than gene conversion accompanied by crossing over , based on examining three known human hotspots . The level of unaccompanied gene conversion might vary between hotspots , and the frequency of conversion at particular markers declines rapidly with distance from the hotspot center [24] . Typically , however , we expect the drive due to conversion unaccompanied by crossover to be as strong as , or even stronger than , drive due to crossover . In Table 1 we present plausible estimates of human drive parameters ( g ) , based on these studies , for various known human hotspots , including the hottest and coldest identified so far . See Text S1 section 2 for full details of how these estimates are obtained . Although there is considerable difficulty in estimating the drive parameter for particular hotspots and hotspot-disrupting alleles , this analysis suggests that a wide parameter range ( 0–200 ) for the drive parameter is likely to encompass the majority of human hotspots . The rate of mutations that disrupt hotspots ( μD ) is unknown . The per base mutation rate in humans is on the order of 10−8 per generation [25] . Clearly , it is probable that only certain mutations within the hotspot will strongly affect its heat . This implies for 1–2kb hotspots that μD is no larger than 2 × 10−5 per generation , resulting in a population scaled mutation rate 4NeμD , of less than 1 ( and probably much less ) . Throughout the paper , we will make use of results that assume such a relatively low population scaled mutation rate . While this slightly reduces generality , we feel that it allows a clear insight into the role of drive in the evolution of hotspots , and it seems likely that biologically plausible parameters will fall within this range . Suppose two species diverged T generations in the past . What fraction of ancestral hotspots ought we to see conserved in both species to the present ? A hotspot is most likely to be conserved in both species in the present day if it was fixed in the ancestral species/population , and so we concentrate on the survival time of a hotspot initially fixed in a population . The probability of a hotspot surviving unaffected in a particular species to the present day is the probability of no hotspot-disrupting allele reaching fixation in the population . We will assume that the mutation rate towards the disrupting allele is low enough that only one mutation that disrupts the hotspot is present at an appreciable frequency within the population at any one time . Further , we assume that both the census and effective population sizes are constant since the two populations split . Alleles that disrupt a hotspot are introduced into the population at rate 2NμD . Using a standard population genetic approximation ( see Text S1 section 3 ) , we can approximate the probability of no disruptive alleles having arisen and fixed in either population by A helpful way to look at the effect of drive on the survival of probability of hotspots is to suppose a mutation at any one of k sites could disrupt the hotspot ( i . e . , let μD = kμs , where μs is the per site mutation rate ) . We need not consider exactly how this disruption takes place; it need not occur by a change within any particular sequence motif . We only require that there is some collection of sites capable of removing the hotspot . Humans and chimpanzees differ on average at about 1 . 23% of homologous sites [26] . This represents a 0 . 0123 probability that a neutral allele has fixed since the time of divergence of human and chimpanzee . This means that at a single site , more than 100 mutations ( for N > 10 , 000 ) will have occurred in one species or the other during the time since divergence , but the vast majority will be lost by genetic drift . Within a hotspot , the bias transmission acts upon this introduced variation , and in the case of an intense hotspot , this dramatically increases the probability that one of these 100 mutations will reach fixation . Given the 1 . 23% level of neutral divergence , we can calibrate μsT accordingly . Assuming a constant effective population size of 10 , 000 in both species , we can plot the number of disrupting sites needed to give a fraction α of sharing between the species , for different values of α and different hotspot intensities ( Figure 2 ) . As can be seen in Figure 2 , the biased gene conversion would strongly influence the chance of survival of DNA3 , NID1 , and DNA2 , and offers an explanation for the observed lack of sharing of hotspots of such heat between the species [5–8] . For a hotspot as intense as DNA3 , only 2–3 sites where hotspot-disrupting mutations can arise are needed to reduce the chance of this surviving to the present day in both species to 10% . However , the drive has little effect on relatively weak hotspots such as DNA1 . This hotspot would need over 180 sites where disrupting mutations could arise to lower the survival probability to the same level . We therefore expect many more weak hotspots to be shared between humans and chimpanzees compared with more intense hotspots . This provides a testable hypothesis , although this prediction is currently difficult to test ( weak hotspots are harder to identify using population genetic data ) . The validity of this hypothesis depends on the historical value of Ne between humans and chimpanzees . A ( much ) higher ancestral population size , as has been proposed by other authors [27–29] , could result in far less sharing for all hotspots . The “recombination hotspot paradox” [11] , asks how any hotspots can arise or persist in the population , if biased gene conversion acts against them . One possible resolution is that if enough mutations introducing hotspots occur that at any given time , a reasonable number of hotspots will be present at high frequency in the population due to simple stochastic drift , despite drive against them . In this section , we explore whether this mutational input offers an explanation for observed human hotspot distributions . First , we examine how many hotspots we expect there to be in a region , if any newly arisen hotspot allele is at a disadvantage due to biased gene conversion in favor of the non-hotspot allele . Later , we consider possible alternatives to this scenario . In order to gain insight into the number of hotspots present in the population , and their distribution of intensities , we make several simplifying assumptions . We assume that alleles that introduce a hotspot experience the same biased transmission against them as alleles switching off such a hotspot experiences in its favor . This assumption is relaxed later . We also assume that hotspots evolve independently of one another; and that mutation towards hotspots is sufficiently rare that any two hotspot-causing mutations create distinct hotspots . Hotspot alleles that experience a biased transmission g against them are introduced by mutation into the population at rate NeμH per generation . Then the expected number of such hotspot alleles with a population frequency in the small interval [x , x + dx] is This formula is equivalent to the often-used frequency spectrum of a selected allele [30] ( see Text S1 section 4 ) . The number of hotspot alleles within the population depends on the relative rates of introduction of hotspots of different heats , μH , which is difficult to estimate sensibly . For simplicity , we assume that hotspot alleles of any heat are introduced at the same rate , i . e . , μH is not a function of g . If recombination were mutagenic ( e . g . , Hellmann et al . ( 2003 ) [31] ) , then the mutation rate μD away from hotspots would be expected to be increasing with rH and hence with g , implying that intense hotspot alleles are even less likely to achieve high frequency in the population , or to be shared between human and chimpanzee . Most hotspot alleles will be lost quickly from the population due to genetic drift and drive; only hotspot alleles that reach appreciable frequency in the population will leave a signal in LD data . To examine the number of hotspots that affect LD patterns in a detectable way , we arbitrarily define “visible” hotspots to have a frequency above a set frequency ( y ) . The total expected number of such hotspots with frequency greater than y is then Plotted in Figure 3 is the expected number of hotspot alleles , with frequency >0 . 5 , for a range of effective population sizes and drive coefficients , 2rH ( 1/2−p ) . The range of effective population sizes used in Figure 3 were chosen to encompass those likely for humans and chimpanzees . We also performed simulations for plausible human population bottleneck scenarios ( see Figure 3 and Text S1 section 5 for full details ) , as well as for a more accurate approximation of the relationship between hotspot heat and that observed in LD data ( Text S1 section 6 ) and find that our results remain essentially unchanged . The first point to note is that considering mutations that create hotspots , and drift , does go some way towards solving the “hotspot paradox” of Boulton et al . ( 1997 ) [11] , since we do see some level of hotspots present in the population at high frequency . However , the number of hotspots at a given frequency drops off exponentially with the drive against them ( i . e . , their heat ) , and in larger populations intense hotspots are very rare , since the drive against them is too great to enable them to reach appreciable frequency . The mutation rate towards such hotspots would need to be unfeasibly large for them to be likely to be observed . In our model , hotspots evolve essentially as deleterious mutations within a population . Larger populations harbor more diversity but strongly resist the influx of negative mutations . If hotspot-promoting alleles do arise that cause a hotspot locally , then the pattern of hotspot heats should be very strongly biased towards weak hotspots . However , the true distribution of inferred heats of hotspots in human populations [2 , 3] shows a much less dramatic skew towards weak hotspots ( although current approaches probably have greater power to infer the presence of hotter hotspots ) . For example , the presence of the hot human DNA3 hotspot is incompatible with drive acting against new hotspots ( Figure 3 ) . In other words , human populations do face a kind of hotspot paradox—how can we explain the existence of our hotter hotspots ? To resolve this question , we must examine alternative hypotheses of how hotspot alleles behave . We consider two possible explanations: first , selection for recombination hotspot alleles to ensure correct segregation , and second , that drive does not act against hotspot alleles immediately upon introduction into the population . To ensure correct chromosomal segregation in meiosis ( in humans and other organisms ) , at least one crossover per chromosome arm occurs [32] . Further , it has been observed [33] that women with a higher crossover rate have more offspring on average . Hotspot evolution could therefore be influenced by direct selection for higher recombination rates . A simple selection model suggests itself; the offspring is inviable unless at least one crossover occurs within a certain region , e . g . , a chromosome arm . We now consider the evolution of a hotspot when crossover within a certain region must occur for the offspring to be viable . Let rH , x , and p be defined as before , and suppose now that the probability of a crossover event elsewhere within the region is w , independently of whether crossover occurs at the hotspot in question ( i . e . , no interference ) . Suppose also that with probability q a DSB at this hotspot results in crossover . As before , this model is equivalent to a model of selection with no dominance , and the population scaled drive coefficient against the hotspot allele under this new model is which can be compared to Equation 1 . Therefore the effect of selection for recombination is equivalent to increasing the probability of transmission of the hotspot allele , as mathematically the parameter p can be replaced by an effective p′ = p ( 1 − q + q/w ) . If the probability of crossover elsewhere in the region ( w ) is small enough , then the hotspot allele is actually ( selectively ) favored , i . e . , p′ > 1/2 . However , if crossing over is very likely elsewhere in the region , w close to 1 , the positive effect of crossover in the hotspot becomes negligible . Models of selection for correct segregation under a weaker scenario in which selection acts in only one sex , and an extreme case in which the fitness is proportional to the number of crossovers , are discussed in Text S1 section 7 . In all cases , the selection for correct segregation will only have a strong effect at an individual hotspot if recombination elsewhere in the region is unlikely ( small w ) and DSBs within the hotspot often results in crossovers ( q close to 1 ) . For most human chromosome arms , w ≥ 1/2 for both males and females [33] . In a number of hotspots studied [24] , gene conversion is much more likely than crossing over , having at most a probability q = 1/5 that DSBs result in crossover . These two estimated quantities ( w and q ) give an effective p′ of ≤ 1 . 2p , thus p′ is still much less than 1/2 , and so the impact of such selection at hotspots may well be slight . This model provides a crude upper limit on the selection for correct segregation , as it does not account for the fact that mis-segregated gametes often abort early in pregnancy [32] , and so individuals can mate again to produce a viable offspring , reducing the effect of a low recombination rate on an individual's fitness . Thus , it is unlikely that the drive against hotspot alleles could be negated by selection for correct segregation . However , these results suggest that there could be preferential survival of hotspots where DSBs resolve mainly into crossover events; for example the NID1 hotspot where q ≈ 4/5 [15] . Looking at the broader chromosomal scale , we can see that selection for correct segregation would have an overall influence on the makeup of hotspots within chromosomes , because the selection will affect the balance between the loss and gain of hotspots . Smaller chromosomes will , all other things being equal , tend to have a lower total mutation rate introducing hotspots . Thus , they will achieve the necessary higher recombination rates by having more hotspots ( because negative drive would occur only at higher hotspot densities ) and hotter hotspots ( because the strength of the drive would be effectively reduced ) . An alternative explanation of the presence of intense hotspots is that there might not in fact be mechanistic drive against some newly arisen hotspots . There are a number of plausible reasons why newly arisen hotspot alleles might not experience drive . First , a change to the DNA sequence at one location may introduce a hotspot at some distant location . The allele causing the hotspot would therefore be unaffected by the drive at the hotspot that it introduces . Second , hotspots appear to compete for a finite amount of recombination with other surrounding hotspots [34–37] . As a result , hotspots that are intense in the present day might have been relatively cool in the past . Hence , the allele that causes the hotspot might have originally experienced little drive . Third , evolution of the recombination machinery could cause whole classes of hotspots to be turned on or off simultaneously; e . g . , if the motif underlying hotspot activity is changed . Hotspot alleles would spread neutrally before activation of the new motif , and only subsequently be subject to biased gene conversion . Fourth , there is evidence from an experiment in yeast that a hotspot allele in heterozygotes can stimulate DSBs on both chromosomes equally [38] . If alleles that introduce hotspots have such a property , or if , conversely , such alleles do not stimulate DSBs in heterozygotes , they will not experience drive . A common feature of all these models of hotspot genesis is that the hotspot allele is initially shielded from the drive and thus is neutral . Subsequently , when disrupting mutations arise within an established hotspot , they would still benefit from drive in their favor . To simplify the modeling of such cases , we concentrate only on the number of hotspots currently fixed in the population . More generally , this result will provide intuition as to the expected number of hotspots at high frequency in the population . We once again assume that the mutation rates are μH towards alleles that generate a hotspot somewhere in a region , and μD towards alleles that disrupt a given existing hotspot , where μH and μD are now both assumed small . This allows us to approximate our model as a model with two stages in the evolution of a hotspot . During the first stage , before any disrupting alleles have arisen , the newly introduced neutral hotspot allele drifts to either loss or fixation in the population . If the hotspot allele reaches fixation , in the second stage , disrupting alleles arise and lead to the removal of the hotspot if one of them fixes in the population . The properties of this model are dictated by the rate at which hotspot alleles arise and fix in the population and the rate at which fixed hotspots are removed from the population by disrupting alleles . The rate at which neutral hotspot alleles fix in the population is given by μH . Similarly , hotspots are removed from the population at rate 8μDNerH ( 1/2 − p ) / ( 1 − exp ( −8NerH ( 1/2 − p ) ) ) , which is the rate at which disrupting alleles arise and get fixed in the population ( see Text S1 section 8 ) . The expected number of currently fixed hotspots in the region is given by the ratio of the rate at which hotspot alleles arise and fix to the rate at which each given hotspot is lost by fixation of a disrupting allele ( see Text S1 section 8 for more details ) . Thus , the expected number of hotspots of heat rH fixed in the population is For strong biased transmission in favor of a hotspot-disrupting allele , this equation behaves as μH/ ( 8μDNerH ( 1/2 − p ) ) . Thus , the number of fixed hotspots of heat rH decays linearly with both Ne and rH . Importantly , this drop-off with large rH is far less severe than in the case in which hotspots experience drive against them from their introduction into the population , where the drop-off with heat ( as can be seen in Figure 3 ) is approximately exponential . Among possibilities we have explored here , the model in which hotspot alleles do not initially experience drive offers by far the most convincing explanation of why we see hot hotspots in the human genome . Hotspots influence patterns of LD , and so it is also of interest to attempt to understand in detail the relationship between hotspots and these patterns , in the case in which hotspot allele frequencies have varied through time . In particular , knowledge of which alleles influence hotspot activity would add to our understanding of hotspots , and an ability to detect such alleles through LD patterns represents an indirect path to finding such alleles . Thus far , we have considered models of alleles that influence the heat of hotspots forward in time; in order to understand the effect of such alleles on current day patterns of diversity , we must consider the ancestry of the sample backward in time . This leads naturally to the use of a coalescent-with-recombination model [39] to describe that ancestry; the model is similar in many ways to the standard coalescent-with-recombination model , but complicated by the fact that the process of recombination must be modified . The process differs from the normal coalescent model in three important respects . First , the two different allelic backgrounds , A and B , where A is the hotspot-allele , will recombine at different rates backward in time . Second , the parental allelic types of a recombinant chromosome are not random draws from the population . Third , there is an asymmetric distribution of material contributed to the offspring by the parental chromosomes . For example , when gene conversion occurs , there is an asymmetry in which parent contributes the majority of the material . The derivation of the process is somewhat involved , and so we restrict ourselves to discussing the likely effects of driven alleles at hotspots . A full description of the scheme is discussed in Text S1 sections 9–12 , and an algorithm for simulating the coalescent process of a region surrounding a segregating hotspot allele is given in Text S1 section 13 . Hellenthal et al . ( 2006 ) [40] have independently studied the first point outlined above , but did not formally develop the latter two points . One interesting case occurs when an allele experiences perfect biased gene conversion ( i . e . , p ≈ 0 ) in a hot hotspot ( rA >> rB ) . Inspection of the model described in Text S1 section 9–12 shows that ancestral lineages recombine at the same rate regardless of whether they are of type A or B , despite the two backgrounds having very different rates forward in time . Initially , this result perhaps seems counter-intuitive; we might expect the A allele haplotype to recombine far more often than the B haplotype , backward in time . However , the A allele is frequently not transmitted when recombination occurs . Thus , the fact that an A allele has been transmitted to the present day implies that it has been involved in few historical recombinations . Therefore , we do not expect type A and B haplotypes to have particularly different patterns of LD; both backgrounds should show similar signals of the hotspot . Further inspection of the model , the second point in particular , shows that the A and the B haplotype backgrounds will look quite similar , reducing the chances of identifying alleles that affect hotspots by haplotypic patterns . When a recombination occurs in an individual with a B allele , it is likely that the individual's other haplotype has an A allele ( as B homozygotes have a much reduced hotspot compared to AB heterozygotes ) . The transmitted allele from this individual will be the B allele , but the transmitted haplotype will be a mixture of the A and B haplotypes . If the gene conversion or crossing-over rate is quite high , many B chromosomes will be descended from ancestors on the A background in the recent past . Thus , the difference between the A and B haplotypes is eroded . There is still some hope of detecting polymorphic hotspots from patterns of LD . In general , the rates of crossover and gene conversion will change backwards in time as the frequency of the allele varies . Eventually , when we reach a time before the introduction of the hotspot allele into the population , crossing over in the region will be much reduced . For recently arisen hotspot alleles , there may be some information about this change , due to their relatively recent introduction into the population , but this could be confounded by the reduced power to observe such hotspots . However , the possibility of such a signal remains an area worthy of future exploration .
Several lines of evidence now support the idea that human hotspots vary in intensity and location through evolutionary time . First , segregating mutations near to the center of the DNA2 and NID1 hotspots affect the recombination rate [14 , 15] . Second , hotspots appear to evolve quickly over evolutionary timescales , with human hotspots typically not conserved in comparisons with our nearest relative , the chimpanzee [7–8] . Finally , examination of a group of hotspots in a 200-kb region of the human genome around minisatellite MS32 [9] revealed strong differences between recombination rates estimated using population genetic data , and male recombination rates estimated using direct sperm typing . Biased gene conversion at hotspots preferentially fixing alleles that disrupt hotspot activity offers a plausible explanation for the above findings . Our aim here was to consider a modeling framework for such biased conversion , enabling us to explore the implications of such a model . Using the model , we sought first to consider whether a model of biased gene conversion could explain the observed rapid evolution of hotspots , while remaining consistent with the large range of hotspot intensities in humans . Beyond this , the use of such a model enables us to make predictions regarding the signature of this phenomenon , both on a broad genomic scale across many recombination hotspots , and at the level of detecting whether an individual hotspot has recently been influenced by a segregating allele , through consideration of the genealogical process under biased gene conversion . Estimates of human parameter values suggest that drive is a sufficiently powerful genetic force to ensure that most hotter hotspots will not be shared with chimpanzees , provided that a sufficient number of sites close to the hotspot exist where a mutation can reduce the hotspot heat . Therefore biased gene conversion could create the observed lack of sharing among species . It is worth noting , however , that in general the probability of sharing among species is strongly influenced by the strength of drive , which increases with the heat of a hotspot . Therefore , intense hotspots are much more vulnerable to extinction through the process of biased gene conversion , other things being equal . A survey in humans and chimpanzees of a number of relatively cold hotspots , perhaps in cold regions where they would more readily be detectable from LD patterns , might well show some proportion of conserved hotspots , if driven alleles are the principal cause of hotspot extinction . In view of the ability of drive to destroy hotspots , a key question is whether one ought to see hotspots at all if drive is acting [11 , 12] . Provided hotspots can arise in the population , there will be some stationary distribution of their number ( and frequencies , considering a hotspot as a potentially segregating allele within the population ) for different intensities of hotspots , even if there is biased gene conversion against any new hotspot allele entering the population . Interestingly , this distribution depends strongly on the effective population size Ne . For larger population sizes , if hotspot alleles must arise against drive , then there is a strong bias towards hotspots of lower heat , with powerful suppression of hotter hotspots . Perhaps the “hotspot paradox” issue [11] that we must address is not how hotspots persist in the face of drive but why there are very hot hotspots within the genome . We considered two possible resolutions of this paradox; direct selection on the chromosomal scale for higher levels of recombination , and a lack of drive affecting newly arisen hotspot alleles . Direct selection for higher rates was proposed recently by Kong et al . ( 2004 ) [33] for maternal recombination . Further , some such selection seems highly credible , given the requirement for around one crossover per chromosome arm in humans and many other organisms . However , for human levels of recombination , such selection seems unlikely to have a significant effect at individual hotspots , except perhaps in a minority of extreme cases such as within the PAR1 pseudoautosomal region , where a large male recombination distance ( 50 cM ) is compressed into a very short stretch of the genome ( 3 Mb ) . The second possibility that we considered as an explanation of the abundance of hot hotspots is that newly arisen hotspot alleles do not compete against biased gene conversion favoring the ancestral , non-hotspot type ( this drive would be comparable to the positive drive affecting an allele suppressing the hotspot ) . In biological terms , drive favoring the ancestral type implies that a hotspot-stimulating mutation creates a hotspot very locally in cis . This assumption may well be inaccurate; hotspot-causing mutations might act remotely and so not suffer from such drive . Under such departures , hotspots of high heat are much more frequently fixed within the population , although these then typically survive for less time than weaker hotspots . A similar dynamic will also be achieved if competition exists between local hotspots for some finite amount of recombination [34–37 , 41] . This competition between hotspots could initially suppress the heat of new hotspots , reducing the drive against the alleles causing them , and thus allowing some of these alleles to rise in frequency due to genetic drift . When hotspots surrounding a new hotspot are removed or cooled by disrupting alleles , the new hotspot allele would increase in intensity and could already have drifted to high frequency in the population . The hotspot could itself then be removed from the population , at a rate proportional to its new heat , by disrupting alleles . Finally , evolution of the hotspot machinery itself could have the most dramatic effect of all , by turning on or off many hotspots across the genome simultaneously . Any of these scenarios , in which hotspots can arise without facing meiotic drive against them but where such drive can cause future extinction , seem broadly consistent with present observations . Although selection for high rates does not in general seem to explain very hot hotspots , it could be an important force in regulating overall recombination rates . Selection for a rate giving at least one crossover event per chromosome arm would essentially multiply ( downweight ) the drive for any allele disrupting a hotspot . On shorter chromosome arms , a selectively favored higher rate would naturally be achieved by a combination of both more , and , on average , slightly hotter , recombination hotspots . This is consistent with observations in Saccharomyces cerevisiae , in which shorter chromosomes have both a significantly higher density of hotspots and hotter hotspots [42] . The various ways in which hotspot intensities can change might well be highly variable and are , as yet , poorly understood . For example , Tiemann-Boege et al . ( 2006 ) [41] find a hotspot that varies in position across men , while Neumann and Jeffreys ( 2006 ) [43] finds two adjacent hotspots in which local sequence does not appear to determine activity . The different ways in which hotspots can vary suggests that the evolution of hotspots is likely to be complex—a fact further suggested by our model-based analysis—and , although biased gene conversion is likely to be a key component in the evolution of fine scale recombination rates , non-local factors are probably equally influential . Finally , we turn to the effect of segregating hotspot alleles on population diversity patterns . The effect is strong , since hotspot activity varies through time , meaning hotspots might appear much colder or hotter from patterns in the data than their present day prevalence and heat would suggest . Inferring this signal directly from population genetic data is far more difficult , since rates on the two allelic backgrounds back in time remain similar to one another , eroding differentiation . In particular , we need to be able to reconstruct ancestral background patterns and to observe ancient crossover events , which will be problematic in practice .
The methods used are included in the Results section and in Text S1 . | Recombination is a fundamental component of mammalian meiosis , required to help ensure that daughter cells receive the correct complement of chromosomes . This is highly important , as incorrect segregation causes miscarriage and disorders such as Down syndrome . In addition to its mechanistic function , recombination is also crucial in generating the genetic diversity on which natural selection acts . In humans and many other species , recombination events cluster into narrow hotspots within the genome . Given the vital role recombination plays in meiosis , we might expect that the positions of these hotspots would be tightly conserved over evolutionary time . However , there is now considerable evidence to the contrary; hotspots are not frozen in place , but instead evolve rapidly . For example , humans and chimpanzees do not share hotspot locations , despite their genomic sequences being almost 99% identical . The explanation for this may be , remarkably , that hotspots are the architects of their own destruction . The biological mechanism of recombination dooms them to rapid extinction by favoring the spread of hotspot-disrupting mutations . By mathematically modeling human hotspot evolution , we find that this mechanism can account for fast hotspot turnover , and in fact makes it very difficult for active hotspots to arise at all . Given that active hotspots do exist in our genome , newly arising hotspots must somehow be able to bypass their self-destructive tendency . Despite their importance , it is difficult to identify mutations that disrupt hotspots , as they hide their tracks in genetic data . | [
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] | 2007 | Live Hot, Die Young: Transmission Distortion in Recombination Hotspots |
For many animals , chemosensation is essential for guiding social behavior . However , because multiple factors can modulate levels of individual chemical cues , deriving information about other individuals via natural chemical stimuli involves considerable challenges . How social information is extracted despite these sources of variability is poorly understood . The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits . Here , we focus on two such traits: a female mouse’s strain and reproductive state . In particular , we measure stimulus-induced neuronal activity in the accessory olfactory bulb ( AOB ) in response to various dilutions of urine , vaginal secretions , and saliva , from estrus and non-estrus female mice from two different strains . We first show that all tested secretions provide information about a female’s receptivity and genotype . Next , we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability . We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability . However , simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units . Furthermore , we show that some traits are more efficiently detected than others , and that particular secretions may be optimized for conveying information about specific traits . Across all tested stimulus sources , discrimination between strains is more accurate than discrimination of receptivity , and detection of receptivity is more accurate with vaginal secretions than with urine . Our findings highlight the challenges of chemosensory processing of natural stimuli , and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons .
Social animals are extremely adept at extracting information about conspecifics and many species rely on chemosensory cues to achieve this goal [1–3] . Yet , deriving information about specific traits pertaining to other individuals can be complicated by several factors [4] . One factor is physical variability , for example due to stimulus source dilution . Namely , if a given trait is associated with a particular level of some compound , dilution or concentration of the stimulus source could confound correct trait detection [4 , 5] . Another factor is the influence of multiple ecologically relevant traits on the levels of any one type of molecule . For example , analysis of mouse urinary compound levels as a function of genetic background and reproductive state indicates that both factors modulate the levels of all tested compounds . In some cases , even the direction of change as a function of reproductive state varies with the genetic background [6] . A third factor involves the stimulus source identity . Many animals , including rodents [7 , 8] , investigate multiple body regions of conspecifics and thus inevitably sample different secretions . Compound concentrations likely differ across secretions and interpretation of their content must thus account for the secretion sampled . Given these sources of variability , reliable detection of any trait becomes a significant computational challenge . Here , we explore the neural representation of genetic background ( a model for identity ) and female estrus-stage ( a measure of receptivity ) , two traits that are critical for a male mouse to guide reproductive behavior [2 , 9 , 10] . We study neuronal responses at the level of the accessory olfactory bulb ( AOB ) , the first brain stage receiving vomeronasal inputs [11] . The vomeronasal system ( VNS ) is ideal for this study as its role in the detection of both of these traits is well established [9 , 10 , 12–16] . In particular , the two donor strains used here ( BALB/c and C57BL/6 ) , are clearly distinguishable by mice in the context of the vomeronasal-system mediated pregnancy block effect ( The Bruce effect ) [17 , 18] . Furthermore , physiological recordings reveal that responses of vomeronasal sensory neurons , and of AOB neurons , are modulated by both the reproductive state [10 , 12 , 19–22] , and the strain [7 , 19 , 23–25] . An important element in this study is the parallel investigation of multiple chemosensory stimulus sources: urine , saliva and vaginal secretions . While urine has been extensively studied as a chemosensory stimulus source in rodents [2 , 22 , 25–27] , saliva and vaginal secretions have received much less attention . Here we show for the first time that neurons in the AOB respond to all these secretions in a strain and reproductive-state specific manner . However , we find a major difference between sensitivity to a certain trait and the ability to reliably detect it in the presence of multiple sources of variability . Thus , while many individual neurons can provide some information about these traits , any one neuron in isolation is generally insufficient to provide invariant information about them . We show , however , that integration of information across multiple neurons considerably improves trait detection , and that the detection does not rely on a small number of specialist neurons . Our study highlights the complexities of extracting socially relevant information from chemosensory cues , yet also suggests that relatively simple networks can overcome these challenges .
We initially tested whether AOB responses to urine , saliva and vaginal secretions can convey the strain and the state of the stimulus donor . Recordings were performed in anesthetized BALB/c males ( BC ) , using multi-electrode arrays . The electrodes were targeted to the AOB external cell layer ( Fig 1A ) which contains the cell bodies of AOB projection neurons , known as AOB mitral/tufted cells [11] . Stimuli were collected from estrus and non-estrus adult BC and C57BL/6 ( C57 ) female mice ( Fig 1B and 1C ) . Fig 2 shows examples of twelve different units , each of which is modulated by the stimulus donor’s reproductive state ( left ) , or its strain ( right ) , for one of the three secretions . While clearly selective to the females’ reproductive-state or strain , the examples shown in Fig 2 are limited to a single pairwise comparison , in which only one trait is varied with all other factors remaining identical . To investigate how invariant representations of strain and state can be attained , we assume the position of a decoder with access to AOB neuronal activity . Unless specified otherwise , we use a simple linear classifier—a perceptron [28]—whose goal is to discriminate among trait values . As inputs , the classifier accepts single-trial responses , defined as averaged firing rate changes calculated during the 40 s period following stimulus presentation . The rate change is defined with respect to the 30 s period preceding vomeronasal stimulation . Fig 1D shows one such single trial ( highlighted by the red rasters ) , used to derive a single value characterizing the neuron’s response ( indicated by bar on right side ) . The broad 40 s time-window was chosen to account for the slow and temporally variable responses of AOB neurons ( see Fig 2 ) . A classifier generally receives inputs from multiple neurons ( Fig 1D ) , with single-trial responses sampled independently from each of the neurons . For each classification , we train the classifier with one set of single-trial responses ( training set , see Materials and Methods ) , obtaining a set of weights and a bias term . Following training , classifier performance is tested on a different set of single-trial responses ( test set ) . We first train the classifier with all units in the dataset , and then sequentially remove the unit assigned with the smallest absolute weight . Unless indicated otherwise , classification results denote an average over 10 training cycles . Repeated training cycles were conducted to account for randomness in the training and testing steps . Vaginal secretions are likely to play an important chemosensory role during anogenital investigation of females [3 , 29] . However , it is not known what type of behaviorally relevant information can be derived from this stimulus by the VNS . We thus first focused on decoding reproductive state from vaginal secretions . Our dataset includes 92 AOB units ( 38 single-units , 54 multi-units , 8 sites from 5 mice , Fig 1C set 1 , see S1 Table ) that responded to at least one of the 12 stimuli at the 0 . 05 significance level . The mode , median and mean number of significant responses per unit is 1 , 3 , and 4 . 0 , respectively ( See S2 Fig , which also shows that the fraction of responsive units increases with stimulus concentration ) . Recording site locations of the 92 responding units spanned the entire anterior-posterior aspect of the AOB , indicating that AOB neurons receiving both basal and apical vomeronasal sensory inputs [11 , 30] were sampled ( S1 Fig ) . The normalized response profiles of all units included in this analysis are shown in Fig 3A . We began with the simplest discrimination , involving two reproductive states , while all other stimulus properties ( dilution and strain ) remain identical . In our dataset , with two strains and three dilutions , this amounts to six simple discriminations ( indicated by the black lines in Fig 3B ) . The classification performance as a function of the number of units , averaged across all 6 pairwise comparisons , is shown in Fig 3C ( mean classifier , solid black lines ) . The traces reveal that even with individual units , all six discriminations can be made with a very high success rate . We next asked if a classifier trained on one dilution performs well on stimuli at a different dilution . For example , how does a classifier trained to distinguish estrus from non-estrus C57 vaginal secretions at 3x dilution perform on a 1x dilution of the same stimuli ? The gray double-headed arrows in Fig 3B indicate the twelve possible tests of generalization . The arrows are double-headed to indicate that two reciprocal tests of generalization can be made with each pair of dilutions . The resulting classifications , whose average is shown by the gray line in Fig 3C , reveal that generalization across dilutions is only slightly better than chance . In this context , it is interesting to note that different urine concentrations were shown to convey different signals , via activation of distinct populations of vomeronasal sensory neurons [22] . See S3A–S3F Fig , for a more detailed description of classification results , and S4 Fig for a detailed examination of generalization across specific dilutions . Do these observation imply that a classifier cannot perform well across different dilutions of a stimulus ? To answer this , we next trained a classifier to discriminate estrus state across all vaginal secretion dilutions ( separately for each strain ) , as illustrated by the orange lines in Fig 3D . Fig 3E shows that robust classification is indeed possible with a simple linear classifier , even across a range of dilutions . Yet , multiple neurons are needed to achieve maximal performance . Using the neuron removal approach , we observe that roughly 10 units allow classification with a success rate of about 95% . These analyses indicate that classification across dilutions requires explicit training and that polling multiple units provides a substantial benefit . We next address the effect of changing one trait on the ability to discriminate another . Specifically , we test how the two dilution-invariant classifiers , each of which was trained on one strain , perform on stimuli from the other strain . These comparisons are indicated by the double-headed arrow in Fig 3D . The averaged classification performance is shown in gray in Fig 3E . Regardless of the number of units used for classification , generalization across strains yields near chance performance , revealing that the effect of one trait on another is indeed significant . Finally , we trained a classifier to discriminate reproductive state across both dilutions and strains ( Fig 3F ) . Although the classifier’s performance plateaued at a lower level , and required more units than the less general classifiers , it clearly yielded above-chance performance ( Fig 3G ) . The unit removal approach often underestimates the performance of the best individual unit . We therefore also include this value , for comparison , in Fig 3C , 3E and 3G as a broken line ( when multiple classifiers are present , the line represents their average ) . The thick solid line in each of these panels shows the maximal perceptron performance . This value represents the best classification performance over the 10 repeated training and classification cycles , with all units considered . Thus , comparison of the thick solid line with the dotted line in each panel shows the benefit of decoding with multiple units over that possible with any individual unit . While this advantage is minor for the simple classifications ( Fig 3C ) , it is substantial for the more general classifications ( Fig 3E and 3G ) . See S11 Fig for additional analyses showing the limited decoding capacity of individual units . Our analysis thus far was based on one stimulus source ( vaginal secretions ) and one type of discrimination ( reproductive state ) . We next expand the analysis to another discrimination ( strain ) and to another secretion ( urine ) . The comparison across discriminations and secretions reveals some general principles but also some notable differences . The urine dataset comprises 51 units ( 25 single units , 26 multi-units ) that responded to at least one of the 12 urine stimuli ( Fig 1C , set 2 ) . Detailed descriptions of the number of units per session and recording site locations , spanning the entire anterior-posterior aspect of the AOB , are given in S1 Table and S1 Fig . For the 51 responding units , the mode , median and mean number of significant responses per unit was 1 , 3 , and 3 . 4 , respectively ( see S2 Fig , which also shows that the number of responsive units increases with stimulus concentration ) . Complete descriptions for VS and urine classifications are shown in S3A–S3L Fig . Fig 4 summarizes the classification performance to facilitate comparison across the various cases . To fairly compare the VS and urine datasets ( the latter containing fewer units ) , we consider performance with 50 units . The similarities are highlighted by noting that for both secretions and for both traits , discriminations involving more sources of variability result in poorer performance ( panels 4A-D ) . Comparison of the left-side panels ( 4A , C ) and the right-side panels ( 4B , D ) , reveals that for both secretions , strain discriminations are more successful than state discriminations . Comparing the upper and lower panels , particularly for the more difficult , general classifications ( red bars ) , shows that while strain discriminations are marginally better with urine stimuli ( 4C vs . 4A ) , state discriminations are considerably more successful with vaginal secretions ( 4B vs . 4D ) . Thus , in the context of vomeronasal chemosensation , different secretions seem optimized for conveying distinct socially relevant traits . Many mammals , and mice in particular , investigate each other in a manner that inevitably samples multiple stimulus sources [7] . From a decoder’s perspective , the challenge is that levels of different cues , and their dependence on particular traits , could vary across secretions . This consideration is particularly relevant in the context of slow vomeronasal sampling , which can lead to mixing of stimuli from different sources within the vomeronasal organ [31 , 32] . In the foregoing analyses , each classifier was trained and tested on a single stimulus source . To investigate how classification criteria based on one secretion apply to others , we recorded responses of AOB neurons to urine , vaginal secretions , and saliva ( Fig 1C , set 3 ) . The dataset contains 164 units ( 101 single-units , 63 multi-units ) that responded to at least one of the 12 stimuli . Detailed descriptions of the number of units recorded per session and recording site locations , spanning the entire anterior-posterior aspect , are shown in S1 Table and S1 Fig . For the 164 responding units , the mode , median and mean number of significant responses across units was 1 , 2 , and 2 . 6 , respectively ( see also S2 Fig ) . Note that in this stimulus set , due to the small volumes of salivary samples , stimuli from four to six females were pooled ( see Materials and Methods ) . This has the potential effect of reducing variability and to some extent simplifying the classification problem . We first trained classifiers for each secretion separately . We began with reproductive-state classifiers that generalize across strains , and strain classifiers that generalize across reproductive states ( indicated by the orange lines in Fig 5A and 5C ) . In these experiments , each stimulus is presented at one dilution , and thus dilution variability does not play a factor . The results of these classifications , shown by the orange lines in Fig 5B and 5D , reveal that in addition to urine and vaginal secretions , salivary cues can also provide information about strain and reproductive-state . Complete classification results for this dataset are given in S3 Fig , panels M-R . Next , we applied classifiers trained on one secretion to the other two ( gray double-headed arrows in 5A , C ) . The analysis shows that application to other secretions results in chance level performance for state discrimination ( Fig 5B , gray traces ) and slightly above-chance performance for strain discriminations ( Fig 5D ) . These results suggest that distinct chemical cues provide information about behaviorally relevant traits across the different stimulus sources . The modest above-chance generalization performance observed with strain classifications across different secretions indicates that some units do respond to strain dependent cues that are common across secretions . Finally , we trained classifiers across secretions , to investigate whether a single classifier ( red lines in Fig 5A and 5C ) can detect particular traits regardless of the stimulus source . Using the unit removal procedure , we observe that about 15 AOB units suffice for approximately 85% correct classification . Best performance with all units , or one unit , is 90% and 70% , respectively . Performance on strain discriminations is higher , with 10 units sufficient to classify strain with a ~95% success rate . Best performance with all units , or one unit , is 98% and 86% , respectively . As with the previous classifications , polling multiple units provides a considerable advantage over individual units . Notably , the ability of one individual unit to provide 86% correct strain discriminations suggests that some neurons are sensitive to strain dependent cues that are common to the three secretions . To gain insight about how social information is decoded from AOB population activity , we investigated the influence ( classifier weights ) assigned to each of the units . Fig 6A and 6B show the vaginal secretion response dataset ( data shown in Fig 3A ) with the rows sorted according to the values assigned by the general reproductive-state and strain classifiers , respectively . As expected , this ordering shows that the linear classifier assigns more weight to units with response profiles that reflect the detected traits . Yet , while unit reordering highlights each of the two traits , it does not reveal if the observed patterns are represented more than expected by chance . To address this , we defined an index that quantifies the impact of the strain and state dimensions on the dataset ( see Materials and Methods ) . Comparison of the index to its bootstrapped distribution under the null hypothesis , reveals that while the strain dimension is significantly represented ( Fig 6D ) , the reproductive-state dimension is not ( Fig 6C ) . The same is true for responses to urine ( strain p-value = 0 . 0035 , reproductive-state p-value = 0 . 55 ) . This observation indicates why strain discriminations are achieved with higher success than state discriminations . Fig 6E and 6F show the units ordered according to the weights assigned by the reproductive-state classifiers , specifically for the BC and C57 strains ( i . e . classifiers indicated by orange lines in Fig 3D ) . Inspection of the columns representing stimuli not used for training the classifier ( i . e . the six rightmost columns in Fig 6E and the six leftmost columns in Fig 6F ) reveals that reproductive state selective responses for one strain are not necessarily associated with similar selectivity for the other . Indeed , examination of matrices in Fig 6A and 6B indicates that even units assigned with the highest absolute weights are influenced by the strain and the dilution . This explains why generalization of reproductive state classifiers across strains is not efficient unless training is done explicitly with stimuli from both strains . The same observations apply with regard to urine stimuli and for strain discriminations across both states , yet the confounding effect of strain on reproductive state discrimination is more dominant than vice versa . As shown in S4 Fig , similar considerations apply to the difficulty of generalizing across concentrations . Generally , we find no systematic relationship between the number of units used and the ability of the classifier to generalize across other instances ( S3 Fig ) . The ability of a classifier to generalize largely depends on the response profiles of the individual units that contribute to it . Specifically , generalization across particular dimensions will be successful if the influential units happen to display invariant responses along these dimensions . To ensure generalization along any given dimension , classifiers must be trained with stimuli that vary along that particular dimension . Across all secretions and traits , our analysis has shown that consideration of multiple units provides better classification performance than is available with any one unit . This is expected , as the entire population of units also includes the “best” unit . This last consideration raises the possibility that the success of classifiers with many units heavily depends on a small number of key units . To address this possibility , we revisited the classification analysis , but instead of removing the unit with the least effect ( i . e . smallest absolute weight ) as done above we instead removed at each stage , the unit with the highest absolute weight . For each classifier , the process was repeated until the classifier’s performance decreased below that possible with the best individual unit ( Fig 7 ) . The average number of units ( across 10 repeated training cycles ) that can be removed without impairing classification below the best one-unit performance are: 27 . 5 ( VS state classification ) , 29 . 6 ( VS strain ) , 17 . 1 ( urine state ) , 15 . 7 ( urine strain ) , 50 . 5 ( across secretions , state ) and 11 . 5 ( across secretions , strain ) . In five out of six cases , about one third of the most influential units can be removed while still maintaining performance above that possible with the best individual unit . S5 Fig illustrates the results of classification with units that were simultaneously recorded in a single session . As expected , classifier performance is substantially reduced in comparison to that obtained with all units available ( S3 Fig ) . Nevertheless , the main conclusions of our foregoing analyses are reproduced even in smaller samples collected in individual sessions . S6–S8 Figs show an analysis of the correlations among the response profiles of the most influential units for each of the general classifiers . The analysis shows that while there are some prominent response patterns among the influential units , the responses of most unit pairs exhibit low correlations . Specifically , of all pairwise correlations among the top 20 units within each classifier , the large majority are smaller than 0 . 5 . ( % correlations below 0 . 5: VS state: 86 . 3%; VS strain: 83 . 7%; urine state: 89 . 5%; urine strain: 86 . 3%; across secretions state: 88 . 4%; across secretions strain: 93 . 7% ) . This analysis thus indicates that to achieve optimal performance , classifiers tend to sample units with a variety of response profiles . Finally , application of Principal Component Analysis ( PCA ) to our dataset revealed only weak correlations between the dominant Principal Components and the dimensions analyzed here ( S9 Fig ) . This result is consistent with the idea that neurons in our sample do not represent a homogenous population with a one stereotypic response profile . Our choice of the perceptron model was motivated by its ability to indicate the contribution of individual units to the classification . Furthermore , in principle , it can be easily implemented by neurons receiving direct excitatory and ( indirect ) inhibitory input from AOB neurons . However , we are not suggesting that the actual downstream readout of AOB activity involves a perceptron-like classifier . Indeed , reliable trait detection may be improved by multiple processing stages realizing non-linear computations . To illustrate this , we also calculated the ability of a more powerful non-linear classifier , a support vector machine ( SVM ) , to discriminate among traits . Each panel in Fig 7 compares the best performance obtained with an SVM to that of the best perceptron classifier . The comparison clearly shows that SVMs provide significantly improved performance , particularly for the more challenging classifications . In one case ( Fig 7D ) , perceptron performance is slightly better . This appears surprising because an SVM classifier can implement any decision rule that a perceptron can . The explanation for the reduced performance observed in this case is that the SVM likely over-fits the training dataset . Comparison of SVM and perceptron performance as a function of the number of units ( S10 Fig ) shows that the SVM advantage is more prominent when more neurons are used . For small ensembles , the differences between the two classifiers are marginal . Overall , this analysis highlights once more the advantage of polling multiple units , and shows that non-linear readout of neuronal activity can yield improved classification performance . The observation that individual neurons can be modulated by more than one trait implies that each could contribute to the detection of multiple traits . To test whether this is the case , we compared the weights assigned by the reproductive-state and the strain classifiers . Fig 6G shows that across the population of units , the weights for state and strain classifiers for the VS dataset ( Fig 6A and 6B ) are clearly not correlated . More generally , over 10 classification cycles , the average correlation coefficient between weights for the two distinct classifiers is 0 . 02±0 . 03 ( mean±SD ) . For comparison , the average correlation coefficient over all 10 repeated training cycles of the state classifier is 0 . 99±0 . 003 . Similar results were observed with the other datasets ( S2 Table ) . These results suggest a model according to which individual units participate in multiple distinct and overlapping networks , each associated with classifying a particular trait ( Fig 8B ) .
In this study , we focused on the mouse AOB to study how socially relevant information can be reliably decoded from natural stimuli . In principle , detection of any trait via chemosensation requires a reliable readout of chemical cues that are correlated with the trait . Fig 8A illustrates a very simple ( hypothetical ) scenario related to the traits addressed here , in which the level of one compound is indicative of estrus state . The example illustrates how factors such as dilution , other traits , or the stimulus source , could modulate compound levels in a manner that confounds discrimination [33] . Even under this simple scenario , trait detection is not a trivial challenge . While there are known cases in which individual molecules elicit well-defined behavioral responses [34–37] , encoding even a relatively robust trait such as sex , seems to involve more than a single molecule [38] . More generally , behaviorally relevant information is conveyed by levels of multiple compounds and the relationships between them [2 , 4 , 20 , 25] . These non-trivial relationships between particular traits and levels of chemical cues imply that trait representation in the AOB can be complex . In one scenario , akin to a labelled line model , the activity of one particular “expert” AOB neuron could provide an unambiguous report of the presence of a particular trait . Under the opposite scenario , the activity of many , potentially all , AOB neurons must be sampled . To provide a definitive answer to this question one would have to test responses of all neurons to an enormous set of chemosensory stimuli varying along multiple dimensions , including: various traits of the stimulus donor ( e . g . genetic makeup , sex , age , physiological status ) , the stimulus source-secretion , and physical properties ( e . g . dilution , freshness ) . In attempt to record many neurons with high temporal resolution and fidelity , we used multisite extracellular recording probes in the AOB . Because of technical limitations on practical dataset sizes , we could only explore a limited stimulus subspace in each single experiment ( Fig 1C ) . Specifically , we tested the responses to urine , saliva , and vaginal secretions of females from different strains and reproductive states . We have shown , for the first time , that AOB neurons have the capacity to respond to all these stimulus sources in a strain and reproductive-state specific manner . Furthermore , compared to urine , vaginal secretions are optimized to convey reproductive state information . Linking these results with behavioral analyses , we note that in hamsters , flank marks are used to detect individuality , whereas estrus state is sensed via vaginal secretions [8] . Adding to previous studies [8 , 39 , 40] , our findings stress the importance of attending to diverse secretions in the context of mammalian chemosensory communication . Based on the responses to these stimuli , we studied how information about a female mouse’s strain and reproductive state can be decoded from the activity of AOB neurons , despite these sources of variability . While some individual units do allow reliable trait discrimination within our ( limited ) stimulus set , most are significantly restricted in their ability to provide reliable information across multiple sources of variability . However , a significant gain in classification performance is obtained by considering multiple units . In this respect , our work is in good agreement with a recent study , which demonstrated that deriving sex and strain information from urine also requires consideration of multiple neurons [23] . Our analyses indicate that generalized discriminations require explicit training with stimuli spanning multiple dimensions of variability . The challenge is similar to that of object recognition . For example , viewing an unfamiliar face once as a static image is generally not sufficient to allow identification across a range of contexts ( e . g . different moods or ages of the person , as well as viewing angles and lighting conditions ) . The ability to reliably identify a face under those contexts requires accumulated exposure that provides sampling under diverse conditions . Here , we observed that strain discriminations generally required fewer units and were performed with higher success than reproductive-state discriminations . Indeed , our previous analysis has shown that in the AOB , sex is represented more prominently than strain [24] . Generally , prominently represented traits may be more immune to variability , explaining why the confounding effect of strain on state discrimination is larger than the reciprocal effect . Similarly , the confounding effect of stimulus dilution depends on its magnitude relative to that associated with different trait values [4] , suggesting why dilution had a minor confounding effect on sex and strain discriminations in the AOB [23] . One may argue that “expert” neurons for trait detection do exist for all behaviorally relevant discriminations , but that our limited sample simply failed to identify them . This possibility cannot be entirely ruled out . However , even if such “experts” were found for each discrimination considered here , there is no guarantee that they would also provide reliable classification across further sources of variability . Our present study suggests an alternative scenario , in which discrimination is achieved by polling a limited number of distinct units , none of which must be an “expert” . The various response profiles associated with AOB neurons provide a rich substrate to realize a large number of discriminations , including of traits not strongly represented by neuronal activity . This was exactly the case for reproductive state discriminations in our experiments ( Fig 6C ) . More generally , when traits are strongly represented in the dataset , a small number of units may suffice to yield reliable discriminations ( Fig 8Bii ) , and complex decoding mechanisms might not be required . Thus , instead of a dichotomy between labelled lines and combinatorial codes , we suggest that decoding distinct traits may require different population sizes , depending in part on how prominently these traits are represented by individual neurons . While the perceptron model provides a clear analogy to a downstream neuron , we do not claim that it actually represents the algorithm used by the brain . Indeed , more powerful non-linear classifiers provide better classification ( Fig 7 ) , especially when a larger neuronal population is considered ( S10 Fig ) . Note also that we quantified neuronal responses as the mean firing rate changes within a long time window , a choice motivated by the relatively long and variable response patterns of AOB units [41] . A more refined examination of individual units’ rate modulations and spike timing relative to stimulus delivery might lead to improved decoding ability . In particular , temporal response profiles may play an important role during bouts of natural investigation , when the vomeronasal pump is likely activated repeatedly [7 , 31] . The dynamics of active sensing could thus substantially affect summation of responses , and hence the ability to discriminate between , or to generalize across , stimuli . Finally , we note that examination of the relationships between individual units’ spike times , and of correlated rate modulations among different units could also lead to improved discriminations . With all these considerations in mind , understanding how AOB activity is indeed read by specific downstream stages remains an important question for future research .
For recordings , adult sexually unexperienced BALB/C ( BC ) male mice were purchased from Harlan Laboratories ( Israel ) . All experiments were performed in compliance with the Hebrew University Animal Care and Use Committee . Stimuli were collected from adult sexually unexperienced female mice of the BC and C57BL/6 ( C57 ) strains ( Harlan Laboratories , Israel ) . For urine collection , mice were gently held over a plastic sheet until they urinated . The urine was transferred to a plastic tube with a micropipette and then flash-frozen in liquid nitrogen and subsequently stored in -80°C . Vaginal secretions were collected by flushing the vaginal region with 30 μl of ringer’s solution . 20 μl were immediately frozen and stored at -80°C for stimulus presentation . The remaining volume was smeared on a glass slide for determination of estrus state . For saliva collection , isoproterenol hydrochloride ( 0 . 2mg/100g ) and pilocarpine ( 0 . 05mg/100g ) were injected I . P . to increase salivation [42] . Saliva was then collected from the oral cavity using a micropipette and immediately frozen in liquid nitrogen and stored at -80°C . All stimulus dilutions were made with ringer’s solution . Stimulus collection was performed 3–5 times per week , usually after 14:00 ( non-reversed light cycle , light on: 7:00–19:00 ) . The estrus stage was determined by examining vaginal secretions smeared on glass slides , dried , and stained with cresyl violet . Slides were examined under a light microscope and the estrus cycle stage determined by cellular morphology [43 , 44] . Stages were classified as either estrus/proestrus ( designated as estrus ) , or meta- or diestrus ( designated as non-estrus ) . For the urine and vaginal secretion datasets ( sets 1 and 2 in Fig 1C ) , stimuli for a given strain were obtained from one individual ( with stimuli collected during different stages of the cycle ) . Dilutions used were 9x ( L , low ) , 3x ( M , medium ) , 1x ( H , high ) for vaginal secretions , and 300x ( L ) , 100x ( M ) and 33x ( H ) , for urine . For comparison of the three different secretions ( set 3 in Fig 1C ) , stimuli comprised a mixture from 4–6 females . In most cases , stimuli for each session were collected from the same females . For comparison of different stimulus sources , we used undiluted samples , as they are thus sampled during natural investigation . With vaginal secretions , this was not possible since they were collected via flushing . Responses to undiluted urine were clearly stimulus specific , and thus did not represent non-specific activation due to urinary potassium S12A–S12D Fig . Unlike in the MOS [45] , where airflow alone can induce activity changes , in the VNS , delivery of ringer’s solution alone does not elicit a response [46] . As examples , S12E and S12F Fig shows recordings of two units , demonstrating a null response to Ringers solution . Because the number of stimuli in our dataset was a limiting factor , we did not use an explicit negative control stimulus here . Experimental procedures were described previously [24] , and are reproduced briefly here , with the differences noted . Mice were anesthetized with 100mg/kg ketamine and 10mg/kg xylazine , a tracheotomy was performed with a polyethylene tube to allow breathing during flushing , and a cuff electrode was placed around the sympathetic nerve trunk with the carotid serving as a scaffold . Incisions were closed with Vetbond ( 3M ) glue and the mouse was placed in a custom-built stereotaxic apparatus where anesthesia was maintained throughout the entire experiment with 0 . 5–1% isoflurane in oxygen . A craniotomy was made immediately rostral to the rhinal sinus , the dura was removed around the penetration site , and electrodes were advanced into the AOB at an angle of ~30° with an electronic micromanipulator ( MP-285; Sutter instruments , Novato , CA ) . All recordings were made with 32 channel probes with 8 channels on each of 4 shanks ( NeuroNexus Technologies , Ann Arbor , Michigan ) . Before recordings , electrodes were dipped in fluorescent dye ( DiI , Invitrogen , Carlsbad , CA ) to allow subsequent confirmation of electrode placement within the AOB external cell layer , which contains the mitral-tufted cells [11] . In each session , stimuli were typically presented 5 times in a pseudorandom order . In a minority of sessions , only 4 repeats were possible . Mean number of repeats across all experiments: 4 . 8 . In each presentation , 2 μl of stimulus was applied directly into the nostril . After a delay of 20 s , a square-wave stimulation train ( duration: 1 . 6 s , current: ±120 μA , frequency: 30 Hz ) , was delivered through the sympathetic nerve cuff electrode to induce VNO pumping and stimulus entry to the VNO lumen . Following a second delay of 40 s , the nasal cavity and VNO were flushed with 1–2 ml of ringer’s solution which flowed from the nostril , into the nasal cavity , and sucked out from the nasopalatine duct via a solenoid-controlled suction tube . The cleansing procedure was 50 s long and included sympathetic trunk stimulation to facilitate stimulus elimination from the VNO lumen . Neuronal data was sampled at 25 kHz using an RZ2 processor , PZ2 preamplifier , and two RA16CH head-stage amplifiers ( TDT , Alachua , FL ) . Signals were band-pass filtered ( 300–5000 Hz ) and custom MATLAB ( Mathworks , Natick , MA ) programs were used to extract spike waveforms . Spikes were sorted automatically according to their projections on two principal components on 8 channels of each shank using KlustaKwik [47] and then manually verified and adjusted using the Klusters program [48] . Spike clusters were evaluated by consideration of their spike shapes , projections on principal component space ( calculated for each session individually ) and autocorrelation functions . A spike cluster was defined as a single unit if it had a distinct spike shape and was fully separated from both the origin ( noise ) and other clusters along at least one principal component projection , and if its inter-spike interval histogram demonstrated a clear trough around time 0 ( of at least 10 ms ) . Clusters comprising more than one single unit were designated as multi-units . Thus , using the present definitions , multi-units could represent the activity of as few as 2 units , or more . Throughout this manuscript , we used both single and multi-unit activity with the aim of increasing the probability of finding individual units conveying robust information . Note that one of our key conclusions is that response profiles of individual units are not sufficiently general , and this cannot be a result of confounding multiple units together . Comparison of classifier weights assigned to single and multi-units ( see Fig 6G ) revealed no systematic relationship between unit type and the magnitude of classifier weight . All data analyses and visualizations were performed using either custom or standard MATLAB code . The response of a unit to a given stimulus was defined as the average firing rate change over a 40 s window following sympathetic nerve stimulation ( change measured compared to the 30 s period preceding VNO activation ) . Response significance of a given unit to a given stimulus was determined by comparing its spiking rate distribution following all repeats to the baseline firing frequency distribution during the 10 s period prior to stimulus application . Response significance ( of a particular unit to a given stimulus ) was determined by a non-parametric ANOVA comparing the set of post-stimulation rates to the set of preceding baseline rates ( preceding rates were pooled across all stimuli ) . | Across the animal kingdom , chemical senses play a central role in guiding social behaviors by conveying information about particular behaviorally relevant traits . However , decoding these traits from profiles of chemical cues is challenging since cue levels are modulated by multiple factors . Here , we investigate how the mouse vomeronasal system , a chemosensory system important for processing social information , detects behaviorally relevant traits from natural stimuli . We focus on detection of a female’s genetic background ( a model for individuality ) and estrus-state ( a measure of sexual receptivity ) by neurons in the first vomeronasal brain relay , the accessory olfactory bulb ( AOB ) . We show that information about both genetic background and receptivity can be obtained from various stimulus sources: urine , vaginal secretions , and saliva . Importantly , while individual AOB neurons can only provide limited decoding ability of these traits , simple networks sampling AOB neuronal ensembles provide considerable improvement . Our analyses highlight an overlooked challenge associated with chemosensory processing and suggest how it can be overcome by downstream neurons that read information from multiple AOB neurons . | [
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"r... | 2016 | Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb |
Listeria monocytogenes is an important cause of maternal-fetal infections and serves as a model organism to study these important but poorly understood events . L . monocytogenes can infect non-phagocytic cells by two means: direct invasion and cell-to-cell spread . The relative contribution of each method to placental infection is controversial , as is the anatomical site of invasion . Here , we report for the first time the use of first trimester placental organ cultures to quantitatively analyze L . monocytogenes infection of the human placenta . Contrary to previous reports , we found that the syncytiotrophoblast , which constitutes most of the placental surface and is bathed in maternal blood , was highly resistant to L . monocytogenes infection by either internalin-mediated invasion or cell-to-cell spread . Instead , extravillous cytotrophoblasts—which anchor the placenta in the decidua ( uterine lining ) and abundantly express E-cadherin—served as the primary portal of entry for L . monocytogenes from both extracellular and intracellular compartments . Subsequent bacterial dissemination to the villous stroma , where fetal capillaries are found , was hampered by further cellular and histological barriers . Our study suggests the placenta has evolved multiple mechanisms to resist pathogen infection , especially from maternal blood . These findings provide a novel explanation why almost all placental pathogens have intracellular life cycles: they may need maternal cells to reach the decidua and infect the placenta .
Infection is a major cause for pregnancy complications including premature labor and resultant maternal and fetal morbidity and mortality ( WHO , 2005 ) . Nevertheless , the underlying mechanisms of placental and fetal infection are poorly understood . The placenta and fetus are vulnerable to infection via two different routes: ( a ) pathogens in the lower genital tract may ascend through the cervix and ( b ) pathogens in the maternal blood or uterus can colonize the placenta and breach the maternal-fetal barrier . The later group includes many viruses , e . g . cytomegalovirus; protozoan parasites , e . g . Toxoplasma gondii; and bacterial pathogens , e . g . Listeria monocytogenes . It is striking that the majority of pathogens that are able to cross the placenta have either facultative or obligate intracellular life cycles . The reason for the predisposition of placental infection toward intracellular pathogens is unclear . It has been postulated that the unique immunological environment in the placenta—necessary to assure tolerance of the fetal allograft—contributes to this phenomenon [1]–[3] , but other aspects of the placenta may also play a role . L . monocytogenes is a ubiquitous bacterial pathogen that causes food-borne disease in humans and many other mammals [4]–[6] . In pregnant women L . monocytogenes can spread to the placenta and fetus , resulting in spontaneous abortion , stillbirth , or preterm labor , depending on the gestational age [7] . The incidence of L . monocytogenes-induced spontaneous abortion during the first trimester is unknown; such early abortions are often due to chromosomal abnormalities [8] and therefore the aborted tissues are not routinely cultured . During the second trimester , L . monocytogenes has been found to cause ∼3% of spontaneous abortions in humans and cattle [9]–[11] . Clinical infections of the mother at term are rare , but when they occur , they can result in neonatal disease with mortality of up to 50% [12] . Among the intracellular microbes known to cross the maternal-fetal barrier , L . monocytogenes is particularly amenable to experimental analysis . L . monocytogenes has been used for decades as a model system to evaluate intracellular pathogenesis and the host's cell mediated and innate immune response to infection ( for recent reviews see [13]–[15] ) . L . monocytogenes can infect professional phagocytic and non-phagocytic cells in many species . A family of bacterial cell wall surface proteins called internalins ( Inl ) promote bacterial adherence and internalization into non-phagocytic host cells [16] . Of these , internalin A ( InlA ) and internalin B ( InlB ) are the best characterized , binding to E-cadherin and c-Met-tyrosine kinase , respectively [17] , [18] . After internalization , the bacterium escapes from the vacuole into the host cell cytoplasm where it multiplies rapidly [19] , [20] . The listerial virulence determinant ActA facilitates spread from infected host cells to neighboring cells without bacterial exposure to the extracellular environment [21]–[24] . Thus , L . monocytogenes is able to infect non-phagocytic cells by two different mechanisms: Inl-mediated direct invasion and cell-to-cell spread . In the work described herein , we determine the placental tissue barriers operative against each mechanism and explore how L . monocytogenes might overcome them . In order to understand the mechanisms leading to placental and fetal infection it is essential to understand the structure and physiology of the placenta . The placenta is made of maternal and fetal tissues . Placentas of different viviparous vertebrates exhibit great variability at the maternal-fetal interface , complicating cross-species comparisons [25] . Humans have a hemomonochorial villous placenta ( Figure 1A ) . Maternal blood from spiral arteries in the decidua ( uterine lining during pregnancy ) flows into the intervillous space where it surrounds thousands of fetally derived floating villi . Some villi invade the decidua and form anchoring villi . The entire villous surface is covered with a continuous layer of multinucleate syncytiotrophoblast ( SYN ) ( Figure 1B ) , which is the major fetal surface in contact with maternal blood . The apical side of the syncytium consists of profuse , branched microvilli [26] , [27] and provides abundant surface area for gas and nutrient exchange between mother and fetus . The syncytiotrophoblast is undergirded by cytotrophoblasts ( CTB ) [28] , which are separated from fetal capillaries in the villous stroma by a basement membrane . Some cytotrophoblasts leave the basement membrane and differentiate along the invasive pathway to form anchoring villi: columns of unpolarized cytotrophoblasts attach to and then penetrate the uterine wall where they give rise to extravillous cytotrophoblasts [29] . Extravillous cytotrophoblasts commingle with resident decidual , myometrial and immune cells . A subset of extravillous cytotrophoblasts breaches maternal spiral arteries in the decidua and differentiates into endovascular trophoblasts that replace the resident maternal endothelium to direct more blood into the intervillous space [29] . The anatomical site and mechanism by which L . monocytogenes breaches the maternal-fetal barrier are controversial . Of particular interest is whether InlA-mediated binding to E-cadherin is essential for transplacental transmission . Infection of isolated human cytotrophoblasts [30] or the BeWo choriocarcinoma cell line [31] with L . monocytogenes deficient in InlA leads to a 100-fold reduction in invasion . However , in vivo , cytotrophoblasts are covered with syncytiotrophoblast and may not be accessible to the bacteria . Lecuit et al . reported that E-cadherin is expressed at low levels on the apical surface of syncytiotrophoblast in explants from human term placentas [31] , and postulated that L . monocytogenes breaches the maternal-fetal barrier by InlA-mediated invasion of the syncytiotrophoblast from the maternal bloodstream [31] . However , other groups have not observed E-cadherin expression on the surface of the syncytium [32]–[36] . Furthermore , InlA or InlB mutants do not affect feto-placental infection in guinea pigs [30] ( unpublished observations ) , and show less than a 5-fold decrease in bacterial numbers in the gerbil placenta and fetus [37] . Wild type InlA does not interact with murine E-cadherin [38] , but infection of wild type mice with L . monocytogenes expressing murinized InlA does not influence the course of feto-placental listeriosis [39] , and in transgenic mice expressing human E-cadherin , InlA and/or InlB have a <5-fold effect on placental and fetal infection [37] . The minimal or absent in vivo phenotype observed with internalin mutants in these four rodent models is surprising given the strong phenotype in isolated cytotrophoblasts and suggests that the syncytiotrophoblast may not be the initial site of infection . InlA/E-cadherin is not the only mechanism for infection—L . monocytogenes can spread from cell-to-cell without exposure to the extracellular environment , and there is evidence that L . monocytogenes traffics to the placenta [40] or the brain [41] inside of cells . Furthermore , we and others have found cell-to-cell spread to be important for fetal infection [42] , [43] . In this report , we probe the human maternal-fetal barrier using first trimester human placental organ cultures , which allow a detailed examination of the most likely sites of transplacental infection by direct incubation with extracellular L . monocytogenes as well as via co-incubation with infected human cells . We found intact syncytiotrophoblast to be resistant to infection by L . monocytogenes . The portal of entry for L . monocytogenes was instead a small subpopulation of E-cadherin-expressing extravillous cytotrophoblasts in anchoring villi that are not readily accessible from the maternal bloodstream in vivo , and infection of these cells occurred via both InlA-mediated invasion and cell-to-cell spread . Surprisingly , these cells were able to restrict the growth of L . monocytogenes . If infection progressed , the bacteria spread along subsyncytial cytotrophoblasts , mostly sparing the syncytiotrophoblast and villous stroma . Our results clarify the mechanisms of crossing the maternal-fetal barrier and provide a unifying explanation for the conflicting in vitro and in vivo results mentioned above .
In order to examine the role of direct invasion and cell-to-cell spread in breaching the human maternal-fetal barrier we turned to first trimester human placental organ cultures , a well-studied model system that allows examination of the trophoblast in a context that retains the cellular architecture of the tissue in vivo [44] , [45] . Placental villous trees are dissected and explanted on substrates of extracellular matrix ( Matrigel ) , where they form floating and anchoring villi ( Figure 1C-D ) . All of the tissue is exposed to the media with the exception of the tips of anchoring villi that result from extravillous cytotrophoblast outgrowth and invasion into Matrigel [45] , [46] . This mimics the conditions in vivo where extravillous cytotrophoblasts invade the decidua while the rest of the villous tree is bathed in maternal blood [29] . The syncytiotrophoblast covers the villi and remains largely intact for at least 24 h ( Susan Fisher , personal communication ) . First trimester placental explants therefore adequately represent the most probable placental sites that are potentially accessible to L . monocytogenes or infected phagocytes: intact syncytiotrophoblast , subsyncytial cytotrophoblasts underlying damaged syncytiotrophoblast , and extravillous cytotrophoblasts ( Figure 1 ) . We infected explants with 2×106 wild type L . monocytogenes ( Figure 2 ) . In order to measure intracellular growth we added gentamicin 1 h post-inoculation to eliminate extracellular L . monocytogenes , and subsequently determined numbers of live bacteria per explant over 24 h ( Figure 2A ) . No significant differences in infection were observed between the wild type strains 10403S and EGDe ( p = 0 . 38 by Student's T-test ) . The average bacterial growth from all placentas was less than 10-fold , which is slow compared to that found in cell lines [20] ( Figure 2A ) , and infection rates were highly variable . Despite the relatively high inoculum , 11% of the explants were not infected and an additional 13% hosted <10 intracellular bacteria at 2 h post-inoculation . The average percentage of intracellular bacteria at 2 h post-inoculation was 0 . 6%±2% SD of the inoculum ( n = 54 explants ) , similar to the bottleneck in the pregnant guinea pig model of listeriosis [40] . Explants vary in size , shape , age , donor and degree of Matrigel invasion , so variability is expected . However , we were able to distinguish two possible courses of infection by examining three explants from the same placenta at each time-point . Roughly half of the placentas exhibited an increase in bacterial numbers from 2 to 24 h ( average = 77-fold , SD = 6 . 4 ) while the others showed a decrease ( average = 0 . 25-fold , SD = 0 . 19 ) . It has been previously suggested that L . monocytogenes invades the syncytiotrophoblast [31] . If this is true for explants , then larger explants should be more highly infected , since >90% of each explant's bacterially accessible surface area is covered by syncytiotrophoblast . But we found no correlation between colony forming units ( CFU ) and explant size ( r2<0 . 05 ) in 30 explants from 11 placentas . Nor did explant age affect CFU ( r2<0 . 02 ) . However , CFU at 2 h post-inoculation did correlate with the number of anchoring villi ( r2 = 0 . 49 , Figure 2B ) , suggesting that extravillous cytotrophoblasts are the preferred sites of L . monocytogenes infection . Examination by immunofluorescence histology revealed only a few foci of infection , usually in extravillous cytotrophoblasts of anchoring villi ( Figure 2C ) . To better characterize which placental cell types are most vulnerable to L . monocytogenes infection , we increased both the inoculum and the time of incubation without gentamicin . These “permissive infections” increase the probability of infection at vulnerable sites . In addition to wild type bacteria , we also used bacteria deficient in ActA ( ΔActA ) that are incapable of intercellular spread , thus ensuring that the L . monocytogenes-containing cells we observe are those initially infected by the bacteria . After 8 h , we examined explant sections by immunofluorescence ( Figure 3 ) . Under these conditions , L . monocytogenes was detectable in three cell types ( Figure 3A ) : subsyncytial cytotrophoblasts , extravillous cytotrophoblasts and syncytiotrophoblast . Infected syncytiotrophoblast could be subdivided into: 1 ) apparently intact syncytiotrophoblast where only the apical surface is exposed to bacteria; and 2 ) basolaterally accessible syncytiotrophoblast ( bSYN ) , where the syncytiotrophoblast is naturally terminated by an invading CTB cell column ( Figure 3A ) or , in rare cases , torn away from the explant , presumably during dissection . We enumerated the total number of infected cells in explant sections ( Figure 3B ) . For syncytiotrophoblast , a “cell” was defined as a circular region similar in size to a CTB , roughly the area surrounding a single nucleus . Infection of subsyncytial cytotrophoblasts was infrequent , which is unsurprising since unlike syncytiotrophoblast and extravillous cyotrophoblasts they are largely inaccessible to bacteria in the media . However , syncytiotrophoblast infection was also low , even though it covers almost all of the explant surface . Roughly 75% of the infected cells were extravillous cytotrophoblasts , which comprise less than 5% of available surface . Furthermore , these cells were ∼5 times more likely to contain multiple bacteria , possibly indicating multiple infections . Transverse sections obscure a full view of the syncytiotrophoblast . To ensure that our observations were not a histological artifact , we fixed and mounted whole explants infected with L . monocytogenes expressing GFP . Confocal microscopy of these minimally manipulated explants confirmed that the bacteria are highly localized within the extravillous cytotrophoblasts of anchoring villi ( Figure 3C ) . Together , these results suggest that extravillous cytotrophoblasts may serve as the primary site of infection . Although in cultured macrophage and epithelial cell lines cell-to-cell spread begins around 4–5 h post-infection [21] , [24] , we found no significant difference between locations of ΔActA and wild type L . monocytogenes at 8 or even 24 h ( Figure 3B , p = 0 . 99 by chi-squared test ) , suggesting that the L . monocytogenes life cycle ( intracellular growth and/or cell-to-cell spread ) is delayed in primary trophoblast cells . E-cadherin is an important host cell receptor for L . monocytogenes binding and uptake . Lecuit et al . suggested that L . monocytogenes extracellular invasion of the placenta occurs via L . monocytogenes InlA interactions with host E-cadherin on the apical surface of syncytiotrophoblast [31] . However , other studies of the placenta have failed to find E-cadherin here [32]–[36] . Our results support this: we never observed E-cadherin staining on the apical surface of the syncytiotrophoblast , although it was expressed strongly on the basal surface ( Figure 4A ) . Like others , we found E-cadherin was most abundant on subsyncytial cytotrophoblasts and proximal extravillous cytotrophoblasts , decreasing as cells migrate away from the villus tip . Since proximal extravillous cytotrophoblasts were the very cells L . monocytogenes infected , we hypothesized that explant infection is InlA-dependent . Indeed , ΔInlA and ΔInlAB mutants were almost completely unable to invade explants ( Figure 4B; p<10−20 by Student's T-test ) . We found no significant difference between invasion of wild type and ΔInlB L . monocytogenes in human explants ( Figure 4B; p = 0 . 68 by Student's T-test ) consistent with previous observations in isolated CTB and BeWo cells ( human choriocarcinoma cell line ) [30] . If the syncytiotrophoblast is relatively resistant to infection , as the preceding data suggest , then removing it should provide L . monocytogenes new sites of invasion . We enzymatically degraded the syncytiotrophoblast by soaking the explants briefly in a collagenase-containing solution before plating [47] . Although the extent of the syncytiotrophoblast removal varied , many subsyncytial cytotrophoblasts were exposed and extravillous cytotrophoblasts increased ( Figure 5A ) . As expected , permissive infections of enzymatically-treated explants allowed for new sites of infection ( Figure 5B ) . The total number of infected cells increased ( from an average of 78 in two sections to 228 with enzymatic degradation ) , and nearly half of the infected cells were now subsyncytial cytotrophoblasts , which express E-cadherin ( Figure 5C; p<0 . 05 by Chi-square test ) . Infection of the placenta by extracellular pathogens in the maternal bloodstream must be mediated by the interaction of pathogen virulence determinants , e . g . InlA with host cell receptors like E-cadherin . However , L . monocytogenes also traffics in vivo to the placenta in a gentamicin-resistant manner [40] , presumably traveling inside phagocytic leukocytes [41] . We wanted to test whether cell-to-cell spread can mediate placental infection , and , if so , what sites are vulnerable . We introduced a fluorescent live cell dye to macrophage-like U937 cells ( differentiated to adherent cells with PMA ) and then infected them with 10403S-sGFP L . monocytogenes . The infected cells were added to explants in the presence of gentamicin to prevent infection of the explant by extracellular bacteria . After 24 h transmission of L . monocytogenes from U937 cells to explants had occurred ( Figure 6A-B ) . As with direct invasion , we found that L . monocytogenes infection by cell-to-cell spread from U937 cells to the placenta was largely confined to extravillous cytotrophoblasts at villous tips ( Figure 6C ) . In fact , the cell populations infected were statistically indistinguishable from InlA-mediated infections after 24 h ( p = 0 . 99 by chi-squared test ) . Invasive CTB express chemokines that attract cells of the monocyte lineage [48]–[50] , and indeed we observed clusters of U937 cells around the extravillous cytotrophoblasts as early as 4–8 h post-inoculation ( data not shown ) . Placental infection was not observed upon co-cultivation with U937 cells carrying ΔActA mutants , which are defective in cell-to-cell spread ( data not shown ) . Regardless of how L . monocytogenes was introduced , the dominant site of initial infection was the extravillous cytotrophoblast at the tip of anchoring villi . Multiple explants from a single placenta showed strikingly similar progression over the course of infection . In three out of six placentas studied , L . monocytogenes advanced significantly beyond the tips of anchoring villi ( Figure 7 ) . By 72 h post-inoculation , subsyncytial cytotrophoblast infection was common in anchoring villi while syncytiotrophoblast remained largely uninfected , suggesting that the syncytium not only resists cell-to-cell spread from macrophage-like cells but also from neighboring cytotrophoblasts ( Figure 7A ) . While infected anchoring villi were always colonized at the distal tips , infection of floating villi always began at proximal junctures shared by anchoring villi . At times floating villi exhibited infection of cytotrophoblasts on both sides of the villus without syncytiotrophoblast infection , indicating that L . monocytogenes trafficked through the subsyncytial cytotrophoblasts ( Figure 7B ) . Spread into the stroma was rare and presumably restricted by the basement membrane underlying subsyncytial cytotrophoblasts . Some stromal cells were infected at later timepoints ( Figure 7C ) . In explants infected with ΔActA L . monocytogenes , bacteria remained in extravillous cytotrophoblasts ( Figure 7D ) . Overall , ∼75 – 100% of anchoring villi were infected ( Figure 7E ) and infection of subsyncytial cytotrophoblasts and stroma increased over 72 h ( Figure 7F ) . In contrast , only 22% of explants exhibited any L . monocytogenes in floating villi ( Figure 7E ) . Taken together , these results describe the cell-to-cell path L . monocytogenes follows in disseminating throughout placental explants over three days: from extravillous cytotrophoblasts of anchoring villi along lateral villous subsyncytial cytotrophoblasts and from there into floating villi and/or stroma , all while leaving the syncytiotrophoblast largely uninfected .
Pathogens present in the maternal bloodstream may colonize the placenta , causing infection , inflammation , and ultimately spontaneous abortion , preterm labor , and neonatal morbidity and mortality [51] . Many pathogenic microbes are found transiently in maternal blood . For example , the simple daily act of brushing teeth is associated with bacteremia [52] , [53] , and L . monocytogenes is ingested frequently by healthy adults [54] . Yet neither result in significant maternal-fetal infection the majority of the time . This is surprising considering that twenty percent of maternal blood can be found circulating freely in the placenta's intervillous space , where it bathes fetal villi that are covered by a syncytiotrophoblast whose surface area ranges from 3000 cm2 in the late first trimester to 125 , 000 cm2 at term [28] . Thus , it seems reasonable to hypothesize that the syncytium forms an extremely effective physical barrier against infection . In this study , we have conclusively shown that the syncytiotrophoblast is resistant to infection by L . monocytogenes and that extravillous cytotrophoblasts are the portal of entry . For internalin-mediated infections the resistance of the syncytiotrophoblast can be reasonably explained by the tissue's lack of E-cadherin on the apical surface . When syncytiotrophoblast was infected , it was more likely to be basolaterally accessible syncytiotrophoblast , in which an E-cadherin expressing basolateral surface was exposed . Even for infections in uninterrupted syncytiotrophoblast , it remains possible that basolateral access was not apparent in the examined section but was available in an adjacent section . It is interesting to note that InlA and E-cadherin interactions mediating intestinal invasion are confined by anatomical and cellular barriers as well [55] . Even more surprising was the near absence of syncytiotrophoblast infection by cell-to-cell spread , either from U937 cells—which were observed near the syncytiotrophoblast—or from neighboring cytotrophoblasts at later timepoints . Three possible explanations exist: 1 ) the syncytiotrophoblast under-expresses unknown host molecule ( s ) required for cell-to-cell spread; 2 ) attachment of leukocytes to syncytiotrophoblast is insufficiently close in time or space for cell-to-cell spread to occur; or 3 ) the syncytiotrophoblast membrane is physically inhospitable to L . monocytogenes' actin-mediated protrusions . The last two are especially plausible when considering the profuse covering of branched microvilli on the apical surface [56]–[61] . The basal surface may also be girded against protrusion by the especially dense cytoskeletal network that is presumably required to resist cytosolic surface tension in the laterally vast syncytium [56]–[59] . Interestingly , interaction of the bacterial virulence factor InlC with human actin regulatory proteins has recently been shown to promote cell-to-cell spread by decreasing cortical tension , thereby enhancing the ability of motile bacteria to deform the plasma membrane into protrusions [62] . The syncytiotrophoblast may act as a general barrier . We have observed that T . gondii is not able to efficiently colonize the syncytium either ( unpublished observations ) , and other groups have reported similar results for herpes simplex virus [63] and cytomegalovirus [64] , [65] . Instead , we present evidence that extravillous cytotrophoblasts , normally not easily accessible from the intervillous space , are the dominant sites of L . monocytogenes colonization from both extracellular and intracellular compartments . Our results are in accord with Lecuit et al . showing that invasion of placental explants by extracellular L . monocytogenes depends on InlA , but our findings differ on the initial site of invasion ( EVT versus SYN , see Figure 8 ) . An important difference is our experimental set-up: we used first trimester placental explants instead of term placentas . Term placental organ cultures do not form anchoring villi after removal from the mother and are maintained in floating culture . Damage of the term explant syncytiotrophoblast has been reported as early as 4 h under selected culture conditions [66] and term explants cannot be used to evaluate extravillous cytotrophoblasts [67] . Thus , first trimester explants better represent the architecture of the maternal-fetal barrier in vivo . However , placental organ cultures from all gestational ages omit the decidua and the maternal vessels . The later are remodeled by extravillous cytotrophoblasts , which differentiate into endovascular trophoblasts that replace the endothelium of the maternal spiral arteries and therefore are in direct contact with maternal blood . Endovascular trophoblasts do not express E-cadherin [32] but it may be possible that they are targets of cell-to-cell spread in vivo . It has recently been postulated that the conjugated action of InlA and InlB leads to breaching of the maternal-fetal barrier [37] . This is particularly intriguing since InlA and InlB are in the same operon and expression of these invasion proteins is most likely co-regulated [16] . Disson et al . show a 10-fold reduction in invasion of the human intestinal cell line Caco-2 with L . monocytogenes strain EGDe , deficient in InlA or InlB , and an almost 100-fold reduction with the InlAB double deletion mutant [37] . Other groups using L . monocytogenes strains derived from 10403S have observed a 2–3-fold effect of InlB on intestinal invasion ( Amieva , personal communication ) . We do not observe a difference between wt and ΔInlB in infection of early gestation placental organ cultures . It may be that the variability of the human placental explants is too high to resolve a potentially small effect of InlB on invasion of placental explants . We did examine the role of cell-to-cell spread from infected U937 cells to the placenta , which seems highly relevant considering the importance of Listeria's intracellular life cycle for virulence [68] and the published evidence that L . monocytogenes traffics between organs inside of cells [40] , [41] . It is striking that extravillous cytotrophoblasts remain the primary portal of entry , leading us to hypothesize that access to extravillous cytotrophoblasts represents the first bottleneck for L . monocytogenes infection of the placenta ( Figure 8 ) . How can L . monocytogenes overcome this hurdle ? Extravillous cytotrophoblasts are present in the decidua and are known to actively recruit macrophages , monocytes and natural killer cells [48]–[50] . Therefore , we postulate that L . monocytogenes reaches the placenta in maternal phagocytes that are recruited to the decidua where they infect extravillous cytotrophoblasts by cell-to-cell spread or internalin-mediated invasion . Another striking finding was that extraordinarily high doses of L . monocytogenes were required to infect placental explants , and that L . monocytogenes growth rates were relatively slow , on average increasing only ∼10-fold over 24 h . However , we could distinguish two placental populations: in about half of the placentas L . monocytogenes did not grow , while in the other half bacterial numbers increased by ∼77-fold . Lecuit et al . used slightly higher doses and reported an increase of ∼100-fold over 24 h . But they also report having removed gentamicin from the culture medium at 2 h post-inoculation . Therefore , their 24 h CFU and histological assays may have included extracellular bacteria that escaped dying cells . In addition , we found little to no cell-to-cell spread at 24 h , while in most cell lines cell-to-cell spread begins as early as 4 h post-inoculation [21] , [24] . In some placentas we observed that infection remained confined to the extravillous cytotrophoblasts for at least 72 h . It is intriguing that the sites of infection correlated with the host cell's proliferative capacity [69] , [70] , which may provide interesting avenues for future studies . The slow rate of intracellular bacterial growth and cell-to-cell spread suggest the possibility that extravillous cytotrophoblasts restrict the intracellular life cycle of L . monocytogenes , thus representing the second bottleneck in the placenta ( Figure 8 ) . Once the placenta is infected , L . monocytogenes can spread to the fetus . One probable route of spread to the fetus is via the fetal capillaries in the villous stroma . Indeed , we observed low numbers of bacteria that had penetrated the basement membrane and infected the stroma suggesting that this is the third bottleneck L . monocytogenes encounters ( Figure 8 ) . It is interesting to note that although some bacteria were observed in syncytiotrophoblast at early timepoints , only anchoring villi acted as an origin of colonization . Our model is consistent with previous findings in vivo that the guinea pig placenta is colonized by 104 times fewer bacteria than maternal liver and spleen , and subsequently only 1 out of 104 bacteria are able to spread from placenta to fetus [40] . It is also in agreement with the epidemiology of human listeriosis , which is a rare disease during pregnancy despite its ubiquity in the environment , as well as the observation that pregnant animals have to be inoculated with high doses of L . monocytogenes to observe consistent placental and fetal infection [37] , [39] , [40] . In addition , our results provide an explanation for the absent or minor phenotype the internalin mutants exhibit in multiple different pregnant animal models [37] , [39] , [40] . Although it may be attractive to describe the route taken by a pathogen as a single mechanism , we do not believe that this accurately reflects what occurs in vivo . There is mounting evidence that pathogens have evolved to exploit multiple strategies to breach the intestinal and blood-brain barriers [71] , [72] , and it is reasonable to expect the same of the maternal-fetal barrier . The mechanisms by which the placenta excludes most pathogens to generate the maternal-fetal barrier are poorly understood , but our results suggest that the syncytiotrophoblast plays a significant role . Given its extensive contact with the maternal blood , this important tissue may have evolved to exclude pathogens , and our model system offers a powerful way to probe the mechanisms by which this occurs . Pathogens that can breach the syncytiotrophoblast or exploit sites of syncytial damage may colonize the placenta via subsyncytial cytotrophoblasts . Our study of L . monocytogenes , a model pathogen that colonizes the placenta , strongly suggests that the placenta's most vulnerable site is the extravillous cytotrophoblast , where cells anchor the placenta in the maternal decidua but have little to no contact with maternal blood . This finding suggests a reason for the observation that almost all pathogens capable of crossing the maternal-fetal barrier are either facultative or obligate intracellular: dissemination in the blood is not enough .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board at the University of California , San Francisco , where all experiments were performed ( H497-00836-28 ) . All patients provided written informed consent for the collection of samples and subsequent analysis . Placentas from elective terminations of pregnancy ( gestational age 4 to 8 weeks ) were collected and prepared as previously described [67] . Briefly , fragments from the surface of the placenta were dissected into 1–3 mm tree-like villi , placed on Matrigel ( BD Biosciences , San Jose , CA ) coated Transwell filters ( Millipore , Bedirica , MA , 30-mm diameter , 0 . 4 µm pore size ) and cultured in Dulbecco's modified Eagle's medium-F12 medium ( DMEM-F12; 1∶1 , vol/vol ) supplemented with 20% fetal bovine serum ( FBS , Fisher Scientific ) , 1% L-glutamine and 1% penicillin/streptomycin ( Invitrogen , Carlsbad , CA ) . For surface area and perimeter measurements , cultured explants were photographed pre-infection on a Leica MZ16F stereomicroscope ( Leica Microsystems , Wetzlar , Germany ) using an Axiocam MR monochrome camera ( Carl Zeiss , Munich , Germany ) . Measurements were made using ImageJ software ( NIH , Bethesda , MD ) . Syncytiotrophoblast was removed from villous trees as previously described [47] . Briefly , placental explants were soaked for 5–15 min in a solution containing Type IA collagenase ( 100 , 000 U ) , hyaluronidase ( 150 , 000 U ) , DNAse ( 120 , 000 U ) and 0 . 1% BSA in PBS without divalent cations ( UCSF Cell Culture Facility , San Francisco , CA ) . Explants were observed continuously via a dissecting microscope and when syncytiotrophoblast degradation was apparent they were transferred to Matrigel . The wild type strain of L . monocytogenes used in this study is 10403S [73] . Mutant strains included ΔInlA ( DPL4405 ) , ΔInlB ( DPL4406 ) , ΔInlAB ( DPL4455 ) [30] , ΔActA ( DPL3078 ) [74] , and sGFP-expressing 10403S L . monocytogenes ( DH-L1039 ) [75] . EGDe L . monocytogenes ( M . Loessner ) was used for some experiments . Bacteria were cultured using brain heart infusion ( BHI ) broth or agar ( Becton Dickenson Company , Sparks , MD ) . Intracellular growth assays of L . monocytogenes were performed as previously described [76] with following modifications: placental explants were incubated in antibiotic free media for 1 h prior to infection , 1×106 bacteria/ml were added for 30 min and gentamicin ( 50 µg/ml ) was added at 60 min post-inoculation . Gentamicin was subsequently maintained in the media , which was refreshed every 24 h . At specified times after infection , explants were removed from Matrigel and homogenized in 1 ml dH2O using a T25 digital Ultra-Turrax ( IKA , Staufen , Germany ) . Aliquots were plated on BHI agar and grown at 37°C . For permissive infections explants were incubated with 2×107 bacteria/ml for 5 h before adding gentamicin . Human macrophage-like U937 cells ( ATCC 1593 . 2 [77] ) were grown in RPMI-1640 ( UCSF Cell Culture Facility ) containing 4500 mg/L glucose , 10% FBS and 1% penicillin/streptomycin ( Invitrogen ) . Forty-eight hours prior to infection , cells were differentiated by addition of phorbol 12-myristate 13-acetate ( PMA; concentration 18 nM; Sigma ) to the medium . On the day of infection , cells were labeled with CellTracker Green CMFDA ( Invitrogen ) and infected with L . monocytogenes for 1 h at an MOI of 1∶1 . Cells were washed once with PBS and lifted from culture plates by incubation in ice cold PBS without divalent cations for 5 min . U937 cells were resuspended in explant medium containing 50 µg/ml gentamicin , and 1×106 cells per transwell were added to the explants . Every 24 h , fresh media containing gentamicin was added . Explants were removed at the times indicated and placed into vinyl cryomolds ( Ted Pella , Redding , CA ) , then covered with optimal cutting temperature ( OCT ) media ( Ted Pella ) and flash-frozen . Histological slicing was performed using a Hacker-Slee cryostat . Glass slides with sections were incubated ∼5 min in acetone at 4°C . All antibody staining was conducted at room temperature . When dry , slides were soaked 60 min in blocking solution ( 1% bovine serum albumin ( BSA , Sigma ) in PBS ) , then rinsed and exposed to primary antibodies in 0 . 5% BSA/PBS . Slides were rinsed three times for 5 min each in 0 . 5% BSA/PBS , then secondary antibodies were added at the indicated concentrations and incubated for 60 min . After three rinses , coverslips were affixed over Vectashield mounting medium with DAPI ( Vector Laboratories , Burlingame , CA ) . Uninfected explants did not stain with anti-Listeria antibodies . Primary antibodies: polyclonal rabbit Listeria O antiserum ( 1∶1000 Becton , Dickenson ) , monoclonal mouse anti-human cytokeratin 7 ( 1X , Clone OV-TL , Dako , Carpinteria , CA ) , monoclonal mouse anti-human E-cadherin ( 1∶200 , Clone NCH-38 , Dako ) , monoclonal mouse anti-human βHCG ( 1∶500 , clone SPM105 , Neomarkers , Fremont , CA ) and monoclonal mouse anti-human EGFR ( 1∶250 , Clone cocktail R19/48 , Biosource , Camarillo , CA ) . Secondary antibodies: Alexa Fluor 594 goat anti-mouse IgG ( 1∶500 ) and Alexa Fluor 488 goat anti-rabbit IgG ( 1∶1000 , both Invitrogen ) . All immunofluorescence conditions were compared to no-primary controls to ensure that non-specific binding did not occur . Slides were viewed using an inverted TE2000-E microscope ( Nikon , Tokyo , Japan ) equipped with a 12-bit cooled CCD camera ( Q Imaging , Surrey , Canada ) . Images were collected using Simple PCI software ( Hamamatsu , Sewickley , PA ) . Counts of L . monocytogenes localization were made by tallying every infected cell in each section at 100X magnification . Whole mount explants were prepared by rinsing explants with PBS and then soaking in 3% paraformaldehyde in PBS ( Ted Pella ) for 12 h at 4°C . Explants were then rinsed three times with PBS and suspended in 1∶100 Alexa Fluor 594 phalloidin and 1∶100 DAPI ( both Invitrogen ) for 24 h at 4°C . Explants were mounted onto glass slides in Vectashield and sealed under coverslips . Imaging was performed at the Nikon Imaging Center at UCSF using an upright Nikon C1 spectral confocal microscope equipped with 405 , 488 and 561 nm lasers . Images were prepared using Photoshop and Illustrator ( Adobe , San Jose , CA ) . RGB color hues were linearly adjusted for better CMYK printing but no non-linear alterations were performed . | Placental infections can lead to severe pregnancy complications as well as infection of the fetus and newborn with significant morbidity and mortality . Pathogens that are able to cross the maternal-fetal barrier typically have life cycles inside host cells . Among these is the facultative intracellular bacterial pathogen Listeria monocytogenes , which is highly amenable to experimental analysis . Our study is the first to use early gestation primary human placental organ cultures to identify the mechanisms by which L . monocytogenes breaches the human maternal-fetal barrier . We found that the placenta has evolved multiple mechanisms to resist infection . The main portal of entry into the placenta was a small subpopulation of fetally derived trophoblast cells ( extravillous cytotrophoblasts ) , which anchor the placenta in the decidua , the lining of the pregnant uterus . These cells could be infected via two mechanisms: direct invasion of extracellular bacteria and cell-to-cell spread . The extravillous cytotrophoblasts are not readily accessible from the maternal blood stream . This is a significant finding because it provides a novel explanation why almost all placental pathogens have intracellular life cycles: they may need maternal cells to reach the decidua and infect the placenta . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/gynecologic",
"infections",
"microbiology/parasitology"
] | 2010 | Placental Syncytiotrophoblast Constitutes a Major Barrier to Vertical Transmission of Listeria monocytogenes |
XRN2 is a 5’-3’ exoribonuclease implicated in transcription termination . Here we demonstrate an unexpected role for XRN2 in the DNA damage response involving resolution of R-loop structures and prevention of DNA double-strand breaks ( DSBs ) . We show that XRN2 undergoes DNA damage-inducible nuclear re-localization , co-localizing with 53BP1 and R loops , in a transcription and R-loop-dependent process . XRN2 loss leads to increased R loops , genomic instability , replication stress , DSBs and hypersensitivity of cells to various DNA damaging agents . We demonstrate that the DSBs that arise with XRN2 loss occur at transcriptional pause sites . XRN2-deficient cells also exhibited an R-loop- and transcription-dependent delay in DSB repair after ionizing radiation , suggesting a novel role for XRN2 in R-loop resolution , suppression of replication stress , and maintenance of genomic stability . Our study highlights the importance of regulating transcription-related activities as a critical component in maintaining genetic stability .
Chromosomes are under constant assault by DNA damaging agents . These insults lead to a variety of DNA lesions [1] that include one of the most severe , the DNA double strand break ( DSB ) [2] . One DSB can be lethal , and if not repaired in a timely and accurate manner can lead to genomic instability and rearrangement , such as translocations , that can contribute to subsequent diseased states [2] . Genomic instability is recognized as one of the hallmarks of cancer [3] . It can arise from a variety of different mechanisms , eventually resulting in mutation or chromosomal aberrations leading to tumor formation or cell death [2] . One of the most common mechanisms leading to DSB formation and genomic instability is aberrant replication , which is found to be a major cause of disease , including cancer [4 , 5] . The cell uses two major pathways , non-homologous end-joining ( NHEJ ) and homologous recombination ( HR ) , to repair DSBs [2] . A number of studies over the last decade have provided evidence that a major source of genomic instability and DSB formation during replication is mediated by transcription , and links between transcription and genomic instability are becoming more apparent [6–9] . In some cases , genomic instability is caused by collisions between the replication and transcriptional machineries , and resultant RNA:DNA hybrids , or R loops [10] . R loops are a consequence of transcription that can form under a variety of conditions and if not properly resolved lead to DSBs and genomic instability [7 , 9] . However , transient R-loop formation is an essential step during certain cellular processes such as immunoglobulin class switch recombination and in some cases RNA polymerase II ( RNAPII ) transcription regulation and termination [11–14] . Transcription termination by RNAPII is an incompletely understood process that requires multiple protein factors [15] . Foremost amongst these are components of the cleavage/polyadenylation machinery , consistent with the long-known requirement of an active polyadenylation signal for subsequent termination [15 , 16] . Other factors involved in termination include: ( i ) XRN2 , a 5’-3’ exoribonuclease that performs a key function in termination by degrading nascent RNA downstream from the 3’ cleavage site [12 , 17 , 18]; recent studies have provided evidence that XRN2 functions in termination of most RNAPII transcripts [19] ( ii ) PSF , which together with p54 ( nrb ) works to recruit XRN2 to pre-determined sites within the genome [20]; ( iii ) Kub5-Hera ( K-H ) , which facilitates localization of XRN2 along the genome [21]; and ( iv ) Senataxin ( SETX ) , an RNA:DNA helicase that in some cases is required to unwind the nascent RNA from its DNA template to allow for its degradation by XRN2 [12] . Interestingly , along with roles in transcription termination , several of the above factors have been implicated in the DNA damage response ( DDR ) and DSB repair . PSF and p54 ( nrb ) have functional roles in both HR and NHEJ [22 , 23] . Loss of PSF or p54 ( nrb ) leads to increased DSB formation , abrogated ATM signaling , delayed DSB repair kinetics and hypersensitivity to ionizing radiation ( IR ) [24 , 25] . Cells deficient in K-H expression display increased R-loop and DSB formation , abrogated NHEJ DSB repair via reduced expression of the DNA endonuclease Artemis , delayed DSB repair kinetics , hypersensitivities to IR and other DSB-inducing agents , and genomic instability [21] . SETX is involved in resolving R loops that form during transcription and lead to DSBs [26–29] . To date , however , a role for XRN2 in the DDR has not been suggested . In this study , we employed genetic , biochemical and cell biological techniques to uncover novel functions of XRN2 in R-loop resolution , DNA damage signaling and repair . Indeed , we found that XRN2 undergoes nuclear re-localization in response to genomic insults , particularly after exposure to ultraviolet ( UV ) and γ-irradiation ( IR ) . Importantly , we found that relocation of XRN2 is dependent on active transcription and R-loop formation . Cells lacking XRN2 demonstrate increased levels of R loops , DSBs , particularly at transcriptional pause sites , genomic instability and are hypersensitive to DNA damaging agents . Loss of XRN2 adversely affects the NHEJ pathway of DNA repair . Finally , XRN2-deficient cells demonstrate increased levels of replication stress and an abrogated DNA repair capability after genomic insult .
We first investigated whether XRN2 associated with known DNA repair factors . To this end , we performed gel filtration chromatography using HeLa whole-cell extracts . The elution pattern of XRN2 ( fractions 23–29 ) coincided with the patterns of the DNA damage repair proteins 53BP1 , Ku70/80 and BRCA1 ( Fig 1A ) . Similar to XRN2 , several DNA repair factors have been found to interact with SETX [27–31] . Among them , BRCA1 mediates SETX recruitment to a subset of transcription pause sites , and aids in SETX-mediated DNA repair [29] . To support the idea that XRN2 interacts with DNA repair proteins , we performed immunoprecipitation using XRN2-specific antibodies . Indeed , we found that 53BP1 and Ku80 both immunoprecipitated with XRN2 ( Fig 1B ) . Unlike what was observed with SETX , we did not detect BRCA1 after XRN2 immunoprecipitation . Notably , Ku80 has also been found to interact with SETX in an affinity purification of FLAG-tagged SETX [31] . These data suggest a possible role for XRN2 in responding to DNA damage , particularly in the NHEJ pathway . Several DNA damage regulators , such as 53BP1 and γ-H2AX , form discrete foci after genomic insults [32–34] . We next examined whether XRN2 formed DNA damage-induced foci . Indeed , XRN2 displayed foci formation in response to both IR and UV exposures ( Fig 1C and S1A , S1B and S1C Fig ) . We observed an average of ~2 XRN2 foci in untreated cells compared to ~6–8 XRN2 foci in IR- or UV-treated cells ( Fig 1C ) . Coincidently , a recent proteomics analysis demonstrated that XRN2 undergoes DNA damage-inducible phosphorylation in response to UV and IR treatments [35] . Importantly , along with purifying 53BP1 by immunoprecipitation , XRN2 foci co-localized with 53BP1 after genomic insult ( S2 Fig ) , further supporting the idea that these two proteins associate with one another . Using previously described human fibroblasts infected with a Kub5-Hera specific shRNA ( shk-h ) [21] , we found that XRN2 foci formation in response to DNA damage was independent of K-H expression ( Fig 1C and S3 Fig ) . This contrasts with XRN2 localization to the 3’ end of genes , where K-H is required [21] . It was recently demonstrated that UV damage leads to the formation of R loops [36] . Interestingly using the S9 . 6 antibody , which recognizes RNA:DNA hybrids [37] , in conjunction with XRN2 antibodies , we observed that foci formation for both RNA:DNA hybrids and XRN2 were significantly increased after UV exposure ( Fig 1D ) . We also observed that XRN2 formed foci after UV damage with kinetics closely mirroring R-loop formation , while there was no change in DSB foci , marked by 53BP1 and γ-H2AX staining ( S4 Fig ) . These observations , and the fact that XRN2 foci also co-localized with R-loop foci ( Fig 1D ) , suggest that XRN2 is recruited to R loops or stalled RNAPII rather than to DSBs . When cells were treated with the RNAPII inhibitor α-amanitin both XRN2 and R-loop foci failed to form after UV treatment ( Fig 1D ) . These data strongly suggest that R-loop formation and active transcription are both required for XRN2 foci formation after genomic insult . We next wished to examine a potential role for XRN2 in the DDR . For this , we employed an XRN2-specific shRNA to generate an immortalized human fibroblast cell line with lowered XRN2 expression levels ( shXRN2 ) , and a non-targeting scrambled sequence shRNA to generate comparable control cells ( shScr ) . We also reproduced our results using XRN2 siRNA in MCF-7 , an ER+PR+ breast cancer cell line [38] . We verified the decrease in steady-state levels of XRN2 protein in shXRN2 cells compared to shScr cells by both western blot and immunofluorescence ( IF ) ( Fig 2A ) . We previously showed that loss of K-H and p54 ( nrb ) , two factors implicated in mediating XRN2 genomic distribution , led to increased DSB formation [21] . Similar to K-H- and p54 ( nrb ) -deficient cells , we observed an increased level of 53BP1 , γ-H2AX , ATM pSer 1981 , and BRCA1 foci/nuclei in XRN2 siRNA-treated MCF-7 cells and in the XRN2 shRNA-expressing fibroblasts ( Fig 2B , 2C and S5A–S5D Fig ) . We also found an increase in the amount of Rad51 foci ( S6A Fig ) in shXRN2 cells compared to controls , suggesting that cells depleted of XRN2 are subjected to an increased level of basal DNA damage . We next examined the ability of shScr and shXRN2 fibroblasts to perform NHEJ . We used for this a previously published plasmid-based NHEJ assay [39] . This assay employed a linearized GFP reporter plasmid , generated by HindIII digestion , leading to a compatible DNA end or I-SceI digestion , resulting in incompatible DNA ends due to restriction site orientation . Significantly , compared to shScr cells , shXRN2 cells could not efficiently repair either compatible or incompatible DNA ends , indicating that loss of XRN2 abrogated the ability of cells to repair DSBs via the NHEJ pathway ( S6B Fig ) . Previously , we showed that K-H-deficient cells ( shk-h ) also lacked the ability to perform NHEJ , but only at non-compatible DSB ends , through loss of Artemis expression [21] . Comparative western blot analyses in shScr , shXRN2 and shk-h cells revealed that loss of XRN2 did not result in a concurrent Artemis loss ( S6C Fig ) , illustrating a significant difference between the two transcription termination factors . We next performed metaphase spreads to examine cytogenetically the extent of genomic instability in the shXRN2 fibroblasts compared to shScr cells . Consistent with increased DSBs and apparent loss of DSB repair ability of XRN2-deficient cells , we noted that shXRN2 cells harbored increased amounts of both chromatid and chromosome type breaks versus shScr cells ( Fig 2D and 2E ) . When we compared shXRN2 with shk-h cells we found similar levels of chromosome-type damage , but loss of XRN2 led to significantly more chromatid-type damage , which was not seen in K-H deficient cells ( Fig 2D ) , again suggesting an important difference between loss of XRN2 and K-H . Cells deficient in XRN2 displayed increased DSBs and genomic instability along with decreased DNA repair capacity . Interestingly , this is similar to previously published reports on K-H and PSF , two factors important in mediating XRN2 distribution along the genome [20 , 21 , 24] . Cells deficient in K-H or PSF expression also demonstrated hypersensitivities to DNA damaging agents , such as IR [21 , 24] . Similarly , XRN2 deficient cells , fibroblast or MDA-MB-231 cells , a triple negative breast cancer cell line [38] , were hypersensitive to various genomic insults as illustrated by decreased colony forming ability after exposure to IR , aphidicolin ( APH ) or hydrogen peroxide ( H2O2 ) ( Fig 3A , 3B , 3E and 3F ) . Notably , both MDA-MB-231 and fibroblast XRN2-depleted cells were also hypersensitive to UV radiation ( Fig 3C and 3D ) . These results reveal a difference between XRN2- and SETX-deficient cells , which show sensitivity to oxidative DNA damage but not IR , and also confirm a difference between XRN2 and K-H deficient cells , which are sensitive to IR , but not UV [21 , 40] . As shown above , cells lacking XRN2 display increased amounts of chromatid damage . This observation suggested that loss of XRN2 may adversely affect cells during DNA replication , as chromatid-type aberrations originate due to DNA damage occurring during S and G2 phases of the cell cycle [41] . Loss of XRN2 also leads to the focal accumulation of several factors required for homologous recombination , such as ATM , BRCA1 and Rad51 ( Fig 2B , 2C and S6A Fig ) . These results suggest that loss of XRN2 can cause replicative stress . Initial experiments revealed that the shXRN2 fibroblasts displayed increased 53BP1 foci formation compared to shScr cells ( Fig 4A ) in cells expressing PCNA , a marker of cells undergoing DNA synthesis . We also observed increased phosphorylation of RPA32 , activated ATR , and the checkpoint kinase CHK1 in both fibroblast and MCF-7 cells ( Fig 4B–4F and S7A , S7B Fig ) , all indicators of replication stress . To measure replication fork impairment in shXRN2 cells directly , we performed DNA fiber analyses and found that nucleotide ( BrdU ) incorporation in shXRN2 cells was ~50% less than in shScr cells ( 15 μm vs 30 μm , respectively ) ( Fig 4G ) . Altogether , these data demonstrate that XRN2-deficient cells undergo significantly increased replication stress . A possible explanation for the observed increase in DNA damage and replication stress is that depletion of XRN2 leads to excess R-loop formation . To investigate this , we examined XRN2 knocked-down ( KD ) cells for basal levels of R-loop formation by IF , using the S9 . 6 antibody . Indeed , MCF-7 and fibroblast cells deficient in XRN2 exhibited an ~4-fold increase in R loops versus control cells ( Fig 5A–5C ) . One caveat to measuring R loops by IF is that it may be difficult to distinguish between R loops formed in nuclear DNA to those formed in other sub-nuclear compartments , such as the nucleolus [7 , 42] . However , it has been observed that R loops that form in the nucleus tend to be sensitive to RNaseH , while R loops within the nucleolus tend to be RNaseH resistant [43] . To support the notion that loss of XRN2 leads to increased R-loop formation within nuclear DNA , we isolated genomic DNA from MCF-7 cells with and without XRN2 and performed dot blot analysis . Again , we found an increase in the amount of S9 . 6 signal with the genomic DNA of MCF-7 cells depleted of XRN2 as compared to control cells ( Fig 5D ) . Importantly , the S9 . 6 signal was strongly diminished after RNaseH treatment . All samples used in the dot blot analysis were treated with RNaseA , to remove any free RNA that the S9 . 6 antibody may cross react with [44] and an antibody against single-stranded DNA was used to ensure equal loading of each sample after DNA denaturation ( Fig 5D ) . To determine how transcription and R-loop formation contribute to the DNA damage observed in shXRN2 cells , we treated shScr and shXRN2 cells with α-amanitin or transfected each cell line with GFP control or GFP-RNaseH expression plasmids to remove R loops , and then measured the number of 53BP1 foci . While we observed the expected increase in 53BP1 foci in mock-treated or GFP-transfected shXRN2 cells , we found that either inhibition of transcription ( with α-amanitin ) or removal of R loops ( with RNaseH-GFP expression ) led to decreased 53BP1 foci in shXRN2 cells , to levels comparable to shScr cells ( S8A and S8B Fig ) . The decrease in 53BP1 foci in shXRN2 cells after α-amanitin treatment correlated well with the decrease in R-loop levels visualized in these cells ( S8C Fig ) . These data confirm a role for XRN2 in R-loop removal and protection from DSB accumulation . Since the loss of XRN2 sensitized cells to IR treatment ( Fig 3A ) , we examined the effects of IR on R-loop formation in shXRN2 cells . Because R loops can directly lead to DSBs , we examined whether active transcription or R-loop formation directly affected DNA repair ( regression of 53BP1 foci ) after IR treatment . shScr or shXRN2 cells were treated or not with α-amanitin or transfected with GFP- or GFP-RNaseH expression plasmids prior to IR exposure and 53BP1 foci/nuclei regression kinetics were assessed at various times after IR exposure . Interestingly , a distinct and significant delay in the disappearance of IR-treated-induced 53BP1 foci in shXRN2 compared to shScr cells was observed , suggesting a defect in DNA repair kinetics ( Fig 6A ) . However , inhibition of transcription by α-amanitin or removal of R loops by RNaseH completely restored DNA repair kinetics after IR exposure in shXRN2 cells ( Fig 6B and 6C ) , suggesting that XRN2-deficient cells , along with an inability to properly perform DSB repair through the NHEJ pathway , are defective in R-loop resolution after IR . Lastly , we examined whether DDR factors accumulate at transcriptional pause sites on genes that undergo R-loop-dependent termination in XRN2-depleted cells . To do this , we performed chromatin immunoprecipitation ( ChIP ) to assay the presence of several DDR proteins at the 3’-ends of three genes subject to R-loop-dependent termination , ENSA , Gemin7 and β-actin , and also Akirin 1 , which are R-loop-independent [12 , 29 , 45] . Strikingly , we found that XRN2 depletion in HeLa cells led to accumulation of ATM , BRCA1 , CtIP , 53BP1 and γ-H2AX at the termination pause site of the ENSA gene ( Fig 7A ) , to an enrichment of ATM , BRCA1 , CtIP , and 53BP1 at the Gemin7 pause site ( Fig 7D ) and to a lesser extent , to accumulation of CtIP and 53BP1 at the 3’-end of the β-actin gene ( Fig 7B ) . We detected no enrichment of any of the DDR factors at the 3’ end of the Akirin 1 gene after XRN2 KD ( Fig 7C ) . We also examined accumulation of the same DDR factors at an intronic region of Gemin7 and did not detect significant changes after XRN2 loss ( Fig 7E ) . These data suggest that XRN2 plays an important role in maintaining genomic integrity at the 3’ pause sites of genes . We note that SETX , which as mentioned above can function in termination by resolving R loops located downstream of certain poly ( A ) signals , has also been implicated in the DDR [26–28 , 40] . In some cases through an interaction with BRCA1 at specific transcriptional pause sites including the three analyzed above [29] . Indeed , similar to the loss of XRN2 , we observed that SETX KD led to slightly increased 53BP1 foci ( S9A Fig ) and initiation of ATM-mediated DNA damage signaling , as measured by increases in Chk2 and H2AX phosphorylation ( S9B Fig ) . In light of these findings , we wished to eliminate the possibility that a concurrent loss of SETX following XRN2 depletion might underlie the effects we have attributed to XRN2 . We measured SETX levels after XRN2 KD by Western blot and found that SETX expression was not altered ( S9C Fig ) . Additionally , SETX immunoprecipitation failed to co-purify XRN2 , while the known SETX-interacting partner Rrp45 [26] was detected ( S9D Fig ) . Together , our data indicate that XRN2 plays an important role in protecting cells from DNA damage accumulation at termination pause sites of a subset of genes .
Regulation of transcription is a critical process essential for cell survival . A consequence of active transcription is the occasional formation of R loops [7] . While R loops can naturally form during transcription , their overly prolonged existence or aberrant formation can be a severe threat to genomic integrity [7] . Therefore , elucidating mechanisms by which cells prevent inappropriate R-loop formation and insure their resolution is imperative to understanding how genomic stability is maintained . Our results provide novel insights into how this occurs , unexpectedly implicating the 5’-3’ exoribonuclease XRN2 in this process . XRN2 functions in RNAPII transcription termination by degrading nascent RNA downstream of the poly ( A ) cleavage site [17] . Our data suggest a requirement of XRN2 in preventing formation of R loops , likely functioning at terminator regions downstream of 3’ cleavage sites , where R loops have indeed been detected [12 , 14 , 29] . Thus a conclusion from our data is that XRN2 is required to ensure that these R loops do not persist or perhaps simply reform after being resolved by SETX . Below we discuss both how XRN2 exerts this function , as well as the newly discovered role for XRN2 in the DDR . Our data has implicated XRN2 as a significant factor in helping cells prevent DNA damage and maintain genomic stability . For example , we showed that loss of XRN2 leads to increased hypersensitivity to ionizing radiation . This is a characteristic seen in cells that have lost factors involved in DNA repair , especially those involved in NHEJ [46 , 47] . Consistent with this , we observed an interaction between XRN2 and factors involved in the NHEJ pathway of DSB repair , such as 53BP1 and Ku80 . Additionally we found that loss of XRN2 leads to accumulation of factors involved in the HR pathway of DSB repair , such as BRCA1 and CtIP , to the 3’ transcriptional pause site of genes undergoing R-loop-dependent transcription termination [12 , 29] . It is worth noting that we detected BRCA1 at 3’ pause sites only after XRN2 KD , while Hatchi et al . found BRCA1 at the same pause sites in normal cells [29] . Although the basis for this difference remains to be determined , our data support the conclusion that this tumor suppressor , XRN2 , functions in the DDR at regions of R-loop-associated transcription termination . Our findings suggest that XRN2 plays a role in DNA repair pathway choice at sites of R-loop-induced DNA damage . Accumulation of CtIP at termination pause sites we observed after XRN2 KD suggests that DNA end-resection has occurred and that the HR repair pathway has been initiated [48] . However , accumulation of 53BP1 at the same sites is intriguing because it is believed that 53BP1 and BRCA1 are antagonistic to one another , with 53BP1 promoting NHEJ repair , and BRCA1 and CtIP promoting HR repair [49 , 50] . Thus we suggest that XRN2 favors the use of NHEJ repair factors through its interactions with 53BP1 , Ku70 and Ku80 , while loss of XRN2 leads to the recruitment of factors involved in the HR DSB repair pathway ( see model , Fig 7F ) . This model however does not explain why 53BP1 still accumulates at pause sites after XRN2 KD . Since 53BP1 foci increase after XRN2 KD , an interesting possibility is that 53BP1 recruits or stabilizes XRN2 to these sites in a similar way that BRCA1 recruits SETX ( 29 ) . Indeed , Hatchi et al . showed that while BRCA1 KD impaired SETX recruitment to 3’ pause sites , SETX KD did not affect BRCA1 accumulation . Since XRN2 interacts with 53BP1 and Ku 80 , but not BRCA1 , and loss of XRN2 decreases the cell's ability to repair DSBs via NHEJ , we propose that XRN2 acts as a scaffolding protein , facilitating recruitment of factors downstream of 53BP1 , such as Ku70 and Ku80 , to the DSB site , thus promoting NHEJ repair . We cannot , however , exclude the possibility that XRN2 interaction with NHEJ factors is an R-loop-independent process that plays a role in global NHEJ , as suggested by the results of our GFP plasmids assays . In addition to its function at gene 3’ ends , SETX has also been implicated in detecting and regulating R loops occurring after replication stress . Yüce and West demonstrated that SETX forms discrete nuclear foci and co-localizes with 53BP1 and γ-H2AX after aphidicolin treatment [28] . SETX has also been shown to interact with factors required for both HR and NHEJ , such as BRCA1 , DNA-PKcs , Ku70 and Ku80 as well as Mre11 [28–31] . Furthermore , Richard et al . provided evidence that SETX , in a sumoylation-dependent manner , interacts with the exosome complex and recruits it to sites of transcription-replication collisions [26] . The exosome is a multisubunit complex containing a 3’ to 5’ exoribonuclease activity and is involved in mRNA turnover and RNA quality control [51] . Importantly , previous studies in human and yeast have also suggested that the exosome can play a role in the DDR and prevention of genome instability [26 , 52 , 53] . The above observations , together with the data presented here , suggest two related mechanisms by which cells resolve R loops and thereby prevent R loop-mediated DNA damage . One is that SETX and the exosome cooperate to disrupt R loops formed during transcriptional elongation and/or replication stress [26 , 30] . In this scenario , SETX would resolve the RNA/DNA hybrid that forms behind a stalled RNAP II and the exosome then degrades the RNA from the 3’ end released from the transcription bubble . This would prevent the RNA from possibly reforming the R loop or causing other deleterious effects . In another mechanism , we propose that SETX and XRN2 function in the resolution of R loops at or near certain transcription termination sites . Following endonucleolytic cleavage of the pre-mRNA at the polyA site , the downstream RNA containing a 5’ monophosphate is degraded by XRN2 as part of the termination process for most RNAPII transcripts [17 , 20] . In some situations , depending on the susceptibility of the sequence to R-loop formation , SETX is also required for termination , to resolve R loop structures that may block XRN2 [17] . In these instances , SETX and XRN2 work together to degrade the RNA at sites of R-loop formation . In the absence of XRN2 , not only would termination be blocked but the R loop could also reform , leading to DNA damage we have described . It is not unlikely that both mechanisms co-exist at some sites of R-loop formation , leading to a 5’-3’ ( through XRN2 ) and a 3’-5’ ( through the exosome ) degradation of the RNA moiety . In summary , our results have shown that XRN2 , previously known to function in transcription termination and RNA turnover , also has an important role in the DNA damage response . Thus , our findings have provided further evidence for the importance of controlling RNA metabolism for maintenance of genomic stability .
shScr , shXRN2 , and shk-h cells derived from immortalized human fibroblasts , were generated using lentiviral shRNA constructs as described [21] under occasional selection with 1 μg/ml puromycin . They were grown in DMEM with 15% FBS , L-glutamine , 100 μg/ml hygromycin , and 1 μg/ml puromycin in a 10% CO2-90% O2 humidified air atmosphere at 37°C . HeLa cells were also used to derive a matched set of shScr and shSETX cells . Antibody recognizing 53BP1 ( A300-272A ) and RPA32 pS ( 4/8 ) ( IHC-00422 ) were purchased from Bethyl Laboratories ( Montgomery , TX ) . The phospho-specific γ-H2AX antibody ( JBW301 ) was obtained from Millipore ( Billerica , MA ) . Mre11 ( 12D7 ) , Ku70 ( GTX233114 ) and Ku80 ( GTX70485 ) were purchased from Genetex . Actin ( C-11 ) , BRCA1 ( sc-642 ) . RNAPII ( sc-899 ) , ATR pS 428 ( sc-109912 ) and Rad51 ( H-92 ) antibodies were obtained from Santa Cruz Biotech ( Santa Cruz , CA ) . Total ( 2662 ) and pT68 Chk2 ( 2661 ) antibodies and total ( 2360 ) and pS317 ( 2344 ) Chk1 were purchased from Cell Signaling . S9 . 6 , an antibody specific for R loops ( RNA:DNA hybrids ) [37] , was provided by Dr . Stephen H . Leppla ( NIH , Bethesda , MD ) . Antibodies used for ChIP: anti-BRCA1 ( Gene-Tex , 6B4 ) , anti-ATM ( Novus Biologicals NB100-305 ) , anti-γ-H2AX ( Abcam , ab2893 ) , anti-53BP1 ( Novus Biologicals , NB100-305 ) , anti-CtIP ( Abcam ) and anti-Gal4 ( DBD ) ( Santa Cruz Biotechnology , sc-577 ) . Antibodies used in western blotting and SETX IP: anti-SETX ( A301-105 ) from Bethyl Laboratories , anti-XRN2 ( NBP1-68149 ) and anti-Rrp45 ( NBP1-71702 ) from Novus Biologicals . shScr and shXRN2 cells were plated onto 60 mm tissue culture plates and allowed to grow for two days . Cells were then exposed to IR , H2O2 , Aphidicolin ( APH ) or UV at various doses as indicated , allowed to grow for 7 days , washed with PBS and stained with crystal violet solution . Colonies with >50 normal appearing cells were counted and percent survival calculated and graphed with dose . To visualize 53BP1 , XRN2 , and γ-H2AX , cells were plated , grown to ~70% confluency on glass coverslips and either mock- or IR-treated . Cells were then washed once with PBS , permeabilized and fixed in methanol/acetone ( 70/30 , v/v ) . To visualize ATM pS 1981 and Mre11 cells were fixed in 3% paraformaldehyde/2% sucrose PBS solution for 10 min at room temperature ( RT ) . Fixation was followed by permeabilization on ice with a 0 . 5% Triton X-100 buffer ( 0 . 5% Triton X-100 , 20mM HEPES , pH 7 . 4 , 50 mM NaCl , 3 mM MgCl , and 300 mM sucrose ) . Cells were then blocked in PBS containing 5% FBS for 30 min at room temperature . Cells were then washed three times with PBS and exposed to primary antibody for 1 h at RT as indicated . Cells were washed three times with PBS , exposed to secondary antibody for 30 min at RT , washed three times with PBS , and mounted onto glass slides . Detection of R loops using the S9 . 6 antibody ( 2 ug/ml ) was performed as described [54] . Visualization was performed using a 100X oil objective lens with fluorescence on a Nikon microscope . For each experiment 100 cells were counted . Were performed as described [39] . Briefly , the pEGFP-Pem1 plasmid was digested with HindIII or I-SceI for 8–12 h to generate free DNA ends . pCherry plasmids were co-transfected with linearized DNA to control for transfection efficiency . shScr and shk-h cells were transfected at ~20–25% confluency and allowed to grow for three days . Transfections were performed using Lipofectamine-2000 using the manufacturer’s instructions . Flow cytometric analyses were performed using a Beckman-Coulter Cytomic FC 500 flow cytometer . Exponentially growing shScr and shXrn2 cells were incubated with colcemid ( 1 μg/ml ) for 2 h before being harvested . Harvested cells were fixed in hypotonic solution containing 75 mM KCl and fixed in methanol:acetic acid ( 1:1 v/v ) . Metaphase spreads were prepared , stained with Giemsa , and examined by light microscopy . Metaphase spreads ( >50 ) were then scored for chromosome and chromatid aberrations as described [21] . shScr and shXRN2 cell pellets were re-suspended in Buffer A ( 10 mM Hepes ( pH 7 . 9 ) , 10 mM KCl , 0 . 1 mM EDTA ( pH: 8 . 0 ) , 0 . 1 mM EGTA , 1 . 0 mM DTT , 0 . 5 mM PMSF ) and allowed to swell for 10 min , 4°C . NP-40 was then added to cell solutions to a final concentration of 0 . 5% and vortexed at low intensity for 30 sec . Isolated nuclei were then harvested by centrifugation ( 2 , 000 X g ) and the nuclear pellets were re-suspended in Buffer C ( 20 mM Hepes ( pH: 7 . 9 ) , 0 . 4 M NaCl , 1 . 0 mM EDTA , 1 . 0 mM EGTA , 1 . 0 mM DTT , 0 . 5 mM PMSF ) for 15 min at 4°C . Nuclear extracts were then isolated by centrifugation ( 25 , 000 X g , 15 min ) and assessed for protein concentrations by Bradford assays . Briefly , after 2 washes with cold PBS cells were resuspended in 1 packed cell volume ( PCV ) of buffer A ( 10 mM Hepes pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCL , 0 . 5 mM DTT and protease inhibitors including NEM ) and incubated on ice for 15 mins . The cells were passed through a 1 ml syringe 5 times and centrifuged for 20 sec . The pellet ( nuclei ) was resuspended in 2/3 of the PCV in Buffer C ( 20 mM HEPES PH 7 . 9 , 1 . 5 mM MgCl2 , 25% glycerol , 420 mM NaCl , 0 . 2 mM EDTA , 0 . 5 mM DTT and protease inhibitors including NEM ) . The extract was stirred using a mini stir bar for 30 min at 4°C . The nuclear debris were pelleted by centrifugation for 5 mins and the nuclear extract was collected in a new tube . For the IP , the glycerol was adjusted at 10% and NaCl at 150 mM . 2 μg of SETX antibody was used to IP SETX complex O/N . The IP was washed 3 times in wash buffer ( 10 mM Tris Hcl pH 7 . 4 , 1 mM EDTA , 1 mM EGTA , 150 mM NaCl , 1% triton ) . SETX siRNA target sequence: AGCAAGAGAUGAAUUGCCA . Extracts were prepared 3 days after siRNAs transfection . Were performed as described [21] . Briefly , HeLa cells were cultured in two 150 mm2 dishes ( up to ~80% confluency ) in DMEM supplemented with 5% FBS and 1 mM L-glutamine in a 5% CO2-95% humidified air atmosphere at 37°C . Cells were trypsinized , harvested by centrifugation and washed with ice-cold 1X PBS . Cells were re-suspended in 1 ml extraction buffer ( 25 mM Tris-HCl [pH: 7 . 7] , 2 mM MgCl2 , 100 mM NaCl , 10 mM β-glycerophosphate , 5 mM NaF , 0 . 5 mM Na3VO4 , 10% glycerol , 0 . 1% NP-40 , 1X protease inhibitor cocktail [Sigma] , 100 units of turbonuclease [Fisher] and 1 mM DTT ) . Cell suspensions were incubated on ice for 5 min and passed through 1 ml syringes with 27G needles until homogeneous suspensions were obtained . Suspensions were incubated on ice for 30 min followed by 10 min at 37°C . Cell lysates were centrifuged at 14 , 000 rpm for 30 min at 4°C using a microfuge . Supernatants were carefully collected as whole cell lysates and used for gel-filtration chromatography . Chromatography steps were performed using AKTA Purifier 10 ( GE Healthcare ) . For fractionation of whole cell lysates , ~3 . 0 mg of protein was loaded onto a 24-ml Superose 6 HR 10/30 column ( GE Healthcare ) pre-equilibrated with chromatography buffer ( 25 mM Tris-HCl [pH: 7 . 7] , 100 mM NaCl , 5% glycerol and 1mM DTT ) and run in the same buffer at a flow rate of 0 . 5 ml/min . Molecular weight standards ( Pharmacia Biotech ) were used to calibrate the column ( as indicated in Fig 1C ) . Studies to monitor the length of DNA synthetic tracks using BrdU were performed as described [55] . 5 μg of specified primary antibody conjugated to Protein A/G beads . 500–1000 μg of Nuclear protein extracts were incubated with antibody:bead complex for 1 hour at 4°C . Each experiment was washed 3 times with NETN solution ( 20 mM Tris-HCL ( pH 8 . 0 ) , 0 . 1 M NaCl , 1 mM EDTA , 0 . 05% NP-40 ) . After washes each sample was separated on 8% SDS-polyacrylamide gel . ChIP experiments were performed using the protocol detailed in Hatchi et al . [29] . HeLa cells were transfected with an siRNA control ( sicont target sequence: UUCUCCGAACGUGUCACGU ) and an siRNA targeting XRN2 ( siXRN2 target sequence: GAGUACAGAUGAUCAUGUU ) at 30 nM with Lipofectamine RNAiMAX ( ThermoFisher ) three days prior chromatin preparation . Chromatin was incubated O/N with protein G Sepharose ( GE Healthcare ) and the appropriate antibody: 4 μg of anti-BRCA1 , 2 μg of anti-ATM , 2 μg of anti-γ-H2AX , 4 μg of anti-53BP1 , 4 μg of anti-CtIP and 2 μg of anti-Gal4 ( DBD ) used as an irrelevant antibody for control . Immunoprecipitates were then washed ( with 1 ml of wash buffer for 5 min each time ) twice with TSE-150 ( 0 . 1% SDS , 1% triton , 2 mM EDTA , 20 mM Tris-Hcl pH 8 , 150 mM NaCl ) , twice with TSE-500 ( 0 . 1% SDS , 1% triton , 2 mM EDTA , 20 mM Tris-Hcl pH 8 , 500 mM NaCl ) , once with in LiCl detergent ( 0 . 25 M LiCl , 1% NP40 , 1% sodium deoxycholate , 1 mM EDTA , 10 mM Tris-Hcl pH 8 ) and finally once with TE . DNA was eluted from the beads with 150 μl of elution buffer ( 1% SDS , 100 mM NaHCO3 ) then supplemented with 300 mM NaCl and 10 μg/ml RNaseA and incubated for 5 hours at 65°C to reverse the crosslink . The samples were then treated with proteinase K and purified using a PCR purification kit from Qiagen . ChIP samples were analyzed by quantitative real-time PCR using Maxima SYBR green master mix from Thermo Scientific and the appropriate primers used in Hatchi et al . [29] . The results were calculated as % Input and then normalized to the negative control ( Gal4 ( DBD ) IP ) . Genomic DNA was isolated from control and siXRN2 cells using the Dneasy Blood and Tissue Kit ( Qiagen ) following the manufacturer’s instructions . 50 and 100 μg of DNA was spotted directly on a nitrocellulose membrane using a Dot Blot apparatus ( Bio-Rad ) and UV crosslinking . Prior to blotting genomic DNA was exposed to 100 μg/ml RNase A from ThermoFisher ( catalog number EN0531 ) . RNase H treatment was performed using RNase H from New England Biolabs ( catalog number M0297S ) at 50 U/ml . Membrane was probed with S9 . 6 antibody ( 1 ug/ml ) for 1 hour at room temp . All experiments ( including Western Blots and immunofluorescence images ) were performed three or more times . Means and standard errors were calculated and differences between treatments were determined by confidence limit calculations using student’s t tests . p values ( 0 . 01 and 0 . 05 ) for 99% and 95% confidence limits , respectively , were considered significant and reported . | Genomic instability is one of the primary causes of disease states , in particular cancer . One major cause of genomic instability is the formation of DNA double strand breaks ( DSBs ) , which are one of the most dangerous types of DNA lesions the cell can encounter . If not repaired in a timely manner , one DSB can lead not only to cell death . If misrepaired , one DSB can lead to a hazardous chromosomal aberration , such as a translocation , that can eventually lead to cancer . The cell encounters and repairs DSBs that arise from naturally occurring cellular processes on a daily basis . A number of studies have demonstrated that aberrant structures that form during transcription under certain circumstances , in particular RNA:DNA hybrids ( R loops ) , can lead to DSB formation and genomic instability , especially during DNA synthesis . Thus , it is important to understand how the cell responds and repairs transcription-mediated DNA damage in general and R loop-related DNA damage in particular . This paper both demonstrates that the XRN transcription termination factor links transcription and DNA damage , but also provides a better understanding of how the cell prevents transcription-related DNA damage . | [
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"interferi... | 2016 | XRN2 Links Transcription Termination to DNA Damage and Replication Stress |
Measles is a highly contagious and severe disease . Despite mass vaccination , it remains a leading cause of death in children in developing regions , killing 114 , 900 globally in 2014 . In 2006 , China committed to eliminating measles by 2012; to this end , the country enhanced its mandatory vaccination programs and achieved vaccination rates reported above 95% by 2008 . However , in spite of these efforts , during the last 3 years ( 2013–2015 ) China documented 27 , 695 , 52 , 656 , and 42 , 874 confirmed measles cases . How measles manages to spread in China—the world’s largest population—in the mass vaccination era remains poorly understood . To address this conundrum and provide insights for future public health efforts , we analyze the geospatial pattern of measles transmission across China during 2005–2014 . We map measles incidence and incidence rates for each of the 344 cities in mainland China , identify the key socioeconomic and demographic features associated with high disease burden , and identify transmission clusters based on the synchrony of outbreak cycles . Using hierarchical cluster analysis , we identify 21 epidemic clusters , of which 12 were cross-regional . The cross-regional clusters included more underdeveloped cities with large numbers of emigrants than would be expected by chance ( p = 0 . 011; bootstrap sampling ) , indicating that cities in these clusters were likely linked by internal worker migration in response to uneven economic development . In contrast , cities in regional clusters were more likely to have high rates of minorities and high natural growth rates than would be expected by chance ( p = 0 . 074; bootstrap sampling ) . Our findings suggest that multiple highly connected foci of measles transmission coexist in China and that migrant workers likely facilitate the transmission of measles across regions . This complex connection renders eradication of measles challenging in China despite its high overall vaccination coverage . Future immunization programs should therefore target these transmission foci simultaneously .
Measles is a highly contagious disease caused by the measles virus , a paramyxovirus , genus Morbillivirus . Before vaccine licensure in 1963 , the disease infected virtually all children . Infection typically causes fever , runny nose , cough , red eyes , and sore throat , followed by a rash spreading over the body . While most cases recover , complications range from diarrhea , otitis media , pneumonia , encephalitis , seizures to death [1 , 2] . Since the implementation of mass vaccination programs , the number of measles infections has declined dramatically and many countries have declared elimination of the disease [2 , 3] . However , measles remains a leading cause of death among young children in developing regions , despite immense public health efforts [2 , 4] . In 2014 , there were 114 , 900 measles deaths globally [5] . In addition , due to decreases in vaccination coverage and importation of cases through travel , the re-emergence of measles has become a concern in recent years for regions free of measles [2 , 3 , 6 , 7] . Prior to the introduction of measles vaccine in 1965 , China recorded an average of 3–4 million measles cases per year , with incidence rates ranging from 200 to 1500 per 100 , 000 population [8] . The country mandated measles vaccination in 1978 and achieved vaccination rates above 95% by 2008 [8–10] . As a result , the average measles incidence rate dropped precipitously to 6 . 8 per 100 , 000 population during 2000–2009 [8] . In 2006 , China committed to eliminate measles by 2012 . To this end , China conducted synchronized nationwide supplementary immunization activities ( SIA ) in September 2010 in addition to routine vaccination programs , which vaccinated 97 . 5% of the targeted population [8 , 9] . However , the measles elimination goal was not achieved . While the annual number of confirmed cases reached record lows of 9 , 944 in 2011 and 6 , 276 in 2012 , it resurged to 27 , 695 in 2013 , 52 , 656 in 2014 , and 42 , 874 in 2015 [11] . Theory predicts , and many observations have confirmed , that high vaccination coverage against measles ( ~90–95% ) will lead to lower outbreak frequency , irregular outbreaks , and eventual elimination [12–18] . The continuous large outbreaks in China prompt speculation on the accuracy of reported vaccination coverage . To access the population profile of measles susceptibility , a recent study [19] tested 2213 people in Tianjin , a major Chinese municipality with regular measles outbreaks [20]; during 2011–2015 , 87 . 8% tested positive for measles IgG antibody regardless of infection history and 91 . 8% were positive among those with a history of measles disease [5% ( 110 ) of the 2213 tested] . A multiplicative adjustment suggests an overall immunity of 95 . 6% , indeed above the 95% threshold . Such high measles immunity in the presence of the concurrent frequent outbreaks observed in China calls for improved understanding of measles transmission dynamics . Previous studies have repeatedly identified three key factors that shape the transmission dynamics of measles [21–27]: 1 ) demographics , particularly birth rates and vaccination coverage , which determine rates of susceptibility , 2 ) seasonal factors that affect human density and contact patterns , e . g . , epidemics during school-terms due to increased mixing among school-aged children in the pre-vaccination era , and 3 ) stochasticity inherent in dynamical systems , e . g . the community size should exceed a certain threshold ( i . e . critical community size ) to avoid extinction of transmission due to stochasticity . In this study , we focus on the geospatial characteristics of measles transmission and aim to identify the key population features that may be contributing to measles persistence in China . Using incidence and demographic data for all 344 cities in Mainland China , we analyze the spatiotemporal patterns of measles across China during the most recent decade and identify clusters and transmission paths of measles epidemics among the 344 cities . Our findings reveal key characteristics defining the cities with the most substantial disease burden . In addition , this study serves as a baseline analysis of measles dynamics in China prior to the implementation of the new nationwide two-child policy in 2016 , which may lead to a baby boom and further complicate measles elimination in the years to come .
Measles surveillance data were compiled from the China Information System for Disease Control and Prevention ( CISDCP ) [28] . The CISDCP system is a web-based real time disease reporting system , established in 2004 , that collects patient-case reports for all notifiable diseases , including measles , from all medical institutions in China . The system collects information on the age , gender , location of residence , and date of onset for each measles case . In the surveillance system , a suspected measles case was defined as any person with fever and rash and one or more symptoms of cough , coryza or conjunctivitis . For suspected measles cases , a serum specimen was collected and tested for measles-specific IgM or IgG , measles virus , or measles viral RNA . A confirmed case must meet one of the following criteria: 1 ) detection of measles-specific IgM in an acute serum specimen collected 3 days after rash onset , a ≥4 fold rise in measles-specific IgG based on testing of an acute serum specimen and a convalescent serum specimen , or a seroconversion between the two tests; 2 ) isolation of a measles virus or viral RNA; or 3 ) meet the clinical definition ( fever of 38 . 3°C or higher , a maculopapular rash , and cough , coryza or conjunctivitis ) and be epidemiologically linked to a confirmed case [9 , 29 , 30] . In this study , we restricted our analyses to confirmed cases . The dataset used included all confirmed cases reported from 1/1/2005 to 12/31/2014 . While the total number of incident cases was large in China , measles incidence was sparse for the majority of counties . Therefore , we aggregated incidence records to the prefectural city level ( i . e . 4-digit geo-division coding level ) [31]; there were 344 cities in total during 2005–2014 . As the generation time , i . e . the mean time interval between infection onset in a primary and secondary case , is approximately two weeks for measles [32] , we aggregated the incidence records to bi-week intervals . Data from the 2010 census in China [33] were used to produce the demographic profiles for each of the 344 cities . We utilized a number of demographic statistics , including 1 ) household registered population ( similar to local population ) and total population ( i . e . local residents plus migrants ) ; 2 ) percentages of urban , rural , or non-agricultural populations among the total population ( same denominator for other percentages unless stated otherwise ) ; 3 ) percentage of minority population ( i . e . any of the 55 non-Han ethnic groups ) ; 4 ) percentages of population aged 0–14 , 15–64 , or ≥65 yr; 5 ) birth rate , death rate , and natural growth rate ( i . e . birth rate minus death rate ) ; 6 ) average years of education and illiterate percentage of population aged ≥15 yr; and 7 ) population mobility data including percentages of migrants from the same county , other counties in the same province , or other provinces . These data were available at the county level and aggregated to the prefectural city level [31] to match the geospatial scale of measles incidence data . Because the population mobility data only recorded immigrants for each city but not emigrants , we used the difference between the total and local population size relative to the total population in each city as an indicator of net influx of migrants ( i . e . immigrants minus emigrants , normalized by dividing the total population size ) . This estimate is reliable because all cities in China employ a strict household registration system ( also known as ‘Hukou’ ) , which records all individuals who are local residents ( i . e . household registered population ) and those from outside cities [34] . We also compiled data regarding municipal socioeconomic development from the China Statistical Yearbook for 2010 [35] . Data for gross domestic product ( GDP ) and per capita GDP were available for 340 of the 344 cities . Another indicator of socioeconomic development is the composition of GDP . The China Statistical Yearbook for 2010 included the shares of GDP from primary ( i . e . agriculture and agriculture-related ) , secondary ( i . e . manufacturing ) , and tertiary ( i . e . service ) industries for 289 of the 344 cities . To visualize the spatial-temporal pattern of measles transmission among the 344 cities , we scaled the biweekly incidence records for each city by dividing the city-specific maximum during 2005–2014 . For each city , we computed the cumulative incidence ( i . e . the total number of reported incident cases ) and cumulative incidence rate ( i . e . cumulative incidence per 100 , 000 population ) . Cumulative incidence and cumulative incidence rate were then mapped to each city to visualize the burden of measles infection across China . Previous measles epidemiological studies [24 , 26 , 36] typically defined a “fadeout” event as zero measles cases reported for more than 2–4 weeks , and endemic transmission as ongoing transmission without any fadeout event . As the numbers of cases per bi-week in each city were in general low during our study period , we relaxed the definition of endemic transmission to allow one instance with no cases over 4 weeks ( i . e . 2 consecutive bi-weeks ) . We identified cities with endemic measles during Jan 2005 –Aug 2010 ( i . e . before the nationwide SIA ) and Jan 2013 –Dec 2014 ( i . e . after the resurgence of incidence ) , respectively . We identified clusters of measles transmission using hierarchical cluster analysis ( HCA ) per two procedures . The first was a traditional HCA per the complete linkage method [37] , which iteratively combines items being clustered based on similarities/dissimilarities between items . Here we used the absolute value of Pearson correlation coefficient ( r ) between the incidence time series as a measure of similarity . The complete linkage method combines groups of items based on the minimal similarity among all intergroup pairs , which results in more conservative clustering . This procedure was carried out using the “hclust” function in R ( https://www . r-project . org ) , and therefore referred to as “hclust” hereafter . As outbreaks occurring simultaneously in adjacent cities are likely parts of a single large outbreak , we developed an alternative HCA procedure to account for such regional clustering . In this procedure ( referred to as “recursive merging” hereafter ) , we first computed the Pearson correlation coefficient of incidence time series for each of the city pair combinations ( e . g . 344×343÷2 = 58 , 996 pairs for the first round of search ) ; we then aggregated the incidence time series of the city pair with the highest correlation coefficient if it exceeded a pre-specified threshold , one pair at a time , and treated the combined city pair as a cluster ‘city’ for the next search . This process was repeated until no more city/cluster pair had a correlation coefficient exceeding the threshold . For both HCA procedures , city pairs with a correlation coefficient ( r ) above 0 . 85 , an arbitrary threshold , were identified as within the same cluster . As a sensitivity analysis , we also tested clusters identified using thresholds of 0 . 80 and 0 . 90 . In addition , we visually inspected the epidemic time series for cities within each identified cluster to ensure the quality of the HCA . To reduce any artificial correlation due to concurrent low incidence following the nationwide SIA in September 2010 , we restricted this analysis to January 2005 through August 2010 for both HCA procedures . To examine the association between the burden of measles infection and population variables , we computed the Spearman’s rank correlation coefficient between the cumulative measles incidence rate over the entire study period ( i . e . 2005–2014 ) and each of the population variables ( see section “Demographic , population mobility , and socioeconomic Dataset” ) among all 344 cities . We used the Spearman’s rank correlation coefficient ( ρ ) , as the relationship between measles burden and population variable is nonlinear . To reduce the false positive rate in multiple comparison , we adjusted the p-values for raw correlations identified as significant at the 0 . 05 level , using the Holm-Bonferroni method [38] . China is presently comprised of 344 cities in 31 provinces , which are further apportioned among six regions ( S1 Fig ) . To test whether there were outstanding demographic or socio-economic characteristics among cross-regional city-clusters compared to regional city-clusters , we pooled cities identified as part of cross-regional clusters by the HCA ( i . e . clusters that include cities from two or more regions ) and those in regional clusters ( i . e . clusters that include only cities from the same region ) , respectively , and computed the Spearman’s rank correlation coefficient for these two categories . This analysis identified two key correlates—the net flux of migrants ( i . e . the difference between total and local population size ) and economic development—for cities in cross-regional clusters , and another two—the percentage of minorities and natural growth rate—for cities in regional clusters . We hypothesize that these two variable pairs , respectively , define different characteristics among cities in cross-regional v . regional clusters . To test this hypothesis , we used bootstrapping to examine whether the preponderance of these two variable pairs , respectively , would occur by chance alone . Specifically , two sets of 10 , 000 random samples with matching spatial structure were drawn from the whole 344-city dataset , one with 67 cities in each sample for comparison with cross-regional clusters , and one with 25 cities in each sample for comparison with regional clusters . For instance , a bootstrap sample matching the cross-regional clusters would include 12 subsamples , each with the same numbers of cities drawn from the same regions as in the 12 cross-regional clusters . This test thus controlled for spatial correlation among neighboring cities ( for both measles burden and population characteristics ) . For each set of random samples , we examined 1 ) the joint distribution of the net flux of migrants and per capita GDP ( an indicator of economic development ) and 2 ) the joint distribution of the percentage of minorities and natural growth rate . We used the portion of cities falling in the first quadrant and the third quadrant of the variable-pair plane , respectively , as a proxy characteristic for the joint distribution; for instance , for the plane of net flux of migrants and per capita GDP , those in the 1st quadrant have both variables higher than the national averages , while those in the 3rd quadrant have both variables lower than the national averages . We computed the p-value for the observed ( either the cross-regional or regional cluster ) based on its location , i . e . percentile , among the distribution of the bootstrapped samples .
A total of 652 , 852 confirmed measles cases were reported in China during 2005–2014 , of which 46 . 7% were confirmed by laboratory test and the rest by clinical diagnosis . Fig 1 shows the biweekly incidence , relative to the city-specific maximum , for each of the 344 cities in 31 provinces , six regions ( i . e . North , Northeast , East , South Central , Southwest and Northwest; S1 Fig ) during 2005–2014 . This ten-year period can be roughly divided into three phases of transmission: ( 1 ) January 2005 to August 2010 , a phase with annual outbreaks; ( 2 ) September 2010 to December 2012 , a lull phase following the nationwide SIA; and ( 3 ) January 2013 to December 2014 , resurgence of incidence . During the first phase ( i . e . before September 2010 ) , measles recurred regularly each year in the majority of cities , except for those in Northwest China ( i . e . Shaanxi , Gansu , Qinghai , Ningxia , and Xinjiang ) , Tibet and Hainan . The nationwide SIA effectively increased the vaccination coverage , which in turn halted transmission in most cities . During September 2010 to December 2012 , tallied over all cities and bi-week periods , measles cases were only recorded 20% of the time ( compared to 56% during January 2005 to August 2010 ) . However , it is evident that some cities in Xinjiang , Qinghai , Gansu , Shaanxi and Tibet still saw substantial numbers of cases ( orange and red colors in 2011 and 2012 in Fig 1 ) , suggesting that the SIA was less effective in those localities . Beginning in early 2013 , measles reappeared in a number of locations , including cities in the Western provinces Yunnan and Qinghai , as well as South Central China ( in particular , Guangdong and Hunan ) and East China ( in particular , Anhui and Shandong ) . By 2014 , this reestablishment has expanded to more cities in Northeast and North China . Not surprisingly , cities with larger populations , mostly located in eastern China ( S1 Fig ) , tended to report larger numbers of cases ( Fig 2A ) . These cities also experienced the most persistent transmission . During the first phase ( i . e . before the nationwide SIA in September 2010 ) , there were 14 cities with endemic measles transmission ( i . e . reporting cases in all months ) . Most of these cities were highly industrialized , populous cities located in eastern China , including Beijing , Tianjin , Suzhou , Nanjing , Shanghai , Wenzhou , Guangzhou , Shenzhen , and Huizhou ( Fig 2A ) . The rest were located in three less developed inland provinces ( Henan , Hubei , and Chongqing ) . During the third phase ( i . e . January 2013–December 2014 ) , there were 16 endemic cities ( Fig 2A and S4 Fig ) . Of these 16 cities , seven ( Beijing , Tianjin , Shanghai , Chongqing , Guangzhou , Shenzhen and Huizhou ) also had endemic measles during the first phase , and eight were located in Guangdong province , an industrialized southeastern province attracting the largest number of migrant workers [39 , 40] . We revisit this connection between measles endemicity and industrialization in subsequent sections . When examined on a per capita basis ( incidence per 100 , 000 people ) , a completely different picture emerges ( Fig 2B ) . Cities in the inland most provinces , i . e . Xinjiang , Tibet , Qinghai , and Sichuan , had the highest measles incidence rates . These inland western regions consistently reported higher incidence rates in all three phases of transmission during 2005–2014 ( S2–S4 Figs ) ; this difference was particularly evident during September 2010–December 2012 when measles incidence was at its lowest levels in China ( S3 Fig ) . This pattern indicates that , while the total numbers of cases were low , the burden of measles infection in these regions was much higher than the rest of China , and that these regions likely served as refuges for measles when transmission was halted elsewhere following the 2010 nationwide SIA . Given the geospatial heterogeneity in measles burden , it is of interest to see whether there existed a characteristic spatial pattern in the measles outbreaks . In particular , were there epidemic clusters in different regions that would allow for targeted intervention ? Further , were there cross-regional clusters that might have facilitated the spread of measles across China ? To answer these questions , we performed hierarchical cluster analyses ( HCA ) , using the correlation of measles incidence time series between cities as an indicator of epidemic synchrony and connectivity . The clusters identified by the two HCA methods are either identical ( e . g . C7-11 in Fig 3 ) or overlapping ( C1-6 in Fig 3 ) . However , our recursive merging algorithm , by design , captured larger and more cross-regional clusters ( e . g . , C1 and C2 in Fig 3A v . 3B ) . For either method , more city clusters were identified when a lower correlation coefficient was used ( S1–S3 Tables and Fig 3 and S5 and S6 Figs ) . Here we report clusters identified using our recursive merging method with r = 0 . 85 . Using the recursive merging HCA method , we identified 21 epidemic clusters ( S1 Table ) . Fig 4 shows four of the largest clusters . As shown by the inserted time series in Fig 4 , epidemic cycles were in concert within the same cluster but differed substantially by cluster . Nine of the 21 clusters were regional . These regional clusters tended to be small ( only 2–5 cities ) and most cities were located in inland and remote regions ( e . g . Fig 4D ) . The close vicinity of cities within each cluster may explain the epidemic synchrony , while their remoteness may explain the isolation and disconnection with cities outside the region . The remaining twelve clusters were cross-regional . These clusters tended to include more cities than the regional clusters ( 2–22 cities ) . In particular , the largest cluster included 22 cities located in five of the six regions ( C1 in Figs 3A and 4A ) ; this cluster captured the connection between the developed East- ( 9 cities ) and underdeveloped Northwest China ( 9 cities ) . Likewise , Clusters 3 , 6 , 7 , and 9 ( Figs 3A and 4C ) likely reflected connections between the underdeveloped inland North and West and the more developed East and South Central regions . These connections suggest that disparity in economic development may be an underlying driver for the synchronous epidemics observed in these cross-regional clusters ( we revisit this in the next section ) . Interestingly , only two of the 14 cities with endemic measles were captured in any of the clusters: Suzhou City ( in C6 of Fig 3A ) and Shanghai ( in C6 of Fig 3B ) . Even when a lower threshold was used ( r = 0 . 8 ) , only 6 endemic cities ( i . e . Shanghai , Nanjing , Suzhou , Guangzhou , Huizhou and Chongqing ) were identified among clusters . To identify key population characteristics that may be associated with measles burden , we examined the correlation between cumulative measles incidence rate and 23 variables related to 1 ) demographics , including population size and density , age structure , birth and death rates , ethnicity composition , and education level; 2 ) population mobility; and 3 ) socioeconomics , including gross domestic product ( GDP ) , GDP composition , and urbanization . Table 1 shows the distributions of the 23 variables along with their Spearman’s rank correlation with cumulative measles incidence rate . Among all 344 cities , variables that correlated significantly with cumulative measles incidence rate spanned all three categories ( i . e . demographics , population mobility , and socioeconomics ) . Among demographic variables , the percentage of 65+ yr olds ( ρ = −0 . 41 , p = 0 ) , death rate ( ρ = −3 . 0 , p = 1 . 28e-7 ) , percentage of minorities ( ρ = 0 . 28 , p = 1 . 63e-6 ) , local population size ( ρ = −0 . 18 , p = 0 . 0046 ) , and percentage of 15–64 yr olds ( ρ = 0 . 17 , p = 0 . 0086 ) were most statistically significant . The Spearman’s rank coefficients ( ρ ) for these variables indicate that cities with larger young adult populations ( 15–64 yr olds ) , larger minority populations , lower death rates , and smaller local population sizes tended to have higher measles burdens . Among population mobility variables , the difference between total and local population relative to total population ( i . e . net influx of migrants; ρ = 0 . 35 , p = 2 . 48e-10 ) , percentage of population migrated from other provinces ( ρ = 0 . 23 , p = 0 . 00015 ) , and percentage of population migrated from counties in the same province ( ρ = 0 . 14 , p = 0 . 029 ) were most statistically significant . These correlations indicate population migration was associated with measles outbreaks , with cities attracting larger numbers of migrants experiencing higher measles burdens . In addition , the highly significant correlation with long-range migration ( i . e . those from outside provinces ) suggests that population migration might link cities identified in the aforementioned cross-regional clusters . Among variables related to socioeconomic development , the share of GDP from agriculture and agriculture-related industries ( ρ = −0 . 28 , p = 1 . 29e-5 ) , the share of GDP from service industries ( ρ = 0 . 15 , p = 0 . 029 ) , and per capita GDP ( ρ = 0 . 13 , p = 0 . 029 ) were most statistically significant . These three correlations all indicate that economically more developed cities were more likely to experience higher measles burdens . This observation may appear counterintuitive , as more developed cities are more likely to have better public health infrastructure ( in particular , better immunization programs ) and one would expect a lower risk of measles transmission; however , developed cities are also those attracting the largest numbers of migrant workers , which , as shown above , were places with higher measles incidence rates . Taken together , the association of higher measles incidence rates with higher migration rates and greater economic development points to a connection with population migration driven by the disparity in economic development among regions in China . Such migration may have facilitated the spread of measles in industrialized cities and linked more developed regions in East China with less developed inland regions . This finding is consistent with the cross-regional clusters identified in Figs 3 and 4 , and with the finding that a large number of endemic cities are highly industrialized cities that attract large numbers of migrant workers . Different demographic and socioeconomic characteristics were associated with the cities identified in the cross-regional v . regional clusters . As shown in Table 2 , for cities in the cross-regional clusters , variables correlated significantly with measles incidence rate were two population mobility indicators ( i . e . net influx of migrants and migrants from outside provinces ) and one economic indicator ( i . e . share of GDP from service industries ) . In comparison , for cities in regional clusters , high measles incidence rates were significantly associated with 13 variables , broadly related to 5 demographic and socioeconomic features: 1 ) high percentages of minorities ( ρ = 0 . 75 , p = 0 . 00032 ) ; 2 ) high rates of population growth , including low percentages of elderly ( ρ = −0 . 86 , p = 3 . 3e-5 ) and high percentages of children ( ρ = 0 . 61 , p = 0 . 011 ) , low death rates ( ρ = −0 . 78 , p = 8 . 7e-5 ) , high natural growth rates ( ρ = 0 . 78 , p = 9 . 1e-5 ) , and high birth rates ( ρ = 0 . 48 , p = 0 . 049 ) ; 3 ) small population size and density , including small local ( ρ = −0 . 62 , p = 0 . 0098 ) and total ( ρ = −0 . 61 , p = 0 . 011 ) population size and low population density ( ρ = −0 . 85 , p = 3 . 3e-5 ) ; 4 ) slow socioeconomic development , as indicated by the low total GDP ( ρ = −0 . 61 , p = 0 . 011 ) ; and 5 ) high population mobility , including high net influx of migrants ( ρ = 0 . 52 , p = 0 . 032 ) and large numbers of migrants from outside provinces ( ρ = 0 . 6 , p = 0 . 011 ) . The larger number of significant correlates identified for regional clusters is not surprising , as cities in the same region tend to have more aligned measles epidemic dynamics , due to their closer spatial vicinity , as well as more similar demographic and socioeconomic characteristics . Further , we note that natural growth rates ( a combination of birth rate and death rate ) and percentages of minorities are highly positively correlated , as shown in Fig 5D . The joint distribution of net flux of migrants and per capita GDP and that of percentages of minorities and natural growth rates showed different characteristics for the two types of clusters ( Fig 5 ) . For cities in cross-regional clusters , the distributions of per capita GDP and net flux of migrants were highly dispersed—i . e . cities were from either highly developed or highly underdeveloped regions—and highly correlated , falling in the 1st and 3rd quadrants among all cities in China ( Fig 5A ) . In comparison , for cities in regional clusters , the joint distribution was narrower—i . e . cities were more clustered around the mean for economic development and migration—and less correlated ( Fig 5C ) . On the other hand , the distributions of minority population and natural growth rate ( i . e . birth rate minus death rate ) were more dispersed and highly correlated for cities in regional clusters , falling in the 1st and 3rd quadrants ( Fig 5D ) , but less so for those in cross-regional clusters ( Fig 5B ) . Based on the above findings , we hypothesized that different mechanisms underlie the connections among cities in cross-regional v . regional clusters . For the former , internal worker migration ( as indicated by the net flux of migrants ) , in response to uneven economic development , connects cities in cross-regional clusters and contributes to the epidemic synchrony among those cities . For the regional clusters , in addition to spatial vicinity , high percentages of minorities and natural growth rates favor similar fluctuation in susceptibility and thus epidemic synchrony among those cities . Indeed , bootstrap sampling analysis ( Table 3 ) , controlled for spatial structure , showed that the cross-regional clusters included a larger portion of cities with both low levels of net migrant-flux ( i . e . more emigrants ) and per-capita GDP ( p = 0 . 011 ) and a larger portion of cities with both low levels of minorities and natural growth rates ( p = 0 . 025 ) , suggesting that migration from economically less-developed cities to more developed regions may have linked cities across regions and facilitated cross-regional measles transmission . In contrast , the regional-clusters included a smaller portion of cities with both high levels of net migrant-flux and per-capita GDP ( p = 0 . 060 ) and a larger portion of cities with both high levels of minorities and natural growth rates ( p = 0 . 074 ) , suggesting that more cities in regional clusters were minority-dominant and less connected to outside regions ( via migration ) .
While reported vaccination rates have been above 90% during the last decade , China continues to document a substantial number of measles cases . Using prefectural city level incidence and demographic data , our study maps the geospatial distribution of measles incidence and incidence rates across China during the past decade . Further , using two approaches—hierarchical clustering analysis and Spearman’s rank correlation—our study reveals the key population characteristics associated with cities that experienced a higher burden of measles infection as well as differing characteristics associated with cross-regional v . regional epidemic clusters . Among the 344 Chinese cities , large population and economic centers not only reported the largest numbers of measles cases , but also recorded cases more often , i . e . had more bi-weeks with nonzero reported cases . We identified 14 cities with endemic measles transmission during January 2005 –August 2010 , of which 10 ( Beijing , Tianjin , Shanghai , Suzhou , Nanjing , Wenzhou , Guangzhou , Shenzhen , Huizhou and Wuhan ) are among the most economically developed [per capita GDP: 74252±23536 Chinese Yuan ( CNY ) v . 31503±23489 over all cities] . Even when evaluated on a per capita basis , these cities still had incidence rates significantly higher than the national average ( 82 . 5±37 . 8 v . 46 . 3± 41 . 0 per 100 , 000 over 2005–2010; p = 0 . 0038 ) . Our analyses of measles epidemic clusters and their population characteristics consistently identify internal migrant movement as a factor contributing to the higher measles burden in these industrial cities . Due to disparities in economic development , each year , over 100 million Chinese leave their hometowns , predominantly in inland regions , to work in big cities , primarily located in eastern China ( e . g . Beijing , Tianjin , Shanghai , Jiangsu , Zhejiang , and Guangdong ) [41] . This massive population movement likely replenished the measles susceptible pools in these industrial cities , fomented outbreaks with higher incidence rates and larger numbers of cases , and contributed to the persistence of measles in those cities . Indeed , recent outbreak investigations indicated that migrant workers were disproportionally infected in big cities [42–44] . Further , our analyses indicate that internal worker migration likely linked measles outbreaks in cities across China . We identified 12 cross-regional epidemic clusters with highly synchronous measles outbreaks; in particular , six of the clusters linked inland regions with eastern China . Among cities identified in these cross-regional clusters , long-distance migration rate was significantly correlated with measles incidence rate ( ρ = 0 . 34 , p = 0 . 011 , Table 2 ) . In addition , while information on the origin of migrants was not available , using the difference in total and local population as a proxy for net influx of migrant , we showed that cities in the cross-regional clusters had either large numbers of immigrants ( i . e . positive net influx ) or large numbers of emigrants ( i . e . negative net influx , Fig 5A ) . It is likely that megacities served as mixing grounds for migrant populations from different regions in China; and the seasonal return of those migrant workers to their hometowns might have facilitated the reintroduction of measles to rural communities . This putative mechanism is also consistent with the timing of the spring outbreaks observed over our study period [9] , as worker migration typically surges round the Chinese New Year ( between late January and late February ) . Further investigation into this potential source-sink interaction and feedback as well as its role in cross-regional transmission and persistence of measles in China is warranted . Remarkably , the identified clusters did not include most of the industrial cities that experienced endemic measles prior to the 2010 nationwide SIA . Mega-industrial cities tend to attract migrant workers from a larger number of locations across China and as a result , the measles epidemic cycle in each city is likely influenced by multiple sources and does not resemble that of one particular source . In addition , those endemic cities did not have synchronous epidemics with one another , and were likely each a transmission hub . For instance , the three endemic cities in Guangdong—Guangzhou , Shenzhen and Huizhou—had asynchronous epidemic cycles despite their close vicinity and high connectivity ( r = 0 . 39 , 0 . 44 , and 0 . 57 , well below the r = 0 . 85 HCA threshold ) . Such asynchrony in endemic cities also creates challenges for eliminating measles in China . Another key population characteristic is the association of high measles burden with a high percentage of minorities , in particular , among cities identified within regional clusters ( Table 2 ) . Minority-dominated cities tend to be located in inland regions and are less connected to outside regions , which likely explains the regional clustering . The reason for the higher measles burden is likely two-fold: 1 ) immunization programs were likely less stringently or effectively implemented in these cities and 2 ) as shown in Fig 5D , these minority-dominated communities tended to have higher birth rates . As a result , these populations were likely to have larger fractions of susceptible persons and to be vulnerable to larger outbreaks should measles infection be introduced from an outside community . These characteristics may have supported the rare , larger outbreaks recorded in some cities , e . g . those in Xinjiang province ( Fig 4D ) . Enhanced vaccination implementation is thus needed in these regions with specific targeting of minority populations . On a related note , high birth rates were only found to be associated with high measles incidence rates among cities in regional clusters ( ρ = 0 . 48 , p = 0 . 049 , after adjusting for other correlates; Table 2 ) , but not among all 344 cities ( ρ = −0 . 03 , p = 0 . 59 ) nor among those in cross-regional clusters ( ρ = −0 . 01 , p = 0 . 97 ) . Birth rate is an important factor in measles epidemiology , as it affects the recruitment rate of susceptible individuals and in turn epidemic frequency [13 , 45 , 46] . The absence of a significant rank correlation over the whole 344-city dataset may be partly due to China’s one-child policy implemented during 1979–2015 , which led to low nationwide birth rates [47 , 48] . It is interesting to note that minorities were allowed to have 2–3 children under China’s one-child policy [47] , and many of the cities with highest incidence rates had large minority populations ( Fig 5D ) . As China has adopted its new two-child policy in 2016 [49] , birth rates may increase in other inland regions . New baby booms in these regions may create further challenges for the elimination of measles . We recognize a number of caveats in this study . First , we do not have city-level measles immunization coverage data to assess the relationship between measles burden and measles vaccination rates . In addition to the synchronized nationwide SIAs in 2010 , multiple subnational SIAs ( typically at the provincial level ) were conducted prior to 2010 [50] , which might have increased the epidemic synchrony among cities undergoing the same SIAs . However , the impact of such forced synchrony is likely small . The majority of cities in China experienced outbreaks annually prior to 2010 ( Fig 1 ) , suggesting that those subnational SIAs were less effective . In addition , most cross-regional clusters identified here included only a small fraction of cities from each province ( S1 Table ) . Should province-specific SIAs be the determinant of epidemic synchrony , more cities within the same province would have been included in the same cluster . Nevertheless , future work should investigate the impact of subnational SIAs on epidemic dynamics when city-level vaccination data become available . Second , while the CISDCP surveillance system includes nearly all medical institutions in China [28 , 51] , underreporting may still exist and reporting rates may vary by city and time . In particular , rural areas likely have less developed health facilities and higher underreporting rates [52]; reporting rates were also likely lower in 2012 for all cities , as China strived to meet the measles elimination goal set for that year . Third , we do not have information on the ethnicity of each measles case . While our analyses indicate significantly higher incidence rates in minority-dominated cities , further studies are warranted to investigate whether these infections indeed occurred within minority groups . Similarly , we do not have information on migration between specific cities or the occupation of each measles incident case ( i . e . whether a case is a migrant worker ) ; as such , our analyses are ecological ( i . e . based on population level variables ) . Further investigations with such detailed information are needed to establish a more direct connection between internal worker migration and the transmission of measles across cities . Finally , we focused on the geospatial characteristics of measles outbreaks and sidestepped other key issues related to measles transmission dynamics , such as the seasonality and age profile of measles cases . Studies to investigate these issues are underway . In summary , our analysis of the geography of measles demonstrates that multiple endemic cities co-exist in China , and that these cities are predominantly industrial cities with large numbers of migrant workers , whose movement likely facilitates the spread of measles across regions in China . In addition , cities with large minority communities and inland , underdeveloped cities likely have lower vaccination rates and are more vulnerable to the resurgence of measles . Measles has reappeared in many cities in China since 2013 . Our findings suggest that elimination of measles remains challenging in China due to the heterogeneity in vaccination implementation and complex connections among regions . | Measles is a highly infectious disease . Eradication of measles can nevertheless be achieved with vaccination of 90–95% of a population , as shown in theory and practice . In China , however , measles continues to infect thousands of people each year despite vaccination coverage above 95% . This conundrum challenges measles elimination in China and worldwide . Here we characterize the geospatial distribution of measles and epidemic connections among cities across China . Using incidence data reported during 2005–2014 for all 344 cities in China , we show that the municipal burden of measles differed substantially and some cities were highly connected and experienced synchronous outbreaks . We identify 14 cities that experienced endemic transmission during 2005–2010 , and 21 transmission clusters , including 6 cross-regional clusters that link the less developed inland regions and the industrial east . We find that three transmission foci coexist in China—cities with large minority populations , inland cities with more emigrants , and mega industrial cities hosting more immigrants—and that migrant workers , connecting the latter two foci , likely facilitate measles transmission across regions . This complex connection , along with the differing disease burden among cities , renders measles elimination challenging in China despite the high overall vaccination rate . Future immunization programs should therefore target these three foci . | [
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"rna... | 2017 | Geospatial characteristics of measles transmission in China during 2005−2014 |
Cell polarization toward an attractant is influenced by both physical and chemical factors . Most existing mathematical models are based on reaction-diffusion systems and only focus on the chemical process occurring during cell polarization . However , membrane tension has been shown to act as a long-range inhibitor of cell polarization . Here , we present a cell polarization model incorporating the interplay between Rac GTPase , filamentous actin ( F-actin ) , and cell membrane tension . We further test the predictions of this model by performing single cell measurements of the spontaneous polarization of cancer stem cells ( CSCs ) and non-stem cancer cells ( NSCCs ) , as the former have lower cell membrane tension . Based on both our model and the experimental results , cell polarization is more sensitive to stimuli under low membrane tension , and high membrane tension improves the robustness and stability of cell polarization such that polarization persists under random perturbations . Furthermore , our simulations are the first to recapitulate the experimental results described by Houk et al . , revealing that aspiration ( elevation of tension ) and release ( reduction of tension ) result in a decrease in and recovery of the activity of Rac-GTP , respectively , and that the relaxation of tension induces new polarity of the cell body when a cell with the pseudopod-neck-body morphology is severed .
Cell polarity , a cell state with an asymmetric distribution of specific molecules and organelles along a geometric axis ( ‘front to back” ) in cell morphology [1–4] , is essential for various kinds of cell functions , including migration [3] and asymmetric division [4] . For example , cell polarization mediates cell migration and the protrusions that extend in direction of migration [3] . Cell polarity involves both chemical systems and mechanical systems [4] . The chemical systems include the cytoskeleton , surface receptors , and polarity proteins [5 , 6] . Among the chemical systems , GTPases ( Rho proteins ) play key roles in cell polarization during cell movement [7] . Activated Rac ( Rac-GTP ) mainly concentrates at the leading edge of the polarized cell [3] . Moreover , it induces actin polymerization and protrusions at the leading edge [7 , 8] . The mechanical factors consist of the force and stress properties of the extracellular matrix ( ECM ) , cytoskeleton and membrane [4] . For instance , ECM geometry determines the direction of cell polarity [9] . Stochastic actin shell rupture induces the formation of one leading edge due to the relaxation of tension [4] . The global actin cytoskeleton interacts with the membrane and modulates membrane tension [10 , 11] . Furthermore , the chemical system is often coupled with mechanical factors during cell polarization . For example , mechanical stress down-regulates lamellipodia formation by inhibiting Rac [12 , 13] . Houk and coworkers showed how membrane tension regulated cell polarity in HL-60 cells [14] . As shown in their aspiration-release experiment , Rac-GTP activity decreased upon aspiration and subsequently recovered after release . In the severing experiment , HL-60 cells formed a tethered morphology with a pseudopod , a long and thin neck and a cell body following brief heat shock . The long , thin neck severely restricted the diffusion-based communication between the pseudopod and the cell body . Surprisingly , the cell body grew new protrusions in tens of seconds if the neck was cut . This result contradicted the common assumption that cell polarity was generated by inhibitors diffusing from the polarized front , suggesting that membrane tension might be the long-range inhibitor [14] . A series of mathematical models of cell polarization have been proposed as a special case of pattern formation in biological systems [15] . Heterogeneous spatial patterns were suggested to arise from simple reaction-diffusion systems in the pioneering paper by Turing [16] . Meinhardt applied a generic reaction-diffusion model to form polar structures induced by local activation balanced with global inhibition [17] . Another method using a local excitation , global inhibition ( LEGI ) model was proposed by Levchenko and Iglesias [18] . The common assumption of these models is that cell polarity is generated by the interaction between two types of molecules: self-activated , slow diffusing molecules and global inhibiting , fast diffusing molecules . Notably , the wave pinning ( WP ) model provides a minimal reaction-diffusion system with bi-stable kinetics to pin the waves into a stable polar distribution [19 , 20] . This model is based on the exchange between the active , membrane-bound form and inactive , cytosolic form of an important polarity protein , Rac [3] . These models , however , neglected the effects of mechanical factors . Moreover , the models addressing mechanical systems of polarity mainly studied the interaction between membrane tension and F-actin [21 , 22] but lacked the interaction between membrane tension and the upstream network in chemical reaction systems . To the best of our knowledge , very few studies have attempted to integrate both chemical reactions and mechanical systems . Previous models are insufficient to explain the aspiration-release experiment and severing experiment [14] . Here , we present a mechano-chemical model of cell polarity by incorporating membrane tension with reaction-diffusion systems . Based on the WP model , we combine the feedback between Rac and F-actin with the repression of actin polymerization by membrane tension [4 , 23–25] . The model generates stable polarity once stimuli exceed certain thresholds . Moreover , polarity is reversed or steered by new stimuli . According to the simulations , membrane tension affects the polarization time and the sensitivity to the attractant , and higher membrane tension leads to better robustness and stability of cell polarization in response to random perturbations . Consistent with the model , the single cell experiment conducted using CSCs and NSCCs indicates that the former has lower membrane tension and subsequently tends to undergo spontaneous cell polarization and change directions . Moreover , for the first time , our mechano-chemical model explains the results of the aspiration-release and severing experiments [14] .
We establish a minimal cell polarity model incorporating the interactions between Rac-GTP , Rac-GDP , F-actin and membrane tension ( Fig 1a ) . The conversion between Rac-GTP and Rac-GDP is formulated by adopting the WP model ( Equations 1 and 2 ) . The feedback loop between F-actin and Rac-GTP is complicated and regulated by various enzymes [24 , 26] . The negative feedback from F-actin to Rho GTPase ( such as Rac-GTP ) was incorporated in the WP model; a variety of patterns from static polarization to actin wave formation are observed as the feedback strength increases [27] . Moreover , positive feedback from F-actin to Rac-GTP has also been proposed to exert a gradient-amplifying effect . This hypothesis has been confirmed in multiple experiments [24 , 26 , 28–32] . A Hill function ( Equations 1 and 2 ) is applied in the model to explore the effect of this positive feedback on cell polarization . In addition , we assume that effective membrane tension ( mt ) functions as a global negative regulator to attenuate F-actin polymerization since the equilibrium of the force on the membrane is on the time scale of milliseconds , which is much faster than the diffusion-based chemical reaction [11] . For simplification , we also assume that mt is a function of the total amount of F-actin ( Equation 3 ) as membrane tension is usually primarily determined by the membrane-associated cytoskeleton ( actin cortex ) instead of the plasma membrane itself [33] . Thus , F-actin effectively has a negative feedback effect on itself and on Rac-GTP activation . Instead of treating the cell as a projection of the membrane and cytoplasm on one line or plane , similar to the traditional one- or two-dimensional ( 2D ) cell polarity models [19 , 20 , 34 , 35] , here , we propose a phase field model , which has been widely used to model vesicle bio-membranes [36] and cell motility [37–39] . By introducing the phase field function to distinguish the interior of the cell from the exterior , the membrane position is naturally determined by the diffuse layer of the phase field function ( Equation 4 ) . This function allows the model to account for the different positions of Rac-GTP on the cell membrane and Rac-GDP and F-actin in the cytosol ( Fig 1a and Equations 5 , 6 and 7 ) . We also use an alternative approach that incorporates a traditional 2D cell polarity model coupled with membrane tension to test the robustness of the mechano-chemical mechanism . We assume this 2D cell presents the projection of a 3D cell on one plane; hence , the cell membrane overlaps with cell cytosol . The regulation of F-actin by membrane tension is described by applying the Brownian ratchet model [25] ( S1 Equation ) rather than Hill functions ( see S1 Text ) . We first confirm that both models are able to capture the common features of cell polarization shown in previous models [19] . First , cells are observed to spontaneously polarize in response to noise , i . e . , a random distribution of stimuli ( S1a and S1b Fig ) and in response to gradients ( Fig 1b and 1d and S1c and S1d Fig ) . Rac-GTP and F-actin mainly concentrate at one end of the cell after cell polarization ( Fig 1b and 1d , and S1 Fig ) , whereas Rac-GDP is nearly evenly distributed across the cell with a concentration of v0 due to its greater diffusion rate . The maximum and minimum concentration of Rac-GTP are the two stable solutions uH and uL for ( ∂u / ∂t = 0 ) when v equals v0 . Second , in the experiments , cells sustain their polarity without external stimuli . Indeed , the asymmetric distribution of Rac-GTP is maintained after the stimulation is removed in our simulations . Third , consistent with the experimental results showing that a polarized cell could redirect its movement in a different direction in response to a new stimulus [19] , the polarized cell could shift its polarity by 90 degrees ( S1 Movie ) or reverse its polarity when it is treated with a transient stimulus presented from another direction . Finally , in response to two simultaneous stimuli with different amplitudes , two polarization fronts initially form in the cell , and subsequently , the front triggered by the stronger stimulus finally ‘absorbs’ the other front ( S2 Movie ) . More importantly , according to our mechano-chemical model , membrane tension strongly influences the spatiotemporal characteristics of the cell polarity . Regarding the temporal characteristics , the polarization time ( which is defined as the duration from the initiation of the stimulus to the time when the concentration of Rac-GTP reaches a higher steady value uH ) increases as membrane tension increases in response to the same stimulus ( Fig 1c ) . Regarding the spatial characteristics , a lower density of Rac-GTP disperses in a larger area in cells with lower membrane tension ( Fig 1b and 1d ) , i . e . , polarized cells with higher membrane tension have sharper fronts . The Rac-GTP concentration may never reach uH if membrane tension is above a certain value in response to the same stimulus , suggesting the existence of a threshold of the amplitude ( ksamp ) and duration ( ksdur ) of the stimulus . Hence , we simulated the Rac-GTP and F-actin dynamics at a specific membrane tension by fixing the duration and varying the amplitude ( S2a Fig ) . Indeed , the cell is only able to exhibit stable polarity as the maximum concentration of Rac-GTP increases towards the higher stable value uH if the amplitude is sufficient . However , when the amplitude is below a certain value , the maximum concentration of Rac-GTP gradually decreases to the lower stable value uL after transiently increasing to a value below uH , indicating that the cell does not polarize under this condition . The F-actin results are similar to the Rac-GTP results . The simulation results are similar to the content discussed above when we vary the durations while fixing the amplitudes ( S2b Fig ) . We further calculated the thresholds for the amplitudes and durations for cells with different membrane tensions . Under the same duration ( amplitude ) of stimulus , the amplitude ( duration ) threshold increases as the membrane tension increases ( S2c and S2d Fig ) . We also calculated the stimulus threshold by varying both the amplitude ( ksamp ) and duration ( ksdur ) for mt0 from 0 . 2 to 1 ( Fig 2a ) . The ksdur vs . ksamp curve shifts away from the origin as membrane tension increases . Thus , cells with lower membrane tension respond to weaker stimuli polarize , consistent with our hypothesis that membrane tension serves as a global inhibitor of cell polarization . As predicted , cells with lower membrane tension have a higher tendency to polarize ( Fig 2b , left ) in response to the same random stimuli ( Equation 9 ) . We tested the prediction of this model by measuring the differences in cell polarization in CSCs and NSCCs ( Fig 2b , middle ) . The Golgi was aggregated in CSCs and NSCCs sorted from MCF-7 cells ( Fig 2c ) , which are known to show dispersed Golgi [40] , and we confirmed that the polarized distribution of Golgi was highly correlated with the cell migration direction ( S3 Fig ) . Furthermore , the initiation of cell polarization triggers the restricted localization of the Golgi at the front side of the polarized cell , and , in turn , secretion from the Golgi toward the proximal plasma membrane domain helps to maintain cell polarity [41] . In addition , the morphology and position of the Golgi are importantly related to the accumulation of F-actin ( cell protrusion ) in migrating cells [42] . Hence , for the MCF-7 cells in our experiment , the morphology of the Golgi served as a surrogate for the usual cell polarity markers , such as the distribution of Rac or F-actin . Of the cells grown on circular ECM patterns without any inducer gradients ( S3 Fig ) , the proportion of polarized CSCs is 77 . 3±5 . 7% ( mean±standard deviation from 3 measurements , the number of cells in each experiment is N = 53 , 75 and 81 ) , more than a two-fold increase compared with the polarized NSCCs ( 33 . 7±6 . 9% , N = 57 , 83 and 95 ) ( Fig 2b , middle ) . Thus , CSCs undergo spontaneous polarization more easily than NSCCs . Based on the prediction of our mechano-chemical model , CSCs would have a lower membrane tension than NSCCs . Therefore , we examined the membrane tension of CSCs and NSCCs using a cell deformation device . As the cell shape reaches the stable state under an applied electric field , NSCCs show little change in shape , but the CSCs exhibit significant deformation ( Fig 2d ) . The relative elongation ( at=15s−at=0at=0×100% , a denotes the cell length along the direction perpendicular to the edges of the electrodes ) of CSCs is 14 . 9±5 . 6% ( N = 10 ) , which is much greater than the value for the NSCCs ( 3 . 7±3 . 1% , N = 13 ) . Moreover , the elongation index ( EI=a−ba+b×100% , where b represents the cell length along the direction parallel to the edges of the electrodes ) is 13 . 3±2 . 4% for CSCs compared to 5 . 9±2 . 8% for NSCCs . Based on these results , Young’s modulus of CSCs is less than the value for the NSCCs [43] . Hence , the membrane tension of CSCs is less than the NSCCs , as membrane tension is proportional to Young’s modulus [44] . We further performed a test experiment to determine whether the proportion of the polarized CSCs will decrease if cell membrane tension is reduced . The addition of myosin inhibitors [12] , stretching the elastic substrate to which the cells attach , or aspirating cell membrane with a micropipette [12 , 14] can modify membrane tension . We chose to alter membrane tension by varying the osmotic pressure [13] , a convenient way to modulate a population of cells . As the CSCs were immersed in hypotonic medium ( DMEM/F12 diluted with the same volume of double distilled water ) , their membrane tension is expected to increase; hereafter , these cells are called CSC-hyper [13] . The proportion of polarized CSCs decreases from 93 . 4±4 . 0% ( N = 80 , 71 and 40 ) in iso-osmotic DMEM/F12 medium to 56 . 2±9 . 3% in hyperosmotic medium ( N = 66 , 68 and 46 ) ( Fig 2b , right ) . These experimental results are consistent with the prediction of the model that higher membrane tension decreases the sensitivity of cells’ polarity in response to stimuli . Moreover , the proportion of polarized CSCs in DMEM/F12 is greater than the proportion in DMEM with 10% FBS ( Fig 2b , middle ) ; hence , factors other than membrane tension also affect the polarization . More experiments , such as manipulating F-actin polymerization with drugs , are required to validate these findings and exclude the effects of other factors . Because higher membrane tension requires a greater stimulus to polarize a cell , we expect that high membrane tension , in turn , will stabilize polarization in response to perturbations . We tested this hypothesis by determining how the cell responds to random stimuli . Stimuli with random amplitudes and durations in random direction are added to the simulations after the cell polarizes . Then , we compare the number of the times the stimulus redirects ( ns ) and the number of the times the polarity redirects ( np ) . If the initial membrane tension is higher , polarity is redirected less frequently ( average np / ns = 0 . 59±0 . 07 ) comparing with the cells with a lower initial membrane tension ( average np / ns = 0 . 80±0 . 10 ) ( Fig 3a ) , suggesting that high membrane tension improves the stability of the polarized cell . We also tested the predictions of this model with single cell measurements and confirmed that the polarity of CSCs varies more easily than the polarity of NSCCs . Polarized cells could change the orientation of their polarity on circular ECM patterns ( Fig 3b ) . We measured the distributions of the angular displacements of CSCs and NSCCs in one hour ( Fig 3c , top ) . We define the rotation angle in 5 minutes as the angular speed . The angular displacement is the total angular speed observed in one hour . According to the distribution of angular displacements , the direction of polarized CSCs varies to a greater extent than the polarized NSCCs and CSC-hyper ( Fig 3c , bottom ) . In fact , most polarity vectors of NSCCs only vibrate around the initial positions . Hence , the polarity of CSCs changes more easily than NSCCs , i . e . , CSCs are more sensitive to perturbations . If the perturbation is from the external environment , CSCs could shift their polarity toward the direction of the perturbation or the source of attractant in a more timely manner , which is critical for metastasis . We further conducted simulations to explain the experimental results reported by Houk and coworkers , which have not yet been mimicked by the conventional reaction-diffusion systems . First , according to the aspiration-release experiment , the activity and concentration of Rac-GTP are reduced upon aspiration and sequentially recovered upon release [14] . To numerically implement this experiment , we assume that the process of aspiration and release occurs instantly , as the details of the tension changes are unclear . As shown in the simulation , if membrane tension is increased upon aspiration , the concentration of Rac-GTP in polarized cells decreases in approximately 50 s ( Fig 4a ) . Subsequently , the relationship between membrane tension and F-actin is resumed when aspiration is released . Approximately 150 s are required for the cell to recover the activity of Rac-GTP . The traditional cell polarity model coupled with membrane tension shows similar results , albeit a slight delay for the cell to recover the activity of Rac-GTP ( Fig 4b ) . Thus , our model closely captures the characteristics of the cell that loses and regains its polarity in the aspiration-release experiment . Our model also successfully solves another puzzle , the pseudopod-neck-body morphology severing experiment . In the simulation , cell occurs in the tethered morphology ( Fig 4c , top ) . F-actin is evenly distributed in the rear cell body at a very low concentration . Since the nonpolarized rear cell body cannot exhibit new polarity without an uneven distribution or perturbation from the internal or external environment , we maintained the random stimuli ( production of Rac-GTP , Equation 9 ) below a certain threshold throughout the simulation . Although this perturbation does not destabilize the polarized cell with a tethered morphology , it does induce polarity , i . e . , asymmetric distribution of F-actin ( Fig 4c , bottom ) , in the nonpolarized rear cell body upon severing when membrane tension of the cell body is reduced to preserve the relationship with the F-actin concentration . In contrast , the cell body does not reanimate if membrane tension is maintained at the same levels before and after severing ( S4 Fig ) . These results were also reproduced with the traditional cell polarity model coupled with membrane tension ( Fig 4d ) . Thus , for the first time , our model recapitulates the phenomena observed in the pseudopod-neck-body morphology severing experiment .
The key of the proposed model for cell polarity is the successful coupling of the chemical systems and membrane tension based on the molecular interactions . In particular , we choose F-actin to link membrane tension and Rac , the key molecule regulating cell polarity . On one hand , we incorporate F-actin polymerization into the Rho GTPase dynamics , based on the positive feedback loops between F-actin and Rac-GTP ( Fig 1a , [4 , 24] ) . On the other hand , we couple membrane tension with the F-actin polymerization dynamics [25] . Although more detailed gene regulation and protein interaction mechanisms are neglected [24] , this minimal model successfully captures the general features of cell polarity , similar to the other models [19] . For example , our model utilizes the stable polarized state , the threshold of stimuli for polarization , and responses to new stimuli . In addition , the stimulus with greater strength increases membrane tension to a greater extent in our new model; consequently , the final stable polarity state has a sharper front ( Fig 1d ) . However , in the WP model , the final polarized state is the same once the stimulus exceeds a certain threshold [19] . Furthermore , the proposed model overcomes the limitations of the previous cell polarity models , which are only based on reaction-diffusion systems [18 , 20 , 45] or mechanical systems [21 , 22] . This model allows us to explore the effects of membrane tension on cell polarity in simulations . For example , membrane tension affects not only the spatial profile of cell polarity but also the sensitivity of the cell to external stimuli . One of the most important achievements of this proposed model is that it recapitulates the aspiration-release experiment and the pseudopod-neck-cell body morphology severing experiment for the first time [14] . Physical forces , such as effective membrane tension and the components regulating cell polarity , form a web of reciprocal interactions [4] . In addition to the interactions formulated in our current model , other possible interactions could also be explored [1 , 4] . Membrane tension has been suggested to directly feed back onto the kinetics of polarity-regulating proteins , e . g . , membrane tension and curvature are sensed by Bar domain proteins and feed back onto the Rac-GTP activation/deactivation rates [33] . For simplification , the current model neglects the effects of cell shape and the curvature of the membrane on cell polarity [46–48] . Moreover , we assume that membrane tension is homogeneous , although there may be inhomogeneous membrane tension [33 , 49] . Furthermore , the dependence of membrane tension on the F-actin concentration may be more complicated than the simple linear relationship formulated in Equation 7 . All these factors described above will be naturally incorporated into our cell polarity model with phase field formulation in future studies to further improve our model . In addition , if we consider that increasing contacts between cells and the extracellular matrix could increase the stiffness and viscosity of the actin cytoskeleton [50] , the model could be extended to studies of the dynamics of cell polarity by coupling chemotaxis and mechanotaxis . From the point of view of the free energy landscape , cell polarization is regarded as an inter-conversion between the nonpolarized state and the polarized state . Since membrane tension affects the threshold of the polarization stimulus ( Fig 2 ) , we naturally postulate that membrane tension modulates the height of the potential barrier between the two states ( S5 Fig ) . The stimulus acts as a driving force by elevating the nonpolarized state to a high-energy state . Only sufficient stimulation above a threshold will help the cell conquer the potential barrier and achieve the polarized state . Although we have not defined the accurate height of the potential barrier because the proposed reaction-diffusion model is not a gradient system [51] , one may still be able to construct the quasi-potential and qualitatively estimate the barrier height by calculating the strength of the stimulus , i . e . , the product of the amplitude and the duration of the stimulation . The curve of the threshold duration for different amplitudes of stimulus under stable membrane tension ( Fig 2a ) exhibits a hyperbola-like profile . The product increases 1 . 8-fold as mt0 increases from 0 . 2 to 1 pN/μm . Based on our model , cells utilize an extra parameter , membrane tension , in addition to the biochemical reaction parameters to tune the sensitivity and persistence of cell polarity . Cells must achieve a delicate balance between the sensitivity of detecting the external gradient and the persistence of the directed migration during chemotaxis to adapt to the complex environment . Cells with a low membrane tension have the advantage of detecting the low external gradient of an attractant . Subsequently , the increase in membrane tension during polarization could prevent the cell from reacting to other perturbations with the same strength ( S5 Fig ) . One advantage would be to avoid the formation of multiple polarization fronts , which often leads to a time-wasting elimination of the extra fronts [20] . The other advantage would be to maintain persistent cell movement in one direction until new polarity forms in response to a stronger stimulus [52] . Both features indicate a better search strategy for cells . Interestingly , this search strategy might have been exploited by CSCs , which are believed to be responsible for tumor metastasis [53] . In addition to resistance to radiotherapy and chemotherapy [54] , CSCs exhibit greater migration and deformation [55] than NSCCs . Since deformation is related to the cytoskeleton , among which F-actin is the most resistant protein to deformation [56] , and the actin cytoskeleton is the main determinant of membrane tension [33] , CSCs may have a lower membrane tension . Indeed , we confirmed that CSCs deform more easily than NSCCs in the single cell deformation measurements . Based on our model , CSCs exhibit higher polarization sensitivity and a better searching strategy during migration . This prediction was further confirmed in the single cell experiment showing that the proportion of spontaneously polarized CSCs is increased more than two-fold compared with the NSCC . These results may facilitate the development of a new therapeutic strategy for tumor metastasis by targeting the signaling pathways regulating the membrane tension of tumor cells . Mechanical factors have been increasingly appreciated for the significant roles they play in diverse cellular processes by interacting with chemical systems . For example , advection and stress affect the spatial patterns in morphogenesis [57] . Tension activates ion channels on the membrane [33] . The stiffness of the substrate plays significant roles in differentiation [58] . To fully understand these biological systems , researchers must surpass the reaction-diffusion models and establish coupling models that integrate both chemical reaction and mechanical systems . Our proposed model in this study provides a minimal system that incorporates membrane tension into biochemical polarization systems . This framework could also be naturally extended to explore the other complex biological networks .
The cell is postulated to exhibit a round shape Ω0 with a radius R = 10 μm , which is of the actual size and is contained in a larger computational domain Ω with the size of 40 μm *40 μm . We extend the WP model to describe the inter-conversion between active Rac-GTP on the membrane ∂Ω0 and inactive Rac-GDP in the cytosol Ω0 , as well as their interactions with F-actin polymerization in the cytosol Ω0 ( Equations 1 , 2 and 3 ) . The concentrations of Rac-GTP , Rac-GDP and F-actin are denoted as u , v and f , respectively . Du , Dv and Df are diffusion coefficients with Du < Df ≪ Dv . The basal conversion rate from Rac-GDP to Rac-GTP is b and the rate at which Rac-GTP is dephosphorylated to Rac-GDP is r . The positive self-feedback of Rac-GTP is described by a Hill function cu2u2+K2 . We add a Hill function term to Equations 1 , 2 and 3 to account for the positive feedback loop between Rac-GTP activation and F-actin polymerization . In addition , ci and Ki ( i = 1 , 2 , 3 ) represent the maximum self-activation rate and the microscopic dissociation constant , respectively . Likewise , the rate of F-actin depolymerization is denoted as df . The formula for the negative self-feedback effects of membrane tension on F-actin is based on two assumptions: 1 ) membrane tension is a function of the total amount of F-actin ( mt ( f ) =mt0 ( 1+λ∫Ω0fds ) , where mt0 is the basal value of membrane tension in the nonpolarized state , λ represents the dependence of mt on the amount of F-actin , and 2 ) the rate of F-actin polymerization is down-regulated by membrane tension , which is inversely proportional to 1+mt ( f ) KF , where KF is a scaling factor for nondimensionalization . Based on the WP model and assumptions listed above , the dynamics of the model system are described using the following equations: {∂u∂t=Du∇2u+ ( b+c1u2u2+K12+c2f2f2+K22 ) v−ru , ( 1 ) ∂v∂t=Dv∇2v− ( b+c1u2u2+K12+c2f2f2+K22 ) v+ru , ( 2 ) ∂f∂t=Df∇2f+ ( c3u2u2+K32⋅KFKF+mt ( f ) ) −dff , ( 3 ) We apply a phase field function to label the intracellular and extracellular regions and to distinguish Rac-GTP ( u ) on the cell membrane ∂Ω0 from Rac-GTP ( v ) and F-actin ( f ) in the cytosol Ω0 . Hence , the cell membrane is described by a narrow transition layer between the interior of cell ( φ ( x , y ) = 1 ) and the exterior of cell ( φ ( x , y ) = 0 ) with a width of ε . The position of the cell membrane is approximated using the function B ( φ ) = φ2 ( 1 − φ ) 2 or |∇φ| , which vanishes outside the narrow interface . Thus , the phase field function is naturally used to account for Rac-GTP on the cell membrane and Rac-GTP and F-actin in the cytosol in a regular computational domain Ω . By coupling the phase field function with the WP model , Equations 1 , 2 and 3 are replaced with the following equations: {∂B ( φ ) u∂t=Du∇⋅ ( B ( φ ) ∇u ) +B ( φ ) ( b+c1u2u2+K12+c2f2f2+K22 ) v−B ( φ ) ru , in Ω ( 5 ) ∂φv∂t=Dv∇⋅ ( φ∇v ) −|∇φ| ( b+c1u2u2+K12+c2f2f2+K22 ) v+|∇φ|ru , in Ω ( 6 ) ∂φf∂t=Df∇⋅ ( φ∇f ) +|∇φ| ( c3u2u2+K32⋅KFKF+mt ( f ) ) −φdff , in Ω ( 7 ) Here , the terms in the cytosol are coupled with φ and the terms on cell membrane are coupled with B ( φ ) or ∇φ . Phase field coupling has been proven to be a consistent approach for modeling the transport , diffusion and adsorption/desorption of material quantities on a deformable interface [59] , and has been successfully applied to many biological models , such as vesicle bio-membranes [36] and cell motility [37–39] . The external stimulus is added in Equations 5 and 6 to increase the rate of transformation from Rac-GDP to Rac-GTP . In simulations , the stimulus is expressed as ksv , where ks is spatially dependent . We fix the spatial relation of the external stimulus and only vary its amplitude ( ksamp ) and the duration ( ksdur ) of ks to simplify the assumption . The following expressions of the stimulus are used: The global graded stimulus ks is ksgrad={ksamp ( R±x ) or ksamp ( R±y ) , 0≤x≤R , 0≤y≤R , 0<t≤ksdur , 0 , t>ksdur , ( 8 ) The local random stimulus ks is ksloc={{ksamp⋅R0 , 0≤x≤0 . 25R or 0≤y≤0 . 25R0 , else , 0<t≤ksdur , 0 , t>ksdur , ( 9 ) where R0 is a uniformly distributed random number between 0 and 1 . The values and units for all parameters are listed in S1 Table . The diffusion coefficients Du , Dv and Df are set according to published values [60 , 61] . The maximum self-activation rate of Rac-GTP c1 and dephosphorylation rate r are chosen based on published values [20 , 62] . We use parameter fitting to determine the values of the other parameters , including Hill coefficients and microscopic dissociation constants ( Ki , i = 1 , 2 or 3 ) , as well as the rate of F-actin degradation ( df ) and maximum activation rates for Rac-GTP and F-actin ( c2 and c3 ) . We also perform sensitivity analyses for all parameters to investigate the effects of the values of these parameters on cell polarity ( S6 Fig ) . The initial conditions affect the spatial profile of Rac-GTP . If the initial concentrations of Rac-GTP and Rac-GDP are too low , their distributions are unlikely to break symmetry and therefore the cell remains nonpolarized , regardless of the strength of the stimulus . Initial homogeneous concentrations of u = 2 μm−2 , v = 6 μm−2 , f = 0 are used to represent the initial nonpolarized state for the simulation of cell polarization . We also analyze the influence of the initial conditions in our simulations ( S7 Fig ) . If we use a stochastic distribution as the initial Rac-GTP and Rac-GDP by adding a Gaussian distribution noise with a mean of 0 and a variation of 0 . 01 , the steady-state spatial profiles of Rac-GTP and Rac-GDP are consistent ( the maximum concentration of Rac-GTP is 0 . 5210 ± 1 . 4853 × 10−5 μm−2 and the total amount of Rac-GTP is 92 . 5253 ± 0 . 0934 ) . We choose the periodic boundary condition and apply the efficient semi-implicit Fourier-spectral method for spatial discretization to solve Equations 5 , 6 and 7 [63] . We also developed another traditional cell polarity model coupled with membrane tension by assuming that the cell membrane overlaps with cell cytosol in 2D to further investigate the effect of membrane tension on cell polarity . In this model , we applied the Brownian ratchet model [25] to describe the mechanism by which membrane tension regulates F-actin . The simulations yielded consistent results with the phase field formulation , supporting the negative effect of membrane tension on cell polarization ( S1c and S1d Fig , Fig 4b and 4d ) . Please see more details in the Supporting Information . Values for the parameters used in this model are shown in S2 Table . The MCF-7 breast cancer cell line was purchased from American Type Culture Collection ( ATCC ) . MCF-7 cells were cultured in Dulbecco’s Modified Eagle’s Medium ( DMEM ) supplemented with 10% fetal bovine serum , 100 μg/mL penicillin , and 100 μg/mL streptomycin at 37°C in a 5% CO2 atmosphere mixed with 95% air . Cells were stained with anti-CD44-PE ( BD Pharmingen ) , and anti-CD24-Alexa Fluor 488 ( BioLegend ) antibodies for sorting with a flow cytometer ( BD FACS Aria II ) . The CD44+/CD24- phenotype was regarded as CSCs , whose proportion was approximately 1 . 6% [64]; the other three phenotypes , CD44+/CD24+ , CD44-/CD24+ , and CD44-/CD24- , represented the NSCCs [65 , 66] . CSCs sorted from MCF-7 cells were cultured in DMEM/F12 ( 1:1 mixture of DMEM and Ham’s F-12 medium ) with 20 ng/mL basic fibroblast growth factor ( bFGF ) , 10 ng/mL epidermal growth factor ( EGF ) , B27 serum-free supplement and N-2 supplement to inhibit the differentiation of CSCs [64] . The culture dish was incubated with Geltrex ( 120–180 μg/mL at 37°C for an hour before use . The phenotype of CSCs was maintained for at least one month . Two planar microelectrodes composed of indium tin oxide on glass slides with a 20 μm gap were ablated with ultraviolet ( UV ) light . A PDMS trough was mounted onto the processed slide , which could accommodate approximately 200 μL samples . Cells were detached from the bottom of dishes using trypsin/EDTA ( Sigma-Aldrich ) and suspended in a mixture of a 0 . 3 M inositol solution and phosphate-buffered saline ( PBS ) . The electric conductivity of the solution was adjusted to 2 mS/m . Two hundred microliters of cell suspension were added to the device . A square waveform signal with an amplitude of 2 V and a frequency of 12 MHz generated by a function generator ( AFG3021B , Tektronix ) was applied to the electrodes to drive the cells’ translational motion and trap them between the gap of the electrodes due to the dielectrophoresis process [67] . The trapped cells were stretched by adjusting the amplitude of the square wave to 5 V for 15 s and then restored by decreasing the voltage to 2 V over the next 15 s . Time-lapse images were captured with a microscope ( IX81 , Olympus ) using a 40X objective with a numeric aperture ( NA ) of 0 . 6 every 33 ms for 30 s . Surface micro-patterns were fabricated using the degassing-assisted patterning method [68] . The diameter of the circular pattern was 25 μm and the interval was 90 μm . The non-fouling material used for micro-patterning was poly L-lysine-graft-poly ethylene glycol ( PLL-g-PEG ) [69] . PLL-g-PEG attaches to negatively charged surfaces , such as the surface of a glass slide treated with oxygen-plasma , and the surface will be resistant to the adsorption of proteins and attachment of cells [70] . A glass slide ( Fisher Brand ) was treated with oxygen-plasma for 3 minutes . The glass slide was covered with a piece of polydimethylsiloxane ( PDMS ) with arrays of pillars ( diameter = 25 μm ) and degassed for 5 minutes . Then , the PDMS mold and the glass slide were returned to atmospheric pressure and PLL-g-PEG ( 0 . 1 mg/mL in 10 mM HEPES ) was added to the edge of the PDMS mold . PLL-g-PEG was aspirated into the space between the PDMS mold and the glass slide . After a 1 hour incubation ( attachment of PLL-g-PEG on the glass slide ) , the PDMS mold was removed from the glass slide . The glass slide was air-dried [71] . A piece of PDMS ( 12 mm×5 mm ) covered the slide when the slide was treated with plasma to avoid plasma-induced damage to the micro-patterns . The profile of the PDMS was depicted on the other side of the slide to mark its position . The glass slide with a bulk PDMS with a small well ( length: 12 mm , width: 5 mm , and height: 5 mm ) was treated with plasma and bonded together by fitting the well with the marked profile ( S1a Fig ) . Two hundred microliters of a mixture of fibronectin ( an ECM protein , 25 μg/mL ) and fibrinogen-Alexa Fluor 488 ( 10 μg/mL , Invitrogen ) was added to the small well , incubated for 1 hour and rinse with phosphate-buffered saline ( PBS ) three times . Before loading , cells cultured in the 24-well plate were stained with 1 μg/mL Hoechst 33342 for 20 minutes at 37°C and rinsed with PBS . Subsequently , they were stained with 333 μg/mL Golgi-Tracker Red ( Molecular Probes ) at 4°C for 30 minutes , rinsed with ice-cold medium and incubated in fresh medium for 30 minutes at 37°C . Then , the cells were loaded into the small well ( S1a Fig ) for spontaneous polarization measurements and into confocal culture dishes to compare the orientation of cell polarity and the direction of migration . Time-lapse imaging was performed on a Zeiss inverted microscope ( Axio Observer . Z1 ( SP ) ) every 5 minutes for at least 1 hour . The objective was 20X with an NA = 0 . 5 in air . The detection channels included bright field , DAPI ( excitation at 352 nm and emission at 455 nm ) and mCherry ( excitation at 587 nm and emission at 610 nm ) using an X-cite lamp . Before being loaded into the small well , CSCs were suspended in a hypotonic medium ( a 1:1 mixture of DMEM/F12 medium and double distilled water ) , which increased their membrane tension [13] . Images were captured after four hours when the cells adhered to the micro-patterns . | The coupling of mechanical and chemical factors has been consistently shown to regulate a variety of cellular processes such as cell polarization , migration and fate determination . However , most of the modeling work on biological systems still focuses on chemical systems . Here , we established a minimal cell polarity model coupling the two factors by linking the interaction between F-actin polymerization and tension with the action of the Rac signaling pathway in cell polarity . We performed single cell measurements to test the predictions of this model regarding the spontaneous cell polarization of two types of cancer cells with different cell membrane tensions . Membrane tension modulated the spatiotemporal properties of polarity and adjusted the cells’ sensitivity to external stimuli . Furthermore , our model recapitulates the experimental results described by Houk et al . , indicating that an increase ( decrease ) in tension results in a decrease ( recovery ) of Rac-GTP activity and that the relaxation of tension induces new polarity of the cell body when a cell with the pseudopod-neck-body morphology is severed . The framework of our model may be naturally extended to explore mechanical effects on other complex biological networks . | [
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"organell... | 2017 | Exploring the inhibitory effect of membrane tension on cell polarization |
Detection of congenital T . cruzi transmission is considered one of the pillars of control programs of Chagas disease . Congenital transmission accounts for 25% of new infections with an estimated 15 , 000 infected infants per year . Current programs to detect congenital Chagas disease in Latin America utilize microscopy early in life and serology after 6 months . These programs suffer from low sensitivity by microscopy and high loss to follow-up later in infancy . We developed a Chagas urine nanoparticle test ( Chunap ) to concentrate , preserve and detect T . cruzi antigens in urine for early , non-invasive diagnosis of congenital Chagas disease . This is a proof-of-concept study of Chunap for the early diagnosis of congenital Chagas disease . Poly N-isopropylacrylamide nano-particles functionalized with trypan blue were synthesized by precipitation polymerization and characterized with photon correlation spectroscopy . We evaluated the ability of the nanoparticles to capture , concentrate and preserve T . cruzi antigens . Urine samples from congenitally infected and uninfected infants were then concentrated using these nanoparticles . The antigens were eluted and detected by Western Blot using a monoclonal antibody against T . cruzi lipophosphoglycan . The nanoparticles concentrate T . cruzi antigens by 100 fold ( western blot detection limit decreased from 50 ng/ml to 0 . 5 ng/ml ) . The sensitivity of Chunap in a single specimen at one month of age was 91 . 3% ( 21/23 , 95% CI: 71 . 92%–98 . 68% ) , comparable to PCR in two specimens at 0 and 1 month ( 91 . 3% ) and significantly higher than microscopy in two specimens ( 34 . 8% , 95% CI: 16 . 42%–57 . 26% ) . Chunap specificity was 96 . 5% ( 71/74 endemic , 12/12 non-endemic specimens ) . Particle-sequestered T . cruzi antigens were protected from trypsin digestion . Chunap has the potential to be developed into a simple and sensitive test for the early diagnosis of congenital Chagas disease .
Trypanosoma cruzi is transmitted to humans via vector , organ transplantation , blood transfusion and from mother to fetus [1] . Initiatives to control Chagas disease have achieved remarkable success , as demonstrated by the decrease in estimated prevalence of infected individuals from 20 million in 1990 to 7 . 8 million in 2005 [2] , [3] . Detection of congenital T . cruzi transmission is considered one of the pillars of control programs . Congenital transmission accounts for 25% of new infections with an estimate of 15 , 000 infected infants per year in Latin America [2]–[5] . The earlier in life congenital infection is detected , the higher the efficacy and tolerability of treatment [6] . Current screening programs employ microscopy of fresh blood specimens concentrated by centrifugation in 4 to 6 heparinized capillary tubes ( a technique known as the ‘micromethod’ ) at birth and one month , followed by serology at 6 to 12 months of age [7]–[17] . However , microscopy has low sensitivity ( <50% in a single specimen ) [7]–[17] and up to 80% of at-risk children fail to complete follow-up in later infancy . Diagnosis is especially problematic in rural areas where tests are not easily accessible or may be poorly performed [8]–[10] . Molecular methods have higher sensitivity than microscopy , but the technical requirements and cost preclude routine use in resource-limited settings . A sensitive , specific and field-friendly screening test is needed to enable effective Chagas disease screening [7]–[9] . Urine antigen detection is an attractive alternative to improve the diagnosis of congenital T . cruzi infection . The non-invasive nature promotes high acceptability by parents . Reported sensitivity varies from 32 . 6% to 100% [18]–[22] , depending on the phase of the infection and the methodology used . T . cruzi antigens were detected with a sensitivity of 80–90% in urine samples from a small number of congenitally infected infants by a sandwich ELISA test using a panel of monoclonal antibodies [20] , [21]; however , this observation was never replicated . In our experience , we achieved a sensitivity of 67 . 5% in urine ultrafiltrate from acutely infected guinea pigs using a polyclonal antibody to trypomastigote excretory-secretory antigen [19] . Antigens exist in urine in very low concentrations and are susceptible to degradation within minutes after collection . A novel nanotechnology , using capturing nano-porous hydrogel particles produced with poly ( N-isopropylacrylamide ) ( poly ( NIPAm ) ) and N , N′-methylenebisacrylamide ( BAAm ) and coupled to chemical baits via amidation reaction , has been proposed for concentration and preservation of antigens in urine [23]–[28] . The nano-porous structure performs size sieving , allowing proteins to penetrate inside the particles depending on their molecular weight and shape . The pore size and molecular weight thresholds can be tuned by the percentage of BAAm crosslinker used in the polymerization reaction . The internal chemical baits capture proteins with extremely high affinity ( KD<10−12 M ) within minutes [23]–[28] . Chemical baits are covalently bound and distributed throughout the 3-dimensional interior space of the particles yielding a high binding surface and very high binding capacity . Captured antigens can be eluted in a small volume yielding a concentration factor proportional to the volumetric ratio between the initial volume of urine and the final elution volume [23]–[28] . The objective of this study was to evaluate the use of nano-porous particles for sequestration , concentration and preservation of T . cruzi antigens in urine , and to apply this technique in urine specimens from congenitally infected and uninfected infants of women with Chagas disease .
The protocols were approved by the institutional review boards of Hospital Universitario Japones , Asociacion Benefica PRISMA ( Lima , Peru ) , Universidad Peruana Cayetano Heredia ( Lima , Peru ) , Universidad Catolica Boliviana ( Santa Cruz , Bolivia ) and Johns Hopkins Blooomberg School of Public Health ( Baltimore , MD ) . The present study is a proof-of-concept study to evaluate the performance of Chunap in the early diagnosis of congenital Chagas disease . Specimens were collected during studies of congenital Chagas disease conducted in Hospital Universitario Japones and Centro de Salud 18 de Marzo in Santa Cruz , Bolivia from 2009 to 2012 . The samples included in this analysis represent a select subset ( 74 specimens from infants without congenital infection and 23 specimens from infants with congenital infection whose urine collection occurred prior to initiation of antitrypanosomal treatment ) . Urine samples from 12 seronegative infants from Lima , Peru were collected as non-endemic negative controls . Trained study nurses explained the protocol to women presenting for delivery and obtained written informed consent . A specimen was collected to screen for maternal T . cruzi infection . The specimen used for screening was a maternal venous blood specimen if labor was not far advanced , or cord blood if the mother was admitted straight to the delivery room in late stages of labor . The screening specimen was tested by 2 rapid diagnostic tests ( RDTs ) : the indirect hemagglutination test ( IHA ) ( PolyChaco , sensitivity and specificity according to manufacturer's instructions: 98% and 99% , respectively ) and Trypanosoma Detect , an immunochromatographic strip assay ( InBios International ) ( Sensitivity: 90 . 7% , Specificity: 100% [29] ) . A study nurse attended each delivery . For women diagnosed as infected in cord blood , a maternal blood specimen was collected after the mother recovered from the delivery and before discharge from the hospital . Mothers with positive results by one or more rapid tests and their infants were asked to return for follow-up at 1 , 6 and 9 months . At the 1-month visit , 5-cc urine samples were collected . Blood samples from infants obtained at 0 and 1 months were evaluated by the micromethod and PCR . The micromethod is the technique used routinely in Bolivian hospitals to screen for congenital Chagas disease . In this technique , cord or neonatal blood is collected in 4–6 heparinized microhematocrit tubes , centrifuged and the buffy coat layer examined microscopically for parasites [30] . Maternal sera and the 6- and 9-month infant sera were tested by at least two of the following IgG serology assays ( sensitivity and specificity according to manufacturer's instructions ) : Chagatest Recombinante ELISA ( Wiener Laboratories , Argentina . Sensitivity: 99 . 3% , and Specificity: 100% ) , Chagatest ELISA with T . cruzi cytoplasmic and membrane antigens ( Wiener Laboratories , Argentina . Sensitivity: 100% , and Specificity: 99 . 6% ) , and the IHA ( PolyChaco ) . Infants were considered to have congenital infection if they had positive results by microscopy or PCR in 0 or 1 month specimens , or positive results by two or more serologic tests at 6 or 9 months . The nurses were blinded to the Chagas status of each infant . Infants with confirmed infection were referred for treatment by the physicians designated by the Bolivian National Chagas Disease Control Program [31] . Hydrogel nano-porous particles were synthesized as previously described [23]–[28] . Briefly , N-Isopropylacrylamide ( NIPAm , 0 . 084 mmol ) , N , N′-methylene bisacrylamide ( BAAm , 0 . 005 mmol ) , and acrylic acid ( AAc , 0 . 015 mmol ) ( Sigma-Aldrich , MO-USA ) were dissolved in 1000 mL of MilliQ water and filtered . The solution was purged with nitrogen at room temperature and medium stirring rate for 1 h , and then heated to 70°C . Potassium persulfate ( Sigma-Aldrich , MO-USA , 0 . 002 mmol/ml ) was added to initiate the polymerization . The reaction was maintained at 70°C under nitrogen for 6 h . Particles were washed with water five times by centrifugation ( 19 , 000 rpm , 50 min ) . Dye molecules containing an amine group ( acid blue 22 , acid black 48 , bismarck brown Y , pararosaniline base , trypan blue and remazol brilliant blue R ) were coupled by condensation to the carboxylic group of acrylic acid present in the poly ( NIPAm-co-AAc ) particles . Activation of the carboxylic group present in the particles was performed as follows . A 50 mL aliquot of the poly ( NIPAm-co-AAc ) particle suspension was centrifuged , the supernatant was discarded and the particle pellet was re-suspended in 50 ml of 0 . 2 M NaH2PO4 pH 5 . Five mL of 1% SDS ( w/v ) , 4120 mg of N- ( 3 Dimethylaminopropyl ) N′ ethyl carbodiimide hydrochloride ( Fluka Analitical ) and 3060 mg of solid N-Hydroxy succinimide ( Sigma-Aldrich ) were added . The reaction was held at room temperature and medium stirring rate for 15 min . Then , the suspension was centrifuged , and the particle pellet was resuspended in 0 . 2 M Na2HPO4 pH>8 . The dyes ( molar ratio of dye/acrylic acid 10∶1 ) dissolved in 0 . 2 M Na2HPO4 buffer pH>8 were incubated with the activated particles at room temperature and medium stirring rate overnight . After washing steps particles were resuspended in MilliQ water . Once made these particles are stable at room temperature for up to 12 months . Dependence of particle diameters on temperature was measured via photon correlation spectroscopy ( N5 Submicron microparticle Size Analyzer , Beckman Coulter ) at increasing temperature from 25°C to 45°C in MilliQ water ( pH 5 . 5 ) . The concentration of particles was determined by weighing the lyophilized particles . In order to evaluate the ability of particles functionalized with different dyes to capture and concentrate T . cruzi antigens , we used: trypomastigote lysate antigen ( TLA ) , a recombinant H49 antigen ( kindly provided by Dr . Jose Franco da Silveira , Universidade Federal de São Paulo - São Paulo , Brazil ) , recombinant 1F8 T . cruzi antigen ( Genway Biotech Inc , CA-USA ) , and trypomastigote excretory-secretory antigen ( TESA ) . The TESA was harvested from cell cultures of T . cruzi Y strain in LLC-MK2 cells , as previously described [32] . For urine antigen detection we used a commercially available , monoclonal antibody against lipophosphoglycan ( LPG ) of Trypanosoma cruzi CL Brener strain ( Cedarlane Laboratories , NC-USA ) , this antibody recognizes bands of 42 kDa and 82 kDa in TESA of T . cruzi Y strain . Urine samples of infants were collected after ingestion of milk . Sterile urine collection bags were maintained in position with adhesive until 5–10 ml of urine was collected or for a maximum of 1 hour . If this volume was not achieved within 1 hour , a new sterile bag was used . After collection samples were immediately centrifuged at 3000 relative centrifugal force ( rcf ) for 10 min and the supernatant was stored in liquid nitrogen or −70°C until use . The supernatant was adjusted to pH 5–6 with 1M HCl . Urine samples ( 5 mL ) were incubated with 7 . 2 mg/ml of particles for 30 min at room temperature under rotation . After incubation , the samples were centrifuged at 16 000 rcf , 20 min at 25°C . Then , the particles were washed three times with MilliQ water by centrifugation at 16 000 rcf , 20 min , 25°C . Elution of the antigen from the particles was done using 300 µl of elution buffer ( 70% acetonitrile and 10% ammoniun hydroxide ) for 10 min at room temperature , following by 5 minutes of sonication . The elution step was repeated twice and the eluates were pooled together . The eluates were dried under nitrogen flow ( Microvap 118 , Organomation Associates , Inc , MA-USA ) at 40°C . The dried eluates were suspended in 40 µl of resuspending buffer {2500 µg trehalose ( Fluka Chemicals , MO-USA ) and 10 µl of 1% ( v/v ) red food dye ( McCormick , MD-USA ) in MilliQ water}; obtaining a concentration factor of 125 folds based on volume ratios ( 5000 µl/40 µl ) . For the Western Blot analysis 20 µl of resuspended antigens were analyzed . In each experiment we included a negative control ( normal urine sample ) and a positive control ( 5 ml of normal urine sample containing 1 ng of TESA antigen ) . The Chunap was carried out by a laboratory biologist who was also blinded to the Chagas status of the patient . In order to evaluate the ability of poly ( NIPAm ) particles to protect T . cruzi antigens from enzymatic degradation , H49 and 1F8 T . cruzi antigens ( 1 µg ) were incubated with 3 ng of trypsin ( Promega , WI , USA ) at 37°C for one hour in 50 mM Tris-HCl pH 7 . 2 , in the presence or absence of particles . After the incubation , antigens were eluted as described above and analyzed with SDS-PAGE analysis . SDS-PAGE analysis was performed using 4–20% Tris Glycine polyacrylamide gel ( Invitrogen Corporation , CA-USA ) using a Novex X-Cell IITM Mini-Cell ( Invitrogen Corporation , CA-USA ) , at 200 V for 50 minutes . Visualization of bands was performed by silver staining or by Western blot . Resuspended antigens ( 20 µl ) were mixed with 4 µl of sample buffer ( 50 mM TrisHCl pH 6 . 8 and treated with 2% SDS , 144 mM 2-mercaptoethanol , 10% glycerol and 0 . 01% bromophenol blue ) and heated to 100°C for 7 min . The antigens were separated on 4–20% Tris Glycine polyacrylamide gels and then were transferred to polyvinylidene difluoride membranes ( PVDF ) ( Millipore , MA-USA ) . The membranes were blocked with casein-based buffer: PBS supplemented with 0 . 1% Tween 20 ( PBST ) and 0 . 2% I-Block ( Applied Biosciences , CA-USA ) for one hour . The membranes were incubated overnight with mouse monoclonal antibody anti-LPG diluted 1/250 in the casein-based buffer . After six washing steps with PBST , membranes were incubated with peroxidase conjugated goat anti-mouse IgG and IgM ( Invitrogen Corporation , CA-USA ) diluted 1/5 000 in casein-based buffer for 60 minutes . Visualization of antigenic bands was done using an enhanced chemiluminescence system ( Supersignal West Dura , Thermo Fisher Scientific , MA-USA ) . In each PVDF membrane we included 5 ml of normal urine sample containing 1 ng of TESA antigen and concentrated with of poly ( NIPAm/TB ) particles . For the Western blot each patient was run twice . The presence of any of the five diagnostic bands ( 22 kDa , 42 kDa , 58 kDa , 75 kDa and 82 kDa ) was considered as a positive result for the Chunap . The criteria for defining a positive band depended on the judgment of a trained analyst . Real time PCR was performed to evaluate levels of parasitemia in 500 µl of cord blood at birth or 200 µl of blood obtained at 1 , 6 and 9 months-old . DNA extraction and quantitative real time PCR ( qPCR ) were performed based on published methods [33] , [34] with the modifications detailed in a previous publication [7] . The primer set Cruzi 1 ( 5′-ASTCG-G-C-T-G-A-T-C-G-T-T-T-T-CGA-3′ ) and Cruzi 2 ( 5′-AAT-T-C-C-T-C-C-A-A-G-C-A-G-C-G-G-ATA-3′ ) was used to amplify a 166-base pair DNA fragment . The probe Cruzi 3 ( 5′-CAC-A-C-A-C-T-G-G-A-C-A-C-CAA-3′ ) was labeled with 5′FAM ( 6-carboxyfluorescein ) and 3′MGB ( minor groove binder ) . STATA 10 . 0 software was used to calculate the sensitivity and specificity of each diagnostic test with 95% confidence interval .
Poly ( N-isopropyl acrylamide ) ( NIPAm ) particles functionalized with all molecular baits tested in this study ( acid blue 22 , acid black 48 , bismarck brown Y , pararosaniline base , trypan blue and remazol brilliant blue R ) captured H49 and TLA antigens to some degree; but poly ( NIPAm ) particles functionalized with trypan blue [poly ( NIPAm/TB ) ] particles were the most effective because they completely sequestered the target protein from the solution ( Figure 1A ) . In order to further characterize the yield of poly ( NIPAm/TB ) particles pre-processing step , we demonstrated by SDS PAGE analysis that H49 antigen was not lost during the washing step and was eluted from the particles with a yield higher than 95% ( Figure 1B ) ( similar results were obtained for TLA ) . In order to investigate whether poly ( NIPAm/TB ) particles have high affinity also for other T . cruzi antigens , particles were incubated with TESA and recombinant 1F8 antigen . SDS PAGE analysis demonstrated that poly ( NIPAm/TB ) particles completely sequestered all the T . cruzi antigens tested ( Figure 1C ) . Our Chagas urine nanoparticle test ( Chunap ) uses poly ( NIPAm/TB ) particles for concentration of T . cruzi antigens in urine . A detection limit ( DL ) of 0 . 5 ng/ml was achieved with an initial urine volume of 5 milliliters after concentration of antigens by poly ( NIPAm/TB ) particles . The DL of western blot without particle concentration preprocessing step was 50 ng/ml , yielding a concentration factor of 100 fold ( Figure 1D ) . Hydrogel particles hydrodynamic dimensions typically exhibit a temperature responsive behavior . The diameter of the poly ( NIPAm/TB ) particles decreased with increasing temperature , as expected ( from 780 nm at 20°C to 320 nm at 45°C ) . The diameter of particles at 25°C and pH 4 . 5 was 758 . 6 nm±15 . 03 . Bait functionalized capturing particles protect captured analytes from enzymatic degradation even if the degradative enzyme is small enough to penetrate inside the particles [24] . In this study , trypsin was captured and concentrated by poly ( NIPAm/TB ) particles ( Figure 2A ) . Even if trypsin was fully captured by poly ( NIPAm/TB ) particles , H49 and 1F8 T . cruzi antigens were completely protected from enzymatic digestion in the presence of poly ( NIPAm ) /TB particles ( Figure 2B ) . As a positive control , complete degradation of H49 and 1F8 T . cruzi antigens was observed in presence of trypsin at 37°C after 1 hour ( Figure 2B ) . The table 1 shows the results of diagnostic testing for each infected infant . Combining the results from the birth and 1-month specimens , the cumulative sensitivity of micromethod and PCR was 34 . 8% ( 8/23 ) and 91 . 3% ( 21/23 ) , respectively . Bands of 22 kDa , 42 kDa , 58 kDa , 75 kDa and 82 kDa were detected in urine samples of infected babies ( Figure S2 ) . The presence of any of these five bands was considered as a positive result for the Chunap . Chunap showed 91 . 3% sensitivity ( 95% CI: 71 . 92%–98 . 68% ) in a single specimen at one month of age , comparable to the 88 . 2% sensitivity of PCR at this time point ( 95% CI: 63 . 5%–98 . 2% ) ( Table 1 ) . Parasitemia levels determined by qPCR peaked at one month of age with subsequent decrease over time ( Figure 3 ) . Chunap specificity was 96 . 5% ( 95% CI: 90 . 1% to 99 . 2% , 71 negative results/74 specimens from uninfected babies in the endemic site , 12 negative results/12 specimens from babies in the non-endemic site ) .
In this study , we demonstrate for the first time that our Chagas urine nanoparticle assay ( Chunap ) detects congenital Chagas disease in a single urine specimen at one month of life with more than 90% sensitivity and more than 95% specificity . The study also shows that poly ( NIPAm ) particles coupled with trypan blue dye efficiently capture and concentrate T . cruzi antigens in urine , and under experimental conditions these particles protect T . cruzi antigens in urine from enzymatic degradation . Evaluation at one month of age provides high sensitivity because this time point is characterized by the highest levels of parasitemia and therefore also excretion of high levels of antigen . Nanotechnology-based tests can be adapted to point-of-care and cost-effective detection of microbial agents , as shown for other infectious diseases [35] . The non-invasive nature of the test will also greatly enhance parental acceptability . We evaluated the performance of poly ( NIPAm ) particles coupled with five different high affinity dyes . Optimal results were obtained with poly ( NIPAm ) particles with trypan blue ( TB ) , which achieved a 100-fold increase in antigen concentration in urine . Our data demonstrate that these nanoparticles can capture and concentrate T . cruzi analytes of different chemical structures , including proteins ( H49 and 1F8 ) , glycoproteins ( TESA ) and lipophosphoglycan . The broad range of antigens captured provides an advantage over other methodologies that target a specific chemical structure [20] , enabling the use of these nanoparticles as a single pre-processing step for sensitive multiplex analysis of several urinary analytes simultaneously . Bait-loaded hydrogel nanoparticles also preserve captured proteins from enzymatic degradation , even when the proteolytic enzyme ( e . g . trypsin , as in this study ) is small enough to penetrate inside the particles . We hypothesize that the mechanism of protection stems from the immobilization of trypsin by the nano-porous particle , which prevents the enzyme from binding substrate proteins . Another possibility is the steric hindrance associated with trapping of the substrate by the affinity-bait groups in the particle , which may prevent enzymes from productively binding target proteins [24] . Antigens in urine can be potentially degraded in the urinary track or bladder; however , studies indicate that this degradation is minimal [36] . Degradation of antigens after urine collection is enhanced due to bacterial contamination , so that the advantage of hydrogel particles is their protective effect immediately after urine collection . Further studies using urine samples collected at different times ( first morning urine vs random spot urine collection ) may help evaluate the extent of degradation in the urinary tract or bladder . However , random spot urine collection makes the test simpler [37] . Two previous studies have utilized urine antigen detection to diagnose congenital Chagas , reporting sensitivities of 80%–100% [20] , [21] . However , these studies had small sample sizes ( n = 10 and 14 ) and all but two of the congenital cases had parasitemia detected by microscopy . By contrast , nearly two-thirds of our infected infants were missed by micromethod , implying lower levels of parasitemia . In this study , PCR showed good sensitivity early in life ( 91 . 3% when results from birth and 1-month specimens were combined ) , as previously reported by other studies [7] , [9] . Two infants had positive results by Chunap but negative results by PCR; the large volume concentrated by the hydrogel nanoparticles may enable detection in some cases when antigen loads in the urine are low . Similar to qPCR , antigenuria also has the advantage of permitting early treatment which is associated with higher cure rates and fewer side effects compared to treatment later in life . Early diagnosis also translates to a much lower rate of missed infections compared to an algorithm requiring 6–12 months of follow-up [7] , [15] . To our knowledge , this study is the first to successfully and consistently detect T . cruzi LPG in urine of Chagas infected infants and to use this analyte for diagnostic purposes . Similarly , LPG of Leishmania has been detected in urine of patients [38] . LPG has been shown to play a key role in host-cell recognition/invasion and in parasite survival . LPG is highly expressed in T . cruzi with a cellular copy number of 4×l05 molecules of LPG glycoconjugates/cell making it a good candidate for diagnosis [39] and a monoclonal antibody is commercially available . The monoclonal antibody that we used was directed against the LPG of T . cruzi CL Brener strain , corresponding to the hybrid genotype VI [40] . This antibody also recognizes LPG fractions ( band of 82 kDa ) in TLA and TESA of T . cruzi Y strain ( genotype II ) and of a genotype I strain isolated from a patient from Bolivia ( Figure S3 ) , suggesting that this antibody can identify LPG preparations of most strains of T . cruzi . However , further studies must be performed in order to determine the ability of this antibody to recognize the LPG of other genotypes . With appropriate adaptation to a field-friendly format , Chunap has the potential to enable early point-of-care diagnosis of congenital Chagas disease in peripheral health facilities . Further steps will be necessary to apply this nanotechnology in developing countries . We are currently optimizing a novel separation method based on magnetic labeling of capturing nanoparticles to enable particle separation from urine without the need for a high speed centrifuge . Finally , although used in this study for Chagas disease , this method could be adapted for detection of other parasitic infections in urine and other body fluids . | Congenital Chagas disease is one of the main pillars for the control of Chagas disease because 25% of new infections occur by this route . Conventional diagnosis of congenital Chagas disease is based on microscopy at birth and serology at 9 months . However microscopy misses many infections and many at-risk infants fail to complete serology at six to nine months . We have developed a Chagas urine nanoparticle test ( Chunap ) for concentration and detection of T . cruzi antigens . Chunap was evaluated in urine samples of 1-month old children . At this age children have the highest levels of parasitemia and therefore also excrete the highest levels of antigen . Parents prefer a urine test to having their baby's blood drawn . Chunap diagnosed congenital infection in a single urine sample as well as PCR in two blood samples . This study also shows that hydrogel/trypan blue particles used in our test efficiently capture , concentrate and protect urinary T . cruzi antigens from enzymatic degradation . Chunap allows for the early diagnosis of congenital Chagas disease , and with appropriate adaptation , may allow early point-of-care intervention . | [
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"infect... | 2014 | Use of a Novel Chagas Urine Nanoparticle Test (Chunap) for Diagnosis of Congenital Chagas Disease |
The tradeoff between the need to suppress drug-resistant viruses and the problem of treatment toxicity has led to the development of various drug-sparing HIV-1 treatment strategies . Here we use a stochastic simulation model for viral dynamics to investigate how the timing and duration of the induction phase of induction–maintenance therapies might be optimized . Our model suggests that under a variety of biologically plausible conditions , 6–10 mo of induction therapy are needed to achieve durable suppression and maximize the probability of eradicating viruses resistant to the maintenance regimen . For induction regimens of more limited duration , a delayed-induction or -intensification period initiated sometime after the start of maintenance therapy appears to be optimal . The optimal delay length depends on the fitness of resistant viruses and the rate at which target-cell populations recover after therapy is initiated . These observations have implications for both the timing and the kinds of drugs selected for induction–maintenance and therapy-intensification strategies .
The failure of antiretroviral therapies to completely suppress viral replication in some patients represents a major difficulty in the management of HIV infection . In therapy-naive patients without clinically apparent resistance mutations , triple-drug therapy with two nucleoside–analog reverse transcriptase inhibitors and a protease inhibitor or a non-nucleoside reverse transcriptase inhibitor is standard [1] . In these patients , treatment success rates , defined as viral load <50 copies/ml at 48 wk , range from 70% to 80%–85% ( reviewed in [2] ) . However , in patients with previous regimen failure requiring salvage therapy , response rates are usually considerably lower [3–5] , and it is frequently not possible to assemble a three-drug regimen with uncompromised activity against all viral strains present . In these individuals , treatment failure often occurs after an initial period of response to a new regimen , and is usually associated with the appearance of multiply drug-resistant viral strains . This has led to attempts to treat highly experienced patients with various deep salvage regimens consisting of four , five , or six individual drugs [6–11] . These patients are particularly vulnerable to the many drug interactions [12] ( also reviewed in [13] ) and adverse metabolic , hematologic , neurologic , cardiovascular , and gastrointestinal side effects that complicate HIV therapy and seriously undermine the success of clinical management [14–20] ( also reviewed in [21] ) . The need to minimize drug resistance while reducing treatment-related toxicities has engendered an interest in induction–maintenance ( IM ) strategies , in which a period of intensified antiretroviral therapy ( induction phase ) is followed by a simplified long-term regimen ( maintenance phase ) [22–25] . Most such trials have yielded higher failure rates in the treatment group than in controls receiving conventional therapy . Failure typically occurs during maintenance therapy , and has been attributed to poor regimen adherence [25] and recrudescence of resistance mutations present before institution of induction therapy [23] . One weakness of existing studies has been that induction therapy consisted of standard three-drug antiretroviral therapy ( ART ) regimens in common clinical use at the time of the study , under conditions now recognized to permit subclinical viral replication [26 , 27] . Moreover , in these early studies , the induction phase only lasted between 3 to 6 mo , which may be insufficient . However , two recent studies have shown the apparent effectiveness of induction therapy for 48 wk followed by maintenance therapy with atazanavir [28] or lopinvir/ritonavir [29 , 30] , and this has led to new optimism concerning IM approaches . We have hypothesized that a longer period of a highly suppressive induction therapy that is appropriately timed relative to the start of maintenance therapy may allow minority resistant variants to decay below a stochastic extinction threshold , allowing for successful long-term treatment with simpler and better-tolerated regimens . To explore this hypothesis quantitatively , we constructed a detailed computer simulation model of the dynamics of sensitive and resistant viruses during a hypothetical IM regimen . We show that the timing and duration of induction therapy relative to maintenance therapy can affect the probability that viruses resistant to the maintenance regimen will be eradicated in ways that are somewhat counterintuitive . Under biologically plausible conditions , we find that 6–10 mo of induction therapy are required to maximize the probability of eradicating these resistant viruses . For shorter induction periods , we find that it is optimal to use a “delayed-induction” regimen administered several days to weeks after the start of the intended long-term maintenance therapy .
The model consists of CD4+ target cells , free viruses , and three types of infected cells: short-lived infected cells with t1/2 of ∼1 d , moderately long-lived infected cells with t1/2 of ∼2 . 5 wk , and long-lived infected cells or “latently” infected cells with t1/2 of ∼3 . 5 y ( Figure 1A ) . The model includes four possible mutations that confer resistance to three antiretroviral drugs; mutations 1 and 2 each confer partial resistance to drug I , whereas mutations 3 and 4 confer a high level of resistance to drugs II and III , respectively ( Figure 1B ) . Our model allows viral recombination , and includes the effects of partial drug efficacy , incomplete viral resistance , and cross-resistance between drugs II and III . Drug-resistant viruses can infect moderately long-lived and latently infected cells , allowing for the formation of latent drug-resistant viral reservoirs . Because the model assumes finite population sizes , the various viral genotypes may fall below a threshold for extinction . Since extinction is a chance event , we used random , stochastic modeling terms to model the rate of change of free viruses and infected cell populations that are near the extinction threshold . In the absence of therapy , viral load rises to a peak of approximately 106 virions/ml by day 25 , then falls to an equilibrium of ∼105 virions/ml by day 100 . Target-cell populations decrease during acute viremia , then recover somewhat as viral load falls to its steady state . ( Analytical formulas for the steady-state concentrations of infected cells and free virus under a model very similar to the one here can be found in [31–37] . ) As observed in [31–37] , our model assumes that resistant viruses have lower fitness in the absence of drug . With our conservative parameter choices , viruses with one , two , and three drug-resistance mutations are generally present at frequencies of 10−3 , 10−6 , and 10−9 , respectively , during the period of acute primary infection , whereas viruses with four drug-resistance mutations are generally absent ( Figure 2A ) . Thereafter , the frequency of mutants and latently infected cells ( unpublished data ) increase slowly to equilibrium . To account for this increase in our simulations , we allowed viral populations to equilibrate over a 4 , 000-d period ( >10 y ) before initiating therapy . With less conservative parameter choices , viruses with three resistance mutations will not generally preexist . In this case , the qualitative results described below can be duplicated with less intensive drug therapies . After initiation of conventional triple-drug therapy , the viral load decays at a rate of 0 . 6/d ( first phase decay ) for ∼10 d , then at 0 . 04/d ( second phase decay ) , until HIV-1 RNA falls below the detection limit of 50 RNA copies ( 25 virions ) per ml of plasma around day 120 ( Figure 2B ) . A population of latently infected cells is assumed to contribute a third phase of decay beginning around day 200 , during which virus decays at a rate of 0 . 00052/d . Viral loads during the third phase are on the order of 1 . 0/ml [40] . Model behavior during primary infection , chronic disease , and ART has been designed to match experimental viral dynamics [38–40] . The minority populations of resistant mutants form a reservoir of drug-resistant viruses that can fuel viral rebound if therapy is prematurely reduced or withdrawn . As expected , at low population densities under conditions prevailing during induction therapy , the appearance and loss of drug-resistant populations behave as random , stochastic processes . We have used this model to investigate two questions about IM therapies . ( 1 ) How long should the induction phase be in order to eradicate viruses resistant to the drugs in the maintenance regimen ? ( 2 ) What is the optimal timing of induction therapy relative to maintenance therapy ? Could IM therapies be improved , for example , if the agents that were unique to the induction regimen were started before starting the maintenance drugs ? In the simulations below , the maintenance regimen consists of drugs I and II , while drug III is applied only during induction therapy ( Figure 3 ) . We define “success” as achieving and maintaining a fully suppressed circulating free virus population for a period of at least 3 y after the end of induction therapy . Figure 3A–3B gives typical results; Figure 3A shows how the probability of success varies with the length of the induction phase . In this simulation , the percentage of success increased dramatically as the length of the induction therapy was increased to ∼120 d , and increased more gradually between 120 and 180 d . Further increases in the length of the induction phase beyond 180 d had little effect with these parameters . Figure 3B shows a typical simulation in which the timing of induction therapy was altered . In these simulations , a 30-d course of therapy intensification was started before maintenance therapy ( start days −30 to −10 ) , at the same time as maintenance therapy ( start day 0 ) , or after drugs unique to the maintenance therapy were started ( start days 10 and higher ) . In the latter case , we refer to the period of intensified therapy as a “delayed-induction” therapy . Interestingly , we note that for induction therapies of limited duration , the highest success rates occurred with delayed-induction therapy initiated ∼40 d after the start of maintenance therapy . Delayed-induction therapy ( also referred to as delayed-intensification or booster therapy ) results in higher eradication rates because drug-resistant viral populations are predicted to decline transiently after the start of maintenance therapy [41–43] . This decline occurs because resistant viruses , which are assumed to be less fit than sensitive viruses [31–37] , are no longer created via mutation once drug therapy interrupts viral replication within the drug-sensitive population . Drug-resistant populations do not increase until target-cell populations increase enough to offset their intrinsic growth rate disadvantage . Specifically suppressing replication of resistant viruses with additional drugs when this population is reduced in size maximizes the net impact of induction therapy . This result can be shown analytically using a simple one-infected cell , one-resistant virus , deterministic version of this model in which wild-type ( WT ) virus is completely sensitive to drug , and resistant virus is completely resistant to drug ( Figure 4A and 4B ) . With these simplifications , Nowak et al . [41] have shown that the dynamics of resistant virus after therapy is approximately where V1 ( 0 ) is the density of the resistant virus at the time that therapy is initiated , m is the turnover rate of target cells at steady state , δ is the death rate of infected cells , R0 = psk/cδm , and R1 = psk1/cδm . R0 and R1 are the basic reproductive numbers ( i . e . , the mean number of new cells infected from a single infected cell in a newly infected host who is not being treated ) for WT and resistant viruses [41] . For t ≪ 1 / m and 0 < R1 < R0 , the second term inside the curly brackets is large compared with the first , leading to transient declines in V1 . As t becomes large compared with 1/m , this second term approaches R1 ( 1 − R0 ) / m , whereas the first term continues to increase linearly with t , allowing for eventual increases in V1 . Setting the derivative of V1 ( t ) equal to zero , it is straightforward to show that V1 reaches a nadir at This indicates that the turnover rate of target cells is of major importance in determining the optimal timing of induction therapy relative to the maintenance therapy ( as illustrated in Figure 4B ) , though the replicative fitness of resistant viruses ( as quantified by values of R1 and R0 ) also plays a role . Although we have focused on reductions in the infection rate constant as the most logical way of modeling fitness reductions , the dependence of tmin on R0 and R1 indicates that we will observe nearly identical results if the resistant viruses have lower fitness due to a lower burst size or a higher clearance rate . The results above suggest that induction therapy should be at least 180 d if started at the same time as the maintenance therapy . It also suggests that the optimal time to initiate short-term induction therapy may be several weeks after the start of maintenance therapy . To explore these results in more detail , and to verify that the results are not overly specific to our parameter choices , we systematically varied the key parameters in the full , stochastic model . We first explored the effect of altering the fitness costs associated with resistance to antiviral drugs ( Figure 5A and 5B ) . As expected , the probability of success decreased with increasing viral fitness under both treatment strategies . Consistent with the equation for tmin above , the optimal time to intensify therapy increased as the fitness of the resistant virus decreased . Interestingly , we found that changing the fitness of viruses resistant to the induction regimen ( drug III ) had little or no effect on the optimal time to intensify therapy: the effects depicted in Figure 5B can be ascribed almost entirely to decreased fitness of viruses resistant to the maintenance regimen . As predicted from the equation for tmin above , we obtained nearly identical results if fitness costs were due to resistant viruses having low burst sizes ( unpublished data ) . Under simple population genetic models , the frequencies of singly and doubly resistant viruses prior to therapy are proportional to μ/s , and μ2/s2 , respectively , where s is the selective disadvantage of a drug-resistance mutation [43] . When viruses resistant to the maintenance therapy suffer large fitness costs ( e . g . , w1 = w2 < 0 . 65 ) , they rarely , if ever , contribute to the pool of long-lived infected cells . However , when these mutations have very small fitness costs ( e . g . , w1 = w2 > 0 . 96 ) , these viruses frequently infect cells destined for latency . ( We note that if the cost of resistance to the maintenance therapy is very low , simultaneous triple therapy will fail as well . ) We conclude , therefore , that the success of maintenance therapy will depend greatly on resistance mutations having measurable fitness costs . We next explored the effects of altering the turnover rate ( m ) of the target-cell population , which we accomplished by simultaneously increasing m and k . From the approximate equation for steady-state viral load: obtained from the simple one-infected cell model , we predict that varying m and k proportionally will change the dynamics of target-cell renewal without affecting pre-therapy viral load ( which is a potentially important confounding factor ) . In the full model , we found that both the optimal time to intensify therapy and the probability that standard IM therapy is successful increased as target-cell turnover rates decreased ( Figure 5C and 5D ) . Success rates are influenced by m because the target-cell populations needed for the growth of resistant viruses recover more slowly when m is small . In the simple one-infected cell model , recovery of target cells after therapy is given by where t is time since the initiation of therapy . From this equation , we see that the rate at which target cells return to their pre-therapy steady state is strongly affected by their death rate , m . To examine the role of the latent viral reservoir , we varied the rate at which latently infected cells are created ( fL ) in the full , stochastic model . ( Unless otherwise specified , all subsequent results are derived from this stochastic model . ) With our canonical simulation parameters ( with its conservative estimate for the number of latently infected cells ) , latently infected cells affected outcomes in only a small percentage of cases . The probability of IM therapy failure changed little within the range of fL = 10−8 – 10−6 , but decreased significantly for fL ≥ 6 . 4 × 10−6 ( Figure 6A and 6B , and unpublished data ) . These results indicate that both IM and conventional triple-drug therapy may fail if the number of latently infected cells is pushed too far above 106 , a value near the upper end of experimentally derived estimates ( Table 1 ) . As expected from the analytical equations above , altering the number of latently infected cells did not change our previous conclusions concerning optimal timing of IM therapy ( Figure 6B ) . Finally , we varied the death rate of the moderately long-lived infected cells . In contrast to our conservative estimate for δL , our canonical value for the death rate of moderately long-lived cells , δM = 0 . 04/d , is at the upper end of what might be inferred from second-phase decay rates [38 , 44–55] . We believe δM = 0 . 04/d is appropriate because imperfect efficacy and/or poor adherence will cause the second-phase decay rate to be less than δM . Second-phase decay rates , furthermore , have been shown to be higher in patients with higher viral loads [55] ( the situation modeled here ) . When we repeated our simulations with lower values for δM , we found , as expected , that the duration of induction therapy needed for successful IM therapy increased ( Figure 6C ) . ( In these simulations , we simultaneously changed δM and fM in order to study the effect of altering δM without affecting the pre-therapy density of moderately long-lived infected cells . ) For the case δM = 0 . 02/d , we observed that induction therapy needed to be at least 300 d to have a high probability of driving viruses resistant to the maintenance therapy to extinction . As expected , changing δM had little effect on the optimal time to intensify therapy ( Figure 6D ) . Our canonical simulation includes somewhat arbitrary choices for IC50 values for both drug I ( for which high-level resistance is assumed to require two mutations ) and drugs II and III ( for which a single mutation confers high-level resistance ) . To explore the effects of varying IC50 values , we conducted simulations under a range of IC50 values for drugs II and III ( Figure 7A and 7B ) and for drug I ( Figure 7C and 7D ) . As expected , we found that the probability of success in eliminating drug-resistant viruses decreased with increasing IC50 values and decreasing drug concentration . As in our previous simulations , the marginal benefit of increasing the length of an induction regimen reached a plateau between 150 d and 270 d . We explored the effect of adding a cross-resistance term wherein resistance to drug II confers partial ( or full ) resistance to drug III , and vice versa . Success rates decreased with increasing degree of cross-resistance , particularly when induction therapy preceded the start of maintenance ( Figure 8A and 8B ) . However , the qualitative results of our previous simulations remained unchanged . All of the delayed-induction therapy simulations above assume a delayed-induction phase of 30 d . To explore the effect of varying the duration and start time of delayed-induction therapy , we repeated our simulations over a range of induction treatment lengths and start times relative to maintenance therapy ( Figure 9 ) . For induction therapies of 40 d or less , the optimal time to initiate induction therapy continued to be 30–50 d , as in previous simulations . When the length of induction therapy was increased to 160 d , however , the curve flattened out considerably , indicating that the benefit of delaying induction is diminished at longer treatment durations . This is intuitively reasonable , since longer induction therapies will cover the critical time when resistant viruses are predicted to hit their nadir , even though they might be started well before the optimal therapy intensification times . The benefit of an optimally timed induction therapy , therefore , is most acute when the length of therapy intensification is short . To explore the effects of viral recombination on these strategies , we extended the model further to account for the effect of recombination between genotypes V12 and V34 . At realistic recombination rates ( i . e . , with r ≤ 0 . 01 ) , we observed virtually no effect on the success rate of IM therapy ( unpublished data ) . This is in part because terms of the form μkTV123 , which approximate the rate of production of V1234 by mutation , are at least an order of magnitude greater than terms of the form rkI12V34 , which approximate the rate of input into the V1234 population by recombination in our model . To achieve a higher-order resistance genotype by recombination , two or more dissimilar resistant virions must coinfect a cell , establish productive infection , and copackage two nonidentical templates to produce a heterozygous virus during virus production . After infection of a new target cell , an odd number of recombination events must occur between templates during reverse transcription , within a locus between the relevant resistance mutations . In the case of drugs targeting protease and reverse transcriptase ( the two most common drugs ) , recombination must occur within a span of ∼900 bp , or roughly one-tenth of the viral genome . Only a fraction of resistant viruses will overcome all of these hurdles . Given published estimates of approximately three recombination events per replication cycle [56] , r = 0 . 01 is reasonable , and perhaps somewhat high . To illustrate the ultimate consequences of very high recombination rates , we also performed simulations with unrealistically high recombination rates ( i . e . , with r ≥ 1 ) . At these extreme values , success rates declined in a manner similar to other perturbations that make therapy less likely to be effective ( unpublished data ) . Thus , biologically plausible recombination rates had little qualitative or quantitative effect on the outcomes observed in our four-mutation model . The fact that effective population sizes are so much lower than census sizes is one of the major riddles of HIV-1 evolution . This controversy arises from the observation that the viral effective population size , as measured using standard tools of population genetics , is orders of magnitude lower than the census size ( physical count of the number of viruses ) . In the simulations shown so far , we have conservatively assumed that the dynamics of viral resistance can be described using a model in which the number of viruses in the body equals a liberal estimate of census size . The controversy over viral effective population size has led to suggestions that the use of viral census size is too conservative [57 , 58] . Unfortunately , it is not clear how to model the effective population size since there is a lack of agreement on why effective population sizes are so low . However , it is possible to explore the effects of some of the more commonly proposed explanations using the modeling framework developed here . One explanation for low viral effective population size is that most of the infected cells and virions assayed by PCR are noninfectious . If this were the entire explanation for extremely low effective population sizes , use of current estimates of census size would be inappropriate . To explore what occurs if very few virions and integrated proviruses are replication-competent , we repeated our simulations with a census size 10 , 000-fold lower than the one used previously . Under this assumption , we obtained qualitatively similar results under a treatment regimen in which both the induction and the maintenance therapies consist of one drug . While a reduced therapy burden would be a welcome finding , two-drug therapies have not been generally successful , suggesting that these conditions are a less accurate approximation of biological conditions . Another possibility is that the effects of a genetic bottleneck during primary infection and rapid turnover of viral populations due to strong immune selection periodically purge HIV-1 populations of genetic variation . Because the effective population size is proportional to the amount of genetic variation , these factors would have a large negative effect on the measured effective population size during primary infection . To examine the impact of these processes on the dynamics of resistant virus , we set viral load to a very low value at the beginning of primary infection , and simulated immune selection for a character unrelated to resistance mutations , starting near day 200 . We found that neither mechanism for low effective population size had a significant long-term impact on the frequencies of drug-resistant viruses ( unpublished data ) . Although these simulations cover only some of the possible mechanisms for low effective population size [59–61] , they indicate that it is possible to appropriately model drug therapy using population sizes similar to the census size , regardless of the calculated effective population size . The results above are all based on a “standard” model that assumes that HIV is limited in vivo by the supply of CD4+ target cells [38 , 45–48 , 62 , 63] . We have chosen to use this standard model because it is supported by independent lines of evidence [64] and is well-studied mathematically , and because there is no clear consensus on appropriate methods to model immune responses . However , some modelers have argued that viral load is determined primarily by the dynamics of the immune response ( reviewed in [65] ) . To verify that our results are not specific to this target-cell limited model , we have performed analogous simulations under a model in which viral populations are limited instead by the immune effectors ( ones that act by preventing virus from infecting cells ) . In Figure 10A , we show using this model that drug-resistant viruses transiently drop in density following drug therapy in a manner very similar to that which occurred under the one-drug , one-cell , one-mutation , target-cell limited model in Figure 4A . When this model was extended to account for moderately long and very long-lived infected cells and varying turnover rates for immune effectors , we obtained results analogous to those for the stochastic target-cell limited model ( Figure 10B ) . In both models , the essential feature is that the environment for resistant viruses improves as viral load decreases , and in both models the length of the dip depends on how rapidly “the environment” improves . In the target-cell limited model , drug-resistant viruses showed a larger transient reduction if target cells regenerated slowly after therapy . In the immune-control model , drug-resistant viruses underwent larger transient declines if the HIV-specific effector cells decayed slowly during drug therapy .
In this study , we have used a detailed differential equation model to investigate induction–maintenance ( IM ) strategies for treating HIV-1 infections . In these strategies , an induction regimen is used to drive viral load to low levels before switching patients to a simpler and potentially better tolerated long-term maintenance regimen . We find that an appropriately deisgned IM regimen is likely to result in long-term suppression of viremia , and may also result in the eradication of minority virus populations resistant to the maintenance regimen . The marginal benefit of increasing the induction phase starts to level off between 4 and 10 mo , depending on the parameter choices . Interestingly , we find that in cases where target-cell populations recover slowly after ART , the optimal time to initiate a short-term induction regimen may be optimally started several days to weeks after the start of maintenance drugs . ( This delayed-induction therapy may also be referred to as delayed-intensification or booster therapy . ) These delays are advantageous because viruses resistant to the maintenance regimen briefly decline after exposure to the maintenance drug , due to reduced mutational input from the majority sensitive population . These resistant viruses do not increase again until the environment for the virus improves ( modeled here as a recovery in target-cell populations ) . Intensifying therapy when the resistant virus population is close to its nadir maximizes the effectiveness of the additional therapy . These results therefore illustrate the importance of considering dynamic feedback mechanisms such as those that occur under classical predator–prey models in ecology [66 , 67] when implementing IM regimens . Although our exploration of this model has caused us to view IM therapy in an optimistic light , our model predicts that IM therapies can fail under a variety of conditions , including situations in which drug resistance imposes little or no fitness costs ( Figure 5A and 5B ) , situations in which latently infected cells are formed at high rates ( Figure 6A and 6B ) , and situations in which the primary mutations responsible for drug resistance have large effects on the IC50 values , either directly ( Figure 7 ) or indirectly via cross-resistance ( Figure 8A and 8B ) . Our specific predictions about the optimal length for the induction period , likewise , depend on the size of the overall viral reservoir and the rate of the decay of moderately long-lived infected cells ( the primary determinant of optimal induction length ) . Finally , as discussed above , our finding that the best time to intensify therapy is often several days to weeks after the start of regular therapy depends critically on two parameters: the fitness of the resistant virus and the rate at which target-cell populations recover after initiation of therapy . The lower the fitness of resistant viruses and the slower the rate of recovery of target cells ( or other factors regulating viral density ) , the later the optimal time to maximize therapy . In cases where target-cell populations increase rapidly , or when other factors that limit viral replication decay quickly during therapy , delaying the induction phase may not be beneficial . These findings may be important in several clinical scenarios . IM therapy may be useful in resource-poor settings where patients have limited access to antiretroviral drugs . In these settings , it is particularly important to minimize the chance of selecting for drug-resistant viruses during the initial attempt to administer antiretroviral drugs . In addition , an intensification–maintenance approach could provide protection against the development of drug resistance in antiretroviral-naive patients , particularly in patients infected by a donor with known poor adherence to medications ( in which case it would be advisable to consider a maintenance phase consisting of three or more drugs , as opposed to the two-drug maintenance regimens modeled here ) . Recent estimates suggest that up to 10%–15% of treatment-naive patients harbor one or more drug-resistance mutations [68–70] , and this problem is likely to increase with increasing availability of ART . Finally , the principle of IM approaches could also be applied to the difficult problem of salvage therapy . The latter two scenarios have not been specifically modeled here . The results presented here must be weighed against several practical considerations: a two-drug maintenance regimen may incur a higher failure risk among patients prone to subtherapeutic drug levels for any reason , since there will be a reduced level of concurrent coverage by other agents in the regimen . It is also essential that the maintenance regimen not include drugs for which the patient previously developed drug resistance , a requirement that is complicated by the problem of cross-resistance . In addition , it would be highly desirable that agents used in maintenance therapy be simple and well-tolerated , with favorable pharmacokinetics , and have a high barrier to the development of resistance—both in terms of the number of mutations required for resistance and the fitness of the resulting mutants . By contrast , the requirements for induction regimens are considerably less stringent: induction therapy must be able to suppress replication of viruses resistant to the maintenance regimen and be free of intolerable adverse effects during short-term use . Although we have gone to considerable lengths to make the model realistic , we still make a number of simplifying assumptions . First , we ignore drug redistribution , and assume that drug levels immediately reach the therapeutic window at the time of initiation , remain constant during therapy , and fall to zero at discontinuation . There will clearly be some deviation from these ideal conditions in vivo because of pharmacokinetic “loading effects , ” individual failure to adhere to treatments , antagonistic drug interactions , and other factors . Although we believe that four mutations are sufficient to capture the basic behavior of drug resistance , this is clearly a simplification , as are some of our assumptions about IC50 values and cross-resistance . Our point is to make a reasonable model that captures key features , not to make a complete model of drug resistance . We have also neglected reversion of drug-resistant variants to WT virus . However , this effect is likely to be small under drug therapy , and would result in lower failure rates than modeled here . In building our model , we assumed that double therapy usually fails and that triple therapy usually succeeds , as has been observed in clinical practice . There are , of course , wide regions of parameter space where double therapy always succeeds and , conversely , where triple therapy always fails , and it is possible that many real patients could fall into one of these two categories . Although the specific simulations presented here would not be relevant to these patients , the same concept ( but with a different number of drugs ) can be applied to these patients . The key to applying IM strategies to such patients would be develop methods for distinguishing among patients whose maintenance therapies would require one , two , three , or more drugs . Finally , our model assumes a degree of fitness cost of resistance to drugs . Several studies have linked the presence of resistance mutations with decreased RT processivity [71] , reduced replicative capacity in vitro [72–75] , a competitive disadvantage against WT viruses in competition assays [75] , lower viral loads , and lower rates of CD4 T cell loss in vivo [72 , 73 , 75] , and have shown a tendency for overgrowth by WT viruses after discontinuation of therapy in cases of mixed infection [76 , 77] . As shown in Figure 5A and 5B , the probability of treatment success drops dramatically as the cost of resistance decreases . An essential feature of any two-drug maintenance regimen , therefore , is that the maintenance regimen includes drugs for which resistant mutations incur measurable fitness costs . In cases where fitness costs are small , it would be advisable to choose maintenance regimens in which four or more mutations are required for resistance ( something that can easily be implemented using a three-drug maintenance regimen ) . Key experiments needed to test the model's assumptions would focus on how the concentration of resistant viruses residing in short-lived , moderately long-lived , and latently infected cells changes during the first 90 d of therapy . Experiments designed to test the prediction that resistant viruses decrease transiently during therapy could be particularly informative . A better understanding of factors that allow for continued replication in the face of various therapies ( e . g . , identification of sanctuary sites in which drugs do not penetrate ) would also be very important . More generally , experiments designed to improve our understanding of viral effective populations size and factors that control viral load in the absence of therapy could lead to the construction of more realistic models for viral dynamics . Also , since our model shows that the probability of therapy success decreases as the number of latently infected cells increases , our study suggests that it would be useful to obtain additional quantitative estimates of the size of the latent viral reservoirs . Most studies of the latent reservoir have focused on blood . If less intensively studied sites such as the lung , brain , or gastrointestinal tract were found to have larger than expected numbers of latently infected cells , it might be necessary to choose more conservative treatment strategies . In addition to HIV-1 , IM approaches are being used for the treatment of a growing number of infectious illnesses , including active tuberculosis [78] , bacterial endocarditis [79] , and prosthetic joint infections [80] , and have widespread application in oncology . In these settings , induction therapy is usually timed to coincide with initiation of maintenance therapy , and maintained for an empirically determined period of time . Although the replication dynamics of the pathogenic elements in these cases ( i . e . , infecting microorganisms or aberrant host cells ) differ significantly from those of HIV , the chronic nature of these conditions , the requirement for long-term therapy , and the potential for developing resistance to drugs and immune responses pose similar challenges to the host . The counterintuitive results that have emerged from our analysis of HIV replication under therapy suggest that it may be beneficial to explore dynamic modeling approaches in these cases as well .
As with most biological models , certain parameters and assumptions are better supported than others . Parameters used in our model are given in Table 1 . These values resulted from a sequential process in which we first fixed parameters , such as viral load , δI , δM , and δ , which have been characterized experimentally . We then manipulated unknown/less-well–characterized parameters to match in vivo data on the viral kinetics during primary infection , during therapy , and after a treatment interruption . Most of these parameters were set to yield conservative ( i . e . , higher than average ) estimates for the number of infected cells . We then varied the drug concentrations and IC50 values ( within estimated ranges ) to match experimental observations that triple therapy is usually successful but double therapy usually fails . After completing these three steps , we performed our key exploratory simulations in which we examined the effects of varying the length and timing of induction therapy . Simulations were repeated across a wide range of reasonable values for parameters that remain poorly characterized by experimental methods ( e . g . , target-cell turnover rates ) . Dynamics of infection were simulated using an extension of a commonly used model for viral dynamics [38 , 41 , 45–48 , 62 , 81–85] that assumes that viral load is limited by the supply of CD4+ target cells . Our model consists of 65 differential equations accounting for target cells , free virions , three types of infected cells , and 16 viral genotypes ( Figure 1B ) . The dynamics of target cells and drug sensitive viruses are given by where I , M , and L represent short-lived , moderately long-lived , and latently infected cells , respectively; V represents free virions; T represents target cells; fM and fL are the fractions of target infected cells that become moderately long-lived and latently infected cells upon HIV-1 infection; fI = 1 − fM − fL; s is the input rate of target cells; m is the death rate of target cells; δI , δM , and δL are the death rates of short-lived , moderately long-lived , and latently infected cells , respectively; pI , pM , and pL are the rates at which short-lived , moderately long-lived , and latently infected cells produce virus; c is the clearance rate of free virus; t is time in days; K is the rate at which WT virus infects cells , and Ki is the rate at which virus with resistance mutation i infects target cells in the presence of therapy . To model the effects of drugs on these different viruses , we assume that infection rate constants K , K1 , K2 , . . . , K1234 decline in the presence of drug-using functions described below ( see Modeling of viral replication under drug therapy ) . The dynamics of mutants partially resistant to drug I , but sensitive to drugs II and III , are given by equations of the form: where μ is the probability that a cell infected with WT virus will acquire a resistance mutation to one of these drugs . The equations of other resistant mutants are straightforward extensions of these equations with sequential mutation accumulation . For example , the dynamics of mutants with high-level resistance to drug I , but sensitive to drugs II and III , are given by the equations . while the dynamics of mutants resistant to all four drugs is given by the equations We note that this model assumes that reverse mutations from resistance to sensitivity is negligible . Another cryptic assumption is that short-lived , long-lived , and latently infected cells are derived from a single population of CD4+ target cells , as modeled by Nowak et al . [41] . In preliminary simulations and/or calculations , we have determined under reasonable conditions that neither of these factors has much effect on our qualitative conclusions . The extinction threshold was set to 3 × 10−9 infected cells/μl , which is roughly equivalent to one infected cell per 2 × 1011 CD4 cells ( the approximate total body CD4 cell population ) . In preliminary work , we found that it is almost impossible to eliminate viruses resistant to any single drug during triple-drug therapy . IM therapy was therefore considered to be successful when the concentration of viruses and cells infected with viruses resistant to both of the drugs in a two-drug maintenance regimen fell to zero or if viral load failed to rebound for a period of 3 y after ending induction therapy . To allow for imperfect drug efficacy against WT virus , we assumed that the infection rate constant for genotype i in the presence of drug j can be modeled as: where k is the baseline infection rate constant for WT virus in the absence of drug , wi is the replicative fitness cost associated with mutation i ( expressed as a percentage of k ) , IC50i , j is the concentration of drug j at which infection rate constant for mutant i is 50% of its original value , and Dj is the concentration of drug j [49] . In our four-mutation system , mutations 1 and 2 confer partial resistance to drug I , while mutations 3 and 4 confer substantial ( though not 100% ) resistance to drugs II and III , respectively . For the “canonical case , ” we assumed that mutations 1 and 2 each confer a 5-fold increase in IC50 value against drug I , resulting in a 25-fold increase in resistance for the double mutant V12 as expected [86] , while mutations 3 and 4 confer 100-fold increases in IC50 values against drugs II and III , respectively . In the figures , we refer to the fold increase in resistance conferred by mutations 1 or 2 as “IC50INT” ( since these mutations confer an intermediate level of resistance ) , and the fold increase in resistance conferred by mutations 3 and 4 as “IC50MUT” ( since these mutations confer high-level resistance; i . e . , they are completely mutated ) . Under this model , resistance to drug I would be analogous to resistance to a protease inhibitor , while resistance to drugs II and III would resemble resistance to nucleoside reverse transcriptase inhibitors and first-generation nonnucleoside reverse transcriptase inhibitors . The resulting IC50 values are summarized in Table 2 . To calculate the infection constants in the presence of multiple drugs , we used generalizations of the IC50 formulas given above , wherein fitness effects and IC50 effects are multiplied together to give the composite infection rate constant . For example , the infection rate constant for the quadruple mutant V1234 in the presence of drugs is given by: where k is the baseline infection rate constant for WT virus in the absence of drug; w1 , w2 , w3 , and w4 are the negative fitness effects associated with each resistance mutation; IC501 , 1 , IC501 , 2 , and IC501 , 3 are the IC50 values for genotype V1 against drugs I , II , and III , respectively; IC501234 , 1 , IC501234 , 2 , and IC501234 , 3 are the IC50 values for genotype V1234 against drugs I , II , and III , respectively; and D1 , D2 , and D3 are the concentrations of drugs I , II , and III , respectively . In the presence of drug , we assumed drug concentration values of 20 ng/ml . In our model , drug concentrations immediately rise to therapeutic levels or fall to zero when therapy is changed . In preliminary calculations , we have determined that pharmacokinetic transients have relatively little effect on our qualitative results under reasonable conditions . Finally , we modeled ( reciprocal ) cross-resistance between mutations conferring resistance to drugs II and III by setting the IC50 value each of these drugs to IC50WT × ( IC50MUT / IC50WT ) α , where α is a coefficient giving the degree of cross-resistance . When α = 0 , the IC50 value equals that of the WT value; when α = 1 , the IC50 value of the mutant equals that of the mutant that is resistant to the other drug . These α values were then converted to percentages , where 0% indicates no cross-resistance , and 100% indicates that mutations conferring resistance to drug II are equally resistant to drug III and vice versa . In models with three mutations , recombination acts only on the same order as the mutation rate , since the triple mutant V123 can be created by either one mutation added to V12 or recombination between V12 and V3 . However , in models with four or more mutations , recombination between V12 and V34 reduces the number of mutation/recombination events needed to create a fully resistant virus . To account for recombination without adding a huge number of terms , we assumed that infection of I34 by V12 or infection of I12 by V34 results in the formation of the quadruple mutant with probability r , where 0 ≤ r ≤ 1 . For example , the equation for short-lived infected cells with virus with all four resistance mutations becomes: Modifications for M1234 and L1234 were similar . To account for random genetic drift occurring at low population densities , we used stochastic terms similar to those used in [46] to model populations near the cutoff for extinction . For each time-dependent variable x ( e . g . , I , V ) , we first determined if x < nsxmin , where ns is the number of copies below which x is subject to stochastic forces and xmin is the concentration at which there is only one virus or infected cell in the body . For x ≥ nsxmin , we set x ( t + h ) = x ( t ) + [B ( x ) − M ( x ) ]h , where h is the step size , B ( x ) is the sum of the “birth” terms , and M ( x ) is the sum of the “mortality” terms . For x < nsxmin , we set x ( t + h ) to x ( t ) − 1 , x ( t ) , or x ( t ) + 1 according to the probabilities hM ( x ) , 1 − [M ( x ) + B ( x ) ]h , and hB ( x ) . Use of deterministic equations for x > nsxmin strikes a balance between the need to account for stochastic effects at low population densities and the need to reduce computation times at higher densities where stochastic effects are negligible . Since preliminary runs with ns = 25 , 50 , 100 , and 200 gave similar results ( but clearly distinct from ns = 1 or ns = 5 ) , we reasoned that ns = 25 would be sufficient to capture most of the stochastic variation that occurs at low density . Probabilities were determined using the random number generator MT19937 [87] . Simulations performed using the random number generator ran2 [88] yielded indistinguishable results ( unpublished data ) . To create a realistic simulation of IM therapy , we adjusted the parameters to match the dynamics of viral decay during potent combination therapies [38 , 40 , 89 , 90] . Prior to the initiation of therapy , we assumed that there are ∼1010 viruses , ∼3 × 108 short-lived infected cells , ∼107 moderately long-lived infected cells , and ∼106 latently infected cells per body . Unless otherwise stated , other parameter values used were: s = 2 . 0 cells/d , m = 0 . 02 cells/d , k = 0 . 0008 cells × μl/d , w1 = 0 . 95 , w2 = 0 . 95 , w3 = 0 . 95 , w4 = 0 . 95 , δI = 0 . 6 cells/d , δM = 0 . 04 cells/d , δL = 0 . 00052 cells/d , f M = 0 . 07 , fL = 10−6 , p = 100 virions/d , pM = 6 virions/d , pL = 2 virions/d , c = 3 d−1 , and μ = 1 × 10−4 . All three drugs ( D1 , D2 , D3 ) are set at 20 ng/ml when these drugs are present . The input rate of target cells , s , was set so that the steady state concentration of target cells is 100 cells/μl , or approximately 10% of a typical peripheral blood CD4 T cell count , since not all CD4+ T cells are susceptible to HIV-1 infection . Units for target cells are based on a total estimate of 2 × 1011 CD4 cells per body , of which 2% are in blood . The stochastic cutoff threshold was set at one infected cell per body , or 3 × 10−9 cells/μl . The death rate of latently infected cells of δL = 0 . 00052/d ( t1/2 = ∼44 mo ) was conservatively set to one of the lower experimental estimates [50 , 89 , 91–93] . The mutation rate was deliberately set to approximately three times the estimated per-base rate to account for the fact that more than one nucleotide mutation may lead to an amino acid change that results in resistance . In all simulations , we assume that fitness effects are multiplicative: that is , that k12 = k1k2 / k , k13 = k1k3 / k , k23 = k2k3 / k , and k123 = k1k2k3 / k2 , as in [94] . The effects of changing less well-quantified parameters , such as m and k , are summarized in the results . Although we focus on the target-cell limited model described above , we also explored a simple immune-control model to determine how dependent our qualitative results are on the factors that regulate HIV-1 density . In our immune-control model , the virus population expands exponentially without limitation in the absence of immunity . Immune effectors , which increase at a rate proportional to the number of infected cells , interfere with the ability of virus to infect cells ( as might happen if immune cells release chemokines and/or neutralizing antibodies ) . We implemented this initially using the following model with one mutation and one type of infected cell: where X is the concentration of immune effectors , sX is the rate of appearance of immune effectors in the absence of immune stimulation , mX is the death rate of immune effectors , kX is the rate at which HIV-1–infected cells activate immune effectors , and Ks is a saturation constant describing the negative effect that the immune effectors have on the ability of HIV-1 to initiate infections . The symbols T , I , I1 , V , V1 , K1 , p , c , δ , and μ have the same meanings as in the target-cell limited model above , though when simulating dynamics under this model , we assume that T does not change over time . To extend this immune-control mechanism to the full , stochastic model , we made analogous extensions , setting dX / dt = sX − mXX + kX ( I + I1 + … + I1234 + M + … + M1234 ) X and multiplying the infection rate constants ( K , K1 , K2 , … , K1234 ) by Ks / ( Ks + X ) , while keeping T constant . To simulate drug treatment for different rates of turnover of immune effectors without also changing pretherapy viral loads , we increased sX , mX , and kX proportionately . ( The latter is needed since steady-state viral load is the sum of terms proportional to sX / kX and mX / kX . ) | Clinicians treating HIV infection must balance the need to suppress viral replication against the harmful side effects and significant cost of antiretroviral therapy . Inadequate therapy often results in the emergence of resistant viruses and treatment failure . These difficulties are especially acute in resource-poor settings , where antiretroviral agents are limited . This has prompted an interest in induction–maintenance ( IM ) treatment strategies , in which brief intensive therapy is used to reduce host viral levels . Induction is followed by a simplified and more easily tolerated maintenance regimen . IM approaches remain an unproven concept in HIV therapy . We have developed a mathematical model to simulate clinical responses to antiretroviral drug therapy . We account for latent infection , partial drug efficacy , cross-resistance , viral recombination , and other factors . This model accurately reflects expected outcomes under single , double , and standard three-drug antiretroviral therapy . When applied to IM therapy , we find that ( 1 ) IM is expected to be successful beyond 3 y under a variety of conditions; ( 2 ) short-term induction therapy is optimally started several days to weeks after the start of maintenance; and ( 3 ) IM therapy may eradicate some preexisting drug-resistant viral strains from the host . Our simulations may help develop new treatment strategies and optimize future clinical trials . | [
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] | 2007 | Optimal Timing and Duration of Induction Therapy for HIV-1 Infection |
Human noroviruses ( huNoV ) are the most frequent cause of non-bacterial acute gastroenteritis worldwide , particularly genogroup II genotype 4 ( GII . 4 ) variants . The viral nonstructural ( NS ) proteins encoded by the ORF1 polyprotein induce vesical clusters harboring the viral replication sites . Little is known so far about the ultrastructure of these replication organelles or the contribution of individual NS proteins to their biogenesis . We compared the ultrastructural changes induced by expression of norovirus ORF1 polyproteins with those induced upon infection with murine norovirus ( MNV ) . Characteristic membrane alterations induced by ORF1 expression resembled those found in MNV infected cells , consisting of vesicle accumulations likely built from the endoplasmic reticulum ( ER ) which included single membrane vesicles ( SMVs ) , double membrane vesicles ( DMVs ) and multi membrane vesicles ( MMVs ) . In-depth analysis using electron tomography suggested that MMVs originate through the enwrapping of SMVs with tubular structures similar to mechanisms reported for picornaviruses . Expression of GII . 4 NS1-2 , NS3 and NS4 fused to GFP revealed distinct membrane alterations when analyzed by correlative light and electron microscopy . Expression of NS1-2 induced proliferation of smooth ER membranes forming long tubular structures that were affected by mutations in the active center of the putative NS1-2 hydrolase domain . NS3 was associated with ER membranes around lipid droplets ( LDs ) and induced the formation of convoluted membranes , which were even more pronounced in case of NS4 . Interestingly , NS4 was the only GII . 4 protein capable of inducing SMV and DMV formation when expressed individually . Our work provides the first ultrastructural analysis of norovirus GII . 4 induced vesicle clusters and suggests that their morphology and biogenesis is most similar to picornaviruses . We further identified NS4 as a key factor in the formation of membrane alterations of huNoV and provide models of the putative membrane topologies of NS1-2 , NS3 and NS4 to guide future studies .
Human noroviruses ( huNoV ) are the most frequent causative agent of acute gastroenteritis worldwide , responsible for over 30% of all cases , subsequently resulting in over 200 , 000 deaths per annum [1] . Still , no vaccine or specific antiviral therapy is available to counteract huNoV infections . Noroviruses are divided into seven different genogroups ( GI-GVII ) and further subdivided into numerous genotypes [2] . Noroviruses grouped into GI , GII and GIV mainly infect humans but also other species , while GV infects mice . The GII genotype 4 ( GII . 4 ) cause the majority of infections with novel outbreak strains emerging every 2–3 years , likely in a response to an immunological pressure of herd immunity [3–5] . Noroviruses belong to the Caliciviridae family and have a positive-sense single-stranded RNA genome of approximately 7 . 5 kilobases ( kb ) ( reviewed in [6] ) . The huNoV genome contains three open reading frames ( ORFs ) , where ORF1 encodes the non-structural proteins ( NS1-7 ) involved in viral replication , ORF 2 encodes the capsid protein and ORF3 encodes a small structural protein . Murine noroviruses ( MNV ) additionally encode an ORF4 from an alternative reading frame located in ORF2 , termed virulence factor 1 ( VF1 ) , involved in antagonism of the host innate immune response [7] . The 5’ end of the genome contains a short 5 nucleotide untranslated region ( UTR ) and the 3’end contains a short UTR and poly-A tail ( reviewed in [8] ) . The norovirus genome is covalently linked at the 5’end with the viral protein VPg ( also termed NS5 ) . ORF1 is translated from the full-length genomic RNA , whereas ORF2 , ORF3 , and ORF4 are mainly translated from a VPg linked subgenomic RNA ( reviewed in [8] ) . ORF1 encodes a large , approximately 200 kDa , polyprotein that is processed by the viral protease NS6 , giving rise to 6 mature nonstructural proteins involved in viral replication and several precursor proteins with potentially additional , yet poorly defined functions ( reviewed in [8] ) . The function of the most N-terminal protein ( termed NS1-2 or p48 ) is unclear . huNoV NS1-2 varies in size ( approximately 40–48 kDa ) and contains an N-terminal disordered region and a C-terminal predicted trans-membrane domain [9] . The central domain further shows homology to the NlpC/p60 superfamily of enzymes , with diverse hydrolase functions [10] . Genogroup I NS1-2 has been shown to localize to the Golgi complex and induce Golgi disassembly , dependent upon the C-terminal hydrophobic region [11] . MNV NS1/2 contains 2 sites cleaved by murine caspase 3 and has been shown to localize to the endoplasmic reticulum ( ER ) upon transient expression [12 , 13] . NS3 ( also termed NTPase , 2C-like and p41 ) has been demonstrated to function as an NTPase in vitro for GI [14] . NS3 has also been shown to co-localize with double stranded RNA ( dsRNA ) during MNV infection [15] . NS4 ( also called p20 , p22 or 3A-like ) function remains unclear , although NS4 has been demonstrated to disrupt ER to Golgi trafficking resulting in Golgi disassembly during norovirus replication [16] . NS4 has also been shown to inhibit actin cytoskeleton remodeling in an epithelial cell line upon transient expression [17] . Upon MNV infection , NS4 was shown to localize to the replication complex [15] , and upon transient expression shown to localize to endosomes [13] . NS5 is linked to the 5’ end of the genome and plays an integral role in the initiation of translation through its interaction with eukaryotic initiation factors and likely primes genome and subgenomic RNA synthesis [18] . The viral protease NS6 ( also called Pro or 3C-like ) is a well characterized cysteine protease and responsible for the cleavage and processing of the viral ORF1 polyprotein [12 , 19] . Lastly , NS7 ( also called RdRp ) functions as the RNA dependent RNA polymerase in viral replication and transcription of subgenomic RNAs [20 , 21] ) . Membrane rearrangements play a key role in the establishment of viral replication complexes for positive strand RNA viruses . In principle these membrane alterations can be subdivided into two morphotypes ( reviewed in [22 , 23] ) . First , the “invagination type” consists of single membrane invaginations of a donor membrane which stay connected to the cytoplasm via a pore and are represented by alphaviruses and flaviviruses . Viral replication takes place inside these vesicles and the viral RNA contributes to their morphology [24] , with the exception of Brome mosaic virus 1a protein which generates spherules in absence of RNA replication [25] . Second , the “DMV-type” consists of vesicular and tubular membrane rearrangements wrapped by one ( single membrane vesicle , SMV ) , two ( double membrane vesicles , DMVs ) and multiple membranes ( multi membrane vesicles , MMVs ) , induced by picornaviruses , coronaviruses and hepatitis C virus ( HCV ) . Most of these structures have no visible connection to the cytoplasm and the functional significance of the different vesicle subtypes as well as the localization of the RNA synthesis machinery is still a matter of debate . However , these structures can typically be induced simply by expression of the replicase proteins in absence of RNA replication , exemplified by picornaviruses [26–29] and HCV [30–32] . Sole expression of individual nonstructural proteins already induces distinct membrane alterations , which are less complex than those derived from the polyprotein . Still , such studies have allowed the identification of those viral proteins contributing to the morphogenesis of viral replication sites and the unraveling of some of their functions [26–28 , 30 , 31 , 33–35] . In the case of HCV , virus induced membrane alterations have been identified as efficient drug targets for silibinin [36] , direct acting antivirals like NS5A inhibitors [37] and host targeting drugs like cyclophilin inhibitors [38] . Our understanding of the ultrastructure of huNoV replication organelles is currently scarce , mostly due to the lack of efficient cell culture models [39] . A replicon model has been established in case of GI noroviruses [40] , but no ultrastructural analysis is currently available . A plasmid driven GII . 3 replicon model allows moderate RNA replication levels , but it remains difficult to dissect the contribution of protein expression and bona fide RNA replication in this system [41] . Recently , tremendous progress has been achieved in cultivating the more clinically relevant GII . 4 strains in both B-cells [42] and enteric organoids [43] , still neither of these models has yet been proven to allow ultrastructural studies . Therefore , most of our knowledge of norovirus induced membrane alterations has been obtained using the MNV model [44] . Previous studies showed accumulations of vesicles in the cytoplasm of infected macrophages consisting of single and double membrane vesicles , which have not been further characterized [44] . In addition , it has been shown that MNV induced vesicle clusters co-localize with all MNV NS proteins and with viral replication intermediates and that these extensive rearrangements of intracellular membranes are mainly derived from the secretory pathway , including ER , Golgi and endosomal membranes [15] . Furthermore , the MNV replication organelles seem tightly associated with the cytoskeleton , probably mediated by the main capsid protein [45] . Little is known so far about the contribution of individual nonstructural proteins to virus induced replication vesicles , but it is believed that NS1-2 and NS4 are the main drivers in this process due to their membrane association and comparison to picornavirus proteins ( reviewed in [8] ) . In addition , NS3 is associated with membranes and recently has been shown to be associated with highly motile vesicular structures [13 , 46] . The current study aimed to investigate membrane alterations induced by clinically highly prevalent GII . 4 using a transient expression system in Huh7 cell lines . Membrane structures induced by expression of the polyprotein of three important outbreak strains ( Den Haag 2006 , New Orleans 2009 and Sydney 2012 ) comprised SMV , DMV and MMV structures . We further observed that SMVs and DMVs were reminiscent of structures found upon MNV infection . The impact of individual GII . 4 NS proteins on intracellular membranes was studied by correlative light and electron microscopy , allowing the localization of each protein within the cellular ultrastructural context . GII . 4 NS1-2 induced membrane proliferation of the smooth ER , which was strikingly different from MNV NS1/2 . NS3 was tightly associated with lipid droplets ( LDs ) and induced convoluted membranes . However , only NS4 expression was sufficient to induce SMV and DMV formation , much like the ability of HCV NS5A and poliovirus ( PV ) 3AB to induce DMVs .
We aimed to study the determinants of membrane alterations induced by huNoV with a specific focus on clinically relevant GII . 4 outbreak strains . We therefore wanted to exploit expression of ORF1 and of individual NS-proteins to assess the morphology of virus induced membrane alterations in Huh7 cells . We chose Huh7 cells for two reasons: first , Huh7 cells have been shown to support RNA replication of a human GI Norwalk replicon [40] and a plasmid based GII . 3 replicon [41] , suggesting that they are in principle permissive for huNoV . Second , Huh7 have been used to study membrane alterations for a variety of positive strand RNA viruses , including HCV , hepatitis A virus ( HAV ) , coronaviruses and Dengue virus ( reviewed in [22 , 23] ) , thereby facilitating the comparison of these structures among different virus groups . We first aimed to evaluate whether structures induced by ORF1 expression indeed resembled those found in infected cells using MNV as a model , ideally using the same cell type as intended for huNoV . We therefore generated Huh7-CD300lf cells stably expressing the MNV receptor [47 , 48] and verified that these cells were indeed permissive for MNV infection by demonstrating the presence of NS3 24h after infection ( S1A and S1B Fig ) . In addition , MNV infected Huh7-CD300lf cells produced similar amounts of progeny virus compared to RAW 264 . 7 cells , albeit with slightly delayed kinetics ( S1C Fig ) . In contrast , Huh7 cells lacking CD300lf failed to amplify the virus inoculum ( S1C Fig ) . Therefore , ectopic expression of CD300lf rendered Huh7 cells fully permissive for MNV infection and supported the entire MNV replication cycle . Ultrastructural analysis of MNV infected Huh7-CD300lf revealed two major phenotypes not observed in uninfected cells ( Fig 1 ) : ( 1 ) Areas containing vesicles with a variety of shapes , sizes and types ( Fig 1A ) . In addition to previously reported SMVs , more complex structures like DMVs and MMVs were found , often in proximity to lipid droplets ( LD ) . This phenotype likely resembled an early replication phase described previously [44] . ( 2 ) A massive rearrangement of the entire endomembrane system consisting of complex structures , often associated with virions , and lacking an organized morphology was observed during what was likely a later stage of the replication cycle [44] ( Fig 1B ) . We found similar phenotypes in RAW 264 . 7 cells ( S2 Fig ) , which have been used in previous studies to characterize the ultrastructure of the MNV replication organelle [15 , 44] , except that these cells lacked LDs ( S2 Fig ) . We next analyzed whether similar structures were generated by expression of MNV ORF1 . MNV ORF1 was expressed in Huh7 T7 cells under transcriptional control of the T7 promoter and translational control of an encephalomyocarditis virus internal ribosomal entry site ( EMCV IRES ) , allowing efficient cytoplasmic expression of proteins of interest in the presence of T7 RNA polymerase ( Fig 2A and 2B ) . For ultrastructural analysis we used chemical fixation ( CF ) or high-pressure freezing ( HPF ) ( Fig 2C ) . Regardless of the fixation technique , we identified basically the same types of membrane alterations observed in phenotype 1 of MNV infected cells: vesiculated areas with SMVs , DMVs and MMVs , again in close proximity to LDs , whereas phenotype 2 ( complex membrane structures lacking organized morphology ) was not found upon expression of ORF1 . We concluded that expression of ORF1 generates membrane alterations comparable to replication organelles found in MNV infection . Therefore , ORF1 expression seemed a valid model to study the morphology of huNoV induced membrane alterations . We used ORF1 sequences of three GII . 4 strains associated with pandemic outbreaks: a Den Haag 2006b variant ( DH ) [49] , a New Orleans 2009 variant ( NO ) [50] and a Sydney 2012 variant ( Syd ) [51] . We first tested the expression of the different ORF1 proteins and their processing to assess the integrity of the polyproteins . In a coupled in vitro transcription/translation system ( S3A Fig ) most of NS-proteins remained buried in precursors , which according to their sizes could represent the entire ORF1 and NS4-NS7 ( S3A Fig ) . Mature cleavage products were only found for NS1-2 and/or NS3 , however the size of these proteins was almost identical . This result was in line with previous data studying ORF1 in vitro processing of GII . 4 [43 , 52] . In addition , we assessed polyprotein expression and processing by Western blotting ( WB ) after transfection of the plasmids encoding the three ORF1 proteins into Huh7 T7 cells . We could detect cleavage products corresponding to NS3 , ( S3B Fig ) , NS7 ( S3C Fig ) and NS1-2 , the latter by expressing N-terminal eGFP tagged ORF1 from the NO isolate since we lacked a specific antibody ( S3G Fig ) . No distinct cleavage products were observed for NS4 , NS5 and NS6 ( S3D–S3F Fig ) , indicating that they might be retained in relatively stable precursor proteins , as suggested by in vitro translation . To investigate membrane alterations resulting from huNoV nonstructural proteins , we expressed the complete ORF1 protein of the three GII . 4 isolates in Huh7 T7 cells and performed EM analysis ( Fig 3 , S4 Fig ) . As for MNV , the expression of ORF1 polyproteins resulted in the formation of complex vesicular structures for all three GII . 4 isolates ( Fig 3 , S4 Fig ) , similar to those found in MNV-infected cells ( Fig 1 , S2 Fig ) and irrespective of the fixation technique used . The average diameter of DMVs was approximately 100–200 nm ( Fig 3B ) , and resembled structures found upon HCV and picornavirus infection [28 , 31 , 53–56] . Most membrane alterations induced by ORF1 expression in Huh7 cells were again found in close association with LDs , similar to MNV . Overall , no consistent differences were found between ORF1 expression and infection regarding the ratio of SMVs , DMVs and MMVs ( Fig 3C ) . However , high variability in vesicles size was observed among cells of the same condition , likely due to differences in protein abundance , time of infection , cell type , etc . Still , SMVs were by far the most abundant vesicle species in all conditions . In summary , expression of ORF1 of different GII . 4 isolates gave rise to a complex set of membrane alterations independent of RNA replication , but similar to structures found in MNV-infected cells . Altogether our data suggested that norovirus replication organelles might belong to the DMV-morphotype , comparable to those observed for enteroviruses and HCV . We focused our subsequent analyses on one of the three GII . 4 strains ( NO ) , since neither polyprotein processing nor the ultrastructural analysis revealed distinct differences upon the expression of ORF1 among the three strains . To gain deeper insights into the morphology and biogenesis of ORF1-induced membrane structures we further analyzed tomograms of cells expressing ORF1 of the NO strain fixed by high pressure freezing ( S1–S5 Movies , Fig 4A and 4B and S5 Fig ) , allowing a better preservation of the cell membranes . Areas appearing as simple accumulations of SMVs , DMVs and MMVs revealed complex structures in close proximity to ER sheets , including clusters of interwoven vesicles delimited by one or several lipid bilayers ( S1 and S2 Movies and Fig 4A and 4B ) . In addition , we found double membrane vesicles ( DMVs ) connecting to multivesicular bodies ( MVBs ) or late endosomes ( S1 and S3 Movies and S5A and S5B Fig ) and autophagosome-like structures ( ALS , S1 and S3 Movies and S5C Fig ) . Since clusters of SMVs , DMVs and MMVs most closely resembled the organization of the MNV replication sites in infected cells , we rendered these areas to address their 3D organization ( Fig 4A and 4B , S4 and S5 Movies ) . SMVs ( white ) , DMVs ( yellow ) and MMVs ( blue ) all appeared rather vesicular than tubular , were tightly attached to each other and mostly found adjacent to ER cisternae . In some serial slices , the membrane of a DMV was found still in continuation with the ER ( Fig 4A , panel 2 ) . MMVs were mainly generated by enwrapping of SMVs with tubular structures , most likely elongated or collapsed SMVs , appearing as multilamellar vesicles in cross-sections ( Fig 4B , panel 2 ) . Alternatively MMVs were originated as SMVs or DMVs engulfing pre-existing DMVs ( Fig 4B , panel 3 , 4 ) . Overall , the morphology and complexity of the membrane alterations , as well as their biogenesis , appeared very reminiscent of later stages of the enterovirus replication sites [53–56] . Altogether , the ET analysis revealed complex interwoven vesicular structures adjacent to the ER , with one or several membrane bilayers , appearing as SMVs , DMVs and MMVs . MMVs were likely generated by enwrapping of SMVs with elongated SMVs , very similar to the mechanism proposed for enteroviruses . Little is known so far about the contribution of individual norovirus NS proteins to the biogenesis of the viral replication complex . We therefore fused NS1-2 , NS3 and NS4 , known to be associated with membranes [13] , N-terminally with eGFP , to study their propensity to induce membrane alterations by correlative light and electron microscopy ( CLEM ) . We first confirmed by WB the expression of stable fusion proteins and the absence of free eGFP ( S6A and S6B Fig ) . The N-terminal protein of norovirus ORF1 is thought to induce membrane rearrangements and is considered to be involved in replication complex formation ( reviewed in [8] ) . First , we examined the localization of eGFP-NS1-2 with respect to subcellular markers by immunofluorescence ( Fig 5A ) . Interestingly , eGFP-NS1-2 had a very peculiar filamentous subcellular distribution in most cells ( Fig 5A ) . A minority of cells showed a focal distribution of NS1-2 or an intermediate phenotype ( Fig 5A , white and yellow asterisk , respectively ) . Since the filamentous localization was observed for N-terminally HA-tagged NS1-2 , an artifact caused by eGFP fusion can be excluded ( Fig 5C ) . We found a significant co-localization of NS1-2 with a marker of the ER , judged by Pearson correlation values above 0 . 5 ( Fig 5A and 5B ) . Next , we used CLEM to determine the ultrastructural morphology of NS1-2-GFP positive structures ( Fig 5D ) . Here , regions with strong eGFP fluorescence , indicating high NS1-2 expression , were represented by a network of tubular membrane protrusions . The absence of ribosomes associated with these structures , and the general co-localization of NS1-2 with an ER marker indicated that these membrane protrusions originated from the smooth ER . It is interesting to note that similar tubular structures can be induced by overexpression of ER-shaping proteins such as REEP1 and CLIMP-63 through direct interaction with microtubules [57] . However , we found no indication for a co-localization of NS1-2 induced ER-tubules with microtubules or intermediate filaments ( S6C and S6D Fig ) . In addition we analyzed co-localization of eGFP-NS1-2 and NS3 upon expression of eGFP-ORF1 ( S6E and S6F Fig ) to assess the impact of the polyprotein on eGFP-NS1-2 localization . Interestingly , we found a variety of phenotypes in various cells , ranging from a focal distribution of both proteins ( upper panel ) to the filamentous localization of NS1-2 observed upon individual expression ( lowest panel ) . This result indicated a mutual impact of the NS proteins on their subcellular localization , retaining significant co-localization in all cases ( S6F Fig ) , as reported for MNV [15] . Since the distribution of GII . 4 NS1-2 was very different from the pattern reported for NS1/2 of MNV [13] , we further analyzed eGFP-NS1/2 of MNV by CLEM ( S7 Fig ) . Interestingly and in concordance with literature , NS1/2 of MNV was widely distributed throughout the cell and co-localized with the ER ( S7B Fig ) . However , we found no indications for specific membrane structures induced by MNV NS1/2 ( S7C Fig ) comparable to those found for GII . 4 NO . The MNV NS1/2 signal could be often correlated with membranes surrounding LDs ( S7C Fig ) , which may represent the ring like structures described in a previous study [13] . Taken together , eGFP-NS1-2 of GII . 4 induced tubular protrusions of membranes likely derived from smooth ER in a focused , mainly perinuclear area . This is in contrast to MNV NS1/2 , which is widely distributed on the ER . This suggests that NS1-2 of GII . 4 probably does not directly induce the vesicular membrane rearrangements observed upon ORF1 expression , but rather may contribute to the proliferation of membranes engaged in replication complex formation . Our results further illustrate differences in the subcellular localization and possible functions of NS1-2 proteins from different norovirus genogroups . We next analyzed the co-localization of eGFP-NS3 of GII . 4 NO with different markers of subcellular compartments ( Fig 6A ) . We observed a distinct , rather dot-like staining pattern for NS3 ( Fig 6A and 6B ) , which was similar to the pattern described for MNV NS3 [13] . NS3 significantly co-localized with markers of the Golgi apparatus , rough ER and LDs ( Fig 6A and 6C ) . We also characterized the subcellular localization of NS3 upon expression of ORF1 ( S8A–S8C Fig ) . Again we found some co-localization of NS3 with ER and LDs ( S8A and S8C Fig ) , albeit to a lesser extent as in case of individually expressed NS3 . In contrast , very little co-localization with several other markers of membranous intracellular organelles was observed , including Golgi apparatus ( S8C Fig ) , suggesting again a mutual impact of the NS proteins on their subcellular localization . The tight association of GII . 4 NS3 with LDs was also validated in CLEM experiments ( Fig 6B ) . Donut like structures of NS3 observed in IF were indeed NS3-studded membrane layers surrounding LDs ( area 1 ) . Interestingly , large foci with strong NS3 but weak LD signal were found to represent highly ordered membrane proliferations and were often observed in close proximity to one or more LDs ( area 2 , 3 ) . Such convoluted membranes were similar to previously described OSER ( organized smooth ER ) membranes with cubic symmetry ( reviewed in [58 , 59] ) . However , we did not observe these structures upon ORF1 expression ( Fig 3 ) . Overall , our findings indicate that GII . 4 NS3 was found on different membrane compartments of the secretory pathway and was closely associated to intracellular lipid storage compartments . The fluorescence pattern of eGFP-NS3 and -NS4 was quite similar , revealing a dot like pattern with donut like and filled structures ( Fig 7A ) located mainly in the perinuclear area . Still , the eGFP-NS4 signal tended to accumulate in larger clusters compared to the majority of eGFP-NS3 ( Fig 7A and 7B ) , but also co-localized with markers of ER , Golgi apparatus and LDs ( Fig 7A and 7B ) . CLEM analysis identified several interesting types of membrane alterations in areas with strong eGFP signal , in agreement with the idea that NS4 is a key driver in the formation of the norovirus replication compartment . Donut like structures were found to be membranes tightly associated with LDs ( Fig 7B , area 2 ) , similar to those found for NS3 ( Fig 6B ) . In addition , large eGFP-NS4 positive foci found in close proximity to LDs consisted of vesicle clusters composed of DMVs and SMVs ( Fig 7B , area 3 , 4; 7C , area 5 ) . The size and morphology of NS4 induced DMVs was very similar to those observed upon expression of the polyprotein ( Fig 7D compared to Fig 3B ) , but their abundance was apparently lower . In contrast , SMVs were much more abundant and heterogeneous in size , with a diameter ranging from 50 nm to 300 nm , although more than ~80% had a diameter <100 nm ( Fig 7E ) . These data suggested that NS4 on its own was capable of inducing vesicle accumulations reminiscent of vesicle clusters of the GII . 4 replication compartment . Finally , similarly to NS3 , highly fluorescent clusters of NS4 within the cells corresponded to regions forming regularly shaped membrane lattices ( Fig 7C , areas 6 and 7 ) . However , NS4 expression resulted in membranes aligned predominantly in tubules with hexagonal symmetry ( Fig 7C , area 6 ) . This hexagonal symmetry appeared very similar to the arrangement observed upon overexpression of the hydroxy-methylglutaryl ( HMG ) -CoA reductase [60] , although regions with cubic symmetry were also observed ( Fig 7C , area 7 ) . Furthermore , NS4 induced crystalline membrane structures were found in proximity to vesicle clusters ( Fig 7C , area 1 ) , suggesting that their formation might be concentration dependent and a consequence of very high local concentrations of NS4 . In essence , our results indicated that NS4 was a key factor in the biogenesis of GII . 4 induced membrane alterations . Specifically , the sole expression of NS4 was sufficient to induce several different types of membrane structures , including SMVs , DMVs , as well as geometric membrane lattices not found in infected cells . Our results obtained from individual expression of eGFP-NS1-2 , NS3 and NS4 of GII . 4 strain NO revealed that they were indeed associated with membranes . Since the functions of all three proteins , in particular NS1-2 and NS4 , are widely enigmatic , we next aimed to generate structural models based on secondary structure analysis and homology searches , allowing for the development of hypotheses accessible to experimental validation . There are no close homologs of known structures for these three proteins , but advanced search methods unambiguously detect distant homologs ( 20% sequence identity ) for parts of both NS1-2 and NS3 . These together with secondary structure predictions show that the 334-residue NS1-2 can be described as a three-partite protein with an unstructured N-terminus ( residues 1–110 ) followed by a papain-like thiol hydrolase domain ( residues ca 120–230 ) that is related to a family of phospholipases and acyltransferases [61] , and finally a hydrophobic domain ( residues ca 250–310 ) with one or possibly two transmembrane helices . We can thus draw two possible topologies for membrane association of NS1-2 ( Fig 8A ) . Interestingly , the catalytic cysteine and histidine of the putative thiol hydrolase are both present in NS1-2 as C205 and H139 . The 366-residue NS3 is a distant homolog of AAA ATPases with an extra 50 residues at the N-terminus comprising a hydrophobic helix that could be transmembrane . Again , we have two possibilities for NS3 membrane association . Finally , we could find no homolog of known structure for the 179-residue NS4 , but secondary structure predictions show that its approximately 140 N-terminal residues are highly structured and end in an amphipathic helix connecting to a natively unfolded C-terminus ( Fig 8A ) . Based on these predictions we finally aimed to gain some evidence for the putative hydrolase domain and its importance for membrane protrusions induced by NS1-2 of GII . 4 . To this end we generated mutants of the highly conserved proposed catalytic residues H139 and C205 in the context of eGFP-NS1-2 . Since these residues are invariant in all norovirus genogroups , we used MNV as a surrogate model to study their functional importance for norovirus replication . Interestingly , both NS1-2 mutants affected the abundance of the filamentous phenotype ( Fig 8B–8D ) , arguing for a contribution of the putative hydrolase domain to the generation of tubular ER protrusions . Albeit mutations H139A and C205A both resulted in an increased abundance of the intermediate phenotype , or a focal distribution of eGFP-NS1-2 , all variants still remained localized to the ER ( Fig 8E ) . Importantly , both residues were indeed essential for norovirus replication , as mutations at the corresponding positions H150A and C216A of MNV NS1-2 abrogated the production of infectious virus ( Fig 8F ) . In summary , our analysis of individually expressed NS1-2 , NS3 and NS4 suggest that these proteins , in particular NS4 , were the main drivers of replication complex formation for GII . 4 . The structural models proposing their membrane topology will allow more in depth studies of their precise functions . Our data further suggest a role of the predicted hydrolase domain in the membrane shaping activity of NS1-2 , but not to the general localization to the ER .
In this study , we analyzed membrane alterations induced by ORF1 expression of clinically relevant GII . 4 isolates and by the individually expressed NS-proteins . ORF1 expression generated vesicle accumulations comparable to those observed in MNV infected cells , mainly consisting of SMVs , DMVs and MMVs . Therefore , norovirus-induced membrane alterations can be generated in the absence of active RNA synthesis , and are reminiscent of picornaviruses and HCV [28 , 31 , 53–56] . Our data indicate that NS1-2 , NS3 and NS4 are the main drivers in the formation of GII . 4 replication organelles . However , only NS4 generated SMVs and DMVs similar to those observed upon ORF1 expression and MNV infection . These data provide the first experimental evidence for the hypothesis that NS4 might be the key factor in the morphogenesis of norovirus replication organelles ( reviewed in [8] ) . Previous studies on the ultrastructure of MNV replication organelles in RAW 264 . 7 cells reported on the appearance of vesiculated areas consisting of SMVs and DMVs at 12h post infection , progressing to a complete destruction of ER and Golgi at later time points , coinciding with the accumulation of virions [44] . We found in principle the same two phenotypes upon MNV infection of Huh7-CD300lf cells and in RAW 264 . 7 cells . ORF1 expression induced structures very similar to the phenotype lacking virions , likely corresponding to an early/intermediate infection stage , but did not progress into the complex endomembrane system observed concomitantly to intracellular virions appearance . Progression to the late stages found in infection might require the presence of the structural proteins or resemble cytopathic effects induced by infection , but not by ORF1 expression . Interestingly , we found no obvious difference in the membrane rearrangements induced by ORF1 or MNV compared to any of the three GII . 4 isolates included in this study , arguing for similar mechanisms driving these processes in various norovirus genogroups and validating MNV as a useful surrogate model to study particular aspects of general norovirus biology . Still , our data obtained upon GII . 4 ORF1 expression will ultimately require validation in a cell culture model with bona fide RNA replication , such as the recently established enteric organoid cultures [43] or infection of B cells [42] . The same holds true for the functional significance of the LD association we found for GII . 4 NS3 and NS4 . While LDs are clearly not essential for MNV replication , since they are not detectable in RAW 264 . 7 cells , we found them close to all virus induced membrane alterations in Huh7 cells , both , upon expression of ORF1 and upon MNV infection . Whether this is co-incidence due to high LD abundance in Huh7 cells or whether LDs have a functional significance in GII . 4 replication remains to be determined . At this point we have not been able to detect membrane alterations that could unequivocally be assigned to norovirus infection by EM in norovirus infected enteroid cultures ( S . Boulant , personal communication ) , likely due to the yet limited efficiency of this model and the high percentage of infected cells required for a thorough EM analysis . However , further optimization of culture conditions will hopefully allow such studies in the future , as well as the establishment of a reverse genetics model for GII . 4 isolates . Our ultrastructural analysis does not allow drawing firm conclusions on the origin and biogenesis of GII . 4 induced SMVs , since we did not observe such vesicles directly connected to cellular membranes . In contrast to PV , we could not find evidence for an interconnected tubular network [55] . Most of our evidence points to membranes of the ER as the origin of SMVs , similar to MNV [15] , due to the co-localization of NS1-2 and NS3 with ER membranes when expressed in the context of ORF1 . Furthermore , our tomograms reveal a close proximity of the induced vesicles to ER membranes . Finally , individually expressed NS1-2 , NS3 and NS4 all at least in part co-localize to and rearrange ER membranes . Therefore , huNoV , like many other viruses , may hijack this cell organelle to generate its replication organelles ( reviewed in [62] ) . Regarding DMVs and MMVs , we were able to identify intermediate structures . Many vesicles appearing as DMVs in a single plane were in fact MMVs in stato nascendi , generated by enwrapping vesicles with collapsed , elongated SMVs or ER cisternae . DMVs might originate as well from collapsing SMVs . This indeed closely resembles the mechanisms demonstrated for picornaviruses [55 , 56] . Finally , our tomograms also showed a variety of complex structures ( e . g . MVBs , ALS ) that might be linked to the biogenesis of these vesicles , however these structures are currently difficult to interpret regarding their functional significance . Overall , our data suggest that norovirus induced membrane alterations are very closely related to picornavirus replication organelles with respect to both morphology and biogenesis . The relatively low abundance of DMVs and MMVs further argues for a non-essential role in norovirus infection , again in line with reports on picornaviruses , where replication most likely occurs on single membrane structures , whereas the appearance of DMVs is associated with later stages of infection [55 , 56] . Expression of the individual nonstructural proteins fused to eGFP and subsequent analysis by CLEM revealed striking phenotypes for NS1-2 , NS3 and NS4 , which are very poorly understood regarding specific functions in viral RNA replication and topology ( Fig 8A ) . The sequence of NS1-2 is highly divergent between different norovirus genogroups with no specific function assigned thus far . Bioinformatic analysis proposed an unstructured N-terminal region , which was confirmed biochemically [9] , a central domain with potential hydrolase function [10] and a C-terminal transmembrane domain [9 , 63] . Norwalk virus NS1-2 was shown to disrupt the Golgi apparatus [11] , whereas MNV NS1-2 mainly localizes to the ER [13] . Our data now show a very peculiar filamentous localization of GII . 4 NS1-2 which could be identified as tubular protrusions of the smooth ER when analyzed by CLEM . Similar structures have been observed upon overexpression of cellular ER remodeling proteins such as REEP1 and CLIMP-63 through direct interaction with microtubules [57] , which appear to be the driving force in protruding the tubules . In the case of NS1-2 , we found no co-localization with microtubules or intermediate filaments , therefore it is currently not clear how the ER-tubules are expanded . Since mutations in the active site of the putative hydrolase domain severely affected the formation of the tubular ER-protrusions , it is tempting to speculate about a role of this predicted enzymatic function in the membrane shaping activity of NS1-2 . However , the functional significance of the ability of GII . 4 NS1-2 to form filamentous ER tubules still remains unclear , since MNV NS1/2 is devoid of this property . Overall these data are suggestive for the presence of an enzymatic activity residing in NS1-2 , but they clearly provide no formal proof , which will require the demonstration of such activity in a purified protein . Interestingly , a recent study identified an interaction of the variable , unstructured N-terminus of MNV NS1/2 with the host proteins VAPA and VAPB , critical for viral replication [64] . VAPA has already been observed to complex with Norwalk NS1-2 [65] , presumably via a different , yet to be defined region . More strikingly , VAPA and VAPB have been implicated in the biogenesis of the HCV replication compartment via interaction with NS5A ( reviewed in [23] ) . Therefore , VAPA and VAPB interactions might contribute to the function of NS1-2 in the formation of the norovirus replication organelle , or even be a common host factor of many DMV-type positive strand RNA viruses . Overall , our data provide a first step to define a role for GII . 4 NS1-2 in the biogenesis of the huNoV replication compartment in proliferating membranes of the smooth ER , thereby generating the material that could be transformed into vesicle clusters by other NS-proteins and suggesting a role of the predicted hydrolase function in this process . Our study demonstrates that NS3 and NS4 both had the ability to create organized ER structures known as convoluted membranes , tubulo-reticular structures , crystalloid ER or cubic membranes , which have been found in a variety of viral infections and upon overexpression of ER membrane shaping proteins ( reviewed in [58 , 59] ) . Interestingly , similar structures have been found upon expression of HAV 2C and 2BC , with the former regarded as the functional counterpart of norovirus NS3 in picornaviruses [34] . These structures clearly resemble OSER membranes with cubic ( NS3 ) and hexagonal ( NS4 ) symmetry . It has been reported that OSER structures can be generated through weak protein-protein interactions , which can even be triggered by overexpression of GFP tagged to cytochrome b ( 5 ) , with the GFP moiety providing the homotypic interactions [66] . Therefore , we currently cannot exclude that the fusion of NS3 and NS4 with eGFP , required for CLEM , also contributed to this phenotype . However , it seems unlikely that these structures are essential for viral replication , since they have not been observed so far in either MNV infected cells or upon ORF1 overexpression [15 , 44] . Therefore , the ability of NS3 and NS4 to induce cubic membranes might point to an intrinsic property , probably weak homotypic interactions , and seems to be linked to strong overexpression of single NS proteins . Upon ORF1 expression and virus infection , such structures might be prevented due to the interactions among NS-proteins and by lower local concentrations of individual NS-proteins . Also in the case of HAV , cubic membranes were only found upon 2C/2BC overexpression [34] but not in HAV infected cells [28] . The strong association of NS3 with ER membranes surrounding LDs as well as its potential to induce convoluted membranes suggests an active function in the formation of GII . 4 replication organelles , which clearly requires more detailed studies beyond the scope of this manuscript . A similar localization pattern was previously found for MNV NS3 in Vero cells , but not tested for LD localization at that point [13] . Interestingly , NS1-2 co-localized to NS3 on LDs in most cells upon expression of ORF1 , whereas this LD association was never found upon sole expression of GII . 4 NS1-2 . This result argues for an additional role of NS3 in recruiting NS1-2 upon formation of the viral replication organelle , probably involving direct interactions . The recently observed localization of MNV NS3 with microtubules and cholesterol rich lipids when transiently expressed in Vero cells [45 , 46] further argues for a function of NS3 in shaping the replication organelle . Whether or not the NTPase activity , which has been demonstrated for the NS3 protein of GI . 1 [14] , is important for localization and membrane activity of NS3 remains to be determined . However , the most obvious putative function of NS3 in RNA replication is a supposed helicase activity , suggested by conserved SF3 helicase motifs [67] . Still to date , only the related 2C protein from the picornavirus Enterovirus 71 has been shown to function as an ATP dependent helicase [68] . NS4 is by far the most enigmatic among the norovirus NS proteins and little is currently known about its structure or function . Here , we provide the first direct experimental evidence suggesting that NS4 indeed might be the central organizer of the norovirus replication complex . In contrast to HCV , where all NS proteins can induce the formation of vesicular structures [31] , NS4 was , in our hands , the only GII . 4 protein capable of vesicle formation upon individual expression . In addition , SMVs and DMVs were found , similar to MNV infection and ORF1 expression , albeit with differing abundance . Altogether , these data suggest that NS4 may be the key driver in the formation of norovirus induced replication vesicles , requiring auxiliary functions of NS1-2 and NS3 to finally shape the replication organelles . This is reminiscent of HCV , where the complexity of the so called membranous web is only found upon expression of a polyprotein precursor encoding NS3 to NS5B , arguing for a concerted action engaging several NS-proteins [31 , 33] . In the case of picornaviruses , 2BC [27] , 2BC/3A [26] , 2C [34 , 69] and 3AB [35] have been found to generate distinct membrane alterations upon individual expression , with 2BC/3A and 3AB generating DMVs . However the ultrastructure of the replication organelles is far more complex upon infection also in case of picornaviruses [55] . Still , it is interesting to note that the unrelated proteins NS5A of HCV and 3A of PV are capable of inducing DMVs and share a similar structural organization . Specifically , both contain a unique structured region lacking enzymatic functions , which has been resolved for HCV NS5A [70] and PV 3A [71] , an intrinsically unfolded region engaged in recruitment of host factors [72–74] and a membrane attachment region . Our current prediction for the organization of NS4 , albeit highly speculative at this point , is strikingly similar regarding subdomain organization and functions ( Fig 8A ) . This model will provide a valuable starting point for further in depth studies regarding the function of norovirus NS4 . Another important aspect that needs to be addressed in future studies is the function of NS4 as part of various stable polyprotein cleavage intermediates . Our study , including three different GII . 4 strains suggests a delayed cleavage of the NS4-NS7 precursor , based on in vitro translation . However , a far more detailed analysis of polyprotein cleavage kinetics using a different GII . 4 strain suggests a number of cleavage intermediates with NS4 , including NS4-NS7 , NS4-NS6 and NS4-NS5 [52] , which might serve specific functions in the norovirus replication cycle . It is particularly tempting to speculate that delayed cleavage of NS4-NS7 might avoid diffusion of the replicase components , since NS4 seems the only protein associated with membranes in this precursor protein . Our model of the putative membrane organization of NS1-2 , NS3 and NS4 ( Fig 8A ) is still highly speculative and contains several uncertainties regarding transmembrane topology . While our predictions are in favor of one transmembrane domain for both , the NS1-2 and NS3 proteins , this topology would require a post-cleavage membrane insertion to keep the cleavage site accessible to NS6 . Alternatively , and still in line with the predictions , NS1-2 could span the membrane twice and NS3 may harbor an amphipathic alpha helix tethering the protein to membranes , thereby keeping the NS6 cleavage site in the cytoplasm . Regardless , the transmembrane topology of NS1-2 and NS3 can be experimentally addressed in future studies using our expression model and , for example , testing the accessibility of N- and C-termini to proteases in cell lysates . In summary , our study reveals a first insight into the organization of the putative GII . 4 replication organelle and the contribution of individual NS proteins to its biogenesis using a protein expression model . In the case of HCV , comparable expression models have been invaluable to study formation and structure of the viral replication organelles as they allow mechanistic studies using replication deficient mutants or inhibitors interfering with replication . Thereby , the contribution of viral NS-proteins could be clearly defined [31 , 33] in addition to the importance of host factors like PI4KA [75 , 76] . Only expression models allowed us to identify viral membrane alterations as targets of direct antiviral agents ( NS5A inhibitors [37] ) and host targeting drugs including PI4KA inhibitors [75] and cyclophilin inhibitors [38] . Similar approaches might help to identify novel strategies to develop drugs targeting norovirus replication in future studies . Furthermore , our study lays the groundwork for an in-depth analysis of the functions of NS1-2 and NS4 in replication complex formation .
All cell lines were cultured in Dulbecco’s Modified Eagle Medium ( DMEM; Life Technologies , Darmstadt , Germany ) supplemented with 10% fetal bovine serum , non-essential amino acids ( Life Technologies , Darmstadt , Germany ) , 100U/ml penicillin and 100ng/ml streptomycin ( Life Technologies ) and cultivated at 37°C and 5% CO2 . The human hepatoma cell line Huh7 ( maintained in our laboratory ) , stably expressing T7 RNA polymerase under blasticidine selection ( 5 μg/ml , Invitrogen , Germany ) [74] , was used for transient expression of plasmids encoding GII . 4 NoV proteins that were analyzed by immunofluorescence and Western blot assays . Huh7 cells expressing the MNV receptor CD300lf were generated by transduction with a lentiviral vector encoding the murine CD300lf cDNA [47] ( generous gift from R . C . Orchard and H . W . Virgin ) . Cells were selected by puromycin to obtain a stable culture of Huh7 cells with CD300lf expression . The murine macrophage cell line RAW 264 . 7 was obtained from ATCC ( Middlesex , UK ) and used for infection with MNV . MNV-CW1 [44] was used at a multiplicity of infection of 1 and analyzed 24 h after infection , unless otherwise stated . HEK293T-cells ( Birke Bartosch , Lyon ) were used for production of MNV virus stocks upon transfection of plasmid pMNV-CW1 ( generous gift from H . W . Virgin ) . The genomes representing consensus sequences of respective patient isolates of three GII . 4 strains including a Den Haag 2006b variant ( DH ) ( GenBank accession no . AB447456 ) , a New Orleans 2009 variant ( NO ) ( GenBank accession no JQ613573 ) and a Sydney 2012 variant ( Syd ) ( GenBank accession no JX459908 ) were used in this study . Coding sequences corresponding to ORF1 of the three isolates were synthesized with protein sequences identical to the GenBank entries in vector pBMH by Biomatik ( Cambridge , Canada ) . Full length ORF1 of NO , Saga , Sydney , MNV were amplified by PCR from the pBMH construct . Restriction sites NcoI and PacI were used to insert fragments into a basic pTM1-2 , AgeI and PacI was used for insertion into basic pTM 1–3 . To generate pTM vectors allowing expression of N-terminally HA- or eGFP tagged individual norovirus nonstructural proteins , respective coding sequences were amplified using primers given in Table 1 and cloned into pTM-HA or pTM-eGFP , respectively using the indicated restriction sites . All PCR amplifications for cloning were performed with Phusion Flash High-Fidelity PCR Master Mix according to the manufacturer’s instructions ( Thermo Fisher Scientific , Germany ) . PCR products were separated by agarose gel electrophoresis and purified with NucleoSpin Gel and PCR Clean-up kits purchased from Macherey-Nagel ( Germany ) . Restriction digests were performed according to the instructions of the manufacturer ( New England Biolabs ) . All parts of plasmid sequences amplified by PCR were analyzed by Sanger-sequencing to verify sequence fidelity and the correct reading frame ( GATC Biotech , Konstanz , Germany ) . MNV stocks were obtained by transfecting plasmid pMNV-CW1 ( generous gift from Herbert W . Virgin ) into 293T cells , as described [77] . 293T cells were seeded in 10 cm tissue culture dish ( Corning , Durham , NC , USA ) at a density of 3x105 cells/ml in 15 . 5 ml of complete DMEM . After 24 hours , cells were transfected with 15 μg of pMNV-CW1 plasmid DNA using TransIT-LT1 Transfection Reagent ( Mirrus Bio LLC , Madison , WI , USA ) following the instructions of the manufacturer . Transfections were incubated for 48 hours . Virus stocks were obtained by harvesting the cells in their culture medium and twice freezing at -80°C and then thawing at 37°C . Lysates were then centrifuged at 2500 x g for 5 minutes and clarified virus stocks were stored at -80°C . To determine virus titers , RAW 264 . 7 cells were seeded in 96 well tissue culture plates ( Corning , Durham , NC , USA ) at a density of 2x104 cells per well . After 24 hours , wells were infected in quadruplicate with serial dilutions of virus stocks diluted in DMEM medium . Assays were harvested 72 hours post infection by aspirating supernatant , washing with PBS and staining with 50 μl of a 1 . 25% ( w/v ) crystal violet solution ( Merck , Darmstadt , Germany ) in a 25% ( v/v ) ethanol solution for 10 minutes at room temperature . The wells were washed twice with distilled water and scored as positively infected or negative . The TCID50/ml was then calculated using the Spaerman-Kärber method [78 , 79] . For the transient expression of norovirus eGFP or HA-tagged proteins , Huh7 T7 cells were transfected with LT1 transfection agent ( Mirus Bio LLC , Madison , WI , USA ) according to the manufacturer's instructions . Cells were processed for IF as described in [76] . Briefly , cells were fixed in 4% paraformaldehyde ( PFA ) for 20 min and permeabilized with 0 . 5% Triton X-100 ( PBS ) for 15 min for co-localization analysis of eGFP-tagged proteins with subcellular markers . Primary antibodies were incubated in 3% bovine serum albumin ( BSA ) for 1 h at room temperature ( RT ) . NS3 was detected with an in-house created rabbit polyclonal serum , animals were immunized with NS3 expressed and purified from E . coli by Davids Biotechnology , Regensburg , Germany . Antibodies detecting NS4/p20 and NS6 of GII . 4 were a generous gift from Stefan Taube [80] . Anti NS5/VPg ( strain SAB60 ) and anti NS7 ( NO-strain ) sera were raised in rabbits ( Eurogentech ) based on purified proteins expressed in E . coli . αNS3MNV PAB was kindly provided by Prof . Dr . Ian Goodfellow , Cambridge University , UK , and was obtained by immunization of rabbits with GV MNV and GI sequences [81] . Subcellular compartments and dsRNA were labeled by the following commercially available antibodies: SEC31A/ COPII Vesicles: BD Bioscience / 612351 ( Becton Dickinson GmbH , NJ USA ) ; Golgi Apparatus: Anti-Golgin 97 ( ab84340 , Abcam ) ; autophagosome: p62/ SQSTM1 ( M162-3 , MBL Life science ) . Mitochondria were labeled with MitoTracker Deep Red ( M22426 ) and LDs were stained with HCS LipidTox Red Neutral Lipid Stain ( Thermo Fisher Scientific Waltham , Massachusetts , USA ) . ER was stained with a polyclonal anti-PDI antibody ( ab31811; Abcam , Cambridge , UK ) unless otherwise stated . In case co-staining did not allow the use of this antibody , a mouse monoclonal antibody against Climp-63 ( mainly rough ER , ALX-804-604 , Enzo ) or reticulon 3 ( RTN3 sc-374599 , Santa Cruz Biotechnology ) was used . The lysosome was marked with anti LBPA1 antibody ( Clone 6C4 , Sigma-Aldrich , Germany ) . All primary antibodies were utilized in a 1:50 dilution . Alexa 488 or 647-conjugated secondary antibodies ( Invitrogen , Molecular Probes ) were incubated in 3% BSA for 45 min at RT with a dilution of 1:1000 . Nuclei were stained using 4 , 6-diamidino-2-phenylindole ( DAPI ) for 1 min at a dilution of 1:4000 after incubation with secondary antibodies . Cells were mounted with Fluoromount G ( Southern Biotechnology Associates , Birmingham , AL , USA ) . Confocal microscopy was conducted on a Leica SP5 AOBS Point Scanning Confocal Microscope ( Leica Microsystems ) . Confocal microscopy was conducted on a Leica SP5 and on Leica SP8 AOBS Point Scanning Confocal Microscopes ( Leica Microsystems ) . Image analysis was performed using the ImageJ software package Fiji ( http://fiji . sc/wiki/index . php/Fiji ) [82] and the Coloc 2 plugin was used to calculate the Pearson's correlation coefficient . A Pearson's correlation coefficient higher than 0 . 5 indicates a strong colocalization . Cells grown on glass coverslips were subjected to chemical fixation and subsequent epon embedding . For overexpression of norovirus proteins , Huh7-Lunet T7 cells were transfected with TransIT-LT1 ( Mirus , Madison USA ) transfection reagent according to manufacturer’s instructions and fixed 24 hours post transfection . RAW 264 . 7 ( ATCC , UK ) were used for infection with MNV-CW1 [83] . For chemical fixation , cells were washed 3 times with 1x PBS and fixed for 30 min with pre-warmed 2 . 5% glutaraldehyde in 50 mM sodium cacodylate buffer ( pH 7 . 2 ) containing 1 M KCl , 0 . 1 M MgCl2 , 0 . 1 M CaCl2 and 2% sucrose . Cells were washed thoroughly 5 times with 50 mM cacodylate buffer and post-fixed on ice in the dark with 2% OsO4 in 50 mM cacodylate buffer for 40 min . Cells were washed with H2O overnight , treated with 0 . 5% uranyl acetate in H2O for 30 min , rinsed thoroughly with H2O and dehydrated in a graded ethanol series at RT ( 40% , 50% , 60% , 70% and 80% ) for 5 min each and 95% and 100% for 20 min each . Cells were immersed in 100% propilene oxid and immediately embedded in an Araldite-Epon mixture ( Araldite 502/Embed 812 Kit; Electron Microscopy Sciences ) . After polymerization at 60°C for 2 days , coverslips were removed and the embedded cell monolayers were sectioned using a Leica Ultracut UCT microtome and a diamond knife . Sections with a thickness of 70 nm were counter-stained with 3% uranyl acetate in 70% methanol for 5 min and 2% lead citrate in H2O for 2 min , and examined with the transmission electron microscope Philips CM120 TEM ( Biotwin , 120 kV ) . Huh7-T7 cells seeded on glass-bottom dishes containing a photoetched gridded coverslip ( MatTek ) were transiently transfected with expression plasmids coding for GFP-fused norovirus proteins . After 24 h cells were washed twice with PBS , fixed for 30 min at room temperature with PBS containing 4% paraformaldehyde and 0 . 2% glutaraldehyde and stained with DAPI and far-red LipidTOX neutral lipid stain ( Thermofisher ) according to the manufacturer's instructions . Samples were analyzed on a Nikon TE2000 Ultraview ERS spinning disc ( PerkinElmer ) . Z-stacks of GFP-positive cells were collected and the positions of the cells of interest were recorded using transmitted light with a differential interference contrast configuration . Cells were then fixed in EM fixative and embedded in Epon/araldite resin , as described above . Seventy nm ultrathin sections were prepared and examined with a Jeol JEM-1400 transmission electron microscope ( Jeol Ltd . , Tokyo , Japan ) . Landmark correspondence plugin from Fiji imageJ distribution was used to correlate the light microscopy and the electron microscopy datasets . Briefly , a single optical section displaying a LDs distribution that matched the one observed in the electron microscopy micrograph was extracted from the z-stack and the corresponding LDs on the two images were used as landmark to calculate the transformation . Cells were seeded onto 3 mm sapphire discs ( M . Wohlwend GmbH , Sennwald , Switzerland ) that had been carbon coated to improve cell adhesion . One day after transfection or infection cells ( Huh7-Lunet T7 and RAW 264 . 7 , respectively ) were frozen after immersion in 1-hexadecene ( Merck , Hohenbrunn , Germany ) using a high-pressure freezer ( M . Wohlwend GmbH ) . Frozen discs were stored in liquid nitrogen until further processing . Freeze substitution was done in acetone containing 0 . 2% ( w/v ) OsO4 , 0 . 1% ( w/v ) UA , and 5% ( v/v ) water by slowly warming the samples from −90°C to 0°C during a period of 20 h [84] . Samples were kept at 0°C and at room temperature for 30 min each , washed with acetone , and embedded in four-step epon series ( Fluka , Buchs , Switzerland ) using 1 h-incubation in 25% , 50% and 75% epon dissolved in acetone and overnight incubation in 100% epon . Epon was exchanged , polymerized for 3 d at 60°C and sapphire discs were removed by immersion in liquid nitrogen . Seventy or 250 nm thick sections for were examined by conventional transmission EM or electron tomography , respectively . Sections of 250 nm thickness were collected on palladium-copper slot grids ( Science Services , Munich , Germany ) coated with Formvar ( Plano , Wetzlar , Germany ) . Protein A-gold ( 10 nm ) was added to both sides of the sections as fiducial markers . Single axis tilt series were acquired with a FEI TECNAI F30 microscope operated at 300 kV and equipped with a 4k FEI Eagle camera over a −65° to 65° tilt range ( increment 1° ) and at an average defocus of −0 . 2 μm . Reconstruction of the tomograms and rendering of their 3D surface was performed by using the IMOD software package ( version 4 . 9 ) [85] ( bio3d . colorado . edu/imod ) . For Western blotting , the cells in a 6-well plate well were lysed and denatured in 150 μL of 6× Laemmli buffer by heating to 95°C for 5 minutes , and loaded onto an 12% polyacrylamide-SDS gel . After resolution by SDS-PAGE , proteins were transferred to a polyvinylidene difluoride ( PVDF ) membrane , with the exception of NS7 , which was transferred to a nitrocellulose membrane ( Amersham Protran 0 . 45 NC , GE Healthcare Life Science ) . NS proteins were detected using NS-protein specific polyclonal rabbit antibodies described in IF section in a 1:1000 dilution . β-actin was detected by monoclonal mouse antibody ( A5441 ) , Sigma-Aldrich . Primary antibodies were detected using αrabbit/ αmouse horseradish peroxidase ( HRP ) -coupled secondary antibodies ( Sigma-Aldrich ) and imaging was done with the ChemoCam 6 . 0 ECL system ( INTAS Science Imaging , Goettingen , Germany ) . pTM based constructs were phenol/chloroform purified and reconstituted in RNase-free double-distilled H2O to a concentration of 1 μg/μL . 0 . 5 μg of the plasmid preparation were then mixed with 10 μL of the L1170 T7 TNT-kit ( Promega , Madison , USA ) and 1μL of 35S Methionine 10 mCi/ml . The reaction mixture was then incubated at 30°C for 90 min . Afterwards , the reaction was suspended in 2x laemmli buffer , and denatured at 95°C for 5 min before being loaded onto a 12% SDS gel for electrophoresis . Radiolabeled proteins were visualized by autoradiography using a phospho-imager ( BioRad , Munich , Germany ) . Stable cell lines expressing the MNV receptor CD300lf [47 , 48] , were created by lentiviral transduction . The lentiviral vectors were created by co-transfecting 293T cells with a gag-pol plasmid ( pCMV∆8 . 31 ) , the retroviral vector containing the CD300lf sequence ( gift from Dr . Herbert Virgin , Washington University at St . Louis , St . Louis , MO , USA ) , and an envelope plasmid ( pMD . G ) mixed in a 3:3:1 ratio , respectively , as described elsewhere [86] . Huh7 cells were seeded at a density of 1x105 cells per well in 6-well plates and transduced with 1 ml of lentivirus suspension mixed with 1 ml of fresh DMEM medium for 12 hours . Media was aspirated and replaced with 1:1 lentivirus suspension and 1 ml of fresh DMEM two more times at 12 hour intervals . After 36 hours , the media was changed and 2 ml of fresh DMEM containing 2 μg/ml puromycin selection was added and selective pressure was maintained during passaging . We used resources of the MPI bioinformatics Toolkit [87] to define domains in NS1-2 , NS3 and NS4 . HHPRED was used to find domains with homologs of known structure and Quick2D to identify structured and non-structured regions and putative transmembrane helices [88 , 89] . Transmembrane helices were further sought with Polyphobius . For putative membrane-peripheral and transmembrane helices , ideal alpha-helices were generated with the “fab” function of PyMOL , as were intervening loops . The NS1-2 central domain ( residues 119 to 213 ) was homology modelled with SwissModel [90] from Protein Data Bank entry 4DPZ [61] . The non-structured N-terminus and helical C-terminus were then generated with PyMOL 1 . 8 . 2 . 0 [91] . The three parts were assembled in PyMOL with the PyMOL sculpting function . NS3 was modelled with I-Tasser [92] and NS4 was modelled with RaptorX [93] , with default parameters . A membrane model was generated with the Charmm-GUI webserver [94] with a lipid composition similar to the endoplasmic reticulum ( Phosphatidylcholine 60%; Phosphatidylethanolamine 25%; Phosphatidylinositol 15% ) [95] . The positions of the transmembrane/peripheral helices relative to this membrane were adjusted with PyMOL . The structures were then minimized with secondary structure restraints with the Phenix geometry minimization function [96] . General statistical analyses as indicated in the corresponding figures were performed using Graphpad Prism Software . | Positive-strand RNA viruses induce membrane alterations harboring the viral replication complexes . In the case of human noroviruses ( huNoV ) , the major cause of acute viral gastroenteritis , these are induced by the ORF1 polyprotein , which is post-translationally processed into the functional nonstructural ( NS ) proteins . Partly due to the lack of efficient cell culture models , little is known so far about membrane alterations induced by huNoV belonging to the most clinically relevant genogroup II , genotype 4 ( GII . 4 ) , nor about the function of individual NS proteins in their formation . We therefore expressed ORF1 proteins of GII . 4 and individual NS proteins in cells to study their contribution to viral replication complex formation . Expression of ORF1 proteins of GII . 4 induced vesicular membrane alterations comparable to those found in infected cells and similar to picornaviruses and hepatitis C virus ( HCV ) . GII . 4 NS1-2 , NS3 and NS4 are contributing to viral membrane alterations . Our work provides new insights into their function in huNoV induced replication complex formation while identifying NS4 as the most important single determinant . This knowledge might provide novel attractive targets for future therapies inhibiting the formation of the membranous viral replication complex , as exemplified by the efficacy of HCV NS5A inhibitors . | [
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"str... | 2017 | Membrane alterations induced by nonstructural proteins of human norovirus |
Human African Trypanosomiasis ( HAT ) is caused by two trypanosome sub-species , Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense . Drugs available for the treatment of HAT have significant issues related to difficult administration regimes and limited efficacy across species and disease stages . Hence , there is considerable need to find new alternative and less toxic drugs . An approach to identify starting points for new drug candidates is high throughput screening ( HTS ) of large compound library collections . We describe the application of an Alamar Blue based , 384-well HTS assay to screen a library of 87 , 296 compounds against the related trypanosome subspecies , Trypanosoma brucei brucei bloodstream form lister 427 . Primary hits identified against T . b . brucei were retested and the IC50 value compounds were estimated for T . b . brucei and a mammalian cell line HEK293 , to determine a selectivity index for each compound . The screening campaign identified 205 compounds with greater than 10 times selectivity against T . b . brucei . Cluster analysis of these compounds , taking into account chemical and structural properties required for drug-like compounds , afforded a panel of eight compounds for further biological analysis . These compounds had IC50 values ranging from 0 . 22 µM to 4 µM with associated selectivity indices ranging from 19 to greater than 345 . Further testing against T . b . rhodesiense led to the selection of 6 compounds from 5 new chemical classes with activity against the causative species of HAT , which can be considered potential candidates for HAT early drug discovery . Structure activity relationship ( SAR ) mining revealed components of those hit compound structures that may be important for biological activity . Four of these compounds have undergone further testing to 1 ) determine whether they are cidal or static in vitro at the minimum inhibitory concentration ( MIC ) , and 2 ) estimate the time to kill .
Human African Trypanosomiasis ( HAT ) is caused by infection with either the trypanosome subspecies Trypanosoma brucei gambiense or Trypanosoma brucei rhodesiense . Decreasing numbers of reported new cases over the last 10 years have been reported - from over 25 , 000 in 2000 to 10 , 000 in 2009 - of which over 95% are caused by T . b . gambiense [1] . However , the World Health Organization ( WHO ) currently estimates the actual number of cases to be around 30 , 000 [http://www . who . int/mediacentre/factsheets/fs259/en/] . HAT is mainly confined within sub-Saharan Africa , where the vector , the parasite and the animal reservoirs co-exist [2] . HAT occurs in two stages , whereby the first stage , also called the haemolymphatic stage , corresponds to the invasion of lymph , blood and other tissues by the trypanosomes , and the second stage is associated with parasites crossing the blood-brain barrier and invading the central nervous system ( CNS ) . Symptoms of the second stage of the disease include mental impairment , severe headaches , fever , chronic encephalopathy and an eventual , terminal somnolent state , if the disease remains untreated . There are currently few drugs available for the treatment of HAT . For the first stage of the disease , suramin is used as the treatment for T . b . rhodesiense and pentamidine for T . b . gambiense infections . Neither of these drugs are able to cross the blood brain barrier and therefore are not effective against the CNS resident , second stage of the disease . In addition , both of these treatments have significant side effects , often resulting in reduced compliance . Suramin is associated with exfoliative dermatitis [2] and renal failure [3] , whilst pentamidine use has been correlated with diabetes mellitus and nephrotoxicity [4] . Melarsoprol , an organoarsenic compound , is most frequently used for the treatment of the second stage of the disease as it is effective against both trypanosome subspecies . However , there have been reports of high failure rates with melarsoprol , and although resistance has not definitively been proven , this does highlight the need for alternative therapies [5] . As a consequence of treatment with melarsoprol , encephalopathic syndromes occur in 5 to 10% of all of treated patients causing between 10 to 70% fatality , depending on the literature source [6]–[10] . The alternative therapy for the second stage of the disease , eflornithine , is a less toxic and a safer alternative however it is unfortunately not effective against T . b . rhodesiense . There are also problems with affordability of eflornithine in many of the disease-endemic countries [11] . The recent inclusion of nifurtimox to the WHO Essential Medicine List in 2009 [11] , [12] , to be used only in combination with eflornithine for the treatment for the second stage of HAT caused by T . b . gambiense , is a significant milestone . Nifurtimox-eflornithine combination therapy ( NECT ) has a shorter and simplified administration regimen and is the only significantly improved treatment option made available to patients in the past 25 years . NECT is now used as the first line treatment for stage 2 HAT caused by T . b . gambiense [13] , [14] . There was some hope for an oral drug for treating the first stage of HAT with the compound , pafuramidine ( DB289 ) . Unfortunately , in an extended phase III trial , liver toxicity and delayed renal insufficiency were observed in a number of patients and consequently the program was discontinued in 2008 [15] . Recent advances which hold promise include the identification of orally bioavailable oxaborole 6-carboxamides which have been shown to cure a murine model of late stage CNS HAT [16] and an orally active benzoxaborole has been selected to enter pre-clinical studies [17] . Despite this there is still a need for the discovery of additional trypanocidal compounds with the potential for further progression in the drug discovery pipeline for HAT . This is particularly evident when one takes into account the toxicity of traditional treatments , the inability of the newer less toxic combination therapies to treat both subspecies or both disease stages , and the historical 90% failure rate of drugs entering the clinic to reach the market [18] . One method for the identification of active compounds against HAT is the application of high throughput screening ( HTS ) methods . HTS against T . b . brucei targets , such as the enzyme TbHK1 ( Trypanosoma brucei hexokinase 1 ) [19] have recently been reported . A potential drawback to target-based HTS is that screening hits may have to undergo significant medicinal chemistry optimisation to impart favourable properties for low serum binding , high membrane permeability and high aqueous solubility in order to register potent activity against the parasite . Whole cell screening is becoming increasingly popular , as although elucidation of the biological target requires deconvolution , active compounds are discovered under conditions that are already physiologically relevant . We have recently reported the development of a 384 Alamar Blue based 384-well viability assay for HTS screening of compounds against T . b . brucei [20] . For this assay , and indeed many in vitro models for studies of HAT , the human non-infective sub-species T . b . brucei blood stream form has been utilised [21] . Alamar Blue ( containing resazurin ) is a fluorometric/colorimetric REDOX indicator . In a reducing environment caused by metabolising cells , resazurin is converted to resorufin , a fluorescent end product . This reagent has been used routinely as an indicator of the viability of mammalian cells . It is thought that cells may induce a reduction in the medium or reduce Alamar Blue intracellularly [22] . We have shown that the fluorescent Alamar Blue signal is linear to the number of T . b . brucei cells in a well , therefore it provides a good indication of viable cell numbers [20] . For this reason we have used this assay to assess the activity of compounds against T . b . brucei whole cells . Here we describe the HTS of a compound library ( WEHI 2003 collection [23] ) using a 384-well whole cell T . b . brucei assay , and the retesting of the identified active compounds against both T . b . brucei and a human cell line , HEK293 , in order to assess mammalian cytotoxicity . The reproducibility of both the primary and retest assays were evaluated by the Z'-factor ( Z' ) , a coefficient which reflects the reproducibility of the assay and is calculated using the positive and negative controls . The Z' takes into account the control signal range and variation , with a value close to 1 considered highly reproducible [24] . Reference compound activities for the T . b . brucei assay were compared with previously published results for the same assay format [20] , [25] . Selectively active compounds were subjected to rigorous chemical analysis taking into account drug like and non-drug like structural properties . The selectivity index ( SI ) was defined as the HEK293 IC50 values divided by the T . b . brucei IC50 value . The compounds selected , with the initial criteria of an SI of greater than 10 times , were ultimately shown to have SI values ranging from 19 and a predicted value greater than 345 . Further testing against T . b . rhodesiense revealed five new classes of active compounds that are recommended as chemical leads for the potential development of therapeutics against HAT . SAR mining revealed components of these hit compound structures that may be important for the observed biological activity , and these will be outlined . Based on compound availability , four compounds were selected for further biological profiling by estimating the time to kill and assessment if the compound action is cidal .
T . b . brucei lister 427 cells [26] were maintained in log phase growth in 25 cm2 tissue culture flasks ( Corning , NY , USA ) by sub-culturing at either 24 or 48 hour intervals . Cells were grown in HMI-9 medium [27] , supplemented with 10% fetal calf serum ( FCS ) and 100 IU/ml penicillin/streptomycin ( Invitrogen , Carlsbad , California , USA ) with incubation at 5% CO2 at 37°C in humidified conditions . HEK293 cells were maintained in high glucose DMEM with L-glutamine , supplemented with 1× non-essential amino acids ( NEAA; Invitrogen , USA ) and 1 mM sodium pyruvate . Growth conditions were in 5% CO2 at 37°C , under humidified conditions . All reagent and cell additions were made with a Multidrop liquid handler ( Thermo Scientific , Newington , NH , USA ) under sterile conditions . Fifty-five microliters of 2000 cells/mL of T . b . brucei in HMI-9 medium were added to a black , clear-bottomed 384-well lidded plate ( BD Biosciences , Franklin Lanes , NJ , USA ) . Cells were incubated for 24 hours at 37°C in an atmosphere of 5% CO2 before addition of 5 µl of compounds/DMSO for control wells . Compounds suspended in 100% DMSO or 100% DMSO as controls were pre-diluted 1∶21 in high glucose DMEM without FCS by using a Minitrack robotic liquid handler ( PerkinElmer , Waltham , MA , USA ) . Five microliters of diluted sample was added to the plate to give a final DMSO concentration of 0 . 417% in the assay . Cells were incubated for an additional 48 hours at 37°C . Ten microliters of 70% Alamar Blue ( Biosource , Bethesda , MD , USA ) was added to each well ( diluted in HMI-9 medium supplemented with 10% FCS ) to a final concentration of 10% in the assay . The plate was incubated for two hours under the same conditions , then incubated for 22 hours in the dark at room temperature . Wells were read at 535 nm ( excitation ) and 590 nm ( emission ) wavelengths on a Victor II Wallac plate reader ( PerkinElmer , USA ) . Specific dilutions are explained further in the primary and retest assay methodology . Reference drugs used in the assay were pentamidine ( Sigma-Aldrich , St Louis , MO , USA ) , diminazene aceturate ( Sigma-Aldrich , USA ) and puromycin ( Calbiochem , San Deigo , CA , USA ) . Pentamidine is used to treat patients with HAT and diminazene is a veterinary drug used against T . b . brucei to combat infections in cattle . Puromycin is a non selective , protein synthesis inhibitor . Cells at 80% confluence were harvested and diluted in growth medium ( high glucose DMEM supplemented with 10% FCS ) to 7 . 27×104 cells/ml . Under sterile conditions , 55 µl of diluted cells per well were added to a black , clear bottomed 384- well lidded plate ( BD Biosciences , Bedford , MA , USA ) with a Multidrop liquid handler ( Thermo Scientific , Barrington , IL , USA ) . Incubation times , compound additions and plate read were as per the trypanosome viability assay , with the exception that Alamar Blue was diluted in HEK293 growth media before addition , and incubation of Alamar Blue at 37°C , in 5% CO2 , was for 4 hours , followed by incubation at room temperature for 20 hours . The activity of compounds against HEK293 cells was used to calculate the SI of mammalian to T . b . brucei cells . The control compound for HEK293 cells was puromycin ( Calbiochem , USA ) . L6 rat skeletal myoblasts [28] , [29] were purchased from the American Type Culture Collection ( ATCC , Rockville , MD , USA; ATCC number CRL 1458 ) . This cell line was used for cytotoxicity testing to calculate an SI against T . b . rhodesiense and screened alongside the T . b . rhodesiense , P . falciparum , T . cruzi and L . donovani assays . L6 were also the host cells for the T . cruzi assay . Assays were performed in 96-well microtiter plates , each well containing 100 µl of RPMI 1640 medium supplemented with 1% L-glutamine ( 200 mM ) , 10% FCS , and 4000 L6 cells . Serial drug dilutions of eleven 3-fold dilution steps covering a range from 100 to 0 . 002 µg/ml were prepared . After 70 hours of incubation the plates were inspected under an inverted microscope to assure growth of the controls and sterile conditions . Ten µl of resazurin solution ( resazurin , 12 . 5 mg in 100 ml double-distilled water ) was then added to each well and the plates incubated for another 2 hours . Then the plates were read with a Spectramax Gemini XS microplate fluorometer ( Molecular Devices Cooperation , Sunnyvale , CA , USA ) using an excitation wavelength of 536 nm and an emission wavelength of 588 nm . Data was analysed using the microplate reader software Softmax Pro ( Molecular Devices , USA ) . Podophyllotoxin was used as a positive control in the assay . T . b . rhodesiense STIB900 stock was isolated in 1982 from a human patient in Tanzania and after several mouse passages cloned and adapted to axenic culture conditions [30] . Fifty microliters of Minimum Essential Medium ( MEM ) supplemented with 25 mM HEPES , 1 g/l additional glucose , 1% MEM non-essential amino acids ( 100× ) , 0 . 2 mM 2-mercaptoethanol , 1 mM Na-pyruvate and 15% heat inactivated horse serum was added to each well of a 96-well microtiter plate . Serial drug dilutions of eleven 3-fold dilution steps covering a range from 100 to 0 . 002 µg/ml were prepared . Four thousand bloodstream form cells of T . b . rhodesiense STIB 900 in 50 µl were added to each well and the plate incubated at 37°C under a 5% CO2 atmosphere for 70 hours . Ten microlitres of resazurin solution ( resazurin , 12 . 5 mg in 100 ml double-distilled water ) was then added to each well and incubation continued for a further 2–4 hours [31] . Plates were then read with a Spectramax Gemini XS microplate fluorometer ( Molecular Devices , USA ) using an excitation wavelength of 536 nm and an emission wavelength of 588 nm . Data was analysed using the microplate reader software Softmax Pro ( Molecular Devices , USA ) . The drug melarsoprol was a positive control against T . b . rhodesiense . Rat skeletal myoblasts ( L6 cells ) were seeded in 96-well microtitre plates at 2000 cells/well in 100 µl RPMI 1640 medium with 10% FCS and 2 mM l-glutamine . After 24 hours the medium was removed and replaced by 100 µl per well containing 5000 trypomastigote forms of T . cruzi Tulahuen strain C2C4 containing the β-galactosidase ( Lac Z ) gene [32] . After 48 hours the medium was removed from the wells and replaced by 100 µl fresh medium with or without a serial drug dilution of eleven 3-fold dilution steps covering a range from 100 to 0 . 002 µg/ml . After 96 hours of incubation the plates were inspected under an inverted microscope to assure growth of the controls and sterility . Then 50 µl of the substrate , containing chlorophenol red-β-D-galactopyranoside ( CPRG ) and Nonidet , was added to all wells . A colour reaction developed within 2–6 hours that could be read photometrically at 540 nm . Data were transferred into the graphic programme Softmax Pro ( Molecular Devices , USA ) , which calculated IC50 values . The drug benznidazole was used as a positive standard in this assay . Amastigotes of L . donovani strain MHOM/ET/67/L82 were grown in axenic culture at 37°C in SM medium [33] at pH 5 . 4 supplemented with 10% heat-inactivated FCS under an atmosphere of 5% CO2 in air . One hundred µl of culture medium containing 105 amastigotes from axenic culture with or without a serial drug dilution were seeded in 96-well microtitre plates . Serial drug dilutions of eleven 3-fold dilution steps covering a range from 100 to 0 . 002 µg/ml were prepared . After 70 hours of incubation the plates were inspected under an inverted microscope to assure growth of the controls and sterile conditions . Ten µl of resazurin solution ( 12 . 5 mg resazurin dissolved in 100 ml distilled water ) [34] were then added to each well and the plates incubated for another 2 hours . The plates were then read with a Spectramax Gemini XS microplate fluorometer ( Molecular Devices , USA ) using an excitation wavelength of 536 nm and an emission wavelength of 588 nm . Data was analysed using the software Softmax Pro ( Molecular Devices , USA ) . Decrease of fluorescence ( = inhibition ) was expressed as percentage of the fluorescence of control cultures and plotted against the drug concentrations . From the sigmoidal inhibition curves the IC50 values were calculated . Miltefosine served as a known drug control in this assay . In vitro activity against erythrocytic stages of P . falciparum was determined using a 3H-hypoxanthine incorporation assay [35] , [36] using the chloroquine and pyrimethamine resistant K1 strain that originates from Thailand [37] . Compounds dissolved in DMSO at 10 mg/ml were added to parasite cultures incubated in RPMI 1640 medium without hypoxanthine , supplemented with HEPES ( 5 . 94 g/l ) , NaHCO3 ( 2 . 1 g/l ) , neomycin ( 100 U/ml ) , Albumax ( 5 g/l ) and washed human A+ red blood cells at 2 . 5% haematocrit ( 0 . 3% parasitaemia ) . Serial drug dilutions of eleven 3-fold dilution steps covering a range from 100 to 0 . 002 µg/ml were prepared . The 96-well plates were incubated in a humidified atmosphere at 37°C; 4% CO2 , 3% O2 , 93% N2 . After 48 hours , 50 µl of 3H-hypoxanthine ( = 0 . 5 µCi ) was added to each well of the plate . The plates were incubated for a further 24 hours under the same conditions . The plates were then harvested with a Betaplate cell harvester ( Wallac , Zurich , Switzerland ) , and the red blood cells transferred onto a glass fibre filter then washed with distilled water . The dried filters were inserted into a plastic foil with 10 ml of scintillation fluid , and counted in a Betaplate liquid scintillation counter ( Wallac , Zurich , Switzerland ) . IC50 values were calculated from sigmoidal inhibition curves using Microsoft Excel . Chloroquine was used as a positive control in the hypoxanthine assay . Primary screening of the library , consisting of 87 , 296 compounds in two hundred and forty eight 384-well plates , was undertaken in single point against T . b . brucei . Stock solutions consisted of test compound at a concentration of 5 mM in 100% DMSO . One µl of each compound stock solution was diluted by the addition of 40 µl of dilution medium ( high glucose DMEM without FCS ) by a multidrop liquid handler ( Thermo Scientific , USA ) . A 5 µl sample of this diluted solution was then added to the trypanosome assay plate . The final concentration of test compound in the assay was 10 . 2 µM and that of DMSO was 0 . 42% v/v . Compounds were screened over a total of 11 days , at an average of 80 plates per day , taking into consideration that the assay incubation was 3 days total . Test compounds were added to plates in batches of 20 at two hour intervals , to maintain the timing of additions and reads . Compound activity was calculated as the percentage inhibition in relation to positive and negative controls . The positive control , pentamidine , was contained in whole control plates , separate to the plates containing compounds , and the negative control ( no effect ) comprised of 0 . 42% DMSO , in column 24 of each test compound assay plate . These in-plate negative controls were used in an effort to normalise compound activity in relation to any plate to plate variation in the assay signal . A whole 384-well control plate was included in each day's screening , one per 20 compound plates containing half a plate of 2 µM pentamidine for the positive assay control , and half a plate of 0 . 42% v/v DMSO as a negative control . The positive assay control was used to calculate compound activity for batches of 20 compound plates . As well providing the positive control data , these whole plate controls were used for the calculation of the Z' to measure the reproducibility of the assay . An active hit was defined as a compound that demonstrated greater than the mean percentage activity of the library , plus three times the standard deviation . A separate plate containing a 13 point dose-response of reference compounds in triplicate was also included per 20 test plates to calculate the sensitivity of the assay . Compounds identified from primary screening were retested against both T . b . brucei and HEK293 cells in duplicate and at varying concentrations to obtain a dose-response curve . Thus , a 5 µl sample of fresh compound stock solution ( 5 mM in DMSO ) was cherry picked into 384-well plates and diluted 1∶10 in dilution medium ( high glucose DMEM without FCS ) . Serial dilutions of these samples were then prepared in the same media by a Minitrak robotic liquid handler ( Perkin Elmer , USA ) . This resulted in a total 13 doses per sample with 41 . 7 µM as the highest concentration of test compound , for which the DMSO concentration was 0 . 83% v/v . A screening dose of 10 . 4 µM was included in the dilution series to enable reconfirmation of primary screening T . b . brucei activity . Compounds with activity against T . b . brucei ≤10 µM , which also displayed an SI of ≥10 , were selected for medicinal chemistry analysis . The DMSO working concentration in the serial dilutions was maintained at 5% , giving a final assay concentration of 0 . 42% DMSO , except for the 41 . 7 µM test compound solution , where as previously stated the corresponding DMSO concentration was 0 . 83% v/v . The concentration of DMSO that can be tolerated in the T . b . brucei assay has been previously determined as 0 . 42% [20] . Therefore the 41 . 7 µM test compound sample with 0 . 83% v/v DMSO was not used in the T . b . brucei assay and thus the top test compound concentration in this assay was 20 . 8 µM . However , as the HEK293 assay can tolerate 0 . 83% DMSO ( results not shown ) the highest test compound concentration of 41 . 7 µM with 0 . 83% DMSO was included in the HEK293 assay in order to maximise the chances of deriving an IC50 value for more weakly cytotoxic compounds . Compound activity in the retest campaign was calculated as percentage inhibition in relation to positive and negative controls , in the same manner as the primary screening campaign . The positive controls , pentamidine ( 2 µM final concentration ) for T . b . brucei and puromycin ( 8 µM final concentration ) for HEK293 , were both screened in whole 384-well control plates . Whole 384-well plate controls were included after every batch of 20 compound plates , and were comprised of half a plate of negative control and half a plate of positive control . The negative control was the vehicle , 0 . 42 µM DMSO . The negative control was also included in column 24 of each compound assay plate to determine signal variation from plate to plate and to calculate compound activity . The exception was the 41 . 7 µM compound dose used in the HEK293 assay . In these plates column 24 contained 0 . 83% DMSO as a negative control . A separate 384-well plate containing a 13 point dose-response of the reference compounds puromycin , pentamidine and diminazene , in triplicate , was also included per 20 compound test plate batch to estimate assay sensitivity . Cluster analysis of the active compounds ( n = 205 ) identified and confirmed from the primary and follow up retest campaign was performed using Pipeline Pilot . A predefined functional class fingerprinting set ( FCFP_6 , average number of molecules per cluster = 5 , max distance to center = 0 . 6 ) was applied , followed by the removal of compounds carrying toxicophores ( n = 35 ) or permanent charge ( n = 25 ) based on filters developed in-house , which include a list of 110 undesirable chemical moieties . The remaining clusters ( n = 93 , total of 137 compounds ) were then independently scored by 3 medicinal chemists with industrial experience . Scoring was based on criteria including activity and selectivity , number of active analogues in the cluster , drug-like structural features , chemical tractability , presence of additional toxicophores not detected by the previously applied filters , potential for CNS penetration , and possible overlap with scaffolds already considered for HAT development at DNDi , or the literature . Following medicinal chemistry analysis , compounds deemed to be of most interest were re-purchased or re-synthesised and analysed by liquid chromatography-mass spectrophotometry ( LCMS ) to confirm expected molecular weight and acceptable purity ( >85% ) prior to retesting of biological activity . These compounds were retested as in dose for N of three replicates , as described above for both T . b . brucei and HEK293 . For SAR mining , hit compounds were compared structurally to the whole primary screening compound collection . A series of substructure searches , performed in ActivityBase , were defined and refined to retrieve analogues most relevant to SAR interpretation . We have undertaken SAR mining for more than 60 HTS campaigns and have found substructure searching to return more meaningful SAR-relevant analogues than similarity searching . This is not surprising as it is well known that fingerprint-derived structural recognition captures medicinal chemistry-based structural recognition in only a rudimentary fashion [38] . The substructures that were used for searches are shown in Figure 1 . A1 and A2 were the basis for searches for analogues of compounds 1 and 2 , B1–B3 were used for compound 3 , C1 for compound 6 , D1 for compound 8 , and E1–E3 for compound 7 . For compounds 1 , 2 , 6 and 7 , the minimum inhibitory concentration ( MIC ) was determined from a concentration response curve , generated using the Alamar Blue assay . The MIC was extrapolated as the minimum concentration at which there was a plateau of activity in the assay ( >95% activity ) . For compound 1 , this was 3 . 97 µM , compound 2 was 19 . 8 µM , compound 6 was 9 . 92 µM and compound 7 had a MIC of 0 . 99 µM . To determine cell counts at this MIC , compounds at their MIC concentration were added following incubation of 2×103 parasites per well for 24 hours in the absence of compound . Cell numbers were determined after 24 , 48 and 72 hours exposure to compounds and compared to controls of puromycin , also at an MIC concentration ( 1 . 15 µM ) . Puromycin was used as a positive control for 100% cell death ( cidal action ) , as since for this drug there were no parasites remaining in the treated wells following 24 hours . The MIC calculated for pentamidine was 0 . 04 µM . IC50 values were determined for compounds 1 , 2 , 6 and 7 following exposure of T . b . brucei to each compound for 29 , 48 and 72 hours . The starting dose was 40 µM , and the IC50 values were determined from a 16 point dose-response curve . The assay conditions were the same as previously described for the Alamar Blue assay , except that 10 µL of a final 10% concentration of Presto Blue in HMI-9 medium was added as the indicator of viable cells , at various time points . At the first time point , following 20 hours of incubation with compounds , Presto Blue was added to the wells and incubation at 37°C continued . Plates were read every hour and returned to continue incubation at 37°C . This was performed to determine at which time point there was a reproducible signal ( Z' of >0 . 5 ) , using puromycin as a negative control and 0 . 42% DMSO as a positive control . This corresponded to a 9 hour incubation , or 29 hours incubation in the presence of the compound . After 45 hours incubation , Presto Blue reagent was added , and the samples incubated for an additional 3 hours , thus read at 48 hours to give a reproducible signal . Similarly , at 70 hours , reagent was added , samples incubated for another 2 hours and read at 72 hours . If a compound reached a plateau of activity and no cells were identified at the MIC , compounds were considered to have been effectively cidal at that time point .
As a hit threshold , three times the standard deviation plus the mean of the activity of the compound collection was calculated at 50% , in an effort to reduce false positives in the assay . Compounds with ≥50% activity were therefore considered active . From the primary screening campaign , 1 , 980 compounds inhibited T . b . brucei growth by ≥50% , a hit rate of 2 . 27% . These were grouped into two classifications , the first containing those compounds that inhibited growth between ≥50% and <80% and the second consisted of compounds with inhibitory activity of ≥80% . Group two was comprised of 1 , 217 compounds and it was these that were progressed to initial retesting . In-plate controls revealed little variation in the assay signal expressed as a ratio of maximum signal to background , throughout the entire test period ( Figure 2 ) . From separate whole plate controls , the Z' was calculated as an average of 0 . 81±0 . 05 ( Figure 3 ) . IC50 values for each of the reference compounds determined over the four screening days are shown in Figure 4 . For each screening day , there were four control plates containing a dose-response of each reference compound , in triplicate , starting at doses of: puromycin 120 µM , pentamidine 70 µM and diminazene 80 µM . One control plate was included for screening per 20 compound plate batch . Thus , there were 4 control plates each in the first 3 days of screening ( 80 library compound plates per day ) and only 1 control plate for the last ( 8 library compound plates ) . Mean IC50 values and standard deviations were therefore calculated from 12 replicates each on days 1–3 , and 3 replicates of dose-response of reference compounds on day 4 . These values were not significantly different from one another , as determined by a one way ANOVA in GraphPad Prism , with a significant difference of P<0 . 05 . An IC50 value was not considered reproducible if varying more than 3 times from the mean . All values fell within three times the mean . IC50 values for the reference compounds were 61 . 9±6 . 8 nM for puromycin , 65 . 4 nM±12 . 5 nM for diminazene and 14 . 7±4 . 7 nM for pentamidine . Of the 1 , 217 primary actives that were retested from stock solutions in duplicate with dose-response curves , 822 compounds ( 67 . 5% ) reconfirmed in duplicate to be ≥50% inhibitory in the T . b . brucei assay at the serial dilution concentration point of 10 . 4 µM ( closest to the primary screening concentration of 10 . 2 µM ) . A dose-response plateau is necessary for IC50 values to be determined for these compounds . Hence , a compound needed to display ≥80% inhibitory at both 41 . 7 µM and 20 . 8 µM in duplicate ( although one singleton was allowed to extend to ≥70% ) . There were 57 . 6% of the 1 , 217 compounds that passed these criteria . For all these compounds , titration data were imported into GraphPad Prism and the IC50 values estimated . Similarly for the HEK293 assay , only data whereby an IC50 value could be estimated were imported in to GraphPad Prism . There were 700 compounds that displayed ≥80% inhibition at both 41 . 7 µM and 20 . 8 µM in duplicate ( although one singleton was allowed to extend to ≥70% ) . As before , criteria included a plateau of activity necessary for the calculation of an IC50 value . The HEK293 IC50 value could be estimated for 10% of the 700 compounds in this manner and this allowed for the determination of the SI . For the remainder of compounds , an estimation of the IC50 against HEK293 cells was possible by observing the lowest concentration in the HEK293 assay that displayed ≥50% inhibition in at least one of the two replicates . Using these analyses , there were 205 ( 29% ) of the 700 re-confirmed compounds that had an estimated SI of ≥10 . Of these compounds , 8 produced a non-sigmoidal curve in the T . b . brucei assay and therefore could not have an IC50 value , nor SI estimated . This may have been due to compound solubility , or the nature of the compound's action , and these compounds were de-prioritised . This left 197 hits that were progressed to medicinal chemistry cluster analysis . Control plates , used as a measure of reproducibility , showed that the T . b . brucei assay had an average Z' of 0 . 74 ( Figure 3 ) . For the HEK293 assay , the mean Z' was 0 . 73 for both 0 . 42% DMSO and 0 . 83% DMSO final assay concentrations . Puromycin was active on both cell lines with an IC50 of 138 . 5±15 . 7 nM against T . b . brucei and 1123±155 nM against HEK293 . Puromycin , a known cytotoxic compound therefore exhibited an SI of less than 10 , supporting the use of the Alamar Blue for the identification of cytotoxic compounds . For T . b . brucei , diminazene exhibited an IC50 value of 29 . 5±5 . 8 nM and pentamidine 7 . 8±3 . 6 nM . Neither pentamidine nor diminazene displayed activity in the HEK293 assay at the doses screened ( 1 µM and 40 µM , respectively ) . Scoring was attributed independently by 3 medicinal chemists with industrial experience and was based on criteria including criterion 1: activity and selectivity ( compounds with IC50 values indicating good activity and high selectivity were favored ) ; criterion 2: number of active analogues in the cluster ( cluster with n>1 were preferred to singletons ) , criterion 3: drug-like structural features ( based on Lipinski's rule of 5 scoring [39] ) , criterion 4: chemical tractability ( based on personal experience , as well as availability of commercial analogs ) , criterion 5: presence of additional toxicophores not detected by the previously applied filters ( personal experience ) , criterion 6: potential for CNS penetration such as molecules with low PSA , low molecular weight , low clogP and low number of H-bond donor/acceptors [40] . It is recognised that this method is internally consistent , however may differ from analysis undertaken by other medicinal chemists [41] . This analysis lead to the selection of 11 compounds for retesting . The 11 compounds identified from medicinal chemistry analysis were either re-synthesised or re-purchased , and re-tested in both the T . b . brucei and HEK293 assays . Following this , the number was reduced to 8 ( Table 1 ) after two resupplied compounds did not confirm activity in the T . b . brucei assay ( <50% activity , results not shown ) . A third compound was found to only be >50% active at the top dose of 65 µM and therefore was unsuitable for IC50 or SI calculation . The IC50 values and calculated selectivity indices of the remaining 8 compounds are outlined in Table 1 , and the structures in Figure 5 . During rescreening of resynthesized compounds the Z' for the T . b . brucei assay was 0 . 81±0 . 02 and 0 . 88±0 . 01 for the HEK293 assay . In the T . b . brucei assay , pentamidine displayed an IC50 value of 3 . 52±0 . 36 nM , diminazene 121±9 . 04 nM and puromycin 58 . 4±0 . 77 nM ( Table 1 ) . In the HEK293 assay , puromycin was active at 518±28 . 1 nM , whilst as expected neither pentamidine nor diminazene displayed activity at the doses screened ( 1 µM and 40 µM , respectively ) . The selectivity index for puromycin was similar to that found at original retest , ( 8 . 9 fold , Table 1 ) , as expected for a non-selective inhibitor . The compounds identified by medicinal chemistry analysis as the most promising were also tested in dose-response against the human infective parasites T . b . rhodesiense , L . donovani and T . cruzi to estimate IC50 values . Data obtained is shown in Table 1 . Rat skeletal L6 muscle cells were also used as an indicator of cytotoxicity and the SI was calculated against all species . Initial analysis of compound activity was made against the HAT reference strain , T . b . rhodesiense , taking into consideration the IC50 value and the SI . Criteria used were as described for the primary screening and retest campaigns , therefore for compounds to be initially considered as favourable hits for further progression , the IC50 cut off was <10 µM and the SI>10 . Compound 5 had an IC50 value <10 µM and a corresponding SI of <10 , and thus was de-prioritised . Compound 4 displayed an SI of 0 . 19 and therefore was also de-prioritised . This left a panel of 6 compounds to be considered for further progression . Table 2 shows the physiochemical properties of these 6 prioritised compounds: the molecular weight , aqueous solubility , polar surface area and cLogP . Reference compounds were used as controls throughout testing with all of these assays and are also shown in Table 1 . For the T . b . rhodesiense assay , the drug melarsoprol displayed an IC50 of 6 . 28±1 . 78 nM . Benznidazole , a drug used to treat Chagas disease , was 1680±1930 nM active and miltefosine , a treatment for Leishmaniasis had an IC50 value of 365±93 . 7 nM . The drug chloroquine was active against P . falciparum with an IC50 of 164±24 . 7 nM . For the 6 hit compounds , ActivityBase was used for substructure searching to identify the relevant analogues to associate with the primary screening data . The refined substructures used for searches are shown in Figure 1 . Tables S1 , S2 , S3 and S4 show structure and activities of these compounds over the T . b . brucei primary screening and retest campaigns . Identified analogues are shown in Supplementary Table S1 ( compounds 1 and 2 ) , Table S2 ( compound 3 ) , Table S3 ( compound 6 ) and Table S4 shows analogues of compound 7 . No analogues of compound 8 were found in the library using these methods , even using the relatively broad substructure definition D1 in Figure 1 . Measurements of the number of T . b . brucei cells , following exposure to the MIC of compounds 1 , 2 , 6 and 7 during a 72 hour period , are shown in Figure 6 . Treatment with three of the 4 compounds at the MIC for 24 hours resulted in cell counts indicating the complete lack of viable trypanosomes . However , the compound pyrido-isoxazol-2-ylanilide ( compound 7 ) , only cleared parasites following 72 hours incubation . At the MIC of puromycin , no cells remained following 24 hours treatment , whereas with pentamidine this effect was not observed until 72 hours incubation at the MIC . The IC50 values for all 4 compounds selected for cidal assessment did not differ between the Presto Blue assay at 72 hours and the Alamar Blue assay ( total compound exposure in this assay is also 72 hours ) , as shown in Table 3 . Thus the Presto Blue assay was considered to also be an accurate indicator of compound activity measured over time and the results were comparable to IC50 values determined in the Alamar Blue assay . All compounds were active at 29 hours , with a plateau of activity displayed in dose-response curves . Compounds 1 , 2 and 7 showed similar IC50 value across all time points , while compound 6 reached a stable IC50 value at 48 hours incubation with the compound ( Table 3 ) . Puromycin and pentamidine were demonstrated to reach a maximum IC50 value after 48 hours exposure .
Due to the many problems associated with current existing treatments for HAT , in particular toxicity , treatment regimes and cost , there exists a tangible need for new compounds to be introduced into early HAT drug discovery . HTS has been utilised by a number of research groups for HAT to identify active compounds for the drug discovery process , however there are few incorporating the use , or development of HTS for whole cells [20] , [42] , [43] . The inclusion of an assay to estimate cytotoxicity as a part of a whole cell HTS campaign is an important consideration for the progression of potential compounds . Here we describe the utilisation of an Alamar Blue HTS assay [20] to successfully screen a library of almost 90 , 000 small molecules . Following medicinal chemistry analysis of the positive hits in the assay , eight compounds with activity against T . b . brucei were identified . These compounds had IC50 values ranging from 0 . 22 µM to 4 µM with associated selectivity indices ranging from 19 to greater than 345 . Both the primary and retest screening campaigns were reproducible as exemplified by the statistical coefficient of the Z' . For the primary screening campaign , the Z' was averaged at 0 . 81 for the T . b . brucei assay ( Figure 3 ) . At retest the respective Z' values were 0 . 74 and 0 . 73 for T . b . brucei and HEK293 assays . Throughout the campaign , reference compounds in the T . b . brucei assay were within the range of the IC50 value of previously reported results for the same assay format [20] . The reproducibility of the reference compounds over primary screening days is highlighted in Figure 4 . The HEK293 assay was validated in this campaign as effective for the identification of cytotoxic compounds by the activity of the compound puromycin . Puromycin is a general cell growth inhibitor of both eukaryotic and prokaryotic cells which disrupts protein synthesis . It was active on both cell lines in the retest screening campaign with an IC50 of 138 . 5±15 . 7 nM against T . b . brucei and 1123±155 nM against HEK293 . This compound would therefore have been correctly identified as non-specifically cytotoxic by our criteria that a potentially useful T . b . brucei active must have an initial SI >10 . This was also shown through the data obtained for the controls from during screening of re-isolated compounds , where the SI for puromycin was 8 . 9 ( Table 1 ) . As anticipated , neither pentamidine nor diminazene , which are registered drugs against HAT and T . b . brucei , respectively , exhibited activity in the HEK293 assay at the doses screened . Diminazene is reported to have an SI of 692 [44] , whilst pentamidine has low µM activity reported for some mammalian cell lines [45] . The 8 compounds identified following reconfirmation of actives from new solids and chemical clustering were subjected to testing against the human HAT infective species T . b . rhodesiense , as well as other protozoal species that cause disease such as T . cruzi ( Chagas disease ) , L . donovani ( Leishmaniasis ) and a chloroquine and pyrimethamine resistant strain of P . falciparum ( Malaria ) . The structures and chemical classes of these compounds , designated compounds 1 to 8 , are shown in Figure 5 . As an additional mammalian cytotoxicity control and one relevant when screening these additional assays for protozoal parasites , the rat skeletal myoblast L6 cell line was used as this cell line is the host cell line used for the T . cruzi assay . The biological activities of these 8 compounds against T . b . brucei , a panel of human infective parasite species , plus the L6 cytoxicity data , with corresponding HEK293 selectivity indices are shown in Table 1 . The activity of the relevant control/reference drugs has also been included . On the basis of this data , 2 compounds displayed relatively low ( Table 1 , compound 5 ) or extremely low ( Table 1 , compound 4 ) SI and thus were not considered favourable for progression . This left 6 high priority compounds , representing 5 distinct structural classes that could serve as a basis for progression in the early drug discovery process for HAT . Structures and key physicochemical properties for selected compounds are listed in Table 2 . For analysis of physicochemical properties , a cLogP of 1–4 is considered favorable; >4–6 is acceptable , while >6 is unfavorable . A preferred solubility is considered to be >10 µM . Polar surface area is considered to be good at less than 70 Å2 and acceptable less than 80 Å2 . A molecular weight lower than 400 is preferred in terms of lead-likeness and blood brain barrier crossing properties . The phenylthiazole amide ( compound 1 ) was active against T . b . brucei with an IC50 value of 0 . 79 µM and an SI of >96 . It was similarly active against T . b . rhodesiense with an IC50 of 1 . 5 µM and an SI of 42 . This compound also demonstrated activity against T . cruzi with an IC50 of 2 . 3 µM . In terms of physicochemical properties , it has a low molecular weight of 306 , predicted good aqueous solubility of 63 µM , a low polar surface area of 42 Å2 suitable for CNS penetration , and a favourable cLogP of 3 . 4 . Phenyltriazol-5-yl-ethylamide ( compound 2 ) , although closely related , was significantly less active against T . b . brucei with an IC50 value of 4 . 0 µM , with also a weaker T . b . rhodesiense activity IC50 of 6 . 8 µM . The SI for compound 2 determined against both HEK293 and L6 cells was approximately 20 . The physicochemical properties of this compound reveal it to be of low molecular weight , with an acceptably low polar surface area of 71 Å2 and a favourable cLogP of 2 . 9 , although the calculated aqueous solubility is low at 8 µM . A literature search revealed no biologically active compounds closely related to these two hit compounds , suggesting that these compounds may represent starting points for novel trypanocides . There were approximately 3 dozen compounds related to compound 1 ( Table S1 ) , approximately two dozen of which ( 1 , 4–7 , 16–24 , 26–28 , 33–36 ) were structurally very similar . Few of these exhibited any activity , suggesting tight SAR around the core structure . The exception to this was the potent thiophene-containing compound ( entry 23 ) , that did not initially pass the medicinal chemistry functional group filters , because of the thiophene group . However , this compound still provides useful SAR and suggests that different hydrophobic amides may be tolerated in this region with retention of potent activity . Remaining compounds were more distant , conformationally constrained , or contained heterocyclic alternatives to the thiazole and none were active . The phenoxymethylbenzamide ( compound 3 ) had moderate activity against T . b . brucei with a retest IC50 of 1 . 1 µM and an SI of >67 . It was similarly active against T . b . rhodesiense with an IC50 of 0 . 85 µM and an SI of 60 . In terms of physicochemical parameters , this compound has a moderately low molecular weight of 353 , a calculated aqueous solubility of 25 µM , a low polar surface area of 39 Å2 and an acceptable cLogP of 4 . 8 . SAR mining revealed 34 analogues related to compound 3 ( Table S2 ) . Some of these compounds had only relatively minor changes ( entries 5 , 11 , 13 , 33 ) but of these , only one ( entry 13 ) showed some activity ( 77% at 10 . 4 µM ) , suggesting both ends of the molecule ( piperidine amide and p-alkoxyphenyl ) are likely to be important for activity . The remaining compounds tended to have more significant changes to both ends or the central unit and none of these were active except for one ( entry 3 ) , suggesting the piperidine could be replaced with a diaminoethane though cytotoxicity would need to be monitored . The activity of the pyrimidin-2-yl-pyrazol-5-ylamide ( compound 4 ) was 3 . 1 µM for T . b . brucei , with an SI of 25 . This compound also demonstrated activity against T . b . rhodesiense IC50 of 4 . 8 µM but with an extremely low SI of 0 . 19 to L6 cells , and it was for this reason that this compound was not included in the top 6 compounds to be considered further . The 7-aminotetrahydroquinoline bis sulfonamide ( compound 5 ) had a moderate retest T . b . brucei IC50 value of 2 . 1 µM and an SI of 36 to HEK293 cells . However the low activity observed against the infective species ( T . b . rhodesiense ) of 14 µM rendered this compound de-prioritised . None of the entries 1 , 2 , 3 or 5 belong to classes associated with any known biological activities as far as the authors can ascertain . However , this is not the case for compound 6 , 6-aryl-3-aminopyrazine-2-carboxamide , which was moderately active with a retest IC50 of 1 . 2 µM and an SI of >65 when cytotoxicity is measured on HEK293 cells . It was similarly active against T . b . rhodesiense with an IC50 of 0 . 97 µM and an SI to L6 cells of 18 . This compound is predicted to have a favourable aqueous solubility of 3 . 2 mM , has a low molecular weight of 270 , an acceptably low polar surface area of 81 Å2 and a favorable cLogP of 2 . 0 . This class is quite heavily patented and associated with numerous biological activities [46]–[50] . Only one compound was a close analogue of compound 6 , a des-N-alkyl carboxamide ( Table S3 ) , however this was inactive , suggesting the alkyl group is essential for activity . The pyrido-isooxazol-2-ylanilide ( compound 7 ) is an isoxazol-2-ylanilide with a fused pyridine ring and displays the best biological activity profile of all compounds , with a T . b . brucei retest IC50 value of 0 . 22 µM and an SI of >345 . It was similarly active against T . b . rhodesiense with an IC50 of 0 . 59 µM and an SI of 39 . This compound also displayed activity against T . cruzi with an IC50 of 0 . 23 µM and an IC50 against L . donovani of 1 . 8 µM , suggesting potential as a broad spectrum anti-kinetoplastid . The physicochemical properties of this compound are favourable , with a moderately low molecular weight , an acceptable polar surface area of 81 Å2 and a favourable cLogP of 2 . 6 . The calculated aqueous solubility is low ( 25 nM ) and it is possible the actual solubility may be improved due to the ortho effect of the 2-chloro substituent . This compound belongs to a class with an isolated report of biological activity , activation of the NAD+-dependent deacetylase SIRT1 [51] , a sirtuin , which also appears to be present and important in trypanosomes [52]–[54] . This compound would appear to present a promising starting point for drug development , though early investigation of aqueous solubility and its improvement could be important . For this compound , there were 19 analogues that provided useful SAR ( Table S4 ) . Several compounds suggested the furan was important for activity , as replacement with substituted phenyl ring ( entries 6 , 10 , 11 , 16 , 17 , 19 ) or extension ( entries 5 , 7 , 9 , 12 , 15 , 18 ) led to inactive compounds . However , replacement with a simple propyl group ( 2 ) led to an active compound suggesting smaller hydrophobics may be acceptable . Two compounds had small changes in other parts of the molecule and were also inactive , suggesting even simple substitution changes to the central phenyl ring ( entry 3 ) are not necessarily tolerated nor small changes to the distal pyridine ring ( entry 4 ) . While more significant in their alterations , all other analogues are still clearly related to the parent compound yet inactive , suggesting tight SAR . The aminoethyl benzoyl arylguanidine ( compound 8 ) displayed a T . b . brucei retest IC50 value of 2 . 6 µM and an SI of >29 . This compound displayed increased activity against T . b . rhodesiense with an IC50 value of 0 . 88 µM and an SI of 150 , whereas the activity against T . cruzi was low ( IC50 of 82 µM ) . The biologically active conformation of this molecule may adopt an intra-molecular hydrogen-bonded form as shown [55] , similar to benzoylureas [56] . In terms of physicochemical properties , this compound has a moderate molecular weight of 380 , a reasonable aqueous solubility of 32 µM , an acceptably low polar surface area of 67 Å2 and a favorable cLogP of 3 . 1 . Closely related compounds are patented as inhibitors of human mitochondrial F1Fo- ATPase [57] , the same molecular target that DB289 has been suggested to target in T . brucei [58] . Oligomycin A , which is known to inhibit mitochondrial membrane associated ATPases in mammalian cells [59] has also demonstrated potent activity against T . b . brucei [60] . Oligomycin sensitive ATPases have been found to be present in T . b . brucei [61] . The aminoethyl benzoyl arylguanidine represents a highly tractable and attractive structure for medicinal chemistry optimization , although consideration will need to be given to the potential for liver toxicity manifested in DB289 , and how this may be overcome [9] . Data mining showed there were no analogues of this compound in the library screened . From the hit chemical classes , compounds 1 , 2 , 6 and 7 underwent further biological profiling to ascertain whether their action was cidal or static at the MIC determined . Of the 4 compounds profiled , all had completely cleared parasites in wells by 72 hours incubation at the MIC ( Figure 6 ) and were therefore considered to have a cidal action . Compounds 1 , 2 and 6 and the control puromycin resulted in complete depletion of trypanosomes at this dose at 24 hours , whilst compound 7 and the control compound , pentamidine , required a 72 hour incubation to attain the same effect . To determine the IC50 values of compounds 1 , 2 , 6 and 7 over time , as an estimation of the kill time , the resazurin-based reagent , Presto Blue , was used . In the presence of live cells this dye converts more rapidly to a fluorescent end product , in comparison to Alamar Blue ( results not shown ) . Dose-response curves of these compounds showed a plateau of activity of the 4 compounds at 24 hours ( considered as 2 doses or more at >90% ) , suggesting that all compounds were active ≤29 hours . Compounds 1 , 2 and 6 at MIC resulted in complete clearance of all parasites at ≤29 hours , with compounds 1 and 2 displaying the fastest cidal activity , with a maximum IC50 value reached at this point ( Table 3 ) . Compound 7 had similar IC50 values over each time interval investigated however at the MIC not all parasites were cleared until 72 hours . Although the MIC would shift slightly over time , at 24 and 48 hours there were 0 . 41% and 0 . 06% of the population remaining , respectively . Additional profiling revealed these compounds were cidal in nature and the speed of action was either similar to , or faster than , the known drug , pentamidine . These compounds will be profiled at reduced exposure times to determine if the time to kill may be less than the exposure times studied here . Estimation of the MIC at each time point would clarify complete parasite clearance . Collation of all of the analyses completed led to the selection of five priority classes: phenylthiazol-4-ylethylamide , phenoxymethylbenzamide , 6-aryl-3-aminopyrazine-2-carboxamide , pyrido-isoxazol-2-ylanilide and aminoethyl benzoylarylguanidine . In summary , these compounds are novel scaffolds for HAT early drug development and represent attractive templates for further biological analysis and medicinal chemistry optimization , to build structure-activity relationships for compounds active against T . b . brucei . Upon confirmation of SAR , the chemistry program would be extended to optimize potency and solubility , in conjunction with early in vitro absorption , distribution , metabolism , elimination ( ADME ) and toxicity assays . Early pharmacokinetic studies ( PK ) , to measure of brain compound levels , as well as in vivo efficacy studies in HAT murine models , would follow upon identification of suitable candidates . Medicinal chemistry efforts are being actively pursued to synthesize new compounds from the starting points discussed here , in a bid to generate leads with improved physicochemical and biological properties . Chemical structures and biological activities of all compounds defined as actives in the T . b . brucei primary screening campaign at ≥80% activity ( 1217 ) , which were retested in dose-response in both the T . b . brucei assay and the HEK293 cytotoxicity assay are available in the CHEMBL-NTD database https://www . ebi . ac . uk/chemblntd . | Human African Sleeping Sickness ( HAT ) is a disease caused by sub-species of Trypanosoma . The disease affects developing countries within Africa , mainly occurring in rural regions that lack resources to purchase drugs for treatment . Drugs that are currently available have significant side effects , and treatment regimes are lengthy and not always transferrable to the field . In consideration of these factors , new drugs are urgently needed for the treatment of HAT . To discover compounds suitable for drug discovery , cultured trypanosomes can be tested against libraries of compounds to identify candidates for further biological analysis . We have utilised a 384-well format , Alamar Blue viability assay to screen a large non-proprietary compound collection against Trypanosoma brucei brucei bloodstream form lister 427 . The assay was shown to be reproducible , with reference compounds exhibiting activity in agreement with previously published results . Primary screening hits were retested against T . b . brucei and HEK293 mammalian cells in order to assess selectivity against the parasite . Selective hits were characterised by chemical analysis , taking into consideration drug-like properties amenable to further progression . Priority compounds were tested against a panel of protozoan parasites , including Trypanosoma brucei rhodesiense , Trypanosoma cruzi , Leishmania donovani and Plasmodium falciparum . Five new compound classes were discovered that are amenable to progression in the drug discovery process for HAT . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"medicinal",
"chemistry",
"drugs",
"and",
"devices",
"chemistry"
] | 2012 | Identification of Compounds with Anti-Proliferative Activity against Trypanosoma brucei brucei Strain 427 by a Whole Cell Viability Based HTS Campaign |
Junín virus ( JUNV ) , the etiologic agent of Argentine hemorrhagic fever ( AHF ) , is classified by the NIAID and CDC as a Category A priority pathogen . Presently , antiviral therapy for AHF is limited to immune plasma , which is readily available only in the endemic regions of Argentina . T-705 ( favipiravir ) is a broadly active small molecule RNA-dependent RNA polymerase inhibitor presently in clinical evaluation for the treatment of influenza . We have previously reported on the in vitro activity of favipiravir against several strains of JUNV and other pathogenic New World arenaviruses . To evaluate the efficacy of favipiravir in vivo , guinea pigs were challenged with the pathogenic Romero strain of JUNV , and then treated twice daily for two weeks with oral or intraperitoneal ( i . p . ) favipiravir ( 300 mg/kg/day ) starting 1–2 days post-infection . Although only 20% of animals treated orally with favipiravir survived the lethal challenge dose , those that succumbed survived considerably longer than guinea pigs treated with placebo . Consistent with pharmacokinetic analysis that showed greater plasma levels of favipiravir in animals dosed by i . p . injection , i . p . treatment resulted in a substantially higher level of protection ( 78% survival ) . Survival in guinea pigs treated with ribavirin was in the range of 33–40% . Favipiravir treatment resulted in undetectable levels of serum and tissue viral titers and prevented the prominent thrombocytopenia and leucopenia observed in placebo-treated animals during the acute phase of infection . The remarkable protection afforded by i . p . favipiravir intervention beginning 2 days after challenge is the highest ever reported for a small molecule antiviral in the difficult to treat guinea pig JUNV challenge model . These findings support the continued development of favipiravir as a promising antiviral against JUNV and other related arenaviruses .
Several New World ( Junín , Machupo , Guanarito , Sabia , and Chapare ) and Old World ( Lassa and Lujo ) arenaviruses cause viral hemorrhagic fever ( HF ) with case fatality rates generally in the range of 15–30% [1] . They are rodent-borne viruses that can be transmitted via the aerosol route therefore making them potential bioterror agents . Exposure to Lassa virus ( LASV ) and mortality associated with Lassa fever ( LF ) in hyperendemic areas of West Africa are estimated to be as high as 300 , 000 infections and 10 , 000 deaths annually [2] . Of the New World arenaviral HFs endemic in different regions of the South America , Junín virus ( JUNV ) , the etiologic agent of Argentine HF ( AHF ) , causes the greatest morbidity and mortality . AHF cases , although reduced in number , continue to be reported despite the vaccination of individuals with the greatest risk of exposure [3] . Immune plasma has proven to be an effective treatment for AHF if administered within 8 days of initial disease symptoms , but has been associated with a late neurological syndrome and is not readily available outside of Argentina [4] . Ribavirin is the only licensed antiviral known to offer protection in cases of LF [5] , but remains largely unproven with only limited data in cases of AHF and Bolivian HF due to Machupo virus infection [6] , [7] . Adverse effects primarily in the form of hemolytic anemia are generally considered to be reversible with cessation of ribavirin treatment [8]–[10]; however , teratogenicity and embryotoxicity are of concern [11] , [12] . One must also consider the added risk of using a drug that can cause anemia to treat arenaviral HF diseases , which have a propensity for bleeding . At present , there are very few promising antivirals which have demonstrated anti-arenavirus activity in vivo [13] . Favipiravir ( T-705; 6-fluoro-3-hydroxy-2-pyrazinecarboxamide ) is a novel antiviral compound developed by the Toyama Chemical Co . , which selectively and potently inhibits the RNA-dependent RNA polymerase ( RdRP ) of influenza [14] . It has been found to inhibit all serotypes and strains of influenza A , B and C viruses against which it has been tested [15] , [16] , including those resistant to currently approved neuraminidase inhibitors [17] . Remarkably , it is also active against alpha- , arena- , bunya- , and flaviviruses , both in cell culture and rodent models [16] , [18]–[20] , and it has shown in vitro activity against members of the paramyxo- , picorna- , and calicivirus families [21] , [22] . To date , oral favipiravir treatment has been shown to be active in lethal hamster and guinea pig arenaviral HF models based on challenge with Pichindé virus ( PICV ) , a New World arenavirus that produces in these species many of the clinical disease manifestations associated with AHF , including vascular leak and thrombocytopenia [23]–[25] . Importantly , favipiravir was efficacious in treating advanced disease even when initiating treatment one week after challenge when clinical disease was clearly apparent in guinea pigs [25] . In vitro studies have demonstrated favipiravir activity against pathogenic strains of JUNV , Machupo , and Guanarito viruses [19]; however , its activity against a bona-fide HF arenavirus in vivo has not been explored until now . In the present study , the antiviral activity of favipiravir against JUNV infection modeled in guinea pigs was investigated .
All animal procedures complied with USDA guidelines and were conducted at the AAALAC-accredited Robert E . Shope , M . D . Laboratory at The University of Texas Medical Branch ( UTMB; Galveston , TX ) under protocol # 0903023 approved by the UTMB Institutional Animal Care and Use Committee . Outbred male Hartley strain guinea pigs ( 300–350 g ) were obtained from Charles River ( Wilmington , MA ) and acclimated for 1 week prior to challenge . Animals were sorted prior to the start of both experiments so that the average group weights were similar . IPTT-300 electronic transponders were subcutaneously implanted for identification and temperature measurement in conjunction with the DAS 6002 scanner ( BMDS , Seaford , DE ) . The Romero strain of JUNV was kindly provided by Thomas Ksiazek ( UTMB ) . The virus stock was grown in Vero cells and the titer determined by plaque assay . The virus was prepared in minimal essential medium ( MEM ) for intraperitoneal ( i . p . ) challenge with approximately 1000 plaque-forming units ( PFU ) . The actual challenge dose for each experiment was determined by plaque titration of the inoculation medium . All live virus work was performed in BSL-4 containment at UTMB in accordance with institutional health and safety standard operating procedures . Favipiravir was provided by the Toyama Chemical Company , Ltd . ( Toyama , Japan ) . Ribavirin was supplied by ICN Pharmaceuticals , Inc . ( Costa Mesa , CA ) . Compounds were suspended in GERBER NatureSelect 1st FOODS carrot food ( ingredients: carrots and water ) for oral administration in the first efficacy study and 2 . 9% sodium bicarbonate solution ( Sigma-Aldrich , St . Louis , MO ) for i . p . dosing in the second study . For virus titration of organs and serum collected from infected guinea pigs , approximately 0 . 2–0 . 5 g of each organ was homogenized in 0 . 5 ml PBS . Serum was separated from whole blood by centrifugation . The homogenates and sera were held at −80°C until titration . The homogenates were centrifuged to remove cellular debris and the cleared homogenate for each organ and the serum were titrated by a focus-forming unit ( FFU ) assay as follows . Vero E6 monolayers were infected with serial 10-fold dilutions of serum or tissue homogenate for one hour at 37°C . For the second experiment , serum was also tested undiluted . Following infection , cells were overlayed with 0 . 8% Tragacanth ( Sigma-Aldrich ) in MEM supplemented with 2% fetal bovine serum and 1% penicillin and streptomycin . After 7 days in culture , the overlay was removed , the cells fixed with 10% buffered formalin for 30 min at room temperature followed by overnight refrigeration . Fixed cells were permeabilized in 70% ethanol for 20 min and washed with phosphate buffered saline ( PBS ) prior to overnight staining with primary antibody ( antisera to JUNV Candid #1 kindly provided by Dr . Robert Tesh , World Reference Collection for Emerging Viruses and Arboviruses , UTMB ) diluted 1∶1000 in PBS with 5% milk and 1% Tween 20 . The cells were then washed with PBS and the secondary antibody , goat anti-mouse IgG labeled with horseradish peroxidase ( HRP; DakoCytomation , Carpinteria , CA ) diluted 1∶500 in PBS with 1% bovine growth serum ( BGS ) , was added to plates and incubated for 4–5 h . After washing with PBS , AEC Substrate Chromagen ( DakoCytomation ) was added for 15 min and the reaction was stopped with distilled water prior to counting of FFUs . Heat-inactivated guinea pig sera from survivors were diluted 1∶10 in MEM supplemented with 1% FBS , and titrated in two-fold serial dilution steps . Equal volumes ( 150 µl ) of JUNV Romero strain containing approximately 1000 PFU/mL and serum dilutions were mixed and incubated for 1 h at 37°C and 5% CO2 . Confluent monolayers of Vero E6 cells ( seeded in 12-well plates ) were infected with 100 µl of the virus–serum mixtures . After 1 h incubation at 37°C and 5% CO2 , the wells were overlaid with 0 . 5% agarose MEM with 1% FBS . The plates were incubated at 37°C and 5% CO2 for 7 days , and then stained with 0 . 25% crystal violet in 10% buffered formalin . The plates were washed and the plaques enumerated . The neutralizing antibody titer of a serum was considered positive at the highest initial serum dilution that resulted in >80% ( PRNT80 ) reduction of the number of plaques as compared to guinea pig serum from the mock-infection . All cell counts were quantified using a Hemavet Mascot hematology analyzer ( Drew Scientific , Dallas , TX ) equipped with veterinary software to measure white blood cell count , red blood cell count , hemoglobin concentration , hematocrit , mean corpuscular volume , mean corpuscular hemoglobin , mean corpuscular hemoglobin concentration , neutrophils , lymphocytes , monocytes , eosinophils , basophils , leucocytes , reticulocytes , and platelet count . Blood chemistry was performed using a VetScan2 Chemistry Analyzer ( Abaxis , Inc . , Sunnyvale , CA ) , which provides a diagnostic panel that includes albumin , alkaline phosphatase , alanine aminotransferase , amylase , total bilirubin , blood urea nitrogen , calcium , creatinine , glucose , potassium , total protein and globulin . Guinea pigs were treated with 100 mg/kg of favipiravir administered by placement of drug prepared in carrot baby food vehicle in the back of the oral cavity with a tuberculin syringe or by i . p . injection of drug prepared in a 2 . 9% sodium bicarbonate solution . Plasma was obtained from 3 animals per group by saphenous vein puncture at 0 . 25 , 0 . 5 , 1 , 2 or 4 h after treatment . Samples were deproteinized and analyzed by HPLC for quantitation of favipiravir as previously described [25] . The Mantel-Cox log-rank test was performed to analyze Kaplan-Meier survival plots . Hematology and blood chemistry were analyzed by the Student's two-tailed t-test . Virus titers were analyzed using one-way analysis of variance ( ANOVA ) followed by Bonferroni multiple comparisons test . All statistical evaluations were done using Prism ( GraphPad Software ) .
Based on studies modeling oral favipiravir therapy in PICV-infected guinea pigs [25] , a dose of 300 mg/kg/day was selected for the initial efficacy trial in the JUNV infection model . Animals were dosed twice daily by oral installation for a duration of 14 days , starting 1 day after challenge with 1300 PFU of the Romero strain of JUNV . As shown in Figure 1A , favipiravir treatment improved survival outcome compared to guinea pigs treated with placebo . Two of the 10 guinea pigs treated with favipiravir survived the infection , and those that succumbed survived , on average , >4 days longer than the animals treated with placebo ( 20 . 3±2 . 6 days and 16 . 2±1 . 2 days , respectively ) . Weight change over the course of the study was used to assess the effect of favipiravir treatment on the condition of the animals . The mean weights of the placebo-treated guinea pigs began to drop on day 10 post-infection , and were below the initial starting weights by day 12 ( Figure 1B ) . In contrast , mean weights in animals treated with favipiravir did not fall below initial starting weights until day 18 . Favipiravir treatment also delayed the onset of fever ( defined as a body temperature of ≥39 . 8°C ) from 8 . 8±2 . 3 days in the placebo group to 16 . 5±3 . 3 days . Ribavirin , included as a positive control [26] , performed similarly to favipiravir as the survival , weight , and temperature curves did not differ significantly between the two drug treatment groups . Blood samples were collected from all animals on day 9 post-infection for analysis of virus titers . The early time point was selected because of concerns that the deep anesthesia required for blood collection from the cranial vena cava may have resulted in the loss of sick animals if the procedure would have been delayed beyond day 9 . Unfortunately , viral titers in the placebo-treated animals were not well developed , as only 3 of 10 animals had detectable levels of virus in the range of 200–1300 FFU/ml . Nevertheless , virus could not be detected in favipiravir- or ribavirin-treated guinea pigs . Viral RNA was not detected in the serum or brain , liver or spleen tissues collected from the surviving favipiravir- and ribavirin-treated animals at the conclusion of the study ( data not shown ) . Moreover all survivors appeared to be in good health by physical examination and all the aforementioned tissues were histologically normal . Despite proven effectiveness in the PICV guinea pig infection model by oral treatment with favipiravir suspended in carrot baby food vehicle [25] , the lower than expected efficacy observed in the first study prompted a PK analysis to compare plasma levels of favipiravir following administration by i . p . and oral treatments . Compared to oral instillation of the compound , i . p . injection of an equivalent dose of 100 mg/kg resulted in higher plasma concentrations of favipiravir within the first hour , and similar levels at the later time points ( Figure 2 ) . The area under the curves ( AUC ) s of the drug concentrations were 56 . 1 by the oral instillation method and 103 . 4 by i . p . injection . Peak favipiravir plasma concentrations approximate 80 µg/ml ( 500 µM ) for i . p . treatment , with >10 µg/ml ( 60 µM ) present at 2 h . These levels are well above the reported 50% effective concentration ( EC50 ) of <20 µM for JUNV in cell culture [19] . Thus , the single dose PK data suggest that favipiravir is more bioavailable through i . p . dosing . A second experiment was conducted wherein favipiravir was dosed by i . p . injection starting 48 h post-challenge with 750 PFU of JUNV . Favipiravir treatment started 2 days post-infection provided a highly significant level of protection ( 7/9 survivors; 78% ) compared to the placebo group ( 1/9 survivors; 11% ) , and performed better than ribavirin ( 3/9 survivors; 33% ) ( Figure 3A ) . The weight data were consistent with the survival curves with the favipiravir-treated animals mirroring the weight gain of the sham-infected animals through day 14 before leveling out temporarily as a few animals became ill ( Figure 3B ) . Guinea pigs in the placebo group began to develop fevers as early as day 6 post-infection , while temperatures were in the normal range in the drug-treated animals through the first two weeks ( Figure 3C ) . Notably , the surviving placebo-treated animal had undetectable ( PRNT80<20 ) levels of JUNV neutralizing antibodies at the conclusion of the 42-day study . In contrast , all guinea pigs treated with favipiravir or ribavirin developed substantial neutralizing antibody titers of 320 . The placebo group survivor also gained weight and maintained normal body temperature throughout the entire experiment suggesting that the virus challenge did not produce the desired infection . In a subset of animals sacrificed on day 14 of infection , favipiravir treatment prevented the thrombocytopenia and leucopenia commonly associated with severe disease and also maintained a number of hematologic and blood chemistry parameters at normal baseline levels ( Table 1 ) . Moreover , we were unable to detect virus in the serum , brain , heart , kidney , liver , lung , and spleen of favipiravir-treated animals ( Figure 4 ) . With the exception of a single animal that had >5 logs of virus per gram of spleen , guinea pigs treated with ribavirin were also free of virus . All surviving guinea pigs were rapidly gaining weight and observed to be in good health by physical examination at the termination of the study . Taken together , the results demonstrate robust inhibition of viral replication and a high level of protection by i . p favipiravir treatment in the difficult to treat JUNV guinea pig infection model .
In the present study , the efficacy of oral and i . p . favipiravir treatment was evaluated in the guinea pig JUNV challenge model . Based on previous success in treating PICV infection in guinea pigs [25] , favipiravir was initially dosed by orally feeding the animals the drug suspended in carrot baby food . The results from this trial did demonstrate a significant protective effect that was comparable to ribavirin , the only small molecule antiviral that has demonstrated activity against severe JUNV infection , which initially showed promise over 25 years ago [27] , [28] . Ribavirin has been evaluated in a small-scale clinical trial in patients with advanced cases of Argentine HF , and did produce some signs of efficacy [3]; however , because it is associated with toxicity primarily in the form of hemolytic anemia , safer and more effective options are needed . The results from the initial guinea pig study were encouraging and supported further consideration of favipiravir through a second experiment designed to improve upon the limited success of the first study . The fact that oral favipiravir provided complete protection against lethal PICV infection in guinea pigs underscores the greater challenge of treating the more virulent JUNV infection , which was also less responsive to ribavirin in the present and past studies [25]–[27] . It is likely that the higher plasma concentrations achieved when administering favipiravir by the i . p . route resulted in greater levels of drug in target organs , which may have contributed to the remarkable efficacy observed in the second experiment . Because the animals generally did not like the taste of favipiravir suspended in the baby food vehicle , it was difficult to accurately deliver the doses in BSL-4 containment . This may have led to diminished amounts of drug actually making it into the gut for subsequent absorption into the circulation . It is likely that higher doses of the well-tolerated oral favipiravir [25] would improve survival outcome in guinea pigs challenged with JUNV; however , the increased viscosity of the favipiravir suspension at higher drug concentrations results in a paste-like consistency that is increasingly difficult to administer by mouth . It is also possible that delaying the initiation of treatment from 24 h in the first study to 48 h for the second study may have elicited a better immune response to the additional 24 h of viral replication , which combined with the inhibitory effects of favipiravir may have facilitated the clearance of the virus and afforded the greater level of protection observed . Notably , the clinical laboratory findings and lack of virus on day 14 correlated well with the survival data . The principal mechanism of action of favipiravir against influenza A virus was shown to be direct inhibition of the viral polymerase [14] . Although direct evidence to support this claim is lacking for other viruses sensitive to the action of favipiravir , findings from arenavirus and norovirus studies are consistent with the RdRP serving as the main target [19] , [21] . A recent report suggests that favipiravir induces lethal mutagenesis in influenza A viruses through selective pressure applied in cell culture [21] . However , it is uncertain whether this mechanism plays any role in vivo . Collectively , the evidence suggests that favipiravir selectively inhibits RNA virus RdRP , with only limited toxicity to cells . This specificity makes favipiravir an attractive candidate for a broadly active therapeutic with potential to treat multiple viral diseases . Our findings with i . p . favipiravir treatment represent the most significant level of protection ever reported for an antiviral drug intervention in the difficult to treat JUNV guinea pig infection model [26] . A study to define the therapeutic window in guinea pigs and efficacy studies in a nonhuman primate model are planned . | Argentine hemorrhagic fever ( AHF ) is a severe and often-fatal disease caused by infection with Junín virus ( JUNV ) . Presently , there is an unmet need to develop new therapeutics to address current medical , public health and national security concerns , as JUNV is considered a potential bioterror agent amenable to aerosolization and intentional release . In the present study , favirpiravir , a promising anti-JUNV drug in clinical development for the treatment of influenza , was evaluated in an experimental small animal model of AHF . Guinea pigs challenged with JUNV were treated with favipiravir twice daily for two weeks starting 1–2 days after infection . Consistent with pharmacokinetic analysis that showed greater plasma levels of favipiravir in animals dosed by intraperitoneal injection , administration by this route resulted in a dramatic protective effect as 78% animals survived the infection compared to 11% in the placebo-treated group . Favipiravir treatment inhibited JUNV replication and prevented the development of disease observed in animals receiving placebo during the acute stage of infection . The high level efficacy observed following post-exposure prophylaxis with favipiravir is the highest ever reported for a small molecule antiviral in the guinea pig JUNV challenge model and thus supports its continued development as a promising antiviral therapy for the treatment of AHF . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Favipiravir (T-705) Inhibits Junín Virus Infection and Reduces Mortality in a Guinea Pig Model of Argentine Hemorrhagic Fever |
During spatial navigation , the frequency and timing of spikes from spatial neurons including place cells in hippocampus and grid cells in medial entorhinal cortex are temporally organized by continuous theta oscillations ( 6–11 Hz ) . The theta rhythm is regulated by subcortical structures including the medial septum , but it is unclear how spatial information from place cells may reciprocally organize subcortical theta-rhythmic activity . Here we recorded single-unit spiking from a constellation of subcortical and hippocampal sites to study spatial modulation of rhythmic spike timing in rats freely exploring an open environment . Our analysis revealed a novel class of neurons that we termed ‘phaser cells , ’ characterized by a symmetric coupling between firing rate and spike theta-phase . Phaser cells encoded space by assigning distinct phases to allocentric isocontour levels of each cell’s spatial firing pattern . In our dataset , phaser cells were predominantly located in the lateral septum , but also the hippocampus , anteroventral thalamus , lateral hypothalamus , and nucleus accumbens . Unlike the unidirectional late-to-early phase precession of place cells , bidirectional phase modulation acted to return phaser cells to the same theta-phase along a given spatial isocontour , including cells that characteristically shifted to later phases at higher firing rates . Our dynamical models of intrinsic theta-bursting neurons demonstrated that experience-independent temporal coding mechanisms can qualitatively explain ( 1 ) the spatial rate-phase relationships of phaser cells and ( 2 ) the observed temporal segregation of phaser cells according to phase-shift direction . In open-field phaser cell simulations , competitive learning embedded phase-code entrainment maps into the weights of downstream targets , including path integration networks . Bayesian phase decoding revealed error correction capable of resetting path integration at subsecond timescales . Our findings suggest that phaser cells may instantiate a subcortical theta-rhythmic loop of spatial feedback . We outline a framework in which location-dependent synchrony reconciles internal idiothetic processes with the allothetic reference points of sensory experience .
A prominent temporal code of neural activity [1–3] is the phase precession of rodent place cell and grid cell activity relative to the septal-hippocampal theta rhythm ( 6–11 Hz ) [4 , 5] , in which firing begins late in the theta cycle and advances to earlier phases as the animal moves across a spatial firing field . Theta-phase precession is strictly unidirectional , which ensures that phase unambiguously encodes the distance traveled through a place field [6] . This unidirectionality may follow from mechanisms such as neuronal adaptation that halts firing before the peak of dendritic excitation [7] , place-cell network plasticity that learns an asymmetric ramp of depolarizing input through experience [8] , or temporal interference between a somatic theta oscillation and a speed-tuned [4 , 9] or spatial [7 , 10–12] dendritic oscillation . In open-field foraging , these mechanisms may lock the phase-distance code of phase precession to trajectory details ( that is , the speed , running direction , and path ) of individual passes through a spatial firing field [13 , 14] , thus preventing a direct mapping of phase to spatial locations . It is unclear whether phase codes with different properties ( for example , bidirectionality , spatial symmetry , or trajectory independence ) operate in other brain areas to process spatial information . Temporal interference models theorized that multiple velocity-controlled oscillators ( VCOs ) [15 , 16] perform path integration to collectively synthesize the hexagonally periodic spatial firing of grid cells [17] . Electrotonic soma-dendrite coupling ruled out dendritic implementations of VCOs [18] , leading to models of neuronal oscillators that project path-integrating phase codes to the grid cell network [19–21] . Experimental evidence for neuronal VCOs includes our previous report of thalamic theta-bursting neurons with the theoretically required burst-frequency tuning of direction [22] and observations of full phase precession at the periphery of grid cell fields as predicted by temporal interference but not continuous attractor networks or ramp depolarization models [14 , 23–25] . Organizing VCOs into ring attractor networks provides some internal stability [26 , 27] , but biological variance in spike timing and local theta cycle periods limits the temporal precision of VCO phase computations [28 , 29] . Likewise , continuous attractor models of grid-cell path integration accumulate position errors , even before considering sources of biological variance . In open environments that allow rotations , and particularly at low speeds , bounded network topologies cause error-inducing ‘ripples’ that perturb an otherwise flat energy landscape [30] . To counter the accumulation of position errors , path integrators must reset to the current position based on environmental cues [31 , 32] . Models combining the continuous attractor and VCO frameworks have proposed resetting VCOs via descending grid cell feedback [27 , 33 , 34] . However , for mice in complete darkness , grid cell patterns are rapidly disrupted [35] while path integration is sufficiently preserved to maintain a global heading angle [36] . Thus , grid cell networks in different species may not have the spatial stability to support a feedback role ( as in the combined attractor/oscillator models ) and may not directly compute the spatial vector maintained by path integration ( as in continuous attractor models ) . Subcortical targets of the hippocampal formation , typically studied as regulators of the theta rhythm ( cf . [37 , 38] ) , may additionally contribute to neural computations of space . In rats , the lateral septum ( LS ) , but not the medial septum , has revealed spatial modulation of firing rates in open environments [39 , 40] that diverged with respect to hippocampal remapping over time [41] . However , LS neurons have also been reported to carry a phase code for one-dimensional ( 1D ) tracks that precisely reflected hippocampal phase precession [42] . The degree to which LS or other spatially-modulated subcortical neurons are computationally dependent on hippocampal activity is unclear , especially in open two-dimensional ( 2D ) environments . In this study , we asked two questions: ( 1 ) Can spatial theta-phase codes be found in subcortical theta-rhythmic structures ? ( 2 ) What computational function might such phase codes serve in downstream circuits related to spatial cognition ? Our approach integrated , respectively , single-unit recordings in rats during open-field foraging , and computational modeling of spatial phase-coding networks and their downstream targets . We found a class of LS and hippocampal neurons with 2D spatial phase codes for which we analyzed the relationship between rate and phase , stability of rate and phase coding , temporal organization by theta , spatial firing patterns , and spatial vs . trajectory-related selectivity . Our analysis was consistent with an absolute , allocentric representation of space , thus we studied models of temporal coding mechanisms distinct from those hypothesized for the relative , field-centered representation of hippocampal phase precession . We suggest the theory that intrinsic neuronal and network processing of convergent hippocampal inputs form an independent and collective encoding of the animal’s current ( not prospective ) position . This spatial transformation may enable rapid and flexible phase-resetting of path integration .
We obtained tetrode recordings from 8 rats as they foraged in an 80-cm cylindrical arena during sessions lasting an average of 2 . 1 hours . Long sessions helped to ensure sufficient sampling of phase differences across the environment . Hippocampal LFP signals were recorded from an electrode located in the hippocampal stratum oriens , referenced to animal ground . Across 110 sessions , LFPs were collected concurrently with 1 , 073 single-unit recordings ( we use ‘recording’ to refer to a unit’s data from one session ) of 671 uniquely identified neurons ( some of which were observed in multiple recordings ) from sites including the LS and medial septum , hippocampus , thalamus , midbrain , and other subcortical areas ( Table 1; Methods ) . In some recordings , units exhibited spatial tuning of firing rate as well as spatial tuning of spike phase with respect to the LFP theta oscillation . Fig 1 shows one such cell from LS that fired preferentially in the west/southwest of the arena ( Fig 1A ) and was moderately theta-rhythmic ( index: 0 . 392; Fig 1A , inset , top; Methods ) and theta-modulated ( index: 0 . 288; Fig 1A , inset , bottom; Methods ) . Across space , the cell’s mean firing rate ( ‘ratemap’; Fig 1B; Methods ) revealed a single-peaked firing field that broadly covered much of the arena . Surprisingly , the spatial distribution of the mean theta-phase of spikes ( ‘mean-phase map’; Fig 1C , left; Methods ) varied in a pattern of spatial modulation that qualitatively matched the ratemap in Fig 1B . The cell fired at LFP theta peaks ( 0 radians ) in locations corresponding to low firing rates ( Fig 1C , left , green regions ) and during example low-firing-rate time intervals ( Fig 1D , top ) . Conversely , the cell fired near LFP theta troughs ( −π or π radians ) in locations corresponding to high firing rates ( Fig 1C , left , pink regions ) and example high-firing-rate intervals ( Fig 1D , bottom ) . To quantify phase reliability during a recording , we computed at every location the mean resultant vector length ( MVL ) of spike phase , which varies from 0 ( uniformly random ) to 1 ( perfectly reliable ) . Thus , we display the full effect of spatial modulation on spike phase with a ‘phase-vector map’ ( or simply ‘phase map’ ) where mean phase is indicated by color hue ( as in Fig 1C , left ) and maximum-normalized MVL by color saturation ( Fig 1C , right; Methods ) . The example cell had typical phase MVL around 0 . 2 except for a high-variance region along the westward wall ( Fig 1C , right , dark pixels ) and a high-reliability region >0 . 3 near the center of the arena ( Fig 1C , right , bright pixels ) . To study the characteristic phase relationships in our data , we examined spiking activity over individual traversals of the arena and whole-session spatial maps . A 15-s trajectory segment illustrates a series of bursts emitted by the example LS neuron ( Fig 1E , left ) . The cell initially burst around theta peak in a low-rate region in the northeast of the arena , precessed to earlier phases in the high-rate region as the animal moved to the southwest , and then shifted back to later phases when the animal returned to a low-rate region ( Fig 1E ) . Burst phase during this short trajectory was noisy , but the activity symmetrically followed the rate-phase regression line in both directions ( Fig 1E , right ) , corresponding first to phase advance and then to phase delay . To measure this phase modulation over the 2 . 2-h session , we regressed the mean-phase map ( Fig 1C , left ) onto the ratemap ( Fig 1B ) , revealing a negatively sloped rate-phase relationship ( circular-linear correlation: n = 3 , 190 map pixels , estimated r ^ = - 0 . 836 , p ^ ≈ 0; Methods ) around which the cell’s spatial data was narrowly distributed ( Fig 1F ) . For this cell , spike phase was symmetrically and bidirectionally coupled to firing rate over multiple timescales . By inspecting our dataset for this phenomenon , we defined ‘phaser cells’ as neurons whose spike phase coded for position and was strongly coupled to firing rate . To classify phaser cell recordings , we imposed criteria on three measures of phase , rate , and space ( Methods ) : ( 1 ) Spatial phase information Iphase quantified the spatial content of spike alignment to LFP theta oscillations as the Shannon mutual information between spike phase and position; ( 2 ) Total phase shift captured the depth of phase modulation as the regressed phase difference from the minimum to maximum rate; ( 3 ) The rate-phase correlation indicated the strength of rate-phase coupling based on a recording’s ratemap and mean-phase map . To determine the criteria , we asked how recordings that carried spatial information in spike theta-phase differed from others . Significant phase-coding recordings ( Iphase shuffled phase test , p < 0 . 02; n = 156 cells; S1 Fig , panel D ) exhibited less variable theta-burst frequency ( variance ratio , 0 . 624; Iphase-significance bootstrap test , p = 0 . 001; Methods ) than non-significant recordings ( n = 570 cells; S1 Fig , panel B ) , suggesting that phase-coding cells were more reliably periodic . Furthermore , significant phase-coding recordings exhibited more variable rate-phase correlation coefficients ( variance ratio , 3 . 87; p = 0 . 001 ) and more broadly distributed total phase shifts ( interquartile range ratio , 1 . 96; p = 0 . 001 ) than non-significant recordings ( S1 Fig , panel E ) . Thus , we classified phaser cell recordings as unit-session data that met each of several criteria: The fourth criterion ensured sufficient levels of spatial activation , at least one spike every other theta cycle , to convey rate and phase relationships . A total of 101 recordings from 5 rats satisfied the phaser cell criteria . Phaser cell recordings revealed moderate firing rates , corresponding to 1 or 2 spikes per theta cycle in preferred regions , and similar theta rhythmicity to other significant phase-coding recordings ( S2 Fig , panel A ) . By analyzing which recordings followed the same neuron across multiple sessions ( Methods ) , we determined that 69 unique phaser cells were observed by the 101 recordings: 50 phaser cells were located in the lateral septum , 15 in the hippocampal formation , and 4 in other subcortical structures ( Table 1 ) . The validity of the above criteria for phaser cells depended on whether they selected a meaningful subset of our data . Fig 2A visualizes the measures tested by the first two criteria ( Iphase and total phase shift ) with respect to their thresholds; the third measure ( rate-phase coupling strength ) is indicated by the size of the plot markers . In Fig 2A , significant phase-coding recordings ( n = 233 ) are shown with individual data points , the distribution of non-significant recordings ( n = 840 ) is represented by contours in the background , and phaser cell criteria ( 1 ) and ( 2 ) above are overlaid as red lines that cross out the region excluded by the criteria . Non-significant recordings ( Fig 2A , contours ) displayed a wide range of Iphase values that failed to achieve statistical significance ( S1 Fig , panel D ) and no relationship with total phase shifts that were narrowly distributed around zero ( S1 Fig , panel E , right ) . However , significant phase-coding recordings ( Fig 2A , circles ) fell into roughly three clusters: ( 1 ) low Iphase , total phase shift near zero , and minimal rate-phase coupling; ( 2 ) moderate Iphase , large positive phase shifts , and moderate coupling; ( 3 ) high Iphase , large negative phase shifts , and strong coupling . The first cluster was excluded , and the latter two clusters were selected as phaser cell recordings . Due to the striking division of the direction of phase shifts between the selected clusters , we labeled them as ‘positive’ and ‘negative’ subtypes . That is , negative phaser cells advanced to earlier phases , like hippocampal phase precession , and positive phaser cells delayed to later phases , unlike previously described spatial phase codes . To verify that differences in the direction of phase shifts were not artifacts of the recording configuration , we inspected our dataset for colocation , stability , and simultaneous observation of the two subtypes . Phaser cells were predominantly recorded from LS ( Table 1; S3 Fig , panel A ) . Two-thirds of phaser cells ( 48/69 ) were negative and one-third ( 24/69 ) were positive . For 19 phaser cells with multiple recordings , all but 3 preserved the sign of phase shift across their phaser-classified recordings ( S2 Fig , panel B , right ) . In some cases , negative and positive phaser cells were recorded simultaneously against the same LFP reference electrode and/or observed on the same tetrode . These observations , together with the fact that the LFP signal was always recorded from the hippocampal stratum oriens , indicate that the direction of rate-phase coupling was a stable property of individual phaser cells and not an artifact of variations in LFP signal polarity . To quantify phaser cell accuracy and reliability , we examined , respectively , a measure of spatial uncertainty and the spatial distribution of spike-phase MVL . We computed spatial uncertainty as R / 2 I phase for arena radius R = 40 cm . Increasing magnitude of total phase shift was associated with lower spatial uncertainty for negative ( n = 65 recordings; mean ± s . e . m . , 33 . 5 ± 0 . 378 cm; linear regression , r = 0 . 363 , p = 0 . 00292 ) and positive ( n = 36; 35 . 4 ± 0 . 349 cm; r = −0 . 441 , p = 0 . 00707 ) phaser cells ( Fig 2B ) . Across spatial locations , MVL was distributed from nearly zero up to a typical maximum value of 0 . 414 ( median , n = 101 recordings; Fig 2C ) . In order to statistically test for differences between subtypes , we averaged values across recordings for unique cells with multiple recordings . Negative phaser cells demonstrated both lower spatial uncertainty ( n = 48/24 negative/positive cells; post hoc Welch’s t = −2 . 32 , p = 0 . 0236 ) and higher phase-code reliability ( mean MVL; t = 2 . 68 , p = 0 . 010 ) than positive phaser cells . Thus , phaser cells exhibited spatial accuracy on the order of body length based on a reliable mapping of spike phase to position in certain locations . If phaser cells contribute to navigation or other spatial functions , then they must stably reflect a given context or environment . Cell-specific spatial modulation and rate-phase coupling should be preserved over both long experiences and multiple days . To analyze spatial stability of phase coding in phaser cells , we compared early vs . late portions ( <1 h ) of each recording to a baseline of pair-wise measurements between different cells ( Methods ) . For spatial stability , the distributions of spatial correlations between ratemaps revealed significant similarity above baseline across the multiple-hour recording sessions ( median , 0 . 502; within-cell ( n = 101 ) vs . between-cell ( n = 9 , 986 ) early-late pairs; Kolmogorov-Smirnov D = 0 . 694 , p = 2 . 07e−43; Fig 2D , left ) . For phase-coding stability , changes in total phase shift were distributed narrowly around zero , significantly lower than baseline ( 1 . 07 radians; D = 0 . 371 , p = 1 . 00e−12; Fig 2D , right ) . Likewise , for the 19 phaser cells with multiple recordings , spatial correlations between different recording days were significantly higher than baseline ( 0 . 345; within-cell ( n = 57 ) vs . across-cell ( n = 4 , 986 ) day pairs; D = 0 . 431 , p = 7 . 52e−10; Fig 2E , left ) and changes in total phase shift were distributed close to zero , significantly lower than baseline ( 1 . 30 radians; D = 0 . 399 , p = 1 . 66e−8; Fig 2E , right ) . Further , all but 3 of these phaser cells maintained similar Iphase values and total phase shifts across days ( S2 Fig , panel B ) , suggesting a global stability of the phase code beyond the pair-wise stability implied by Fig 2E . The stability of Iphase and total phase shift is necessary for phase-code stability , but those are spatially averaged measurements and relative phase shifts remain constant even if phase-code angles systematically drifted . Thus , we addressed the relationship between specific locations and the magnitude of changes in mean-phase angles . We calculated absolute phase differences between the early and late mean-phase maps from the analyses in Fig 2D . To relate these phase differences to spatial variation of phase reliability ( Fig 2C ) , we display them according to spike-phase MVL . Low/high MVL locations would be expected to show larger/smaller phase differences over time . Fig 2F shows MVL and absolute early-late phase differences for the LS cell from Fig 1; the wedge shape reflects the expected relationship , but the placement of the bulk of the data distribution revealed that typical MVL values coincided with phase differences of <π/4 radians ( that is , 1/8th of a theta cycle or ∼17 ms ) . Averaging across phaser cell recordings revealed a similar pattern in which the region of highest spatial density corresponded to absolute phase-code changes of <1/8th of a theta cycle ( Fig 2G ) . As in Fig 2B+2C , positive phaser cells demonstrated weaker phase-coding than negative phaser cells , as shown by the relatively higher density of the ‘tail’ leading up to maximal phase difference ( |Δ| = π ) at low MVL ( Fig 2G , right ) . Thus , phase reliability ( Fig 2C ) implied location-dependent phase-code stability over multiple hours ( Fig 2G ) . The spatial and phase-coding stability of phaser cells across hours and days was consistent with functional contributions to the spatial computations of the hippocampal formation . We asked what theoretical mechanism could support our observations of the spatial phase code carried by phaser cells . We considered the crucial feature that spatial data points , such as the conditional spike-phase distributions in Fig 1F , were tightly coupled to the rate-phase regression . Strong rate-phase coupling suggested that the rate-phase relationship was maintained across spatial locations and that rate and phase did not systematically diverge over short or long timescales . We surmised that , on average , rate and phase deflected together on approaches to a preferred location ( that is , a high mean firing-rate region ) , and then symmetrically retraced those deflections on leaving the preferred location ( Fig 3A ) . Thus , we theorized that the phaser cell code was a spatially homogeneous coupling of rate and phase that was symmetric and , because they deflect and retrace , bidirectional . In contrast , Souza & Tort ( 2017 ) [43] examined hippocampal place-cell theta-phase at low firing rates and revealed a distinct angle-shaped rate-phase relationship across place fields . The resulting curve ( adapted in Fig 3B ) reflects the combination of two effects that progress from entry to exit of hippocampal place fields: ( 1 ) the strict unidirectionality of spike theta-phase precession [4] , and ( 2 ) the single-peaked rise and fall of firing rate , which may be symmetric or skewed with respect to the field center [12 , 44] . To reconcile these differences , we suggest that symmetric , bidirectional phaser cell coding ( Fig 3A ) and asymmetric , unidirectional hippocampal phase precession ( Fig 3B ) reflect experience-independent vs . experience-dependent models of temporal coding , respectively . Mehta et al . ( 2002 ) [8] proposed that theta-rhythmic inhibition combines with spatially asymmetric input learned from the place-cell network to monotonically shift spike phase across place fields . However , absent learning , that mechanism generates a symmetric rate-phase relationship mediated by the rise and fall of external input ( Fig 3C ) . Thus , theta-rhythmic inhibition combined with depolarization by external inputs may explain the rate-phase relationship of negative phaser cells ( Figs 1F and 3A ) . As noted in Mehta et al . ( 2002 ) [8] , coupling phase to rate precludes a precise mapping between phase and specific locations within a place field . Instead , a rate-coupled phase signal in a 2D environment is restricted to encoding isocontours of the depolarizing spatial input ( Fig 3D; Discussion ) . Our observations of positive phaser cells , which modulated timing in the opposite direction to negative phaser cells , presented a conundrum . In models described below , we suggest a network mechanism to account for this difference , but the key prediction is that positive modulation requires theta-rhythmic excitation instead of inhibition . A consequence of theta excitation is that positive cells would fire at theta peak ( 0 radians ) at low firing rates , and then delay to later phases at higher rates . Negative phaser cells based on a symmetric ramp mechanism ( Fig 3C ) would fire following the minimal inhibition of the theta trough ( −π or π radians ) at low firing rates , and then advance to earlier phases at higher rates . This distinction implies a temporal segregation of phaser cell activity . To assess this temporal organization , we show rate-phase regressions for every phaser cell recording according to subtype ( Fig 3E ) . Negative and positive phaser cells fired during the rising phase [−π , 0] at low firing rates , and , with increasing firing rate , followed opposing paths to the falling phase [0 , π] , thus complementarily spanning the theta cycle ( Fig 3E ) . Positive phaser cell activity clustered before theta peak at low rates ( Fig 3E ) as predicted by theta excitation and a high threshold . Distributions of typical spike phases , computed as the spatial average of mean-phase maps to avoid the sampling biases of time averages , show that the subtypes were segregated by theta phase: negative/positive phaser cells typically fired at theta trough/peak ( Fig 3F ) . Thus , temporal segregation by subtype may reflect underlying differences in theta drive . Negative phaser cell ratemaps revealed diverse spatial representations including place-like fields , broad gradient-like fields , and boundary ( including on/off ) responses along the arena wall ( Fig 4A; recordings #444 and #768 produced remarkably similar rate and phase maps from different rats ) . Maximal firing rates ( Fig 4A , top ) corresponded to pre-theta-trough timing ( Fig 4A , middle , blue/pink ) . Conditional spike-phase distributions ( Fig 4A , bottom ) revealed a tendency for phase modulation to halt after approximately one-half theta cycle , perhaps indicating a minimum latency to spike following theta-peak inhibition; this nonlinearity means that some rate-phase regression lines ( Fig 3E ) overestimated the total phase shifts . Positive phaser cells likewise showed diverse spatial modulation , but the responses were more subtle , involving higher baseline firing rates and heterogeneous compositions of boundary-like and place-like selectivity ( Fig 4B , top ) . Maximal firing rates typically mapped to post-theta-peak timing ( Fig 4B , middle , green/blue ) and the rate-phase relationships were weaker ( n = 24 cells; median , rate-phase correlation r ^ = 0 . 42; Fig 4B , bottom ) than those of negative phaser cells ( n = 48; r ^ = - 0 . 54; Fig 4A , bottom; absolute values , post hoc Welch’s t = 2 . 053 , p = 0 . 0442 ) . Thus , subtype differences in patterns of spatial modulation reinforced our analysis showing higher spatial uncertainty and weaker phase stability in positive phaser cell recordings ( Fig 2B+2C and 2G ) . To quantify spatial modulation , we calculated spatial rate information Irate using a standard measure of position coding in place cells [45] and determined its statistical significance in phaser cell recordings with a spike-train shift test ( criterion p < 0 . 02; Methods ) ; 47/48 negative and 24/24 positive phaser cells attained significance . As expected from prior analyses , negative phaser cell spikes carried significantly higher Irate ( n = 47 significant cells , p < 0 . 02; 0 . 381 ± 0 . 06 bits/spike , mean ± s . e . m . ) than positive phaser cell spikes ( n = 24 , 0 . 111 ± 0 . 048; log values , post hoc Welch’s t = −3 . 92 , p = 0 . 0002 ) . The least-squares optimized slope between Irate and Iphase was 0 . 640 ( n = 101 recordings; S3 Fig , panel B , left ) , indicating that spike phase contributed substantial spatial information ( ∼56 . 3% ) in excess of firing rate alone . Most of the phaser cell recordings ( 10/16 ) with the highest Irate values ( >0 . 6 bits/spike ) were from hippocampal sites ( S3 Fig , panel B , left ) and most of those ( 9/10 ) were negative phaser cells , consistent with place cells that may have reflected phaser cell activity ( Discussion ) . However , our hippocampal sample was too small to draw clear conclusions . Thus , negative and positive phaser cells may represent diverse spatiotemporal relationships resulting from circuits combining theta-rhythmic inhibition or excitation with varied patterns of spatial drive . Our thesis that phaser cells map spike phase to spatial isocontours ( Fig 3D ) requires that spiking is predominantly driven by allocentric spatial factors ( that is , external cues in a world-centered reference frame ) . To compare allocentric spatial modulation with other factors , we calculated the spike information content of speed ( an idiothetic self-motion signal ) and movement direction ( an allocentric , but not spatial , signal; Methods ) . In contrast to the Irate comparison , the least-squares optimized slopes between Iphase and directional ( 0 . 086; n = 101 recordings ) or speed information ( 0 . 023; S3 Fig , panel B ) indicated minimal coding overlap between Iphase and other trajectory-based factors . However , it is possible that the spatial modulation apparent in ratemaps ( Fig 4 ) was a spurious by-product of trajectory-based factors and biased spatial sampling of the arena . Firing-rate modulation indices ( Methods ) for direction ( median , 0 . 379; n = 101 recordings ) and speed ( 0 . 318; S3 Fig , panel C ) were suggestive of possible trajectory dependence . Such a confound can result from directionally biased visits to particular locations for which a recorded cell happened to have a similar directional preference . For example , a cell responding to clockwise movement around the arena may produce a spatial ‘wall’ representation if the rat only moved clockwise when contacting the wall . To isolate spatial-behavioral confounds , we studied a Poisson-distributed generalized linear model ( GLM ) of spatial ( allocentric ) and trajectory-based ( idiothetic speed , and allocentric non-spatial direction ) variables . GLMs have been shown to learn independent spatial and directional contributions to firing that avoid trajectory-driven biases [46 , 47] . To capture inhomogeneous changes in spatial or trajectory-dependent selectivity , we fitted GLMs independently to every phaser cell recording for data restricted to sections of a 3 × 3 spatial grid spanning the arena ( Methods ) . The model was trained to predict the spike count for any 300-ms interval i Y ^ i = β ^ 0 + β ^ L L i + β ^ Q Q i + β ^ W W i + β ^ S S i + β ^ D D i ( 1 ) where L and Q are linear and quadratic spatial variables , W is a sigmoidal wall-proximity signal , S is linear speed , and D is movement direction . L , Q , and W are purely spatial whereas S and D capture the rat’s trajectory as a velocity vector . Thus , we termed this spatial family of GLMs the ‘LQW-SD’ model . To train LQW-SD , we standardized the position and trajectory data from our recordings , but several properties of the data needed to be addressed: ( 1 ) statistical dependence among the predictors contributed to an ill-posed problem; ( 2 ) spatial predictors had more reliable short-timescale correlations than the trajectory-based predictors; and ( 3 ) variable data density across spatial grid segments reduced the validity of model comparisons across the arena . To mitigate these issues , we imposed constraints on model coefficients by training LQW-SD as a ridge regression with ℓ2- regularization [48] . Further , to maximally expose the spatially inhomogeneous directionality that could have produced behavioral confounds , we chose the regularization penalty that optimized the trade-off between maximizing model directionality and minimizing spike-prediction errors ( S4 Fig , panel B+C; Eq ( 14 ) ; Methods ) . While we did not cross-validate spike-count predictions from the model , our analysis goal was not prediction but to statistically isolate consistent drivers of phaser cell spiking versus spurious factors that may have arisen due to behavioral biases . However , training the model independently within the 3 × 3 grid sections effectively performed a 9-fold cross-validation in space . We asked whether phaser cell recordings demonstrated directional selectivity that could produce spurious spatial modulation . To quantify directionality , we computed a directional homogeneity index ( DHI ) on [0 , 1] measuring alignment of the 9 βD vectors ( Eq ( 1 ) ) across the 3 × 3 grid; additionally , we computed a directional strength index ( DSI ) on [0 , 1] measuring the magnitude of βD relative to the other predictors ( Methods ) . The DHI of phaser cells ( median , 0 . 265; n = 69 unique cells with at least one phaser-classified recording ) revealed higher homogeneity than nonphaser cells ( 0 . 213; n = 602; post hoc Mann-Whitney U = 15 , 423 , p = 0 . 0005 ) . The DSI of phaser cells ( median , 0 . 0248 ) and nonphaser cells ( 0 . 0127 ) indicated low overall directionality ( U = 15 , 268 , p = 0 . 0003 ) , but it was more widely distributed for nonphaser cells ( range , [0 , 0 . 199] ) than phaser cells ( [0 . 003 , 0 . 105] ) . Thus , phaser cells excluded both homogeneous ( high DHI , high DSI ) and inhomogeneous ( low DHI , high DSI ) directionality . Our analysis was predicated on the ability of the model to explain firing patterns . To verify that LQW-SD could reproduce patterns of spatial modulation , we generated spike-count predictions across the 3 × 3 grid to reconstruct firing ratemaps ( Methods ) . Quantifying accuracy as the vector cosine similarity between ratemaps , we found phaser cells ( median , 0 . 986; n = 69 unique cells with at least one phaser-classified recording ) and nonphaser cells ( 0 . 908; n = 602 ) to have highly accurate reconstructions ( post hoc Mann-Whitney U = 16 , 960 , p = 0 . 012 ) . Actual and LQW-SD-predicted ratemaps are shown in Fig 5A for the negative phaser cells in Fig 4A with overlaid arrows representing the modeled directionality ( βD ) of each grid section . To verify that LQW-SD also captured strong directional ( high DSI ) cells accurately , examples of homogeneous ( high DHI ) and inhomogeneous ( low DHI ) directionality are shown in S5 Fig . Thus , LQW-SD provided a high-fidelity account of single-unit firing in our dataset , including spatial and directional cells . What does the LQW-SD model reveal about spatial vs . trajectory-based predictors ? Like DSI for directionality , we computed the relative strength of each model variable ( Eq ( 15 ) ; Methods ) . Box plots ( Fig 5B ) show the distribution of variable weights for phaser cells ( n = 69 unique cells with at least one phaser-classified recording ) and nonphaser cells ( n = 602 ) . Both cell types had similar central tendencies with nonphaser cells exhibiting wider ranges of variable strengths . The second-order spatial variables ( L and Q ) overwhelmed the wall and trajectory variables , constituting approximately 30% and 60% of the model weight , respectively . Wall/boundary cells were ( by inspection ) a small number within the dataset , but we considered that the trajectory-based factors ( S and D ) might be non-normally distributed , leading to artificially low coefficients . Thus , we computed the importance of model variables by their maximal contribution to predictions over the length of the recording . For variable X , we computed its maximal contribution Contribution ( X ) = max i | β ^ X X i | ( 2 ) across time intervals i and sum-normalized the variables ( Methods ) . The contribution profile ( Fig 5C ) was also dominated by L and Q , but the W , S , and D contributions were enhanced relative to the strength profile in Fig 5B . Wall and direction variables each constituted ∼8% of the total contribution and nonphaser cells revealed a wide range of speed contributions ( Fig 5C , S , gray ) consistent with the availability of speed signals throughout space-related brain areas [49 , 50] . Sorted recording data confirmed this pattern by showing an inverse relationship between spatial and speed-based contributions for phaser cells ( S6 Fig ) ; this relationship held for both negative and positive phaser cells ( S6 Fig , panel E ) . Thus , LQW-SD revealed a trade-off between allocentric spatial coding and idiothetic speed modulation , and that phaser cells were overwhelmingly spatial , not directional . To gain insight into the possible mechanisms and functions of phaser cell populations , we developed computational models based on minimal dynamics for intrinsic processing of spatial and theta-rhythmic inputs . Crucially , our models assumed that postsynaptic averaging of convergent hippocampal-LS projections produces input to phaser cells that is independent of hippocampus-specific coding ( Discussion ) . Our modeling approach balanced two goals: ( 1 ) qualitatively capture salient neurocomputational features of the data , and ( 2 ) minimize degrees-of-freedom to avoid model complexity and parameter fine-tuning . Our neuron and network models were broadly tuned to recapitulate several phenomena: ( 1 ) theta-bursting rhythmicity ( Fig 1A+1D; S2 Fig , panel A , right ) , ( 2 ) symmetric and bidirectional rate-phase coupling ( Figs 1E+1F and 3A; S1 Fig , panel E , left ) , ( 3 ) negative/positive phase-shift subtypes ( Figs 2A–2C , 3E and 4 ) , ( 4 ) temporal segregation of subtypes ( Figs 3E+3F and 4 ) , and ( 5 ) allocentric phase coding of spatial isocontours ( Figs 1B+1C and 2E; S2 Fig , panel B , right; Figs 3D and 5B+5C ) . Thus , to ensure rhythmicity and realistic spike timing , we based our neuron models on two-variable dynamical systems ( Eq ( 5 ) ; Methods ) featuring intrinsic bursting dynamics and spike initiation tuned to the activity of hippocampal low-threshold bursters [51 , p . 310] . To outline the computational role of phaser cells , our simulations focused on feedforward models in which phasers project to targets that ‘read out’ the phaser cell code . ( We will refer to model phaser units as ‘phasers , ’ ‘negative phasers , ’ or ‘positive phasers’ to distinguish them from our observed ‘phaser cells . ’ ) In the following sections , we present model simulations in several stages: ( 1 ) single-neuron phaser models with 1D external inputs , ( 2 ) a demonstration model of a small phaser network with artificial 1D spatial inputs and a downstream target cell , and ( 3 ) a realistic model of a large phaser network with 2D spatial inputs and several downstream target networks . Model negative phasers combined inhibitory theta input and excitatory external input ( Eq ( 6 ) ) with parameters ( Tables 2 and 3 ) that enabled theta-bursting ( Methods ) . Fig 6 shows phaser simulations in which the external input varied up and down over its full range ( Eq ( 8 ) ) . For low levels of excitatory input , the negative phaser ( Fig 6A+6B , Low1 and Low2 ) emitted single spikes near theta peak every few theta cycles . For high excitatory input ( Fig 6A+6B , High ) , the negative phaser burst with spike triplets near the theta trough on alternating theta cycles . This cycle-skipping rhythmicity is reminiscent of observations in medial entorhinal cortex and the head direction system [52 , 53] , but this model has no relationship to those phenomena: cycle skipping was a side-effect of the particular theta-bursting parameters ( Table 2 ) that we chose to qualitatively match phaser cell characteristics , which do not include skipping . ( The skipped cycles entailed that the resultant spike phase signal was perhaps weaker than if the units had fired every cycle . ) Expanded time intervals ( Fig 6B ) clearly show that the negative phaser shifted to earlier phases of the reference theta wave at high input levels . The model’s rate-phase correlation ( n = 399/512 nonzero input-level bins; r ^ = - 0 . 809 , p ^ ≈ 0; Fig 6D ) revealed strong , consistent phase modulation from peak ( 0 radians ) to trough ( −π ) . That is , spike-phase advanced during rising inputs ( Fig 6A; 0–10 s ) and then delayed to later timing during falling inputs ( 10–20 s ) . The simulated rate-phase coupling is symmetric and bidirectional as predicted ( Fig 3C ) and it advances to the theta trough as observed for negative phaser cells ( Fig 3E ) . To model positive phaser cells , we proposed a circuit mechanism whereby a bursting unit driven by excitatory theta input is suppressed by a negative phaser and does not directly receive spatial inputs . We modeled the feedforward inhibition as incrementing a slow 100-ms inhibitory conductance in the positive phaser for each presynaptic spike from the negative phaser ( Table 3; Eq ( 9 ) ; Methods ) . The positive phaser burst at the peak of every theta cycle when disinhibited by low external input to its presynaptic negative phaser ( Fig 6A+6B , Low1 and Low2 ) . As the external input rose and fell ( Fig 6A ) , the negative and positive phasers fired in complementary patterns: low/high input silenced the negative/positive phasers ( Fig 6C ) . The model’s rate-phase correlation was indeed positive ( n = 351/512 nonzero input-level bins; r ^ = 0 . 705 , p ^ ≈ 0; Fig 6E ) , but weaker and with a shallower phase modulation than both the negative phaser ( total phase shift , 0 . 654 vs . −2 . 44 radians; Fig 6D ) and the positive phaser cell data ( ∼83% of the low end of the observed range ) . Positive phaser weakness in the model was commensurate with the higher spatial uncertainty ( Fig 2B ) , lower phase reliability ( Fig 2C ) , and lower phase-code stability ( Fig 2G ) of positive phaser cells in our dataset . Crucially , negative and positive phasers were temporally segregated according to rate-phase coupling direction ( Fig 6D+6E ) as in the phaser cell recordings ( Fig 3E ) . Thus , a simple connectivity pattern between theta-bursting models qualitatively recapitulated phaser cell temporal organization . To demonstrate how a downstream target may learn to decode phaser cells , we constructed an artificial 1D spatial paradigm with which to study a model network of 128 negative and 128 positive phasers . The top half of Fig 7 ( panels A+B ) presents the phaser network and its outputs , and the bottom half of Fig 7 ( panels C-G ) presents the inputs and outputs of a target neuron model . To emulate the spatial diversity of phaser cells ( Fig 4 ) , we created two sets of spatial inputs that each drive one-half of the phaser network: ( 1 ) 64 place-like tuning functions ( Fig 7A , spatial information flows from the middle to the top of the network diagram ) , and ( 2 ) 64 inverted place-like tuning functions that we termed ‘notch’ functions ( Fig 7A , spatial information flows from the middle to the bottom of the network diagram; Methods ) . A notch function is equivalent to a corresponding place function that has been vertically flipped about its middle so that it is active everywhere except for one location; it is a spatial function and not a frequency filter as the term is used in other domains . Example joint space-phase distributions show the spatiotemporal firing patterns ( Fig 7B ) that were expressed by the phasers at the spatial mid-point of the network ( position 0 . 5; Fig 7A , highlighted phasers ) . The four network layers represent the possible combinations of spatial input type ( place vs . notch ) and phaser subtype ( negative vs . positive ) . These space-phase patterns ( Fig 7B ) , replicated at each of 64 positions across the 1D space , were available as presynaptic inputs to a downstream theta-bursting neuron ( ‘target burster’; Fig 7B , right ) . We next demonstrate how this downstream target can utilize phaser activity to learn a spatial phase code . To demonstrate how phaser inputs can entrain a downstream target , we devised an artificial 1D phase code consisting of two modes: theta-trough timing to the left ( position 0 ) and theta-peak timing to the right ( position 1 ) ( Fig 7C ) . This code associated opposite ends of the 1D space with opposing theta phases . We tuned the target burster model ( Table 4; Eq ( 11 ) ; Methods ) to emit spike doublets without cycle skipping ( Fig 7C , inset; S7 Fig , panel A ) . Its intrinsic burst rate approximately matched the reference theta frequency ( 7 . 5 Hz ) of our simulations , but a small deviation caused the burst phase to slowly precess over time ( S7 Fig , panel B ) . That is , the target burster was an intrinsic theta generator independent of other model elements . To amplify its independence , we injected a noisy current ( Table 4; Eq ( 11 ) ; S7 Fig , panel C ) that caused its burst phase to randomly drift ( 0 . 924 angular s . d . over 30 s , n = 36 trials; S7 Fig , panel D ) . To determine feedforward weights from phaser network inputs , we computed the vector cosine similarity between the space-phase distributions of each phaser ( as in Fig 7B ) and the supervised phase code ( Fig 7C ) . Inputs with the highest similarity were selected by k-winners-take-all ( kWTA; k = 25 negative + 25 positive phasers; Table 4; Methods ) . The resulting weights showed that the theta-trough mode to the left was supported by place/negative phasers , the middle part of the space was not strongly represented , and the theta-peak mode to the right was supported by notch/positive phasers ( Fig 7D ) . The total weighted phaser-network input revealed a qualitative match to the supervised phase code ( Fig 7C ) . In a 1-h simulation without injected noise , the target burster’s phase revealed distinct stereotyped phase trajectories for movement to the right or the left ( Fig 7F , arrows ) . Importantly , phaser network activity was not directional ( Fig 7B ) ; however , the target burster was directional because its phaser input was effectively released in the middle part of the space ( Fig 7D ) . Thus , in the middle , the target preserved its most recently entrained phase until the simulated spatial trajectory approached the other phase mode . This entrainment dynamic was visibly preserved in a simulation with injected noise ( Fig 7G ) : moving left caused a smooth phase advance to the theta-trough mode , while moving right slowly delayed toward the theta-peak mode until discontinuously jumping ahead of it . The vertical extent of the burst-timing channels at either side ( ∼π/2; Fig 7F+7G ) indicated the degree of phase misalignment allowed by this competitive phaser-target burst-synchronization mechanism . While the entrainment did not act perfectly , it prevented the target burster from substantially drifting from the phase code across a range of parameters ( S8 Fig ) . Thus , a phaser network robustly entrained a noisy target cell to a phase code in an artificial 1D space . To model realistic phaser cell activity , we drove our model phasers ( Eqs ( 5 ) – ( 10 ) ; Tables 2 and 3; Fig 6 ) with spatial input functions sampled from a generative model of the open-field spatial modulation of phaser cells ( S10 Fig , panel A ) . The generative sampling model was based on the ‘LQW’ model ( Eq ( 3 ) ) , a reduced LQW-SD model that was trained on full recording data ( that is , a 1 × 1 grid instead of the 3 × 3 grid ) without the trajectory-based variables S ( speed ) or D ( direction ) . The result is a seamless model of allocentric spatial selectivity F LQW ( x ( t ) ) = β ^ 0 + β ^ L L ( x ( t ) ) + β ^ Q Q ( x ( t ) ) + β ^ W W ( x ( t ) ) ( 3 ) for any trajectory x ( t ) inside the 80-cm recording arena . In the same way that LQW-SD was optimized to expose directionality ( Eq ( 14 ) ; Methods ) , LQW was optimized to expose wall signals ( S4 Fig , panel A ) to ensure that the less prevalent boundary/wall responses were captured . The generative model processed and randomized LQW representations to synthesize novel patterns of spatial modulation ( S10 Fig , panel A ) for negative phasers ( as only negative phasers received direct spatial inputs ) . Given a sampled input function F LQW * , the external input current followed I ext ( t ) = g e F LQW * ( x ( t ) ) ( 4 ) with excitatory input gain ge ( Eq ( 8 ) ) and other parameters unchanged ( Table 3 ) . We simulated 1 , 000 pairs of negative ( S9 Fig , panel A ) and positive ( S9 Fig , panel B ) phasers , in which the negative phaser inhibited the positive ( Eq ( 9 ) ; Fig 6 ) . Simulated phasers expressed place-like , gradient-like , and boundary/wall-like responses ( S9 Fig ) similar to our phaser cell recordings ( Fig 4 ) . We next demonstrate how this realistic phaser network can entrain a downstream target cell . To demonstrate realistic phaser entrainment of a single cell , we simulated a target burster neuron using an actual behavioral trajectory ( 1 h from Fig 1A ) . Without phaser input , the target’s bursting phase map illustrated the baseline spatial modulation ( Fig 8A; maximum MVL , 0 . 486 ) to be expected from a randomly drifting oscillator ( S7 Fig , panel D ) . We devised spatial phase codes representing oscillatory path integration ( Discussion ) that spanned the arena and the theta cycle . Two such codes with different phase offsets represented path integration of movement in the 45° direction at the scale of the arena ( Fig 8B ) . As in Fig 7D , we calculated the 2D kWTA weights ( k = 35 negative + 35 positive phasers; Table 4 ) based on spatial phase-tuning similarity between phasers and the supervised phase code . As in Fig 7E , the total weighted phaser-network inputs to the target burster revealed a spatial phase pattern that approximated the desired phase code ( Fig 8C ) . This input pattern comprised a post-theta-peak band ( π/2; Fig 8C , top , blue ) , due to positive phasers , alternating with a theta-trough band ( π; Fig 8C , bottom , pink ) , due to negative phasers; the location of these bands ( Fig 8C ) tracked corresponding phase stripes in the phase codes ( Fig 8B ) . With phaser input , the target’s phase maps revealed two broad modes of high burst-phase reliability ( Fig 8D; bright colors; maximum MVL , 0 . 994 , top; 0 . 973 , bottom ) reflecting location-dependent phaser entrainment . The division between the post-theta-peak and theta-trough modes was visibly sharper ( Fig 8D , dark stripe ) than in the phaser input itself ( Fig 8C ) , suggesting an attractor-like nonlinearity in the input-output phase transformation of phaser-target burst-synchronization . Further , the two entrainment modes were expanded and shifted in the 45° direction relative to phaser input ( Fig 8C+8D ) , analogous to the directionality and delayed onset of entrained bursting observed in the 1D phase trajectories ( Fig 7F+7G ) . Thus , for a single target cell , realistic phasers controlled the spatial distribution of burst timing , but the limited spatial frequency and phase-modulation depth of phaser activity ( especially positive phasers , Fig 6E ) dynamically constrained the phase-code output . To overcome the constrained output of single target cells , we asked whether a downstream network of multiple cells with phaser inputs would provide a stronger position signal . We considered target networks to be simple collections of target burster units ( Eq ( 11 ) ; Table 4 ) ; each unit had its own set of competitive synapses carrying input from the 2D phaser network . We constructed three target collections of 64 units ( Fig 8E; S10 Fig , panel B ) . By analogy with oscillatory ring-attractor models of path integration [26 , 27] , we created the ‘Ring’ collection with identical preferred directions but a full range [0 , 2π] of phase offsets ( Fig 8E , top ) . Because a single ring network is directionally biased , we expected that it would not support a clear open-field position signal on its own . The remaining two collections were constructed with a full range [0 , 2π] of preferred directions but identical phase offsets across units ( Fig 8E , bottom ) . These collections , ‘Phase 1’ and ‘Phase 2 , ’ were equivalent to taking a single-phase slice across a population of ring attractor networks ( S10 Fig , panel B ) . For each collection , every unit’s phase code ( Fig 8E ) was learned via kWTA competition and simulated with a 600-s behavioral trajectory . Due to the feedforward phaser-target connectivity , all units were simultaneously entrained by the same open-field phaser network ( as in Fig 8C+8D ) . The phaser input and unit output maps are shown as movies for the Ring ( S1 Movie ) , Phase 1 ( S2 Movie ) , and Phase 2 ( S3 Movie ) collections . Thus , realistic 2D phasers enabled functionally flexible phase-code entrainment of many downstream targets . To uncover the collective position signal in these collections , we applied the method of Bayesian spike-count decoding of position [54] to the phase domain ( Eq ( 13 ) ) to infer estimated trajectories from simulated burst timing ( Methods ) . If this position signal were to support the resetting of path integration , then it should be quantified in terms of position-error correction . Example 6-s trajectories with maximum a posteriori ( MAP ) estimates of position revealed that , as expected , the Ring network poorly tracked the trajectory ( Fig 8F , top left ) , but the Phase 1 and Phase 2 collections more closely approximated the trajectory’s position and shape ( Fig 8F , top right and bottom ) . To quantify error correction , we decoded a benchmark trajectory across collections and bootstrap unit samples ( Methods ) . The mean squared error ( MSE ) , based on the distance between actual positions and MAP estimates ( Methods ) , showed that the Ring network consistently performed poorly , but the Phase 1 and Phase 2 collections’ performance substantially improved by collectively decoding larger numbers of units up to the total of 64 ( Fig 8G ) . Phase 1 , Phase 2 , and the combination of all collections exhibited average decoding errors of 8 . 25 , 11 . 6 , and 8 . 70 cm , respectively . To be useful , phase resets should occur quickly . To measure the timescale of error-correction in phaser-entrained targets , we computed temporal auto-correlations of decoding errors for the benchmark trajectory ( S10 Fig , panel C ) . We quantified the typical timescale of error-correction as the correlation’s half-width at half-maximum ( HWHM; Methods ) . Across target collections , the HWHM timescale ( Fig 8H ) revealed subsecond correction in the Phase 1 ( 0 . 667 s ) and Phase 2 ( 0 . 267 s ) collections and 1-second correction in the combined collection ( 1 . 067 s ) . In our framework , correcting path integration errors depended on populations of ring networks ( as represented by the Phase collections ) or other structures with diverse preferred directions . As expected , a single ring network ( or other directionally homogeneous integrator ) would be insufficient to support a 2D position signal . Further , our target units were not performing path integration: they were noisy , intrinsic theta-bursters . Thus , error-correction performance in our models provided a lower bound: presumably , a path-integrating target would have fewer errors to correct than randomly drifting oscillators .
Hippocampal place fields [55] were studied extensively as a spatial firing-rate code prior to the characterization of spike theta-phase precession [4 , 6 , 56] . Theoretical models and in vivo manipulations have explored how interacting oscillations , ramp currents , or intrinsic dynamics may account for the link between phase precession and firing rate [7–12] . An analysis of pooled hippocampal activity highlighted the asymmetry of phase precession ( Fig 3B ) by finding clear theta coupling before the animal entered the classical rate-based place field [43] . This extended oscillatory coupling may reflect a critical role for phase precession in compressing place cell activity [57] into the timescale of synaptic plasticity [58 , 59] . If phase precession is primarily involved in the internal temporal organization of place cell activity , then spatial and theta-rhythmic input from the hippocampus may be transformed for other functions by other brain areas . Our analysis characterized the rate-coupled phase code of phaser cells as distinct from hippocampal phase precession . Most phaser cells in our dataset were located in LS ( Table 1 ) , a primary subcortical target of dense , convergent hippocampal efferents [42 , 60] that had previously been shown to carry a degraded spatial rate code [39–41] . Tingley & Buzsáki ( 2018 ) [42] reported that many LS neurons recorded during track running carried spatial phase codes that were similar to phase precession except for rate independence and larger spatial extents than typical place fields . Their analysis [42] indicated that the LS phase code depended specifically on hippocampal phase precession coordinating theta sequences in CA3 and CA1 inputs . However , this leaves open the questions of what LS phase codes in the open field look like and whether previously described LS rate-coding neurons also carry a phase code . Examining a single open-field behavioral condition , we found that 15 . 6% ( 50/321 ) of LS neurons yielded phaser-classified recordings according to our criteria ( 16 medial septal cells were not phaser cells; Table 1 ) . Unlike the Tingley & Buzsáki [42] phase code on tracks , LS phaser cells had strongly rate-coupled phase modulation and a wide range of spatial patterns including wall/boundary responses [61–63] that may be available to the LS via subicular afferents [60] . LS phaser cells demonstrated a symmetric and bidirectional code for allocentric space ( Fig 3A ) , whereas hippocampal phase precession is an asymmetric and unidirectional code for distance relative to the boundaries of a place field ( Fig 3B ) . Thus , rate-coupled phaser cells and rate-independent precession may represent distinct neuronal populations or distinct operating modes within LS and/or other structures , possibly mediated by heterogeneous connectivity patterns . Delay-based phase codes as in our positive phaser cells have not , to our knowledge , been previously demonstrated . Three of our positive phaser cells were located in the dentate gyrus , which receives input from a LS-supramammillary pathway [60] , suggesting possible hippocampal entrainment by LS phaser cell activity . Hippocampal negative phaser cells with strong spatial rate codes ( and place-like selectivity ) additionally demonstrated stronger directional and speed coding ( S3 Fig , panel B ) , thus contributing to the trajectory component of the space–trajectory trade-off observed in our GLM analysis ( S6 Fig ) . Our sample of hippocampal cells was too small to draw conclusions , but that relationship suggests that some hippocampal phaser cells may have been place cells reflecting phaser-entrainment signals from subcortical pathways . Our positive phaser model was based on theta excitation and negative-phaser inhibition ( Fig 7 ) , consistent with the prevalence of GABAergic neurons and recurrent collaterals in LS [60] . Our bursting models showed that , given convergent spatial and theta-rhythmic input , phaser cells could operate intrinsically without inheriting phase relationships from CA3 or CA1 . Convergent inputs allow the possibility that the longitudinal-to-vertical-band topography of the hippocampus-LS projection [60] averages over the spatial and theta-rhythmic activity of many place cells , effectively displacing hippocampal tuning specificity so phaser cells can exploit hippocampal input while computing distinct codes . Thus , both extrinsic and intrinsic phase transformations of hippocampal spatial information may arise in the LS and/or other structures depending on contextual and behavioral requirements . Early theoretical models suggested that hippocampal sequences , learned via phase precession and/or temporally asymmetric synaptic plasticity , enabled context-dependent predictions of future positions [64–68] . Experimental studies revealed theta-rhythmic forward-sweeping sequences during active locomotion [69 , 70] that mentally probed paths ahead of the animal’s current position to guide navigational decisions [71 , 72] . This research suggests a major function of theta-rhythmic information processing along the trisynaptic circuit of the hippocampal formation is to generate memory-guided predictions of future states given the current state . The current state may be reflected in CA3 or CA1 activity at the trough of local theta waves [56] , but it could also be directly encoded by other theta-rhythmic structures . Specifically , if recurrent network plasticity and phase precession enable future-oriented sequences , then phase codes in extrahippocampal circuits without those elements may be more likely to encode the current state by default . Such phase codes would be symmetric and bidirectional , similar to phaser cells as well as hippocampal place fields during initial exposure to a novel environment [8 , 44 , 73] . Thus , phaser cells may provide an experience-independent temporal code for the current state . The phaser cell spatial transformation is inherently less precise than phase precession . Its bidirectionality assigns the same phase to different locations: for example , a single phase would map to opposite edges of a 1D place field on a track ( Fig 3C ) or a concentric ring ( isocontour ) of a 2D place field ( Fig 3D ) . In contrast , the unidirectionality of phase precession enhances the rate-coded position signal of a place cell by contributing unambiguous information about distance traveled through its place field [4 , 6] . Phase precession constructively adds to coding precision , but the phaser cell code may serve to directly transform spatial information . We showed that the phaser cell code was stable across hours and days , suggesting that it may contribute to the context-dependent spatial computations of hippocampal/entorhinal circuits . LS spatial modulation has been previously shown to exhibit distinct responses to context changes compared to hippocampal place cell remapping [41] . Our study did not address context-dependence , but it did reveal spatial heterogeneity across phaser cells ( for example , Fig 4 ) , thus supporting our theoretical notion that phaser cell responses provide a basis for flexible spatial learning across contexts . One benefit of a bidirectional phase code is that positive phase modulation can coexist with negative phase modulation in the same network . To illustrate the spatiotemporal activation of symmetric rate-coupled phase codes , we could imagine layers of negative and positive phaser cells with 2D bell-shaped spatial tuning and uniformly distributed fields . At the trough of a theta wave , negative phaser cells representing the current location fire first and strongest , followed by their neighbors in all directions . Activation continues in a radial wave extending outward and dissipating by theta peak . Positive phaser cells , conversely , follow a reverse radial wave that begins with a wide concentric circle of weak firing at theta peak and collapses onto the current location with strong firing before theta trough . This expansion and contraction of radial waves would collectively span the theta cycle as a consequence of the theta-segregation of negative and positive phaser cells ( Fig 3E ) . Thus , phaser cells may form a spatiotemporal cursor marking the present . The neural mechanisms of path integration are not well understood . In rats , experimental inactivation of the medial septum has been shown to reduce the theta rhythm and disrupt grid cell firing [74 , 75] , but preserve the spatial firing of hippocampal place cells [76] except in conditions such as large environments ( or wheel running ) in which performance would be expected to rely more on path ( or time ) integration than external cues [77] . Similarly , septal inactivation of theta using gabazine ( but not muscimol or tetracaine ) was demonstrated to preserve hippocampal spatial activity while impairing navigation to a hidden goal [78] . In mice , path integrating behavior is preserved in the dark ( cf . the control animals tested by [36] ) even though spatial grid cell activity has been shown to require visual input [35] . These findings suggest the theta rhythm is critical to path integration independent of place field maps or grid cell periodicity , raising the question whether it plays a direct computational role or a supporting role ( such as phase reset ) , or contributes to both . The temporal interference models based on VCO units [19–21] posited a direct role in which relative phases between oscillators constitute a spatial vector anchored to a previous reference point . We previously showed that a generalized VCO model could be effectively calibrated by extended sensory cue interactions that mediated phase-code feedback [31] , although that study was agnostic to the feedback mechanism . Here , we demonstrated burst-synchronized entrainment of target neurons that learned VCO-like activity patterns ( Fig 8 ) . However , detecting collective synchrony among a population of phaser cells is a general decoding mechanism that could theoretically support a continuous attractor network of grid cell activity [30] . In that case , temporal coordination within the theta cycle might act as a signal boost for spatial feedback to reset the location of the activity bump ( cf . [32] ) . Additionally , a main criticism of VCO theories followed from the finding of grid cells in bats without continuous theta oscillations [79] . However , like VCO-based path integration ( see discussion in [25] ) , a phaser-based reset does not necessarily require rhythmic periodicity: synchrony could arise from structured latencies due to shared arrythmic inputs . Indeed , phase locking and phase coding by hippocampal and medial entorhinal neurons in crawling bats has been reported to be organized by nonoscillatory LFP fluctuations [80] . While our phaser models required theta rhythmicity , the mechanism of spatial synchrony that they demonstrated could be generalized to nonoscillatory systems . Despite widely varying navigational and perceptual requirements across species , synchronous ( but not necessarily oscillatory ) neural activation may be organized by allocentric features . The main requirement is that path integration reset must be linked to the current state of the world . Thus , LS phaser cells in rats may operate a present-focused reset mechanism parallel to future-focused hippocampal dynamics . The phaser models assumed that temporal contiguity , as measured by spatial phase-tuning similarity , promotes associative synaptic weights [58 , 59] between phaser cells and their targets . The supervised competitive mechanism was not realistic , but our modeling goal was to demonstrate the functional implications of having competitively weighted phaser inputs . The simplified learning mechanism represented the end result of an animal’s familiarization with a given environment . During exploration , we supposed that path integration produces a ‘teacher’ signal that associates internal states with external cues represented in phaser cell inputs . This would be a noisy signal in novel environments or disoriented animals , but investigatory behaviors in those situations emphasize incremental exploration and active management of path integration [81]: shorter excursions , direct returns to home base , and more visual fixations and/or head scanning [82] . These behaviors may stabilize the teacher signal to allow the path integrator to learn new weights from phaser cells ( or other inputs ) . For example , in a VCO-based path integrator , relative phases between ring networks would coherently advance and delay relative to idiothetic motion signals [26 , 27] . As long as those phase modulations were relatively continuous between sensory fixations , then any resulting spatial structure in the relative phase pattern would serve to reinforce itself by enhancing co-active inputs from phaser cells with similar spatial phase tuning . Our supervised phase codes ( Figs 7C and 8B+8E ) temporally collapsed the process of learning a teacher signal into a single pattern . An additional complication for VCO-based path integration is that learning requires theta-rhythmic coupling between the target and its phaser inputs . However , the burst frequency of VCOs increases with movement in the preferred direction [19 , 22] . Thus , phase-coupled synaptic modification would be restricted to the subset of VCOs with preferred directions orthogonal to the animal’s current direction . This limitation would be mitigated by ring attractor organization of VCO cells [26 , 27] , in which learning would be continuous because every orthogonal direction would be represented by a cell in the network . For continuous attractor-based path integration in grid cells , phaser cells and grid cells would be phase coupled via the shared hippocampal-entorhinal theta rhythm [83] , but phase locking of layer III grid cells to the local theta trough [5] could restrict learning to negative phaser cell inputs . Future studies are needed to determine biologically plausible learning mechanisms . The continuous activity of phaser cells further raises the question of how a path integrator would switch from internally integrating self-motion to receiving phase-code feedback to reset errors . Presumably , both processes could not occur concurrently . Our models ( including [31] ) suggest that resetting to stabilize the spatial representation of a familiar environment requires theta-phase coupling ( similarly to learning ) but it only needs to punctuate path integration briefly enough to achieve burst synchronization ( Fig 8H; S7 Fig ) . Punctuated resets could be adaptively driven by investigatory behaviors like head scanning [82] or boundary visits [84] , or by error signals mediated by grid cells [27 , 85] . Ring attractor organization of VCOs could enhance the robustness of phase-code resets by propagating updated phase offsets via intrinsic connectivity . Furthermore , our examination of LS phase codes may be biased by our sample of recording sites . Tingley & Buzsáki ( 2018 ) [42] found a dorsal-ventral dissociation in LS phase coding properties , including evidence that local theta is a traveling wave in the dorsal-ventral and medial-lateral directions . Thus , the theta-phase diversity of phaser cells is potentially much broader than our sample , enabling additional entrainment or switching mechanisms in downstream targets . Theories of the neural circuits of spatial cognition should go beyond representations to describe how target brain areas read , decode , and translate signals along the path to decisions and behavior . We presented exploratory single-unit data revealing a rate-coupled spatial phase code in neurons found in the LS , hippocampus , and other subcortical areas . Dynamical bursting models helped to explain observations in the data , but they also demonstrated how collective synchronization codes among phaser cells could be learned and decoded by target cells and networks . Our data and models suggest a subcortical phase-code feedback loop for allocentric space may be mediated by phaser cells in LS and/or other regions . Future studies of the role of theta oscillations in spatial navigation may consider the phaser cell mechanism or our theorized feedback pathway to provide a useful perspective . Further research is needed to determine which pathways might support this feedback , but the LS is ideally positioned to translate hippocampal spatial and theta-rhythmic output to downstream subcortical areas [60 , 86] that regulate the theta rhythm [37 , 38] and theta-bursting thalamic nuclei [22 , 87 , 88] including the nucleus reuniens with hippocampal and entorhinal projections [60 , 89 , 90] . Spatial synchronization codes may resonate through limbic loops to reconcile internal maps with external sensory experience .
Rats were chronically implanted with recording devices under deep isoflurane anesthesia . All experiments were conducted in accordance with the U . S . National Institute of Health Guide for the Care and Use of Laboratory Animals ( NIH Publications No . 90-23 ) , and were approved in advance by the animal subjects review committee at the University of California , Los Angeles . We define a quadratic integrate-and-fire model [51] of intrinsic bursting with a fast variable for the spiking limit cycle ( V ) and a slow adaptive variable for terminating bursts ( u ) . The dynamics follow τ V ˙= Φ ( V ) - u + I ( t ) τ u ˙= a ( b V - u ) ( 5 ) where I ( t ) is a cell-specific time-varying input , Φ ( V ) = 0 . 04V2 + 5V + 140 is a quadratic nonlinearity for spike initiation , a and b control adaptive feedback , and τ sets a shared time-scale for spiking and bursting ( in addition to the time constants implicit in Φ ( V ) and a ) . Whenever V > Vt , a spike is recorded , V is reset to c , and u is incremented by d . Bursting parameters are listed in Table 2 . While V is approximately millivolt scale , we treat this system as a qualitative , not biophysical , model for which the parameters are in arbitrary units . For theta-rhythmic inputs and recording theta phase , simulations tracked a reference theta wave at frequency fθ = 7 . 5 Hz , matching the typical burst rate in our single-unit recordings . For negative phasers , we set the time-varying input ( Eq ( 5 ) ) to the combination I ( t ) = I θ ( t ) + I ext ( t ) ( 6 ) of sinusoidal theta inhibition ( for inhibitory gain gθ < 0 ) I θ ( t ) = g θ [ 0 . 5 ( cos ( 2 π f θ t ) + 1 ) ] ( 7 ) and external excitatory input ( for excitatory gain ge ) I ext ( t ) = g e F ext ( t ) ( 8 ) where the external input function Fext ( t ) had range [0 , 1] . The positive phasers had theta gain gθ > 0 and followed Eq ( 5 ) with negative-phaser input I ( t ) = I neg = - g inh ( V - E inh ) ( 9 ) where ginh was a slow inhibitory conductance τ inh g ˙ inh = - g inh ( 10 ) that was incremented by dinh with every pre-synaptic spike ( Table 3 ) . The target bursters had a shorter time-constant ( ↓τ ) and lower burst excitability ( ↑d; Table 2 ) . In place of Eq ( 5 ) , the fast variable followed τ d V d t = Φ ( V ) - u + I syn ( t ) + I const + σ ξ τ d t ( 11 ) where normalized white noise ξ was controlled by gain σ , and Isyn ( t ) was the total synaptic drive from the phaser network I syn ( t ) = ∑ k ∈ { neg , pos } [ g k ∑ j = 1 n p W k j δ ( t - t k j ) ] ( 12 ) where np was the number of phasers in each subtype layer , gneg and gpos were subtype-specific feedback gains ( Table 4 ) , Wneg and Wpos were the phaser weight vectors ( for example , Fig 7B ) , and tneg and tpos were most-recent-spike vectors . Constant input current was tuned ( Iconst , Table 4 ) so that the intrinsic burst rate , without noise or synaptic input , was close to reference theta frequency ( 7 . 519 s−1 compared to fθ = 7 . 5 Hz ) . Spiking neuron and network models were implemented in the equation-based Brian simulator [91] . Simulations were integrated in 1-ms timesteps . Phaser layers and the target burster without noise were evolved with Runge-Kutta 4th-order integration; the target burster with noise used the Euler-Maruyama method . Burst timing in simulations was determined as spike times following interspike intervals ≥ 25 ms . For 1D spatial simulations , place tuning functions were Gaussian functions with bandwidth 1/64 normalized to the range [0 , 1] and centered at 64 evenly-spaced positions from 0 to 1 . Each notch tuning function was 1 minus a place tuning function . The gain of phaser input onto the target burster ( Table 4 ) was manually tuned for visually matched ‘middle of the road’ synchronization at both fixed points . For 2D spatial simulations , phase code gratings had 80-cm spatial periods so that one cycle covered the environment . Phaser gain onto the target burster ( Table 4 ) was manually tuned to roughly equalize the size of negative and positive synchronization modes across different reference phases . Based on 1-hr training simulations , we generated joint space-phase distributions from phaser spikes: 15 × 36 ( x × ϕ ) bins for 1D simulations; 15 × 15 × 36 ( x × y × ϕ ) bins for 2D simulations . The supervised phase code was either directly specified as a binary array for 1D simulations or binned from a spatial grating function for 2D simulations . We computed the vector cosine similarity between the space-phase distributions of the phasers and the supervised phase code as the basis for feedforward synaptic weights from the phaser layers to the target burster . To determine competitive weights , we chose the kWTA negative and kWTA positive phasers ( Table 4 ) with the highest similarities and normalized those similarities to the range [0 , 1] via [ ( similarity − min ) / ( max − min ) ] . Inactive weights were set to 0 . Total phaser input ( Figs 7E and 8C ) was computed as the product-sum of the weight vector and an array of all space-phase distributions . We simulated target networks with 64 bursting units that each learned different ranges of phase offsets and preferred directions ( Fig 8E ) . Burst timing was decoded in 267-ms sliding windows ( 2 theta cycles ) that were incremented in 133-ms steps ( 1 theta cycle ) . For each unit , the average burst phase was computed in each window; the previous average was used if no bursts occurred in the window . Analogous to methods for decoding spike counts [54] , we calculated the posterior probability distribution of spatial position P ( x|ϕ ) for an array of phase values ϕ as P ( x | ϕ ) = P ( x t | ϕ , x ^ t - 1 ) = C ( τ , ϕ ) exp ( - | | x ^ t - 1 - x t | | 2 σ c 2 ) ∏ i = 1 n exp ( cos ( ϕ i - Φ x , i ) ) ( 13 ) where xt was the position for the current window , x ^ t - 1 was the MAP position estimate for the previous window , C was a normalization factor based on ϕ and window-size τ that ensured ∑x P ( x|ϕ ) = 1 , σc = 15 cm was the Gaussian width of a spatial contiguity prior , n was the number of units , and Φx , i was the phase value at position x of the 2D spatial phase code that was used to train unit i . Decoding MSE was computed as the mean squared Euclidean distance between the MAP position and the average of recorded trajectory samples within each window across a 60-s trajectory segment used as a performance benchmark . We decoded the activity from three target-burster networks with 64 units ( Fig 8E; S10 Fig , panel B ) and the combination of all three networks with 192 units . Each network condition was bootstrapped by sampling ( or subsampling to smaller network sizes as in Fig 8G ) with replacement the units in the network and then decoding the sample’s activity and computing the MSE as described . Temporal autocorrelations ( S10 Fig , panel C ) were computed using full-size networks ( 64 or 192 units ) by correlating each bootstrap MSE time-series with itself and normalizing the minimum and maximum of the mean bootstrap correlations to [0 , 1] . HWHMs were calculated as the time lag of the earliest window with normalized correlation <0 . 5 for each bootstrap; data are shown ( Fig 8H ) as means and empirical 95% confidence intervals of bootstrap HWHMs . Male Long-Evans rats ( 350–400 g ) were individually housed and kept at 85% of ad libitum weight . They were trained over 5 d to forage for food pellets in an enclosed environment . Under deep isoflurane anesthesia , rats were chronically implanted with tetrode arrays targeting ( across rats ) the medial and lateral septum , dorsal hippocampus , anterior thalamus , midbrain , and/or other subcortical areas . Each rat was implanted with 16 tetrodes ( 64 electrode channels ) that were grouped into four independently drivable bundles of four tetrodes each . Data collection methods including conduct of recording sessions , video tracking analysis , and single-unit acquisition have been described previously [22] . Spike trains recorded during different sessions were considered to be from the same cell if ( 1 ) they were obtained from the same tetrode , ( 2 ) the tetrode had been advanced <80 μm between recordings , and ( 3 ) cluster boundaries and waveform shapes were visually similar on all tetrode channels for both sessions . The phase of the septal-hippocampal theta oscillation was quantified from the LFP signal on a reference electrode in the hippocampal stratum oriens . In one subject ( rat 11 ) , a strong theta-rhythmic cell was used as phase reference instead of the LFP signal and was not included in data analysis . All analysis data was filtered for linear movement speeds >5 cm/s . To handle large variance in spatial data density from long recordings , we computed spatial maps with adaptive scaling kernels . We used a KD-tree algorithm to generate a nearest-neighbor model of the data points for the map . For every pixel to evaluate , we found the enclosing radius of the nearest 4% of data points . If the radius was <8% or >30% of the arena diameter , then it was fixed at 8% or 30% , respectively . A Gaussian kernel set weights for each data point in this evaluation radius . For ratemaps , we computed weighted averages of trajectory data and spike data to create occupancy and spike density maps; dividing the spike density by the occupancy map produced the ratemap . For phase maps , we computed weighted mean resultant phase vectors from which we retrieved the mean phase and MVL . The mean phase across pixels produced the mean-phase maps; otherwise , the MVL was maximum-normalized and composited as a color saturation overlay onto the mean-phase map to produce the phase-vector map . Phase maps used colors drawn from the CIELUV color space to maintain perceptual uniformity of intensity across hues . The rhythmicity index and burst-frequency estimates were derived from spike-timing autocorrelations . We adaptively smoothed 128-bin 0 . 5-s correlograms to find stable estimates of the first trough and first ( non-central ) peak of the correlograms . Rhythmicity was calculated as the ratio [ ( peak − trough ) /peak] . Burst-frequency was calculated as the average of the first-peak mode estimate and an estimate based on a weighted-average of the first-to-second-trough correlations . The theta modulation index was computed from a 10° binned phase histogram on [−π , π] . We circularly convolved the histogram with a 10° bandwidth Gaussian kernel for smoothing . Theta modulation was calculated as the ratio [ ( max − min ) /max] of the smoothed histogram . We implemented the method of Kempter et al . ( 2012 ) [92] for computing circular-linear regressions with stable estimates of the correlation coefficient and p-value . This method was used for all rate-phase regression lines and rate-phase correlation values . For a given unit recording , the input data consisted of the common trajectory-sampled pixels from the 64 × 64-pixel ratemap and mean-phase map computed ( as described above ) from the unit’s spike data , LFP theta signal , and spatial trajectory . To compute the total phase shift , we multiplied the estimated rate-phase regression slope by the range of firing rates [max − min] in the ratemap . We calculated spatial correlations as the mean-adjusted cosine vector similarity between the common trajectory-sampled pixels in 64 × 64-pixel ratemaps computed with the adaptive kernel ( as described above ) . We calculated changes in total phase shift as the absolute difference between total phase shifts computed from rate-phase regressions on 64 × 64-pixel ratemaps and mean-phase maps . For the early-late within-session comparisons , the early portion consisted of up to 1-h after the start or the first half of the recording session data ( whichever was shorter ) ; the late portion consisted of up to 1-h before the end or the last half of the recording session data ( whichever was shorter ) . The across-cell baseline consisted of each recording’s early portion paired with the late portion from every recording of all other identified cells . For the multiple-day comparisons , spatial correlations and changes in total phase shift were computed using the ratemaps and mean-phase maps based on the full recording session data ( as in every analysis apart from the early-late comparisons ) . The within-cell comparison consisted of all unique pairs of a given cell’s recordings for all cells with multiple recordings . The across-cell baseline consisted of each recording from a cell with multiple recordings paired with every recording of all other identified cells . We computed spatial phase information Iphase as the mutual information between phase ( ϕ ) and position ( x ) I ( ϕ ; x ) = ∑ x ∑ ϕ p ( ϕ , x ) log 2 ( p ( ϕ , x ) p ( ϕ ) p ( x ) ) based on joint space-phase distributions of spikes binned into 15 × 15 × 36 ( x × y × ϕ ) arrays . This measure yielded information in units of bits . We permuted spike phases 1 , 000 times to calculate p-values . We computed spike information content based on Skaggs’ formulation [45] I K = 1 F ∑ k ∈ K p ( k ) f ( k ) log 2 ( f ( k ) F ) where K was position , direction , or speed of the trajectory; p was the occupancy density; f was a firing-rate function; and F was the mean firing rate . Position was binned into 15 × 15 arrays on [0 , 80] cm along the x and y axes; direction into 36 bins on [0 , 2π]; and speed into 18 bins on [5 , 50] cm/s excluding bins with <3 s occupancy . These measures yielded information rates in units of bits/spike . We randomly shift-wrapped spike trains with 20-s minimum offsets and re-interpolated trajectory data 1 , 000 times to calculate p-values . The direction modulation index was computed as the ratio [ ( max − min ) /max] of a smoothed firing-rate function of movement direction . Average firing rates in 36 direction bins on [0 , 2π] were circularly convolved with a 10° bandwidth Gaussian kernel . The speed modulation index was computed as the ratio [ ( max − min ) /max] of a firing-rate function of speed . Average firing rates were calculated for 14 bins on [5 , 40] cm/s excluding bins with <8 s occupancy . Ridge regression models were trained on 9 scalar predictors representing the vector components of the 5 model variables: L = ( x , y ) , Q = ( x2 , y2 , xy ) , W ( scalar ) , S ( scalar ) , and D = ( ux , uy ) . The wall predictor W was a sigmoid proximity signal [1/ ( 1 + exp ( −k ( r − w0 ) ) ) ] for radius r from arena center , k = 0 . 5 , and w0 = 30 cm . S was linear trajectory speed . D was the unit vector along the movement direction . Training samples were 300-ms bins and predictors were interpolated at the midpoint of each bin . Each predictor was standardized by subtracting its sample mean and dividing by its sample standard deviation . The response variable was the log spike-count Y for each bin , as in a Poisson-distributed GLM . The trajectory was divided into equal-sized 2 × 2 or 3 × 3 grids based on data limits . For each grid section , the GLM was trained on all data samples inside the section according to interpolated ( x , y ) position . Estimated model intercepts and coefficients for each recording and grid section were stored for analysis ( or for the reduced LQW generative model ) . To regularize the model , tuning parameter α determined the ℓ2- norm penalty for least-squares optimization β ^ = arg min β [ ∑ i = 1 n t ( Y i - Y ^ i ) 2 + α ∥ β ∥ 2 2 ] where nt was the number of training samples . We maximized model directionality ( or , similarly , the wall response W in the LQW generative model ) by choosing α ^ = arg max α [ 1 n r ∑ k = 1 n r e ∥ β D , k ∥ 2 · n t , k ∑ j ∈ { L Q W S D } e ∥ β j , k ∥ 2 ∑ i ( K i , k - K ^ i , k ) 2 ] ( 14 ) which maximizes ( over nr = 1 , 073 single-unit recordings ) the softmax directional coefficients while minimizing spike-count ( K = exp ( Y ) ) prediction errors ( MSE; S4 Fig ) . The value α = 1 . 2496 from the 2 × 2 model was used for analysis because of higher likelihood , lower MSE , lower penalty , and complete wall contact across grid sections compared to the 3 × 3 model . The relative strengths of GLM variables were computed as normalized vector norms Strength ( X ) = ∑ i = 1 g ∥ β X i ∥ 2 2 ∑ j ∈ { L Q W S D } ∑ i = 1 g ∥ β j i ∥ 2 2 ( 15 ) for variable X ∈ {L , Q , W , S , D} across g grid sections . Thus DSI was computed as Strength ( D ) and DHI was computed as 1 minus the angular s . d . of the βD vectors across the grid . The maximal contributions of GLM variables were computed similarly to Eq ( 15 ) but with maximum linear predictors ( Eq ( 2 ) ) instead of coefficient vector norms . The sum across variables for both relative strength and maximal contribution was normalized within recordings and then averaged by unique cell ( Fig 5 ) . Grid matrix plots ( S6 Fig , panel A+C ) show these values prior to the grid summations ( Eq ( 15 ) ) . To reconstruct ratemaps , we used the midpoints of grid-specific training samples to predict spike counts from the model for each grid section . We collated the counts and sample positions across grid sections to reconstitute a complete dataset for generating the ratemap . To create the LQW generative model , we used a COBYLA search to find the arena-bounded minimum and maximum of the linear predictor for each recording . We normalized the LQW parameters to [0 , 1] and applied a clipping sigmoid [1/ ( 1 + exp ( −10 ( f − 0 . 5 ) ) ) ] to smoothly enforce the range of the resulting spatial function . To sample the generative model , we randomly selected a negative phaser’s spatial function , added 20% Gaussian noise to its LQW parameters , and rotated the function about the center by a random angle . Data analysis and modeling were conducted using custom python packages that depend on libraries from the open-source ecosystem: numpy , scipy , matplotlib , seaborn , pandas , scikit-learn , pytables , Brian2 , and others . The source code , including a complete specification of the python environment , is available at doi . org/10 . 6084/m9 . figshare . 6072317 . | Spatial cognition in mammals depends on position-related activity in the hippocampus and entorhinal cortex . Hippocampal place cells and entorhinal grid cells carry distinct maps as rodents move around . The grid cell map is thought to measure angles and distances from previous locations using path integration , a strategy of internally tracking self motion . However , path integration accumulates errors and must be ‘reset’ by external sensory cues . Allowing rats to explore an open arena , we recorded spiking neurons from areas interconnected with the entorhinal cortex , including subcortical structures and the hippocampus . Many of these subcortical regions help coordinate the hippocampal theta rhythm . Thus , we looked for spatial information in theta-rhythmic spiking and discovered ‘phaser cells’ in the lateral septum , which receives dense hippocampal input . Phaser cells encoded the rat’s position by shifting spike timing in symmetry with spatial changes in firing rate . We theorized that symmetric rate-phase coupling allows downstream networks to flexibly learn spatial patterns of synchrony . Using dynamical models and simulations , we showed that phaser cells may collectively transmit a fast , oscillatory reset signal . Our findings develop a new perspective on the temporal coding of space that may help disentangle competing models of path integration and cross-species differences in navigation . | [
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"crystallo... | 2019 | Spatial synchronization codes from coupled rate-phase neurons |
Mutations in PINK1 and Parkin cause familial , early onset Parkinson's disease . In Drosophila melanogaster , PINK1 and Parkin mutants show similar phenotypes , such as swollen and dysfunctional mitochondria , muscle degeneration , energy depletion , and dopaminergic ( DA ) neuron loss . We previously showed that PINK1 and Parkin genetically interact with the mitochondrial fusion/fission pathway , and PINK1 and Parkin were recently proposed to form a mitochondrial quality control system that involves mitophagy . However , the in vivo relationships among PINK1/Parkin function , mitochondrial fission/fusion , and autophagy remain unclear; and other cellular events critical for PINK1 pathogenesis remain to be identified . Here we show that PINK1 genetically interacted with the protein translation pathway . Enhanced translation through S6K activation significantly exacerbated PINK1 mutant phenotypes , whereas reduction of translation showed suppression . Induction of autophagy by Atg1 overexpression also rescued PINK1 mutant phenotypes , even in the presence of activated S6K . Downregulation of translation and activation of autophagy were already manifested in PINK1 mutant , suggesting that they represent compensatory cellular responses to mitochondrial dysfunction caused by PINK1 inactivation , presumably serving to conserve energy . Interestingly , the enhanced PINK1 mutant phenotype in the presence of activated S6K could be fully rescued by Parkin , apparently in an autophagy-independent manner . Our results reveal complex cellular responses to PINK1 inactivation and suggest novel therapeutic strategies through manipulation of the compensatory responses .
Parkinson's disease ( PD ) is the most common neurodegenerative disease affecting movement and currently there is no cure . A pathological hallmark of PD is the reduction of dopamine content in the brain , caused by the selective dysfunction and degeneration of DA neurons in the substantia nigra . The causes of DA neuron loss are complex and likely involve both environmental insults and genetic predisposition . Increasing evidences suggest that mitochondrial dysfunction may be linked to the pathogenesis of both sporadic and familial forms of PD . Recent genetic studies of rare familial forms of PD identified multiple disease genes , including PINK1 and Parkin [1] , [2] . PINK1 encodes a Ser/Thr kinase with a mitochondrial targeting sequence and is partially localized to the mitochondria [3] , [4] . Parkin is an E3 ubiquitin ligase that is largely cytosolic under normal conditions . The inactivation of Drosophila orthologs of PINK1 or Parkin resulted in similar phenotypes , with the formation of enlarged , swollen mitochondria preceding muscle degeneration , DA neuron loss and spermatogenesis failure [5]–[7] . Further analysis showed that overexpression ( OE ) of Parkin could rescue PINK1 mutant phenotype , but not vice versa , suggesting that PINK1 and Parkin may function in the same pathway , with Parkin acting downstream of PINK1 [5]–[7] . Interestingly , promoting mitochondrial fission by either overexpression of mitochondrial fission protein Drp1 or downregulation of mitochondrial fusion proteins Marf ( the D . melanogaster homolog of mammalian mitofusin ) or Opa1 could completely rescue PINK1 or Parkin mutant phenotypes , suggesting that PINK1 and Parkin might regulate mitochondrial dynamics by interacting with the mitochondrial fusion/fission machinery [8]–[10] . PINK1 and Parkin have also been suggested to collaborate to form a mitochondrial quality control system [11] , [12] . Despite being mainly cytosolic under normal conditions , Parkin can be mobilized to damaged mitochondria that have decreased membrane potential [11] . This translocation of Parkin requires the function of PINK1 , which is stabilized and accumulates on damaged mitochondria [12] . Parkin recruited to damaged mitochondria can further ubiquitinate mitochondrial proteins to mark the damaged mitochondria for degradation by autophagy [11] , [13] , [14] . These studies offered an attractive molecular mechanism linking the inactivation of PINK1 or Parkin to the accumulation of dysfunctional mitochondria . However , most of these studies were carried out in cell culture . Their in vivo relevance remains to be determined . The target of rapamycin ( TOR ) protein is an evolutionarily conserved serine/threonine protein kinase that functions as a master regulator of many crucial cellular processes , including protein translation , mRNA transcription , autophagy and cytoskeletal organization [15] . TOR exerts its regulatory function by integrating diverse cues ranging from extracellular growth factors to intracellular levels of ATP , amino acids and oxygen [15] . In response to ATP depletion , for example , TOR signaling is suppressed by the AMP-activated protein kinase ( AMPK ) , leading to subsequent inhibition of S6 kinase ( S6K ) -mediated protein translation and activation of Atg1-mediated autophagy [16]–[18] . In an effort to further understand the mechanisms of PINK1 and Parkin pathogenesis , we performed genetic screens to find modifiers of PINK1 mutant phenotypes . We identified S6K and Atg1 as strong modifiers of PINK1 . We found that activated S6K acts through protein translational regulation to significantly enhance muscle degeneration and DA neuron loss in PINK1 mutant . Together with our previous study of LRRK2 [19] , this result supports that impaired translational control is an integral part of PD pathogenesis . We also found that Atg1 OE could suppress PINK1 mutant phenotypes even in the presence of constitutively active S6K , and the rescuing effect of Atg1 OE was dependent on its ability to directly promote autophagy , suggesting that enhancing autophagy represents another efficient way to combat PINK1-related Parkinson's disease . Since reduced S6K activation and enhanced autophagy were already observed in the PINK1 mutant background , the protective effects observed after further strengthening these processes suggest that they represent compensatory responses to PINK1 inactivation . Pharmacological augmentation of these responses thus holds significant therapeutic value .
To better understand PINK1 pathogenesis , we performed both gain-of-function and loss-of-function genetic screens to find genetic enhancers and suppressors of PINK1 mutant phenotypes . PINK1 mutant flies exhibit an easily observable abnormal wing posture ( either held-up or drooped ) caused by indirect flight muscle degeneration . Therefore , we used the Mhc-Gal4 driver to direct the expression of UAS-PINK1 RNAi transgenes specifically in the muscle and used the penetrance of the abnormal wing posture phenotype as an indicator of genetic interaction in our screens . PINK1 RNAi line , which exhibited weaker wing posture phenotype than the PINK1 null mutant , allowed us to screen for both enhancers and suppressors in the same screens by scoring the percentage of flies exhibiting abnormal wing posture at young and old ages . In our genetic screens , we first took an unbiased approach by screening a collection of ∼300 EP lines , and based on the information obtained from this unbiased screen we implemented a more targeted approach by screening genes involved in specific pathways or processes . In our unbiased screen , we uncovered ∼30 lines that either enhanced or suppressed PINK1 RNAi phenotype ( data not shown ) . These lines are associated with genes that have diverse functions , suggesting that PINK1 may functionally interact with many different cellular pathways . The strongest modifiers of PINK1 RNAi phenotype were identified in our targeted screen and one of the strongest enhancers of PINK1 RNAi-induced abnormal wing posture is S6 kinase ( S6K ) . S6K is one of the downstream effectors of TOR , a master regulator of cell growth and proliferation . In response to cellular growth stimuli , TOR phosphorylates S6K to upregulate the synthesis of ribosomal proteins and translation initiation and elongation factors [20] . When wild type ( WT ) S6K was overexpressed in the muscle of PINK1 RNAi flies , the penetrance of the abnormal wing posture phenotype was greatly enhanced in an age-dependent manner ( Figure 1A ) . Even more dramatic enhancement of the abnormal wing posture was observed when S6K-TE , S6K-STDE , or S6K-STDETE , the phosphomimetic , constitutively active forms of S6K [21] , were co-expressed ( Figure 1A ) . In these cases , more than 50% of the flies had abnormal wing posture at 1-day old , whereas virtually none of the PINK1 RNAi flies of the same age showed the phenotype . The phenotype became stronger in 14-day-old flies . Overexpression of WT or constitutively active S6Ks in wild type background did not affect wing posture , even after the flies were aged for weeks , suggesting that the effect on wing posture caused by S6K was specific to the PINK1 RNAi background ( Figure 1B ) . On the other hand , when we reduced S6K function through S6K RNAi , it effectively attenuated PINK1 RNAi effects ( Figure 1A ) . Similar genetic interaction between PINK1 and constitutively active S6K was observed in PINKB9 mutant background ( Figure S1 ) . This result , together with the observation that S6K OE did not affect PINK1 protein level ( Figure S2 ) , suggested that the genetic interaction between PINK1 and S6K was not due to a possible regulation of PINK1 expression by S6K . The stronger effects of the phosphomimetic , constitutively active forms of S6K than WT S6K is consistent with the fact that S6K function is tightly controlled by TOR through ordered phosphorylation of multiple Ser/Thr residues [22] , [23] . As S6K is one of the downstream effectors of TOR that regulate protein translation , we further tested whether other components of the TOR pathway that regulate translation also interact with PINK1 genetically . The eukaryotic translation initiation factor 4E ( eIF4E ) /eIF4E-binding protein ( 4E-BP ) axis of translational control is also regulated by TOR . TOR signaling leads to 4E-BP phosphorylation , weakening its binding to eIF4E and releasing its inhibition on translational initiation [24] . Reducing translation by 4E-BP OE mildly suppressed the abnormal wing posture phenotype in PINK1 RNAi background . Conversely , eIF4E OE greatly enhanced the abnormal wing posture phenotype in aged PINK1 RNAi flies . However , these flies at 1-day old showed relatively normal wing posture , indicating that the effect of eIF4E is milder compared to constitutively active S6K . Consistent with the above genetic interactions , dTOR OE , which phenocopies dTOR loss-of-function effects in Drosophila [25] , also suppressed PINK1 RNAi phenotypes . Together , these results support a strong functional interaction between PINK1 and TOR-mediated translational regulation . In addition to enhancers , we also recovered strong suppressors of PINK1 RNAi phenotypes , with Atg1 being one of them . Atg1 is a kinase that has been suggested to play an essential role in the initiation of autophagy . Previous studies have shown that Atg1 OE in Drosophila fat body was sufficient to induce autophagy , and high level of Atg1 expression could cause growth arrest and apoptosis [26] . When we expressed high level of Atg1 in the muscle using the Mhc-Gal4 driver and strong UAS-Atg1 transgenes , the flies either failed to eclose or showed strong abnormal wing posture , possibly due to excess apoptosis in the flight muscle ( data not shown ) . However , when we used a weaker Atg1 OE line ( UAS-Atg1GS10797 ) [26] , the flies enclosed with normal wing posture and flight ability ( Figure 1B , data not shown ) . Interestingly , this mild overexpression of Atg1 completely suppressed the abnormal wing posture caused by PINK1 knockdown ( Figure 1A ) , which was not due to a change in PINK1 expression level ( Figure S2 ) . Conversely , PINK1 RNAi phenotypes were exacerbated by the overexpression of Atg1 RNAi or Atg1K38Q , a kinase-dead form of Atg1 that acted in a dominant-negative fashion [26] . Similarly , RNAi-mediated knockdown of Atg3 , Atg13 and Atg18 , all of which are essential for autophagy in Drosophila [27] , [28] , also enhanced the abnormal wing posture phenotype in PINK1 RNAi background ( Figure 1A ) . These data indicate that autophagy is physiologically relevant to PINK1 pathogenesis and that mild induction of autophagy protects against PINK1 pathogenesis . To further validate the effectiveness of our screen , we tested genes that were known to genetically interact with PINK1 . We found that Parkin OE and Marf RNAi could completely suppress the PINK1 RNAi phenotypes ( Figure 1A ) , consistent with previous findings [5]–[10] . In contrast to the rescuing effect of Marf RNAi , overexpression of Marf alone in the muscle , which would lead to excessive mitochondrial fusion , caused lethality at third instar larval stage ( Figure 1B ) . Flies overexpressing a dominant-negative form of Drp1 ( Drp1DN ) , which inhibited mitochondrial fission , were viable but showed strong abnormal wing posture phenotype ( Figure 1B ) . When Drp1DN was expressed in PINK1 RNAi background using the Mhc-Gal4 driver , a synthetic lethality phenotype was observed ( Figure 1A ) . These results are consistent with previously reported genetic interactions between PINK1 and the mitochondrial fusion/fission machinery [8]-[10] . In addition , we tested the effects of antioxidant genes , some of which have been shown to rescue PINK1 and Parkin mutant phenotypes [29] , [30] . All four antioxidant genes tested , catalase ( Cat ) , glutathione peroxidase homolog with thioredoxin peroxidase activity ( GTPx-1 ) , Glutathione S transferase S1 ( GstS1 ) , and superoxide dismutase ( SOD ) , could partially rescue the abnormal wing posture in PINK1 RNAi background when overexpressed . However , they were less effective compared to Atg1 and Parkin OE or Marf knockdown ( Figure 1A ) . In addition to abnormal wing posture , PINK1 mutant flies typically exhibit enlarged mitochondria , energy depletion , muscle degeneration and DA neuron loss [5] , [8] . To better understand the genetic interaction between S6K and PINK1 , we tested whether S6K affected these phenotypes as well . In one-day-old flies , Mhc-Gal4-directed co-expression of constitutively active forms of S6Ks ( S6K-TE , S6K-STDE and S6K-STDETE ) in PINK1 RNAi background completely abolished their flight ability ( Figure 2A ) , significantly decreased ATP level in the muscle ( Figure 2B ) , and dramatically increased thoracic indentation ( Figure 2C , 2D ) , which all indicate increased muscle degeneration . In contrast , overexpression of a S6K RNAi transgene in PINK1 RNAi background partially rescued these phenotypes ( Figure 2A , 2B , 2C ) . Overexpression of constitutively active S6K or S6K RNAi transgenes in wild type flies had no obvious effect in these assays ( Figure 2A , 2B ) , suggesting that their effects on muscle degeneration were specific to the PINK1 RNAi background . We further used transmission electron microscopy ( TEM ) to examine muscle degeneration in detail . At one-day after eclosion , the thoracic muscle of PINK1 RNAi flies showed only small lesions , while large areas devoid of muscle tissues were observed in the thoraces of PINK1 RNAi flies expressing constitutively active S6K , supporting the conclusion that S6K enhances the muscle degeneration in PINK1 RNAi background ( Figure 2E ) . In comparison , the muscle morphology of flies expressing constitutively active S6K alone appeared indistinguishable from that of wild type flies , with healthy , electron-dense mitochondria laying in between muscle fibers in an organized fashion ( Figure 2E ) . We further examined mitochondrial morphology in DA neurons using a mitochondrially targeted GFP ( mitoGFP ) as a marker . When we used the tyrosine hydroxylase ( TH ) -Gal4 driver to induce the expression of mitoGFP in wild type DA neurons , most mitochondria exhibited tubular-shaped mitochondrial network ( Figure 3A ) . Overexpression of constitutively active S6K-TE did not change the overall mitochondrial shape , although mitochondrial content appeared increased . PINK1 mutant ( PINK1B9 ) showed enlarged mitochondria in DA neurons ( Figure 3A ) , consistent with previous reports [6] , [8] . When S6K-TE was expressed in PINK1B9 background , mitochondrial sizes were further increased , frequently doubling those in the PINK1 mutant in diameter ( Figure 3A , 3B ) . As enlarged mitochondria or mitochondrial clusters are hallmarks of PINK1-related parkinsonism , this further increase of mitochondrial size after S6K-TE overexpression , which is possibly the consequence of inefficient mitophagy ( see Discussion and Figure S3 ) , suggests exacerbation of the disease process . Consistent with this idea , the expression of S6K-TE in PINK1B9 mutant background also promoted DA neuron death , as the number of DA neurons in the protocerebral posterior lateral 1 ( PPL1 ) cluster was further decreased compared to PINK1B9 mutants in aged flies ( Figure 3C ) . In summary , the overexpression of constitutively active S6K in PINK1 mutants significantly enhanced muscle degeneration and DA neuron loss . Although S6 kinase has been shown to have multiple substrates [31] , its best-known function is to phosphorylate the 40S ribosomal subunit S6 ( RpS6 ) and upregulate the translation of proteins involved in ribosomal biogenesis and protein synthesis [20] . To directly examine whether S6K enhances PINK1 mutant phenotype through increasing translation , we expressed an RpS6 RNAi transgene together with S6K-TE in the PINK1 RNAi background . Significantly , RpS6 RNAi efficiently blocked S6K-TE's enhancing effect on PINK1 RNAi-induced abnormal wing posture ( Figure 4A ) , thoracic indentation ( Figure 4B ) , increased mitochondrial aggregation ( Figure 4C ) and ATP depletion in the muscle ( Figure 4D ) . RpS6 RNAi also partially suppressed such phenotypes in PINK1 RNAi background without S6K-TE co-expression ( Figure 4A , data not shown ) . The effects of RpS6 RNAi were more obvious in young flies . In aged flies , the rescuing effect of RpS6 RNAi was mild in both the PINK1 RNAi and PINK1 RNAi/S6K-TE OE backgrounds ( Figure 4A , 4D , data not shown ) . These results suggested that S6K did act through RpS6 to genetically interact with PINK1; however , reduction of translation through RpS6 RNAi only provided partial suppression of PINK1 RNAi phenotypes . This could be due to either inefficient knockdown of RpS6 function by RNAi or the involvement of other pathogenic pathway ( s ) . To further confirm the genetic interaction between PINK1 and the protein translational control pathway , we screened more than 20 EP lines expressing cytosolic or mitochondrial ribosomal subunits to see if any of these lines could also modify PINK1 RNAi phenotypes . Interestingly , one line that overexpresses ribosomal protein S9 ( RpS9 ) greatly enhanced the PINK1 RNAi phenotypes ( Figure 4A ) . Further , RpS9 RNAi was as effective as RpS6 RNAi in blocking S6K-TE's enhancing effects on PINK1 RNAi phenotypes ( Figure 4A–4D ) . Previously , RpS9 knockdown was shown to significantly reduce the rate of protein synthesis and cell proliferation in primary human fibroblasts and tumor cell lines [32] . It is therefore likely that RpS9 RNAi mitigates the effects of constitutively active S6Ks through downregulating translation . To test whether S6K and the related translational control pathway is normally involved in PINK1 pathogenesis , we examined the levels of S6K and phosphorylated S6K in PINK1 mutants . We found that the level of total S6K in PINK1 RNAi or PINK1B9 mutant flies was largely unchanged . However , the level of phosphorylated , active form of S6K was significantly decreased ( Figure 4E ) , suggesting that there is decreased TOR signaling and protein translation in PINK1 mutant background . Since protein translation is a very energy-consuming process , reduction of translation could serve as a compensatory response to the mitochondrial dysfunction caused by PINK1 inactivation . As mentioned earlier , Atg1 emerged as a strong suppressor of PINK1 RNAi phenotype in our screen . To confirm this result , we tested the effect of Atg1 OE in PINK1B9 mutant , which exhibited stronger phenotypes than PINK1 RNAi flies . Similar to the results obtained using PINK1 RNAi flies , Atg1 OE could efficiently suppress the abnormal wing posture and thoracic indentation phenotypes of PINK1B9 flies ( Figure 5A , 5B ) . Of note , our behavior test and muscle ATP level analysis indicated that the rescuing effect of Atg1 OE was not as strong as Parkin OE or Marf RNAi ( Figure 5C , 5D ) . When we used mitoGFP to examine mitochondrial morphology in the muscle , Parkin OE and Marf RNAi could almost completely suppress the mitochondrial aggregation phenotype , but we could still see enlarged mitochondria in PINK1 mutant overexpressing Atg1 ( Figure 5E ) . Similar to Atg1 OE , 4E-BP OE only partially rescued PINK1B9 mutant phenotypes ( Figure 5A–5D ) . The overexpression of Catalase , GTPx-1 , RpS6 RNAi or RpS9 RNAi also could not fully rescue the abnormal mitochondrial morphology phenotype in the PINK1 mutant ( Figure 5E ) . Therefore , these genes modified PINK1 mutant phenotypes without effectively rescuing mitochondrial morphology , suggesting that they might act downstream or in parallel to the mitochondrial dynamics pathway . Atg1 is a Ser/Thr protein kinase involved in the initiation of autophagosome formation , which is under the control of TOR signaling . The loss of TOR signaling promotes the association of Atg1 with Atg13 and Atg17 , which further recruit other Atg proteins to the pre-autophagosomal structure to mediate the formation of autophagosome [33] . In Drosophila , Atg1 OE alone is sufficient to induce autophagy in the fat body [26] . In addition to being a downstream effector of TOR , Atg1 can also exert feedback inhibitory effect on TOR . Atg1 OE has been shown to cause reduced phosphorylation of Drosophila S6K ( dS6K ) at T398 , indicating downregulation of TOR signaling by Atg1 [26] , [34] . Since S6K OE and Atg1 OE exert opposite effects in PINK1 RNAi background , we next tried to distinguish whether the rescuing effect of Atg1 OE was due to the inhibition of S6K function or induction of autophagy . To test whether Atg1 suppressed PINK1 RNAi-induced abnormal wing posture through inhibition of S6K , we expressed Atg1 together with S6K-TE in the PINK1 RNAi background . S6K-TE harbors the T398E mutation that mimics the phosphorylated form of S6K , which is constitutively active and cannot be suppressed by Atg1 [21] . Strikingly , Atg1 OE could strongly rescue the abnormal wing posture and ATP depletion phenotypes in the PINK1 RNAi/S6K-TE OE background ( Figure 6A , 6B ) , suggesting that the rescuing effect of Atg1 did not rely on its known effect on S6K phosphorylation . Similarly , Parkin OE or Marf RNAi also significantly suppressed the abnormal wing posture and energy depletion phenotypes in PINK1 RNAi/S6K-TE OE background ( Figure 6A , 6B ) . In contrast , overexpression of 4E-BP or the anti-oxidant genes , such as GTPx-1 , Cat and SOD , were not as effective ( Figure 6A , 6B ) . To test whether Atg1 OE rescued PINK1 RNAi phenotype by inducing autophagy , we first tested whether Atg1 OE could directly induce autophagy in the muscle , as observed in the fat body [26] . We used LC3-GFP as a marker to examine the lipidation of LC3 in different genetic backgrounds and used GFP antibody to detect mobility shift of LC3-GFP . Compared to the control , Atg1 OE led to an increased level of LC3-II , indicating induction of autophagy ( Figure 6C ) . To test whether PINK1 deficiency affects autophagy in vivo , we introduced LC3-GFP into PINK1 RNAi and PINK1B9 backgrounds . Elevated autophagy was observed in both cases ( Figure 6C ) . Thus , autophagy is basally induced in PINK1 mutant and further enhancement of autophagy is protective , suggesting that similar to decreased protein translation , increased autophagy also represents a compensatory response in PINK1 loss-of-function background . To further prove that Atg1 OE-induced autophagy was critical for the suppression of PINK1 RNAi phenotype , we attempted to block the Atg1 OE effects with Atg18 RNAi . The co-expression of Atg18 RNAi could largely abolish the rescuing effects of Atg1 OE in PINK1B9 mutant or PINK1 RNAi/S6K-TE OE backgrounds ( Figure 7A , 7B ) , suggesting that Atg1 OE rescued PINK1 mutant phenotypes mainly through inducing autophagy . Recent cell culture studies showed that Parkin is specifically recruited to dysfunctional mitochondria to mediate their elimination by the autophagy pathway , and the lack of PINK1 prevented this process , suggesting that Parkin plays an essential role in the selective elimination of damaged mitochondria [11] . We sought to directly test the role of autophagy in Parkin's rescue of PINK1 mutant phenotypes in vivo . In stark contrast to the efficient blockage of Atg1's rescuing effect by Atg18 RNAi , Atg18 RNAi failed to block Parkin's rescue of the abnormal wing posture ( Figure 7A ) and energy depletion ( Figure 7B ) phenotypes in PINK1B9 and PINK1 RNAi/S6K-TE OE flies . Similarly , Atg1 RNAi also could not block the rescuing effect of Parkin ( Figure 7A , 7B ) . Consistent with these results obtained in the muscle , Atg1 RNAi or Atg18 RNAi failed to block the ability of Parkin to rescue the mitochondrial morphology phenotype in PINK1 mutant DA neurons ( Figure 7C ) . These results suggest that Parkin might act through other mechanisms to rescue PINK1 mutant phenotype than solely promoting selective autophagy . Atg18 RNAi also failed to block the rescuing effects of Marf RNAi in PINK1B9 and PINK1 RNAi/S6K-TE OE backgrounds ( Figure 7A , 7B ) , suggesting that decreased mitochondrial fusion can rescue PINK1 mutant phenotype independent of the autophagy pathway .
The occurrence and the progression of Parkinson's disease can be determined by both genetic predisposition and environmental insults . Recent human genetic studies have identified many genes responsible for the heritable forms of the disease , greatly enhancing our understanding of disease pathogenesis [35] . By analyzing the cellular pathways that interact with these genes , hopefully we will ultimately find ways to better understand and treat this devastating disease . Previously , PINK1 and Parkin have been suggested to interact with mitochondrial fusion/fission machinery and the autophagy pathway [8]–[11] . In this study , we found that PINK1 also genetically interacted with the protein translation pathway . Increased global protein translation with S6K or eIF4E OE exacerbated PINK1 mutant phenotypes , while decreased translation had the opposite effects . Overexpression of constitutively active S6Ks dramatically enhanced muscle and DA neuron degeneration in PINK1 mutant flies , which could be mitigated by the co-expression of RpS6 RNAi or RpS9 RNAi , supporting that the TOR/S6K pathway modifies PINK1 mutant phenotypes through regulating global translation . Recently , we have reported that pathogenic leucine-rich repeat kinase 2 ( LRRK2 ) , which represents the most frequent molecular lesions found in Parkinson's disease , promotes 4E-BP phosphorylation , resulting in increased eIF4E-mediated translation , enhanced sensitivity to oxidative stress , and DA neuron loss [19] . Taken together , our results support the idea that deregulated protein translation is generally involved in the pathogenesis of Parkinson's disease . Deregulated translation affects Parkinson's disease pathogenesis most likely at the level of energy metabolism , since protein translation is a very energy-consuming process , of which ribosomal biogenesis is the most costly , consuming approximately 80% of the energy in proliferating cells [36] . Here we show that forced upregulation of ribosomal biogenesis in the fly muscle by the overexpression of constitutively active S6K was well tolerated in WT flies; however , such manipulation in PINK1 RNAi flies completely abolished their flight ability , depleted ATP in the muscle and enhanced muscle and DA neuron degeneration . The tolerance of increased protein translation by wild type flies is probably due to the existence of an intact mitochondrial quality control system containing PINK1 and Parkin , which can either eliminate damaged mitochondria generated during elevated energy production or minimize damages caused by increased ROS generated during energy production . However , in PINK1 or Parkin mutants that lack a functional mitochondrial quality control system , increased protein translation and the corresponding energy demand will translate into increased ROS generation , accumulation of dysfunctional mitochondria , and eventual energy depletion and tissue degeneration . Since downregulation of translation through knockdown of S6K , RpS6 , or RpS9 is beneficial to PINK1 mutant flies , and S6K activity is already tuned down in PINK1 mutant flies , reduction of translation likely represents one of the cellular compensatory responses to the energy deficit caused by mitochondrial dysfunction in PINK1 mutants . Interestingly , partial reduction of S6K activity prolonged fly lifespan , whereas increased S6K activity had the opposite effects on longevity [37] . The effects of S6K on animal lifespan and PINK1 mutant phenotypes can both be explained by the energy metabolism hypothesis and they offer a tantalizing link between aging and the pathogenesis of Parkinson's disease . Supporting the energy metabolism model , we show that downregulation of protein translation by knocking down positive regulators of translation ( S6K , RpS6 , RpS9 ) or overexpressing a negative regulator ( 4E-BP ) could rescue PINK1 mutant phenotypes . These manipulations presumably act by preserving cellular energy and reducing the workload and ROS production of mitochondria . Previously , 4E-BP OE was suggested to rescue PINK1 mutant phenotype by upregulating Cap-independent translation of stress related genes , including antioxidant genes [30] , and boosting antioxidant gene activity has been suggested as a therapeutic strategy in the PINK1 and Parkin models of Parkinson's disease [38] . We found that although overexpression of antioxidant genes , such as Catalase , GTPx-1 , SOD and GstS1 , all showed some degree of rescue of PINK1 mutant phenotypes , their effects were in general weaker than that of Atg1 OE , Parkin OE , or Marf RNAi , particularly in the PINK1 RNAi/S6K-TE OE background . These data suggest that increasing autophagy and mitochondrial fission might be better choices to combat PINK1-related Parkinson's disease . Autophagy is a conserved cellular process through which cytoplasmic content or defective intracellular organelles can be eliminated or recycled . Although autophagy is usually induced under adverse conditions to provide means for survival , basal level of autophagy in the cell is just as critical to the physiological health of the organism , since defects in autophagy are frequently associated with cancer , neurodegeneration , and aging [39] . The induction of autophagy leads to the de novo formation of double membrane structure called isolation membrane , which expands to form a sealed compartment named autophagosome that will engulf materials destined for degradation . The large size of mitochondria likely poses a challenge for the autophagy machinery , as engulfment of an entire mitochondrion requires a significant amount of building materials for autophagosome formation . This is especially the case in PINK1 mutant where dysfunctional mitochondria becomes grossly swollen or aggregated . Previously , we and others showed that increased mitochondrial fission or Parkin OE could efficiently rescue the enlarged mitochondria phenotype in PINK1 mutants [5]–[10] . The rescuing effect by increased mitochondrial fission could be due to the fact that it decreases mitochondrial size and makes it easier for the autophagosome to engulf the entire mitochondrion during mitophagy ( Figure S3 ) . In addition , increased mitochondrial fission could facilitate the segregation of the healthy part of a mitochondrion from the unhealthy part , thus enhancing the selective elimination of dysfunctional mitochondria through mitophagy [40] . Supporting the mitophagy model , Parkin has been proposed to promote the efficient removal of damaged mitochondria by selectively ubiquitinating proteins on damaged mitochondria [11] . A key prediction of the mitophagy model is that the protective effects of Parkin OE and increased mitochondrial fission as in the case of Marf RNAi will depend on the autophagy pathway . Surprisingly , we found that blocking autophagy through Atg1 RNAi or Atg18 RNAi failed to block Parkin OE or Marf RNAi's rescuing abilities in PINK1 mutant , although Atg18 RNAi was effective in blocking the rescuing ability of Atg1 OE . This result suggests that the rescuing effect of Parkin OE or Marf RNAi is not entirely dependent on autophagy , and that other processes are likely involved . For example , Parkin has been suggested to promote mitochondrial biogenesis [41] and regulate protein translation [42] . Further studies are needed to elucidate the exact molecular functions of Parkin that are critically involved in mitochondrial function and tissue maintenance in vivo . Given the well-established catabolic role of autophagy in degrading cytoplasmic contents , it helps recycle nutrients and provide energy source needed for survival under harsh conditions . In PINK1 mutants that suffer energy deficit due to mitochondrial dysfunction , induction of autophagy would present as a compensatory response to cope with the limited energy supply . Indeed , we found that basal autophagy is induced in PINK1 mutant , and further increase of autophagy through Atg1 OE protects against PINK1 pathogenesis . Thus , decreased translation and increased autophagy both represent compensatory responses in PINK1 mutant flies , and further augmentation of these responses can effectively protect against the toxic effects of PINK1 inactivation . A previous study in cultured mammalian cells also indicated that autophagy is induced in response to PINK1 inactivation [43] . Thus , the in vivo compensatory responses revealed in this study are likely relevant to PINK1 pathogenesis in mammals . Pharmacological interventions that promote these responses offer potential new treatment strategies for Parkinson's disease .
Flies were raised according to standard procedures at indicated temperatures . dPINK1 null mutant line dPINK1B9 was a gift from Dr . Jongkeong Chung [6] . The TH-GAL4 line was a gift from Dr . Serge Birman [44] . UAS-mitoGFP line was a gift from Dr . William Saxton . UAS-Atg1[6A] , UAS-Atg1[6B] , UAS-Atg1KQ , and UAS-Atg13 RNAi lines were gifts from Dr . Thomas Neufeld [26] . UAS-Atg1GS10797 line was a gift from Dr . Eric Baehrecke . UAS-Marf line was a gift from Dr . Alex Whitworth . UAS-PINK1 RNAi and UAS-Parkin were generated as described [5] , [45] . UAS-Atg1 RNAi , UAS-Atg3 RNAi , UAS-Atg18 RNAi , UAS-RpS6 RNAi , UAS-RpS9 RNAi , and UAS-S6K RNAi lines were from Vienna Drosophila RNAi Center . All the other lines were from Bloomington Stock Center . Muscle histology with toluidine blue staining and transmission electron microscopy analysis was performed essentially as described [45] , except that Epon resin was used for embedding . For mitochondrial morphology analysis using mitoGFP , indirect fly muscle was dissected out in PBS and examined by live imaging . For abnormal wing posture analysis , male flies were aged at indicated temperature with 20 flies per vial . The abnormal wing posture penetrance was calculated as the percentage of flies with either held-up or drooped wing posture [5] . For each experiment , at least 60 flies were scored for their wing posture phenotype for each genotype . Each experiment was repeated at least three times . For jump/flight ability analyses , 5 to 10 flies were put into each vial . The jump/flight events were counted for two consecutive minutes , during which vials were gently tapped to initiate those events . Data were normalized and represented to reflect the jump/fly activity of 10 animals . Each of these analyses had been repeated at least three times . The thoracic ATP level was measured using a luciferase based bioluminescence assay ( ATP Bioluminescence Assay Kit HS II , Roche Applied Science ) . For each measurement , two thoraces were dissected out ( with wings and legs removed ) and immediately homogenized in 100 µl lysis buffer . The lysate was boiled for 5 min and cleared by centrifugation at 20 , 000 g for 1 min . 2 . 5 µl of cleared lysate was added to 187 . 5 µl dilution buffer and 10 µl luciferase , and the luminescence was immediately measured using a Lumat LB 9507 tube luminometer ( Berthold Technologies ) . Each reading was converted to the amount of ATP per thorax based on the standard curve generated with ATP standards . At least 3 measurements were made for each genotype . Whole-mount immunohistochemistry for TH and mitoGFP was performed as described [46] . Rabbit anti-TH antibody ( 1∶500 ) [5] and chicken anti-GFP antibody ( 1∶5000 ) ( Chemicon International ) were used . The images of DA neurons of the protocerebral posterior lateral 1 ( PPL1 ) cluster were collected at 0 . 5 mm steps along Z-axis on a Leica confocal microscope , and all the images shown were generated by Z-stack deconvolution . Western Blot was performed following standard protocol . Rabbit anti-dS6K antibody was a gift from Dr . George Thomas . Rabbit anti-Phospho-S6K ( Thr398 ) antibody was from Cell Signaling Technology , and chicken anti-GFP antibody was purchased from Chemicon International . Rabbit anti-dPINK1 antibody was generated as described [5] . | Parkinson's disease is the most common neurodegenerative disease affecting the aging population . Clinically it manifests as tremor , muscle rigidity , slow movement , and postural instability . Parkinson's disease is a chronic disorder , and its occurrence and progression are determined by genetic backgrounds and environmental factors . Although the most common forms of Parkinson's disease , the so-called “idiopathic” forms , generally affect people older than 50 , some familial forms of the disease occur before age 40 . Mutations in PINK1 and Parkin genes have been associated with the latter forms of Parkinson's disease . The inactivation of PINK1 or Parkin causes dysfunction of mitochondria , the powerhouse of the cell , leading to the degeneration of tissues such as the brain and muscle that have high energy demand . In an effort to understand how genetic mutations in PINK1 result in disease and to find effective ways to intervene , we have performed genetic studies in the model organism Drosophila melanogaster and found that reduced protein translation or increased autophagy can efficiently mitigate the phenotypes caused by PINK1 inactivation . Our result suggests that pharmacological manipulations of these newly identified PINK1-interacting pathways may prove beneficial for the treatment of Parkinson's disease . | [
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Zika virus ( ZIKV ) , a flavivirus transmitted primarily by Aedes aegypti , has recently spread globally in an unprecedented fashion , yet we have a poor understanding of host-microbe interactions in this system . To gain insights into the interplay between ZIKV and the mosquito , we sequenced the small RNA profiles in ZIKV-infected and non-infected Ae . aegypti mosquitoes at 2 , 7 and 14 days post-infection . ZIKA induced an RNAi response in the mosquito with virus-derived short interfering RNAs and PIWI-interacting RNAs dramatically increased in abundance post-infection . Further , we found 17 host microRNAs ( miRNAs ) that were modulated by ZIKV infection at all time points . Strikingly , many of these regulated miRNAs have been reported to have their expression altered by dengue and West Nile viruses , while the response was divergent from that induced by the alphavirus Chikungunya virus in mosquitoes . This suggests that conserved miRNA responses occur within mosquitoes in response to flavivirus infection . This study expands our understanding of ZIKV-vector interactions and provides potential avenues to be further investigated to target ZIKV in the mosquito host .
Zika virus ( ZIKV ) is a flavivirus related to dengue virus ( DENV ) , West Nile virus ( WNV ) and Yellow fever virus ( YFV ) that is transmitted to humans by Aedes mosquitoes . In the urban transmission cycle , Aedes aegypti is thought to be the dominant vector , while several Aedes species are implicated in transmission in the sylvatic cycle [1 , 2] . The virus was originally discovered in the Ziika forest in Uganda [3] and has likely been circulating in monkey and human populations in Africa and Asia . In the last 10 years , an Asian virus lineage has rapidly spread on an unprecedented timescale around the pacific and the Americas . In humans , the neurotropic virus causes microcephaly in newborns and has been implicated in other neurological disorders such as Guillain-Barre syndrome [4] . The explosive spread of the virus and its effect on infants created a public health emergency and stimulated research efforts to investigate new treatments and vaccines to reduce these conditions . Although significant progress has been achieved concerning the interaction of ZIKV with the mammalian host since the outbreak , we still have a poor understanding of the molecular interplay between the virus and the mosquito host . As vector control is the only viable option for alleviating the diseases caused by ZIKV , a more thorough understanding on these interactions is critical . Arbovirus infection of mosquitoes elicits complex interactions between the host and the virus . In some cases , the mosquito’s innate immune pathways , which can be antagonistic to viral infection , are provoked by arboviruses . However , these immune pathways appear to be virus specific as the Toll and JAK-STAT pathways are antagonistic to DENV yet do not appear to influence other arboviruses such as Chikungunya virus ( CHIKV ) or ZIKV [5–8] . In addition to these classical immune pathways , RNA interference ( RNAi ) and microRNAs ( miRNAs ) are important components that dictate host-microbe interactions for arboviruses and their mosquito vectors [9–11] . PIWI-interacting RNAs ( piRNAs ) , another group of noncoding small RNAs of 25–30 nt , could also potentially be involved in arbovirus-mosquito interactions [12] . miRNAs are small non-coding RNAs ( ~22 nt ) that regulate gene expression post transcriptionally . In mosquitoes , miRNAs are important in many developmental processes and nutrition [13 , 14] and it is becoming clear that these molecules are critical in host-pathogen interactions [9 , 10 , 15] . Several studies have shown that pathogen infection alters the miRNA expression profile in mosquitoes ( reviewed in [11] ) . This alteration could be due to the host responding to the pathogen or by the pathogen attempting to alter gene expression in the host to make its environment more suitable . For example , the mosquito-borne alphavirus North American eastern equine encephalitis virus ( EEEV ) alters a host miRNA to avoid the host’s immune response [16] . In Ae . aegypti , infection with DENV alters the miRNA profile [17] , with temporal variation in miRNA expression observed with 23 miRNAs altered at 9 day post infection ( dpi ) compared to five or less at 2 and 4 dpi . In the Asian tiger mosquito , Aedes albopictus , the miRNA , miR-252 , increased after a DENV infected blood meal , and inhibition of this miRNA resulted in increased viral copies while overexpression of the miRNA suppressed virus [18] . Taken together , these studies demonstrate that miRNAs can contribute to the complex interactions occurring between invading arboviral pathogens and their mosquito host , and that this interplay likely dictates vector competence . While our understanding of these pathways on arbovirus vector competence is expanding , there is a dearth of knowledge related to how ZIKV may alter the miRNA profile in the vector or the human host . To address this issue , here we used high throughput sequencing to examine the small RNA profiles after viral infection of the primary ZIKV vector Ae . aegypti . We examined host miRNA , virus-derived short interfering RNA ( viRNA ) and piRNA profiles at various time points post-infection . Our results provide the first molecular evidence that infection of ZIKV alters the miRNA profile of a host and the mosquito host mounts an RNAi response against the virus .
The ZIKV strain was acquired from the World Reference Center for Emerging Viruses and Arboviruses at the University of Texas Medical Branch ( Galveston , TX , USA ) . The virus was originally isolated from an Ae . aegypti mosquito ( Chiapas State , Mexico ) . ZIKV protocols were approved by the University of Texas Medical Branch Institutional Biosafety Committee ( Reference number: 2016055 ) . Four-six day old female Ae . aegypti ( Galveston strain ) mosquitoes were orally infected with ZIKV ( Mex 1–7 strain ) at 2 x 105 focus forming units ( FFU ) /ml ) in a sheep blood meal ( Colorado Serum Company ) . At 2 , 7 and 14 days post-infection ( dpi ) RNA was extracted from whole mosquitoes using the mirVana RNA extraction kit ( Life Technologies ) following the protocol for extraction of total RNA . Viral infection in mosquitoes was confirmed by Taqman qPCR on ABI StepOnePlus machine ( Applied Biosystems ) using a ZIKV-specific probe and primers ( S4 Table ) . RNA from ZIKV positive samples was pooled ( N = 5 ) for time points 7 and 14 . Limited ZIKV positive samples were detected at day 2 , likely due to the virus titer being at the limits of detection for qPCR . For this time point , at least 1 qPCR positive individual was included in each pool . For all time points , three independent pools were used to create libraries for infected and uninfected samples . Control mosquitoes were fed with blood devoid of ZIKV and collected at the same time points and processed in the same way as infected ones . Small RNA libraries were created using the New England Biolabs small RNA library protocol ( New England Biolabs ) . Library construction used a two-step ligation process to create templates compatible with Illumina based next generation sequence ( NGS ) analysis . Where appropriate , RNA samples were quantified using a Qubit fluorometric assay ( Thermo Fisher Scientific ) . RNA quality was assessed using a pico-RNA chip on an Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Library creation uses a sequential addition of first a 3’ adapter sequence followed by a 5’ adapter sequence . A cDNA copy was then synthesized using ProtoScript reverse transcriptase ( New England Biolabs ) and a primer complimentary to a segment of the 3’ adapter . Amplification of the template population was performed in 15 cycles ( 94°C for 30 sec; 62°C for 30 sec; 70°C for 30 sec ) and the amplified templates were PAGE ( polyacrylamide gel electrophoresis ) purified ( 147 bp DNA ) prior to sequencing . All NGS libraries were indexed . The final concentration of all NGS libraries was determined using a Qubit fluorometric assay and the DNA fragment size of each library was assessed using a DNA 1000 high sensitivity chip and an Agilent 2100 Bioanalyzer . Sequence analysis was performed using the rapid run platform and single end 50 base sequencing by synthesis on an Illumina Hi-Seq 1500 using the TruSeq SBS kit v3 . CLC Genomic Workbench ( version 7 . 5 . 1 ) was used to remove adapter sequences and reads with low quality scores from datasets . We applied the quality score of 0 . 05 as cut off for trimming . As described in CLC Genomic Workbench manual the program uses the modified-Mott trimming algorithm for this purpose . The Phred quality scores ( Q ) , defined as: Q = -10log10 ( P ) , where P is the base-calling error probability , can then be used to calculate the error probabilities , which in turn can be used to set the limit for which bases should be trimmed . Reads without 3’ adapters or with less than 16 nt were also discarded from the libraries . Clean data were considered as mappable reads for further analysis . We used small RNA tool in CLC Genomic Workbench to extract and count unique small RNA reads with minimum five sampling count . Tab separated files with the read sequences and their counts were used as input file for novel and homologous miRNA analysis using sRNAtoolbox [19] . All known Ae . aegypti precursor miRNAs reported in miRBase 21 were used as reference for miRNA annotation [20] . The ultrafast short read aligner Bowtie was used to align the reads to the Ae . aegypti genome and the miRNA database . The alignment type “n” was selected and we allowed a maximum of one mismatch in the Bowtie seed region for genome , and known and homologous miRNA database in our mapping parameters . The seed alignment length for Bowtie was 20 for all the analyses . Differential expression of miRNAs between two conditions was calculated and normalized based on the DESeq package with EdgeR [21] on sRNAtoolbox server , and final fold change values were given in log2 scale . To understand the RNAi activity against ZIKV , we mapped all the small RNAs to the viral genome ( Accession No . KX247632 ) . We implemented strict mapping criteria ( mismatch , insertion and deletion costs: 2: 3: 3 , respectively ) . The minimum similarity and length fraction of 0 . 9 between a mapped segment and the reference were allowed in mapping criteria . We ignored reads with more than one match to viral genome in mapping parameters . Mappable reads in all libraries were filtered and only reads with 21 nt in length were selected to check their mapping pattern to negative and positive strands of the virus genome . We also sorted all mappable reads between 25–30 nt to the viral genome for checking any potential piRNA signature . We used three different algorithms including RNA22 [22] , miRanda [23] and RNAhybrid [24] to predict potential miRNA binding sites in all the Ae . aegypti annotated genes ( GCF_000004015 . 3_AaegL2 ) and ZIKV genome ( KX247632 ) . The small RNA sequence was hybridized to the best fitting portion of the mRNA or viral genome by RNAhybrid . We did not allow G:U pairing in the seed region ( nucleotides 2–8 from the 5’ end of the miRNA ) and forced miRNA-target duplexes to have a helix in this region . Maximum 5 nt were approved as unpaired nucleotides in either side of an internal loop . miRanda also considers matching along the entire miRNA sequence but we ran the program in strict mode which demands strict 5’ seed region ( nucleotide 2–8 from the 5’ end ) pairing . It takes the seed region into account by adding more value to matches in the seed region . RNA22 v2 is a pattern based target prediction program which first searches for reverse complement sites of patterns within a given mRNA sequence and identifies the hot spots . In the next step , the algorithm is searched for miRNAs that are likely to bind to these sites . We allowed maximum 1 mismatch in the seed region and minimum 12 nt matches in the entire binding site . We set the sensitivity and specificity thresholds to 63% and 61% , respectively . miRNA binding sites on Ae . aegypti mRNAs , which were predicted by all the three algorithms are considered as highly confident miRNA binding sites . RNA samples were converted to cDNA using a miSCRIPT II RT kit ( Qiagen ) using the HiSpec buffer to assure that the cDNA produced was derived only from mature miRNA molecules . 5μL of RNA was used per reaction with an average 605ng per sample . One additional reaction was prepared with no RNA template . The reaction was heated on a Mastercycler-Pro thermal cycler ( Eppendorf ) . Real-time PCR was performed using an IQ5 cycler ( BioRad ) and with Quantitech SYBR master mix ( Qiagen ) . The process was performed using the proprietary-sequence universal primer provided with the kit as the reverse primer and 10 μM of one of nine miRNA-specific forward primers ( IDT ) , the sequence of which is listed in S4 Table . The cDNA was diluted with 60 μL of nuclease-free water per 30 μL of RT product solution , and 2 μL of diluted cDNA was used per reaction . The volumes of the master mix and primers used were those recommended by their manufacturer . Each sample was run in duplicate and the Ct values averaged for further mathematical processing . The amplification program began with 95°C for 15min , followed by forty cycles of 94°C for 15s , 55°C for 30s , and 70°C for 30s . Gene expression analysis was performed using the ΔΔCt ( Livak ) method [25] . The miRNA expression in each sample was normalized to the expression of U6B small nuclear RNA ( RNU6B ) . Our RT-qPCR results confirmed that U6B remained quite stable across infected and non-infected samples ( S1 Fig ) . For each day , six RNA samples were used: three from mock-infected mosquitoes , and three from ZIKV-infected mosquitoes . For each day post-infection , individual ΔCt values for both mock and ZIKV samples were used to calculate relative difference of expression . “No-template” controls were included on each plate run . The accession number for the raw and trimmed sequencing data reported in this paper is GEO: GSE97523 .
Illumina small RNA deep sequencing platform was used to produce small RNA profiles in ZIKV-infected and non-infected Ae . aegypti mosquitoes . RNA samples were extracted from whole mosquitoes collected at 2 , 7 and 14 days post-infection ( dpi ) to explore host miRNA and RNAi responses to ZIKV infection . ZIKV infection was confirmed in individual mosquitoes by RT-qPCR , which indicated increases in viral load as infection progressed ( S2 Fig ) . We obtained 59 . 5–61 . 8 million combined raw reads from the non-infected libraries in day 2 , 7 and 14 samples , respectively ( S1 Table ) . From ZIKV-infected libraries , 54 . 7–84 . 8 million reads were acquired after combining all the three biological replicates in day 2 , 7 and 14 post-infection , respectively ( S1 Table ) . 15–25% of reads were discarded in different libraries due to their low-quality score or lack of adapter sequence . We detected most of the annotated Ae . aegypti miRNAs present on miRBase in our data representing 10–17% of clean reads in different libraries . In all libraries , total read numbers over different lengths showed a peak at 21–22 nucleotides ( nt ) representing the typical length of miRNAs and short interfering RNAs ( siRNAs ) ( Fig 1 ) . Another smaller peak at 27–29 nt was obtained probably pertaining to PIWI-interacting RNAs ( piRNAs ) , which are common in most insect small RNA libraries . Small RNA libraries from ZIKV-infected Ae . aegypti mosquitoes showed alteration of miRNA profiles compared with non-infected controls at 2 , 7 and 14 dpi . However , only 17 miRNAs were identified as differentially modulated at all the time points , with the majority of them significantly depleted in response to ZIKV infection ( Table 1 ) . At day 2 , 10 Ae . aegypti miRNAs showed significant changes in their abundance in response to infection . The maximum fold change ( FC ) was found in aae-miR-286a , aae-miR-2944b-3p and aae-miR-980-3p with log2 FC of -1 . 82 , -1 . 54 and -1 . 43 , respectively ( Table 1 ) . Among all the differentially regulated miRNAs , aae-miR-308-3p showed the most considerable depletion ( -3 . 78 ) at 7 dpi . These values are comparable with miRNA changes seen after DENV infection [17] . However , our study and the DENV study [17] , sequenced miRNAs using RNA extracted from whole mosquitoes . More pronounced changes are likely to be observed when using specific tissues that are infected with virus . Furthermore , comparison of infected and uninfected tissues may be useful in determining tissue-specific versus systemic changes in miRNAs . Only miRNAs aae-miR-2940-3p , which is mosquito specific , and aae-miR-1-5p were significantly enriched in ZIKV-infected libraries at this time point . We spotted less alteration in miRNA profile at 14 dpi libraries despite mosquitoes at this time point having the highest viral load ( S2 Fig ) . Overall , among all the differentially expressed miRNAs due to ZIKV infection , significant declines in miRNA abundances are more pronounced than enrichment . A similar observation was also reported in a previous study with DENV2 , where only 4 miRNAs out of 35 modulated miRNAs during the course of infection were enriched in response to DENV infection [17] . Further studies investigating the effect of distinct flaviviruses on miRNA expression in Aedes mosquitoes are required to confirm if depletion is a general response to infection . The abundance of a few miRNAs was altered in more than one time point after ZIKV infection including , aae-miR-309a , aae-miR-308 , aae-miR-286b , aae-miR-2941 and aae-miR-989 . To validate the differentially expressed miRNAs , nine miRNAs were selected . For this , RNA samples extracted from non-infected and ZIKV-infected whole mosquitoes at 2 , 7 and 14 dpi were subjected to miRNA-specific RT-qPCR . Our results showed broad agreement between qPCR and NGS values . While it is not uncommon to find inconsistences between these two quantification approaches [26 , 27] , in 18 out of 27 cases , the direction of gene expression was the same ( i . e . both enriched or both depleted ) ( Fig 2 ) . Where discrepancies were observed , the trend was for NGS data to indicate depletion of the miRNA , while the qPCR suggested no significant changes . A notable inconsistency was seen with the miRNA miR-308-3p that was seen to be enriched by qPCR but depleted by deep sequencing at 7dpi . A cell line study using Ae . aegypti Aag2 cells found miRNAs were only mildly affected by DENV infection [28] , but in contrast a number of mosquito studies , reported differentially abundant miRNAs in response to a number of arboviruses . However , in most cases , follow up studies to explore the functional significance of those changes and effects on host target genes and virus replication are lacking . Therefore , below we mainly compare the miRNA changes identified in our study with those in previous ones . In Ae . aegypti mosquitoes infected with DENV2 , five , three and 23 miRNAs were differentially expressed at 2 , 4 and 9 dpi , respectively [17] . Among those , miR-308-3p and miR-305-5p ( 9dpi ) overlap with those in ZIKV-infected mosquitoes at 7 and 14 dpi; in both host-virus systems both miRNAs showed depletion . In Ae . albopictus DENV2-infected mosquitoes , overlapping differentially abundant miRNAs with ZIKV-infected mosquitoes from this study are miR-2940-3p ( depleted in DENV , but enriched in ZIKV ) , miR-263a-5p ( depleted in both ) , miR-308-5p ( enriched in both ) , miR-989 ( depleted in DENV , but enriched in ZIKV ) , and miR-2941 ( depleted in both ) [27] . In another study from the same group with Ae . albopictus and DENV2 infection specifically in the midgut tissue , three miRNAs ( miR-2941 , miR-989 , miR-2943 ) were differentially expressed [29] , the first two also with change in abundance upon ZIKV infection in this study . Furthermore , miR-989 was found to be depleted in Culex quinquefasciatus mosquitoes by 2 . 8-fold when infected with WNV [30]; although this miRNA was enriched by about 1 . 8-fold at 2 and 14 dpi with ZIKV in the present study . miR-980 was also differentially expressed in the Cx . quinquefasciatus-WNV interaction [22] . It appears that the identified differentially expressed miRNAs in different host mosquitoes upon flavivirus infections overlap more with each other than infections with other viruses , such as alphaviruses . For example , none of the major Ae . albopictus miRNAs that were differentially abundant after CHIKV infection ( miR-100 , miR-283 , miR-305-3p , miR-927 ) [31] were found among the list of differentially expressed miRNAs from this study; although some of the differentially expressed miRNAs as a result of ZIKV infection could be found among miRNAs showing low levels of differential expression in the CHIKV-mosquito interaction . The similarities in miRNA changes in mosquitoes when infected with flaviviruses as compared to alphavirus infections could be due to ( 1 ) antigenic differences between flaviviruses and alphaviruses that may elicit slightly different host responses , or ( 2 ) differences in replication strategies; for example , production of subgenomic flavivirus RNA ( sfRNA ) by flaviviruses , which could function as decoys or sponges against host derived miRNA , suppress the RNAi response , and play other important roles in mosquito-virus interaction [32–34] . Interestingly , sfRNA from WNV has been shown to efficiently suppress siRNA and miRNA-induced RNAi pathways in mosquito cells and its engineering into a Semliki Forest virus ( SFV , an alphavirus ) replicon led to enhanced replication of SFV in RNAi-competent mosquito cells [32] . While alphaviruses do not produce such RNAs and must rely on other mechanisms to deregulate the host RNAi response . The hypothetical binding sites for all the differentially abundant miRNAs upon ZIKV infection were predicted by command line tools miRanda , RNAhybrid and RNA22 v2 using their default parameters . High confidence potential targets were defined as those containing a unique binding site for each miRNA in all the algorithms , with a maximum of 10 nucleotides shifting . We predicted 898 mRNAs , which can potentially be regulated by the differentially abundant miRNAs upon ZIKV infection ( S2 Table ) . Among these predicted target genes , 247 binding sites were identified for aae-miRNA-980-3p while only six predicted binding sites were detected for aae-miR-308-3p . Although this miRNA showed more profound regulation in response to viral infection ( day 7 ) , we only identified Rho GTPase as its predicted target gene ( S2 Table ) . Other predicted binding sites for this miRNA are located on coding regions of some hypothetical proteins . Rho proteins are small signaling G proteins , which are involved in a wide range of cellular functions such as cell polarity , vesicular trafficking , the cell cycle and transcriptome dynamics [35] . Among the predicted targets , a number of immune-related genes were found , such as leucine-rich immune protein and Toll-like receptor , possibly indicating the ability of ZIKV to modulate mosquito immunity . While the list of targets provides a catalogue of high confidence targets of Ae . aegypti differentially abundant miRNAs upon ZIKV infection , further investigations are required to experimentally establish miRNA-target interactions . Whilst miRNA-target studies have not been carried out on any of the miRNAs reported to be differentially abundant following viral infection in mosquitoes ( previous section ) , except aae-miR-2940-5p , the role of some of these miRNAs are known in other aspects of mosquito or Drosophila biology . For example , a number of the differentially expressed miRNAs upon ZIKV infection were also found differentially expressed upon blood feeding in the fat body tissue [36] . These include , miR-308-5p , miR-263a-5p , miR-305-5p , miR-989 , miR-2941 , miR-286b , miR-2946 . miR-309a , specifically was shown to control ovarian development by targeting the Homeobox gene SIX4 [36] , and miR-375 was found highly induced in blood fed mosquitoes regulating a number of mosquito genes , including upregulating cactus and downregulating Rel1 [37] . Application of miR-375 mimic in Aag2 cells led to enhanced DENV replication . While this miRNA was found to be mostly depleted after ZIKV infection ( Fig 2 ) , it will be interesting to experimentally test if manipulation of this miRNA could have any effect on ZIKV infection by regulating the Toll pathway . In D . melanogaster , the role of miR-308 in development [38] , miR-980 in memory [39] , and miR-305 in homeostasis [40] have been reported . We also screened the ZIKV genome for potential miRNA binding sites of all the 17 modulated miRNAs . Eighty-five possible interactions were identified by three different target predicting algorithms ( miRanda , RNAhybrid and RNA22 ) . S3 Table summarizes highly confident binding sites that were predicted by more than one tool . Some miRNAs such as aae-miR263a-5p , aae-miR-286 , aae-miR-305-5p , aae-miR308-5p , aae-miR-989 and aae-miR-980-3p can potentially bind to more than one place in the viral genome . Previously , targeting of genomes of RNA viruses by host miRNAs have been reported in mammalian cells [41] . In particular , a number of human miRNAs ( hsa-miR-133a , hsa-miR-548g-3p , hsa-miR-223 ) with potential binding sites in the 5’ and 3’UTRs of different DENV serotypes have been shown to negatively affect replication of the viruses when overexpressed in mammalian cells [42 , 43] . In mosquitoes , a midgut-specific alb-miR-281 from Ae . albopictus was shown to target the 5’UTR of DENV2 thereby enhancing replication of the virus [44] . Flaviviruses generally produce dsRNA intermediates during their replication , which are the target of their invertebrate host RNAi machinery [10] . The long dsRNAs are recognised and subsequently diced by the ribonuclease Dicer-2 into 21 nt virus-derived short interfering RNAs ( viRNAs ) that are double stranded and induce the formation of the RNA induced silencing complex ( RISC ) . One of the strands of the duplex is degraded and the other one guides the RISC complex to viral target sequences with complete complementarity . This binding results in the cleavage and degradation of viral RNAs produced during replication of the virus . To investigate potential RNAi activity against ZIKV , we mapped all the small RNAs to the viral genome ( accession no . KX247632 ) . In total , 3 , 288 , 20 , 360 and 57 , 867 reads mapped to the viral genome at 2 , 7 and 14 dpi , respectively , ranging in size from 15–35 nt . The total number of reads at 14 dpi that mapped to the virus genome accounted for 0 . 16% of the total small RNA reads at this time point after infection ( 36 , 115 , 068; S1 Table ) , which is close to the percentage ( 0 . 05% ) found in DENV2-infected Ae . aegypti whole mosquitoes at 9 dpi [45] . The number could possibly be higher if small RNAs are analysed in specific tissues where virus infection primarily occurs . Using whole mosquitoes , which is a mixture of infected and non-infected tissues , may result in dampening of the percentage of virus-specific small RNAs . While at 2 dpi the distribution of small RNAs was across different sizes , at 7 and 14 dpi the majority of the mapped reads were at 21 nt , typical of viRNA size in mosquitoes ( Fig 3A ) . When only the 21 nt reads were mapped to the viral genome , the number of viRNAs increased dramatically during the course of infection; 201 ( 2 days ) , 6 , 250 ( 7 days ) , and 20 , 732 ( 14 days ) . This also confirmed successful replication of the virus in the mosquitoes . In addition , the viRNAs mapped across the entire length of the viral genome , on both positive and negative strands of the viral genome ( Fig 3B ) . The pattern of mapped reads indicated a bias towards the positive strand; 62% to the positive strand and 38% to the negative strand–the percentages were very similar both at 7 and 14 dpi . We did not find distinct hot-spots ( large number of viRNA production ) across the viral genome , except one towards the end of the NS5 region at both 2 dpi and 7 dpi , which is also present at 14 dpi but not as a pronounced peak among others ( Fig 3B ) . These results confirm that ZIKV is exposed to the mosquito host RNAi response , with the replicative dsRNA intermediates being the major substrate for Dicer-2 . These findings are consistent with other examples of flaviviruses [28 , 45–50] . Virus-derived piRNA-like small RNAs ( 25–30 nt ) , which are also referred to as viral-derived piRNAs ( vpiRNAs ) , have been identified in insects infected with flavivirues , bunyaviruses and alphaviruses [45 , 51–54] . It has been shown that knockdown of the piRNA pathway proteins leads to enhanced replication of arboviruses in mosquito cells , suggesting their potential antiviral properties in mosquitoes . For example , knockdown of Piwi-4 in Ae . aegypti Aag2 cell line increased replication of the mosquito-borne alphavirus , SFV [51] . In another study in the same cell line , specifically silencing Ago3 and Piwi-5 led to significantly reduced production of vpiRNAs against another alphavirus , Sindbis virus ( SINV ) [55] . To find out if any virus-derived piRNA-like small RNAs are produced in Ae . aegypti mosquitoes infected with ZIKV , we mapped 25–30 nt small RNA reads from the three time points post-infection to the viral genome . The number of reads increased as infection progressed , and they mapped to the entire ZIKV genome with no particular hot spots identified ( Fig 4 ) . However , we found a significant bias for reads mapped to the positive strand; for example , in 14 dpi samples 5 , 300 of 25–30 nt reads mapped to the positive stand and only 60 reads mapped to the negative strand ( Fig 4 ) . In DENV2 infected Ae . aegypti mosquitoes , the number of 25–30 nt reads that mapped to the negative strand of the virus were also extremely low . Further , no bias for a specific base or sequence-specific piRNA signature ( U1 and A10 bias ) was observed in this study , as would normally be expected for ping-pong derived piRNAs [56] . Similar observations were reported in other flavivirus-infected mosquitoes or mosquito cell lines . We recently demonstrated that in Ae . aegypti mosquitoes infected with an insect-specific flavivirus ( Palm Creek virus ) , small RNA reads in the range of 25–30 nt do not harbor any of the classical sequence-specific piRNA features [57] . Hess et al . ( 2011 ) also showed that DENV2 piRNA-like sequences do not display any bias for U in the first position and only a slight bias for A10 [50] . However , in mosquito cells infected with alphaviruses SFV [51] and SINV [52] , and bunyaviruses La Crosse virus [52] and Rift Valley Fever virus [58] clear U1 and A10 ping-pong piRNA signature was observed . Hence , currently we do not have enough evidence to classify the 25–30 nt reads that mapped to the ZIKV genome as vpiRNA since they might be artefacts of viral genome degradation . In summary , we found that ZIKV infection in Ae . aegypti altered the small RNA profile of mosquitoes with peaks seen at 21–22 and 27–29 nt . Overall , ZIKV infection modulated 17 miRNAs with the majority of these small RNAs being depleted . Several immune related transcripts were the predicted targets of differentially abundant miRNAs suggesting that ZIKV may interact with mosquito immunity . At 7 and 14 dpi , viral infection initiated an RNAi response indicated by the presence of viRNAs . At these times points , virus-derived small RNAs in the size range of piRNAs were also found in infected mosquitoes , although they lacked the typical piRNA signature . This study increases our understanding of ZIKV-mosquito interactions and broadens our comprehension of the Aedes miRNA response to flavivirus infection . | Vector-borne viruses have immense impacts on human health by causing mortality and morbidity . Control of diseases caused by these viruses have mostly concentrated on vector control or inhibition of virus transmission by the vectors . This requires a thorough understanding of vector-virus interactions . In this study , we investigated the RNA interference ( RNAi ) response in Aedes aegypti mosquitoes infected with the Zika virus ( ZIKV ) strain isolated from the current pandemic using deep sequencing technologies . We found that infection alters the microRNA ( miRNA ) profile between infected and uninfected mosquitoes and that changes in miRNA expression occur over time . The short interfering RNA pathway , which is the main mosquito defense as part of the RNAi pathway , was also induced by ZIKV infection with the number of short interfering RNAs increasing significantly as infection progressed . Our results indicate that ZIKV induces the mosquito host defense similar to infection with other flaviviruses . | [
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"... | 2017 | Zika virus alters the microRNA expression profile and elicits an RNAi response in Aedes aegypti mosquitoes |
High-throughput RNA sequencing enables quantification of transcripts ( both known and novel ) , exon/exon junctions and fusions of exons from different genes . Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads . To address this challenge , we implemented an RNA-Seq mapping pipeline within the LifeScope software . We introduced new features including filter and junction mapping , annotation-aided pairing rescue and accurate mapping quality values . We combined this pipeline with a Suffix Array Spliced Read ( SASR ) aligner to detect chimeric transcripts . Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system , we called 40 gene fusions among over 120 , 000 splicing junctions . We validated 36 of these 40 fusions with TaqMan assays , of which 25 were expressed in MCF-7 but not the Human Brain Reference . An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1 , and another involving the RPS6KB1 ( Ribosomal protein S6 kinase beta-1 ) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample .
The transcriptome comprises the set of all transcripts in a cell and their quantity at a specific stage and time . RNA-Seq enables hypothesis-neutral investigation of the expression of the transcripts including non-coding RNA and viruses [1] . RNA-Seq provides advantages over microarray technology such as the detection of novel transcripts ( both truly novel as well as those arising from alternative splicing ) and sensitivity over a greater range of expression [2] . Methods to more comprehensively analyze RNA sequencing data are being developed , with particular focus on normalization of differential gene expression , annotation of the transcriptome , and characterization of the splicing junctions [3]–[12] . Paired-end RNA-Seq further enhances quantification of alternative transcripts [13]–[16] . Analysis of tissue and single-cell-specific RNA is revealing cellular gene expression diversity and phenotypy [17]–[19] . Gene fusions arise from mutations including translocations , deletions , inversions , or trans-splicing . Fusion genes are thought to cause tumorigenesis by over-activating proto-oncogenes , deactivating tumor suppressors , or altering the regulation and/or splicing of other genes which lead to defects in key signaling pathways [20] . Fused RNAs are found to occur in significantly higher frequency in cancer than in matched benign samples and may be potential biomarkers [21] . For example , 95% of patients with clinical chronic myeloid leukemia ( CML ) express the BCR-ABL gene fusion in their leukemia cells due to a reciprocal translocation between the long arms of chromosomes 9 and 22 [22] , [23] . BCR-ABL is also found to be a factor in 30% to 50% of adult acute lymphoblastic leukemia cases [24] . Imatinib is a specific tyrosine kinase inhibitor targeting BCR-ABL and is an effective treatment for CML [25] , [26] . Gene fusions are also detected repeatedly in other tumors . Examples include ETV6-NTRK3 in mesoblastic nephroma , congenital fibrosarcoma , and breast carcinoma [27]–[29] . MYB-NFIB in head and neck tumors [30] , TMPRSS2-ERG/ETS in prostate cancer [31]–[34] , and EML4-ALK in lung cancer [33] , [35] . Most lung tumors with ALK rearrangements are shown to shrink and stabilize when patients are given the ALK inhibitor Crizotinib [36] . Hypothesis-neutral gene fusion detection with RNA-Seq was recently demonstrated by different groups [37]–[46] . For example , the FusionSeq software uses paired-end reads to find candidate fusions , and applies a set of filtration modules to remove false positive candidates [41] . FusionSeq applies misalignment filters for large- and small-scale homology , low complexity repetitive regions , and mitochondrial genes particularly considering reads that fall on SNP regions or on RNA edited transcripts that may cause misalignments . deFuse guides a dynamic programming based spliced read detection module with paired-end alignments [42] . Both of these methods reply upon paired-end alignments as the initial evidence and apply spliced read mapping on the candidate regions . PERAlign relies upon mapping spliced reads to the whole genome first and then guiding them with paired-end alignments [38] . In this study , we describe a new method which considers spliced-read and paired-end alignments independently from each other , enabling detection of fusions from single fragment or paired-end experiments . We also introduce techniques for mapping of spliced-reads to a suffix array based virtual gene fusion reference with annotation-aided pairing rescue and methods for quality assessment of alignments and splice junctions . We tested our analysis tool by calling the exon/exon junctions and gene fusions from data generated by sequencing three paired-end RNA-Seq libraries , each with two technical replicates . We also compared our results to TopHat and FusionSeq software on the MCF-7 sample . Next we validated candidate MCF-7 gene fusions using TaqMan® assays and showed that 90% of the calls were valid and over 65% were specific to MCF-7 . We also identified what appears to be an early breakpoint bias at the 5′ fused genes . Finally , we surveyed a subset of MCF-7 and UHR fusions on a panel of breast cancer cell lines and discovered evidence for recurrence .
We prepared strand-specific , paired-end RNA libraries from the Universal Human Reference ( UHR ) , the Human Brain Reference ( HBR ) , and the breast cancer cell line MCF-7 using the Total RNA-Seq kit from Applied Biosystems . These RNA libraries were sequenced using ligation-based high throughput SOLiD™ system [47] . Fragments were gel-selected for insert sizes between 100–200 base pairs ( Figure S1 in Text S1 ) . Using a new transcriptome alignment pipeline in which each pair of reads is mapped to genome , junction , exon and filter references and paired with a pairing quality value ( PQV ) , we obtained total of 580 million read pairs that were confidently mapped to the human genome ( Table S1 in Text S2 ) . Histograms of gene expression showed a wide range of distribution , and average R2 correlation of gene expression between replicates ranged from 0 . 95 to 0 . 96 ( Figures S2 , S3 in Text S1 ) . Splice junctions were discovered by combining three approaches: ( 1 ) BRIDGE evidence found by paired-end reads in which the forward read maps on an exon and the reverse read maps on another exon with a PQV above a confidence threshold; ( 2 ) SPAN evidence found by single reads ( of paired-end reads ) in which the read alignment spans the breakpoint of a set of known and putative splice junctions; ( 3 ) Fusion SPAN evidence found by fusion alignments spanning hypothetical breakpoints of any two exons discovered using the SASR aligner which assesses all exon-exon combinations in the genome ( Figure 1 ) . Using this strategy , for each sample , we identified an average of 133 , 000 RefSeq and 15 , 315 non-RefSeq ( putative ) splice junctions and 5 to 56 candidate fusion breakpoints ( Table 1 and Table S1 in Text S2 ) . To assess the performance of mapping quality values generated with the system , we compared fold-change ratio ( Log2 [UHR/HBR] ) of gene RPKM values with gene expression assays from the MicroArray Quality Control ( MAQC ) project [48] , [49] . We compared correlation of four different PQV ( 1 , 10 , 20 and 40 ) thresholds with the assays ( Figure S4 in Text S1 ) . Pearson correlations of data from TaqMan assays with that of the data from the SOLiD system were not significantly different between PQV thresholds . The slope ( m ) of the regression fits , however , was significantly affected by the threshold settings . As PQV is increased from 1 to 40 , the slope increased dramatically from 0 . 77 to 0 . 88 , indicating significantly greater accuracy compared to a “gold standard” qPCR method . The increase in accuracy is likely a result of increased specificity . Essentially , the log ratio dynamic range increases with increasing PQV settings ( Figure S5 in Text S1 ) . RPKM distributions show an increase in low-end signal for lower PQV . If this increase in “sensitivity” represents additional noise , it can contribute to a loss of accuracy in the fold change calculations . The increase in the low end suggests that these reads may be spurious ( Figure S6 in Text S1 ) . In order to find optimal filters for detecting splice junctions with our combined approach , we compared three quality thresholds using data from the UHR and HBR barcoded libraries: ( 1 ) one SPAN evidence ( 1-SR ) , ( 2 ) two unique SPAN evidences ( 2-SR ) , and ( 3 ) one BRIDGE and one SPAN evidences ( 1-PE-1-SR ) . In addition to these tested thresholds , we applied default filter of choosing only primary alignments with PQV>10 . The results , as illustrated in Figure 2 , suggest an increased number of false positives for 1-SR evidence even though it may have greater sensitivity . 1-PE-1-SR threshold reduced false positives especially for fusions , generating less calls than 2-SR threshold . For the analyses described later in the text , 1-PE-1-SR threshold was chosen for calling splice junctions and 2-PE-2-SR for calling gene fusions . On average , 82% of junctions identified in the libraries were present in the RefSeq database . 84% of these known junctions and 26% of the putative junctions were shared between at least two of the three libraries ( Figure S7 in Text S1 ) . The highest number of library-specific known junctions was observed in HBR , and the highest number of library-specific putative junctions was observed in MCF-7 . Next , we formulated a Junction Confidence Value ( JCV ) and investigated its utility to identify true versus false junctions . Details of JCV and its formulation are explained in supplementary methods in Text S1 . One type of false positive fusion junction is likely called between highly expressed exons for which the random chance of encountering a misalignment or mispairing is elevated . Homology between highly expressed genes would also increase this type of false positive . We created JCV to test the quantity and quality of BRIDGE evidences when compared to an ‘error expectation metric’ . This metric is defined as the estimation of the null hypothesis of encountering a random junction between the two exons . Increasing JCV increased known/putative junction ratio which was predictive of the false discovery rate and at the same time distinguished significant number of novel junctions to either lower or higher score bins ( Figure 3A ) . Known/putative ratios ranged from 0 . 15 for JCV cutoff of 0; 3 for JCV cutoff of 50 and 16 for JCV cutoff of 100 . In order to test the sensitivity of JCV , we simulated 1 , 000 , 000 junctions based on a combination parameter model of true junction expression ratio and false junction misalignment ratio . True positive rate ( TPR ) and false positive rate ( FPR ) were calculated by comparing whether a called junction was real ( Figures S8 , S9 , S10 in Text S1 ) . These simulations showed that JCV was predictive of true junction calls and higher JCV thresholds resulted in much less FPR and slightly less TPR . We performed a separate simulation of gene fusion detection using reads from a DH10B ( E . coli ) DNA sequencing experiment where introns , exons and a gene model were simulated to make the data similar to RNA-Seq experiments . Our algorithm was able to detect 86 out of 93 simulated fusions in this experiment ( Figure S11 in Text S1 ) . Next , we ran TopHat ( v1 . 3 . 0 ) on the MCF-7-1 dataset by using default paired-end parameters for color space . TopHat reported 124 , 236 , 156 mapped reads of which 33 , 634 , 800 were properly paired whereas LifeScope reported 404 , 901 , 929 mapped reads , of which 300 , 341 , 259 pairs were mapped to the same chromosome and 158 , 050 , 096 were properly paired ( Table S1 in Text S2 ) . Of note , TopHat allows 2 mismatches on mapped reads by default and does not report pairs of reads mapped across different chromosomes . Next we identified appropriate score threshold for calling TopHat junctions . For each junction found , TopHat reports a score which corresponds to the number of reads that span the junction . TopHat reported 1 , 391 , 319 total junctions without any score filter and with score>5 threshold this number reduced to 53 , 402 ( Figure S12 in Text S1 ) . We used TopHat candidate junctions with score>5 for comparison to RefSeq known and Lifescope candidate junctions ( Figure 3B ) . There is some evidence that score>10 may yield more specific results for TopHat ( Figure S12 in Text S1 ) . Of note , known ( RefSeq ) junctions called by TopHat dropped from 129 , 316 ( score>0 ) to 106 , 962 ( score>5 ) . These results suggest that TopHat detects a large number of putative novel junctions yet is not as sensitive when distinguishing false positives . LifeScope detected 15 , 074 putative novel junctions between known exons of the same gene that weren't called by TopHat . We could distinguish that more than half of these ‘LifeScope-only’ junctions were likely true positives by looking at their JCV; 3 , 520 had JCV = 0 and 8 , 481 had JCV = 100 with the rest having scores between 0 and 100 . Using the combined BRIDGE&SPAN approach described above on the UHR sample , we called and validated previously reported gene fusions including BCR-ABL1 , GAS6-RASA3 , ARFGEF2-SULF2 , NUP214-XKR3 and BAT3-SLC44A4 [37] , [39] , [40] . These fusions were not described in the literature for HBR , and as expected were not identified in the HBR samples sequenced . In MCF-7 , a total of 40 putative fusions were identified in the first sequencing run ( 50×25 paired-end ) , of which 26 were detected again in a second run ( 75×35 paired-end ) out of a total 56 fusion calls ( Table S2 in Text S2 ) . We also analyzed first MCF-7 sequencing dataset using FusionSeq ( Sboner et . al . ) . Six of the forty gene fusions identified by LifeScope were also called by this software . FusionSeq's confidence value ( RESPER ) for these calls ranged from 1 . 15 to 4 . 53 . Of importance , the ribosomal filter and single read validation module of FusionSeq ( version 0 . 7 ) did not handle color space data or data with different read length pairs adding to 5807 total calls with RESPER>1 ( Table S3 in Text S2 ) . Based on the calls from the first MCF-7 sequencing experiment , we prepared 40 TaqMan fusion assays and run them on the UHR , HBR , and MCF-7 samples along with the prostate cell line PC-3 as an additional control . 36 ( 90% ) of the fusions were validated with the assays and 25 ( 63% ) were found to be specific to MCF-7 and UHR ( Table 2 and Table S4 in Text S2 ) . To note , 19 of these “specific” fusions were called with our algorithms in the second run of MCF-7 . JCV values correlated with whether a fusion was called again , and also with the number of unique start points ( Figure S13 in Text S1 ) . Real-time PCR Cycle Threshold ( CT ) values showed that each of the MCF-7 gene fusions was expressed in UHR with around ten-fold less expression ( Table 2 ) . This suggests that MCF-7 or one of its parent or sister cell lines is very likely part of the UHR pool . According to the information provided by the supplier , UHR RNA is prepared from a pool of ten different cancer cell lines , one of which is an ‘adenocarinoma , mammary gland’ . MCF-7 is an adenocarcinoma cell line from mammary gland . From the RNA-Seq calls , nine of the MCF-7 gene fusions were detectable in UHR with ∼200 million confidently mapped reads whereas these fusions were detectable in MCF-7 with only ∼80 million reads . It is likely that deeper sequencing of the UHR pool would have identified the remaining fusions . Many MCF-7 fusions were between genes in three bands of Chr 1 , Chr 17 and Chr 20 ( Figure 4 ) . These bands were previously described as rearrangement “hot-spots” [50] . Of the total 11 inter-chromosomal or inverted intra-chromosomal fusions , five had premature stop codons ( not in frame ) , while six were in frame . Two of the fusions were alternatively spliced including the fusion from the second exon of ESR1 to the sixth and seventh exon of C6orf97 , and the fusion from the first and second exon of ADAMTS19 to the tenth exon of SLC27A6 . We also found several new intra-chromosomal gene fusions mostly between adjacent or neighboring genes ( Table S4 in Text S2 ) . We observed an enrichment of fusions for which the breakpoints were in the first intron of a gene , a similar bias explained also in Inaki et al . , 2011 . This pattern was not observed for the UHR and HBR samples ( Figure 5A–B ) . On average , first introns in the RefSeq database ( hg18 ) constitute 22% of a gene . We asked whether the large intron size alone might explain the breakpoint bias at the 5′ introns . We used a parametric bootstrap approach to test the hypothesis that gene fusions are more likely to occur towards the 5′ end of a gene; for example , after the first exon . Assuming that the breakpoint was in the middle of the intron following the fused exon , we considered breakpoints for 23 fusions from Table 2 ( omitting multiple splices for ESR1 and ADAMTS19 ) . We simulated 100 , 000 gene fusion locations in these 23 genes from a uniform distribution within the gene . We normalized the location of the real gene fusions by gene length ( defined as the distance between the start of the first exon and the end of the last exon ) . We calculated the mean fusion location of the 23 genes , in the observed fusions and in the simulated fusions . In the real fusions , the mean insert location was 0 . 2587 ( 26% of the length of the gene , Figure 5C ) . In 100 , 000 simulated sets of 23 fusions , the mean was 0 . 5 and the standard deviation was 0 . 06 . Only three in 100 , 000 of the simulated sets of fusions had a value less than 0 . 2587 . Thus the observed location of the gene fusions is statistically significantly biased towards the 5′ end of the gene , with a p-value estimated at 3×10−5 . To test recurrence , we selected 24 fusions from UHR and MCF-7 and investigated their expression in 20 cancer cell line samples ( Figure 6 ) . UHR fusions BCR-ABL1 and BAT3-SLC44A4 were found expressed in the myelogenous leukemia cell line K562 but with eightfold higher expression than in UHR . GAS6-RASA3 fusion was expressed only in UHR . Most of the fusions in MCF-7 were also expressed at a low level in the Du4475 cell line with a higher CT value ( >35 for most cases ) . Both MCF-7 and Du4475 cell lines are traced to a 69/70-year old Caucasian female from Georgetown , but contamination , mixing , mislabeling , or differences in culturing may have caused the observed expression . Two of the intra-chromosomal gene fusions were expressed in multiple samples: ESR1-C6ORF97 and RPS6KB1-TMEM49 . The first of these fusions , between the estrogen receptor alpha gene ESR1 and its neighboring gene C6ORF97 on Chr 6 was expressed in two other ER+ breast cancer cell lines in addition to UHR , MCF-7 , and Du4475 . This fusion may have occurred due to an inversion or rearrangement , as normally the ESR1 gene is downstream of C6ORF97 on the genome ( on the same strand , 128 , 831 base pairs apart ) ; yet the fusion junction was observed to be from the second exon of ESR1 to the sixth exon of C6ORF97 , in the reverse order of expected transcription . We noted that these two genes were considerably expressed in MCF-7 ( RPKM 16 and 46 average ) , though not expressed at all in HBR ( RPKM<0 . 5 ) , and weakly expressed in UHR ( RPKM 1 and 1 . 8 ) . The second recurring fusion , RPS6KB1-TMEM49 , was found expressed in four cancer cell lines including HCC2157 and HelaS3 . We further tested the presence of 24 candidate fusions in cDNA from 48 Clinical Samples of Normal and Breast Tumors ( Origene ) . We found ESR1-C6ORF97 expressed in one ER+ tumor , and none of the other fusions were expressed .
RNA-Seq allows interrogation of known and novel transcript expression and discovery of gene fusions . We describe a new suffix array algorithm to find fusion breakpoint spanning reads in a hypothesis-neutral fashion . We combine this algorithm with a new paired-end mapping approach to detect gene fusions sensitively and reliably . Our mapping method works with a predefined set of exon boundaries which is readily available for the human genome from RefGene or Ensembl databases . To detect novel splicing sites different from the known junctions , one can first find novel expressed islands of reads with tools such as TopHat [8] , and next add the predicted novel exons to the gene model , prior to using our tool . Other de novo assembly algorithms , as long as they generate de novo exon boundaries , and mapped back to genome coordinates , may also be used with our tool [51]–[54] . By sequencing and analyzing the MAQC samples UHR and HBR , and the breast cancer cell line MCF-7 , we validated 25 gene fusions specific to MCF-7 and UHR . Of these , five were not in frame and had premature stop codons . These fusions might still deploy a negative constraint on the fused genes by increasing non-sense mediated decay ( NMD ) [55] . In addition , several gene fusions that occur at the genomic level might not have been detected by messenger RNA sequencing ( mRNA ) because their pre-cursor mRNAs would have been degraded by the NMD mechanism . Such fusions may be identified by DNA-level sequencing . Of the 29 intra-chromosomal fusions called in MCF-7 in this study , 12 were not described in investigated literature ( Table S4 in Text S2 ) . Interestingly , most of the adjacent MCF-7 gene fusions did not fit the standard definition of “read-through” since they did not occur between last and first exons of the fused genes and in some cases they occurred in inverse order of expected transcription . This indicates that these fusions may had arisen due to trans-splicing or structural mutations such as deletions or inversions . These hypotheses may be tested by directly sequencing the DNA from these regions . By surveying cancer cell lines with TaqMan assays , we observed that two of the MCF-7 fusions involving adjacent genes , ESR1-C6ORF97 and RBS6KB1-TMEM49 were expressed recurrently . Fusions of the ESR1 gene may disrupt estrogen signaling pathways and thus events involving this gene may be significant . RBS6KB1-VMP1 fusion was described as a recurrent event recently by another group [45] . VMP1 is another name for TMEM49 . Amplification of the RPS6KB1 loci ( Ribosomal protein S6 kinase beta-1 ) was described in other breast cancers as an oncogene event [56] . Still , it is possible these recurrent fusions arise only in immortalized cell lines rather than being driver mutations . In fact , the ESR1 fusion tested positive in only one of the 48 clinical breast samples , while the RPS6KB1 fusion was not expressed in any of them . Of interest , six of the fusions originated on the band 17q23 , which was previously identified as a common region of amplification in cancer [57] . In many of the MCF-7 fusions , the first or early introns of the 5′ genes harbored the gene fusion breakpoint . A similar pattern was observed in prostate cancer: the complete exon-1 of TMPRSS2 was identified to fuse with ETV1 or ERG as one of the most recurrent rearrangements [31] . Recent studies on prostate cancer found extended breakpoints at the androgen receptor binding sites possibly due to LINE-1-induced ORF or topoisomerase-II beta . These enzymes , when co-recruited with an androgen receptor , were linked to increased chromosomal translocations of the TMPRSS2 , ETV1 , and ERG genes [58] , [59] . Presence of the early 5′ breakpoints in MCF-7 genes suggest that recurrent double-stranded breaks may occur in breast tumors at the gene promoter and early splicing sites due to factors not mediated by the androgen receptor . In conclusion , we presented a novel method of splice and fusion detection from RNA-Seq data . We sequenced MCF-7 , UHR and HBR , and demonstrated high specificity in finding splices and fusions de novo . We further showed that two of the MCF-7 gene fusions are expressed recurrently in a number of tumor cell lines .
Reads were aligned to a reference using the Mapreads module of the BioScope 1 . 3 and LifeScope 2 . 0 software ( http://www . lifetechnologies . com/lifescope ) . Four fasta references were used for increased throughput and accuracy: ( 1 ) genomic reference , ( 2 ) junction reference , ( 3 ) exon reference , and ( 4 ) filter reference ( Figure 1B ) . Filter reference contained polyA , polyC , polyG , polyT , ribosomal RNAs , tRNAs , LINE , SINE , LTR and satellite repeats , rRNA , scRNA and snRNAs , as well as adaptor , barcode , and primer sequences . In our experiments , most reads filtered to ribosomal RNAs and merged adaptor-barcode-primer sequences . When aligning reads to the genome , two mismatches were allowed on the seed , and alignments were extended when possible based on a dynamic scoring function . The junction reference library was generated from a list of known and putative exon-exon pairs within RefSeq transcripts and contained approximately two million fasta entries . Reverse reads in our experiments were shorter than forward reads ( 25 vs 50 , or 35 vs 75 ) . To increase the mapping rate for the shorter reverse reads , they were additionally aligned to an ‘exon reference’ by allowing three mismatches on the seed . This exon reference contained each known exon as a separate reference entry . An exon rescue step was performed for reads where one pair was mapped within a gene and its pair was unaligned , by aligning the unmapped read within the downstream exons of the same gene with up to six mismatches . The genome , exon , junction , and rescued alignments were merged to generate a single set of alignments for the forward and reverse tags separately . Reads that aligned confidently to the filter reference were subtracted from these alignments . A final pairing step was performed to find most probable alignment pairs and assign a pairing quality value ( for formulas see Methods in Text S1 ) . These final paired alignments were put in a genome-coordinate BAM file which represents the summary of mapped alignments except for fusion alignments found by SASR . For reads that were admissible as a candidate to be spliced on a fusion junction ( see Methods in Text S1 for admission criteria ) , we performed a suffix array search as follows . A read was defined to provide evidence of a splice junction between an exon X and exon Y if and only if ( 1 ) exon X maps to the prefix of the read , ( 2 ) exon Y maps to the suffix of the read and ( 3 ) the sum of the two map lengths is equal to the length of the read . For 50-bp long reads ( or 49 colors plus a leading base ) , the suffix data structure was simply a list ( an array ) of all suffixes of length 10 through 38 from all exons . The suffixes were stored in lexicographically increasing order . A string s = s1s2…sm is lexicographically ( i . e . alphabetically ) less than a string t = t1t2…tn if s1<t1 or s1 = t1 , and string s2s3…sm is lexicographically less than string t2t3…tn . Each suffix was represented compactly by a pair of integers ( an integer and a byte in the implementation ) : an index to the relevant exon in the input exon list , and the length of the suffix . Such a data structure is called a suffix array [60] . Because of the lexicographic order proper , all suffixes that start with any given decamer were consecutive in such a list . Therefore , one may quickly find all matching suffixes with a binary search into the suffix array . Once the list of exons that mapped to the prefix and suffix of the read were identified , it could be determined whether the read provided evidence for a unique junction . A read was considered to be a SPAN evidence for a junction X-Y between two exons if it was already junction mapped or if it was discovered by SASR as described above . A paired-end read was considered BRIDGE evidence for a junction X-Y if one read of the pair mapped to exon X and the other mapped to exon Y with PQV>10 . Candidate junctions were stored , each with a count of evidence , number of unique start points and corresponding PQV , in a sparse , directed graph . In this graph , exons corresponded to nodes , and SPAN and BRIDGE evidences corresponded to two types of edges between nodes . After all evidence was collected , junctions were called by evaluating each candidate and assigning a junction confidence value . At least one and two unique evidences of each type were respectively required to call same-gene and different-gene junctions ( fusion ) . Exons could partially overlap , allowing for junctions with different donor and acceptor sites to be counted as alternative splices as long as at least two alternatives were detected . Genes with overlapping annotations were not counted towards a gene fusion if the evidence was ambiguous . Human Breast Adenocarcinoma ( MCF-7 ) Total RNA and FirstChoice® Human Brain Reference RNA ( HBR ) were obtained from Ambion . Universal Human Reference Total RNA ( UHR ) was obtained from Stratagene . Oligo ( dT ) selection was performed twice by using MicroPoly ( A ) Purist™ kit ( Ambion ) according to the manufacturer's recommendations . After polyA selection , 500 ng polyA RNA was fragmented using RNase III . 50 ng fragmented RNA was then subjected to hybridization and ligation using the SOLiD Total RNA-Seq Kit ( Ambion ) according to the manufacturer's instructions . Duplicate libraries , with three different insert sizes ( 100–200 bp , 100–300 bp , 150–250 bp ) , were generated from HBR and UHR RNAs . A total of 12 libraries were multiplexed using the SOLiD RNA Barcoding Kit ( Applied Biosystems ) and pooled at an equi-molar ratio . Two libraries were made from same lot of MCF-7 polyA RNA with standard insert size ( 100–200 bp ) . The final purified products were quantitated using a NanoDrop® instrument , and the size range of the products was confirmed by Bioanalyzer™ instrument analysis . The samples were then diluted and used for emulsion PCR . Libraries were sequenced utilizing 50 or 75 bp forward and 25 or 35 bp reverse paired-end sequencing chemistry on the SOLiD system [47] . TaqMan probes and primers were designed for selected fusion targets . For each putative fusion call , the target region for assay design was composed of 200 bases around the fusion point: the first 100 from the 5′ gene exon and the second 100 from the 3′ gene exon . If either of the exons was smaller than 100 bases , the entire exon was taken but no bases from a further exon were used . Therefore , any target region had a maximum of 200 bases . These target sequences were then used to select TaqMan assay probes and primers which were ordered from Applied Biosystems . These assays were used to validate the novel fusion candidates in Universal Human Reference RNA sample ( Stratagene ) , MCF-7 RNA ( Ambion ) , Human Brain Reference RNA ( Ambion ) , and a no template control sample . cDNAs were generated from 2 . 5 ug total RNA from each sample using the High Capacity cDNA Archive Kit and protocol ( Applied Biosystems ) . The resulting cDNA products were diluted twenty-fold and four replicates were run for each gene for each sample in a 384-well format plate on 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . 24 selected fusion targets ( Figure 6 ) were screened across 20 cancer cell line RNAs and negative template control ( NTC , Table S5 in Text S2 ) using TaqMan probe and primers . Real-time PCR reactions were run as described above . The same selected 24 fusion targets were also screened in 48 breast cancer clinical samples ( Origene ) using TaqMan probe and primers . cDNAs were generated from 2 ng total RNA from each sample using the High Capacity cDNA Archive Kit and protocol ( Applied Biosystems ) . The resulting cDNA was subjected to a 16-cycle PCR amplification followed by real-time PCR reaction using the manufacturer's TaqMan PreAmp Master Mix Kit Protocol ( Applied Biosystems ) . Preamplifed cDNA products were diluted twentyfold and four replicates were run for each gene for each sample in a 384-well plate on a 7900HT Fast Real-Time PCR System ( Applied Biosystems ) . | Advances in sequencing technology are enabling detailed characterization of RNA transcripts from biological samples . The fundamental challenge of accurately mapping the reads on transcripts and gleaning biological meaning from the data remains . One class of transcripts , gene fusions , is particularly important in cancer . Some gene fusions are prominent markers in leukemia , prostate , and other cancers and putatively causative in certain tumor types . We present a set of new RNA-Seq analysis techniques to map reads , and count expression of genes , exons and splicing junctions , especially those that give evidence of gene fusions . These tools are available in a software package with a straightforward graphical user interface . Using this software , we called and validated several gene fusions in a breast cancer cell line . By testing the presence of these fusions in a larger population of tumor cell lines and clinical samples , we found that two of them were expressed recurrently . | [
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] | 2012 | RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm |
It was recently reported that the production of Reactive Oxygen Species ( ROS ) is a common mechanism of cell death induced by bactericidal antibiotics . Here we show that triggering the Escherichia coli chromosomal toxin–antitoxin system mazEF is an additional determinant in the mode of action of some antibiotics . We treated E . coli cultures by antibiotics belonging to one of two groups: ( i ) Inhibitors of transcription and/or translation , and ( ii ) DNA damaging . We found that antibiotics of both groups caused: ( i ) mazEF-mediated cell death , and ( ii ) the production of ROS through MazF action . However , only antibiotics of the first group caused mazEF-mediated cell death that is ROS-dependent , whereas those of the second group caused mazEF-mediated cell death by an ROS-independent pathway . Furthermore , our results showed that the mode of action of antibiotics was determined by the ability of E . coli cells to communicate through the signaling molecule Extracellular Death Factor ( EDF ) participating in mazEF induction .
Traditionally , antibiotics are classed as either “bactericidal , ” meaning that they can kill bacteria , or “bacteriostatic , ” meaning that they can only inhibit bacterial growth . Bacteriostatic drugs may be effective , because inhibiting bacterial growth allows the body's defence mechanisms to eliminate the pathogenic bacteria [1] . The mechanisms of antibiotics actions were well studied , particularly in relation to their targets interactions . Accordingly , they fall into three main groups: DNA damage-causing agents , inhibitors of protein synthesis , and inhibitors of cell wall turnover [2] . Recently , a downstream common mechanism of antibiotics leading to cell death has been reported . It was shown that the three major groups of bactericidal antibiotics , regardless of their targets interactions , stimulate the production of hydroxyl radicals in Gram-negative and Gram-positive bacteria , which ultimately causes cell death . In contrast , the bacteriostatic antibiotics do not produce hydroxyl radicals [3] . We have previously shown that some antibiotics trigger cell death by the activation of the built-in death system mazEF of Escherichia coli [4–6] . mazEF is a toxin–antitoxin ( TA ) module found on the chromosomes of many bacteria , including pathogens [7–10] . E . coli mazF specifies for the stable toxin MazF , and mazE specifies for the labile antitoxin , MazE . In vivo , MazE is degraded by the ATP-dependent ClpAP serine protease [11] . MazF is a sequence-specific endoribonuclease that preferentially cleaves single-stranded mRNAs at ACA sequences [12 , 13] , and thereby inhibits translation [12 , 14] . MazE counteracts the action of MazF . Because MazE is a labile protein , preventing MazF-mediated action requires the continuous production of MazE . Thus , any stressful condition that prevents the expression of the chromosomally borne mazEF module will lead to the reduction of MazE in the cell , permitting the toxin MazF to act freely . Such stressful conditions can be caused by antibiotics , including ( i ) those that inhibit transcription and/or translation like rifampicin , chloramphenicol , and spectinomycin [4]; and ( ii ) those that cause DNA damage like mitomycin C , nalidixic acid , and trimethoprim [6 , 15–17] . Each of these antibiotics is well known to cause bacterial cell death [18 , 19] . It is obvious that antibiotics belonging to the first group prevent mazEF expression . As for antibiotics belonging to the second group , we have shown that thymine starvation by tirmethoprim inactivates the major promoter P2 of mazEF [5] , and we have speculated that this inactivation may be caused indirectly by the induction of ppGpp synthesis , known to inhibit the mazEF P2 promoter [11] , and/or by some specific protein ( s ) that could sense the damage to the DNA . The nature of the mechanism that is involved in mazEF P2 promoter inactivation by trimethoprim , and whether it is involved by other DNA-damaging antibiotics , is still unknown . In addition , we recently reported that E . coli mazEF-mediated cell death is a population phenomenon requiring a communication signaling molecule that we call the Extracellular Death Factor ( EDF ) [20] . EDF is the linear penta-peptide NNWNN . Each of the five EDF amino acids is important for its mazEF-mediated killing activity , and the terminal asparagines are the most crucial . EDF production involves the Glucose-6-phosphate dehydrogenase , Zwf , and the protease ClpXP [20 , 21] . Here we asked: Does the action of mazEF-mediated cell death in E . coli involve the production of Reactive Oxygen Species ( ROS ) ? We treated E . coli cultures with one of two groups of antibiotics: ( i ) inhibitors of transcription and/or translation , traditionally considered as being bacteriostatic; and ( ii ) DNA-damaging agents , traditionally considered as being bactericidal . We found that antibiotics belonging to both groups caused: ( i ) mazEF-mediated cell death; and ( ii ) the production of ROS , which is generated through the action of MazF . However , although both groups of antibiotics caused ROS production , only antibiotics belonging to the first group caused mazEF-mediated cell death that is ROS-dependent . In contrast , antibiotics belonging to the second group caused a mazEF-mediated cell death pathway that is ROS-independent . Thus , our results suggest that there are at least two mazEF-mediated cell death pathways: ( i ) ROS-dependent; and ( ii ) ROS-independent . Furthermore , our results indicate that the mode of action of antibiotics was a function of the ability of E . coli cells to communicate with each other through the signaling molecule EDF .
In every aerobic organism , respiration results in the formation of ROS , including hydrogen peroxide , superoxide anion , and hydroxyl radicals . Theses ROS have been implicated in programmed cell death in eukaryotes [22 , 23] , including yeast [24 , 25] , and in the action of some antibiotics [3] . Cells have developed mechanisms for detoxifying ROS and for repairing oxidative damage [26–30] . Rifampicin , through its action on the β subunit of E . coli RNA polymerase [19] , prevents the transcription of mazEF , thereby leading to mazEF-mediated cell death [4] . In these previous experiments , we have shown that when mazEF is transiently induced , wild-type cells die whereas the ΔmazEF derivative does not die . Here , using similar inducing agents and experimental conditions , we found that mazEF-mediated cell death is prevented by adding catalase from outside the cells or by over-expression of catalase or superoxide dismutase ( Figure 1 ) . These enzymes are well known to detoxify ROS or to inhibit the formation of ROS [26–30] . Previously , we showed that mazEF–mediated cell death is triggered by DNA-damaging agents [5 , 6] including ( i ) nalidixic acid , an inhibitor of the topoisomerase gyrase [31] , and ( ii ) trimethoprim , an inducer of thymine starvation [18] . The latter was shown to inhibit transcription from the promoter P2 of mazEF [5] . Here we found that , in contrast to mazEF-mediated cell death induced by inhibitors of transcription ( Figure 1A ) or translation ( Figure 1B and 1C ) , when mazEF was induced by the DNA-damaging agents trimethoprim ( Figure 1D ) or nalidixic acid ( Figure 1E ) , mazEF-mediated cell death was not prevented by the presence of catalase or superoxide dismutase . In these cases , even increasing the amounts of the ROS-detoxifying enzymes by about five times did not prevent mazEF-mediated cell death ( unpublished data ) . 2–2′-dipyridyl is an iron chelator that prevents the Fenton-mediated hydroxyl radical formation [32] . A knockout of iscS substantially impairs iron sulfur cluster synthesis capability and therefore it prevents the Fenton reaction [33 , 34] . Here we show that similarly to catalase and superoxide dismutase , 2–2′-dipyridyl ( Figure 1 ) and a knockout of iscS ( Figure S1 ) prevented mazEF-mediated cell death triggered by inhibitors of transcription and/or translation , but not by antibiotics causing DNA damage . We further confirmed that rifampicin is bactericidal due to its action through the mazEF-mediated cell death network , which takes place via oxidative pathways . We show that in E . coli strain MC4100 , deleted for the important TCA-cycle component genes—either icdA ( coding for isocitrate dehydrogenase A ) or acnB ( coding for aconitase B ) cell death induced by rifampicin was prevented ( Figure S2 ) . Thus , our results show that ROS-detoxifying enzymes prevent mazEF-mediated cell death induced by the inhibition of transcription and/or translation , but not by DNA damage . Having found that ROS-detoxifying enzymes prevented mazEF-mediated cell death ( Figures 1A–1C ) , we expected that triggering the mazEF module would induce ROS formation that would be reflected in the cellular level of protein carbonylation . To detect oxidized proteins carrying carbonyl groups , we used the immunochemical assay from the Chemicon Oxyblot Kit . The triggering of mazEF was carried out in E . coli MC4100relA+ ( wild type ( WT ) ) with rifampicin ( Figure 2A and 2B ) , or nalidixic acid ( Figure 2C and 2D ) for 10 min , or trimethoprim ( Figure 2E and 2F ) for 1 h . After removing the antibiotics , we prepared crude protein extracts from the treated cultures . Activating mazEF in WT cells by each of these three antibiotics led to an increase in the level of protein carbonylation within 60 min ( Figure 2 ) . Under the same conditions , we observed no such change in the very low basal level of carbonylation in the ΔmazEF derivative strain ( Figure 2 and Figure S3 ) . Furthermore , when we quantified the relative levels of protein carbonylation , we found that the DNA-damaging agents nalidixic acid and trimethoprim each induced significantly higher levels of mazEF-dependent carbonylation than did the transcription inhibitor rifampicin . For example , after the addition of trimethoprim , the relative carbonyl level increased by 27 times ( Figure 2F ) , but after the addition rifampicin , it increased only by about 3 . 5 times ( Figure 2B ) . Note also that , while in the case of rifampicin , mazEF-dependent carbonylation remained constant after 1 h ( Figure 2A and 2B ) , in the case of DNA damage , a gradual increase in the level of carbonylation from 1 to 3 h was observed ( Figure 2C–2F ) . We found that adding or causing the overproduction of catalase completely prevented mazEF-dependent protein carbonylation ( Figure S4A–S4E ) . When we induced mazEF with rifampicin , adding as little as 20 μg/ml of catalase was sufficient to prevent protein carbonylation ( Figure S4A and S4B ) . However , when we induced mazEF with trimethoprim , five times more catalase ( 100 μg/ml ) was required to completely prevent protein carbonylation ( Figure S4D and S4E ) . This increased requirement seemed reasonable , since much more carbonylation resulted from activating mazEF by trimethoprim ( Figure S4E ) or nalidixic acid ( Figure 2C and 2D ) than by rifampicin ( Figure 2A and 2B ) . Based on these results , we asked: would the overproduction or the addition of catalase at concentrations that prevented mazEF-dependent carbonylation also prevent mazEF-mediated cell death ? This clearly occurs when mazEF is induced by rifampicin ( Figure S4C ) , however , not in the case of the DNA-damaging agent trimethoprim ( Figure S4F ) . Here , although high concentrations of catalase ( 100 μg/ml ) were sufficient to prevent mazEF-dependent carbonylation ( Figure S4D and S4E ) , they could not prevent cell death ( Figure S4F ) . Then we asked: would artificially overproducing MazF induce carbonylation ? If so , in cultures in which MazF were artificially overproduced , would preventing carbonylation by increasing the concentration of catalase prevent cell death by MazF ? We used a plasmid-borne mazF gene under the regulation of the E . coli araBAD promoter , permitting the expression of mazF by the addition of arabinose to the cells and its repression by the addition of glucose [35] . We found that , over a period of 15–120 min , overproducing MazF led to a gradual increase in the level of carbonylated proteins ( Figure 3A and 3B ) . Causing the overexpresseion of katE , the gene for catalase , or sodA , the gene for superoxide dismutase ( unpublished data ) , before MazF was overproduced completely prevented protein carbonylation ( Figure 3B ) . However , although overproducing of either KatE or SodA led to significant increases in cell survival ( about 60 times ) , MazF-mediated cell death was not completely prevented ( Figure 3C ) . Thus , elevated levels of ROS are produced by either inducing mazEF by stressful conditions , or by the overproduction of MazF . However , cell death is only completely prevented by catalase when mazEF is induced by stressful conditions that inhibit transcription and /or translation . Mistranslated proteins are substrates for carbonylation [36] . Because MazF is an endoribonuclease that cleaves mRNAs containing ACA sequences [12 , 13] , we hypothesized that the action of MazF could produce mistranslated proteins that could then be substrates for carbonylation . We wished to distinguish if the effect of MazF on protein carbonylation ( Figure 3 ) that we have reported here could be attributed to the formation of ROS or to the mis-translation of proteins . Under conditions of completely anaerobic growth , we expected that mistranslation would still occur but ROS would not be formed . To avoid the production of ROS , we studied mazEF-mediated cell death triggered by various antibiotics under completely anaerobic conditions , comparing those results to those of cultures grown aerobically . We activated mazEF by adding rifampicin to inhibit transcription ( Figure 4A ) , by adding spectinomycin to inhibit translation ( unpublished data ) , or by adding nalidixic acid to cause DNA damage ( Figure 4B ) . Under conditions of aerobic growth , we observed mazEF-mediated cell death when the mazEF module was activated by rifampicin at concentrations between 10 and 30 μg/ml ( Figure 4A ) . When we added the same ( 10–30 μg/ml , Figure 4A ) or even higher concentrations ( up to 60 μg/ml , unpublished data ) of rifampicin to WT cells grown anaerobically , mazEF-mediated cell death was completely prevented . We observed similar results under anaerobic conditions when mazEF was activated by spectinomycin ( unpublished data ) . However , when the mazEF module was activated by nalidicxic acid ( 2–3 mg/ml ) , we observed mazEF-mediated cell death even under anaerobic growth conditions ( Figure 4B ) . Thus , in respect to cell death , inhibitors of transcription and/or translation behave differently than DNA damaging agents . When MazF is overproduced under anaerobic conditions ( Figure S5 ) : ( i ) ROS is not produced , further indicating that ROS is not generated due to mistranslation; and ( ii ) MazF still mediates cell death , although in a much less extend than under aerobic conditions . This result further support our model on the existence of a MazF/ ROS-independent form of cell death ( see Discussion ) .
No direct method is available to quantify intracellular levels of ROS . A method recently used by Collins and colleagues [3 , 38] is based on derivatized fluoresceins , and was recently criticized [39] . Here , we used a different method in which we detected oxidized proteins carrying carbonyl groups . Protein carbonylation is caused by ROS [40–42] . In E . coli , we found that overproducing MazF ( Figure 3 ) , or transiently activating chromosomal mazEF by various antibiotics ( Figure 2 ) , led to significant increases in the levels of oxidized ( carbonylated ) proteins . Therefore , the endoribonuclease MazF that produces truncated proteins , including ROS-detoxifying enzymes , can lead to an increase in the level of ROS and thereby to an increase in the level of protein carbonylation . Here we have offered two lines of evidence supporting that ROS formation is involved in mazEF-mediated cell death , at least under conditions of aerobic growth and when activated by the inhibition of transcription or translation . ( i ) The ROS detoxifying enzymes catalase or superoxide dismutase , and the iron chelator 2–2′-dipyridyl completely prevented death when they were induced before the activation of mazEF by antibiotics that inhibit transcription and/or translation ( Figure 1 ) . ( ii ) We observed similar results when mazEF was activated under completely anaerobic growth conditions ( Figure 4 ) . Thus , we found that when cell death was activated by the inhibition of transcription and/or translation , mazEF-mediated cell death was ROS-dependent . In contrast , it was clear that , when induced by DNA damage , mazEF-mediated cell death was independent of ROS since cell death was not prevented by the presence of ROS detoxifying enzymes or the iron chelator 2–2′-dipyridyl ( Figure 1C and 1D ) or by anaerobic conditions ( Figure 4 ) . Based on our results , we suggest that antibiotic-induced mazEF-mediated cell death is a developmental process for which there are at least two pathways: ROS-dependent ( Figure 7A pathway ( a ) ) and ROS-independent ( Figure 7A pathway ( b ) ) . The ROS-dependent mazEF-mediated cell death takes place when mazEF is induced by inhibitors of transcription or translation . Under those conditions , cell death is prevented either by anaerobic conditions ( Figure 4A ) or by ROS detoxifying enzymes or the iron chelator ( Figure 1A–1C ) . MazF due to its endoribonucleolytic effect inhibits bulk protein synthesis , including ROS detoxifying enzymes , thereby elevated levels of ROS are produced leading to cell death . Therefore , when mazEF is activated by the inhibition of transcription or translation , ROS , and not MazF , is the mediator of cell death ( Figure 7A pathway ( a ) ) . The ROS-independent pathway takes place when mazEF is activated by agents causing DNA damage . Under those conditions , we found that although high levels of ROS were formed ( Figure 2D and 2E ) , neither catalase nor superoxide dismutase , nor anaerobic conditions prevented cell death ( Figure 4 ) . We suggest that when certain antibiotics cause damage to the DNA , the endoribonucleolytic action of MazF triggers a downstream cascade leading to cell death . That such a downstream cascade exists is supported by our results showing that MazE can reverse MazF toxicity only during a short window of time [17 , 35] . Moreover , our recent experiments have shown that although MazF leads to the inhibition of synthesis of most E . coli proteins , it still permits the synthesis of a small group of proteins that participate in cell death ( Amitai S , IK-G , Hananya-Meltabashi M , Sacher A , HE-K , unpublished data ) . This downstream cascade might be initiated by a special mechanism through which MazF would cleave mRNAs at specific sites [12 , 13] leading to the selective synthesis of proteins encoded by mRNAs that are resistant to the cleavage of MazF . Our results suggest that such proteins may function as executioners of cell death independent of ROS , possibly by acting more rapidly or efficiently than does ROS ( Amitai S , IK-G , Hananya-Meltabashi M , Sacher A , HE-K , unpublished data ) ( Figure 7A pathway ( b ) ) . In addition , our recent experiments also show that inducing mazEF by inhibitors of transcription and/or translation also leads to a selective synthesis of “death” proteins acting downstream to MazF , which are different from these induced by DNA damage ( Amitai S , IK-G , Hananya-Meltabashi M , Sacher A , HE-K , unpublished data ) . The “death” proteins induced by inhibitors of transcription and/or translation are probably less potent in the death process than these induced by DNA damage , and therefore in order to induce cell death , they may have to act in combination with ROS . The level of ROS produced by inhibitors of transcription and or translation is about 10 times lower than that obtained by DNA damaging agents ( Figure 2 ) . Therefore , though blocking translation by antibiotics would increase the level of ROS , the threshold of ROS obtained may be too low to enable cell death ( by itself ) and may still require the assistance of the death proteins that are selectively synthesized by MazF . It is well known that there are two classes of antibiotics: ( i ) bacteriostatic , including inhibitors of transcription and or translation , and ( ii ) bactericidal , including DNA damage agents [1] . Recently , Kohanski and colleagues have reported that bactericidal antibiotics lead to the production of ROS , and thereby cell death , but that bacteriostatic antibiotics do not [3] . However , the experiments on which they based their report were carried out in E . coli strain MG1655 , which is defective in the production of the communication factor EDF [21] that is required for the activation of E . coli mazEF [20] . Here we show that antibiotics can induce several alternative pathways leading to cell death ( Figure 7 ) , and the communication factor EDF determines if the mazEF-mediated pathways take over ( Figure 7A pathways ( a ) and ( b ) ) . We found that the addition of synthetic EDF to strain MG1655 switches the culture from a mazEF-independent growth arrest ( Figure 5B and 5D ) , or mazEF-independent cell death ( Figure 6B and 6D ) into a mazEF- dependent cell death ( Figures 5A , 5C , 6A , and 6C ) . Under the experimental conditions in which the EDF-mazEF–mediating pathways operated , the transcriptional and translational inhibitor rifampicin , traditionally known as being bacteriostatic , became bactericidal when it activated the EDF-mazEF system ( Figure 5 ) . The free MazF led to the production of ROS , which led to ROS-dependent death . Antibiotics causing DNA damage , like nalidixic acid , also activated the EDF-mazEF system so that the free MazF caused ROS production ( Figure 2 ) . But in this case , cell death was ROS-independent ( Figure 6 ) . We suggest that the ROS-independent pathway requires a selective production of proteins whose mRNA are resistant to the action of MazF [15] . We have also shown that during a long period of treatment with antibiotics causing DNA damage , an EDF-mazEF–independent cell death takes place which is ROS-dependent ( Figures 6 and 7B pathway ( d ) ) . We suggest that this was actually the mode of action of DNA-damaging antibiotics reported by Collins and colleagues [3 , 38] . Until recently , the modes of action of antibiotics were studied mainly as a function of their target of interactions [2] . Kohanski and colleagues [3] were the first to show that ROS formation is a common mechanism downstream of the action antibiotics , and that it is the ROS that lead to cell death . Our work has revealed yet another dimension to the mode of action of some antibiotics . We found that it is determined by the ability of E . coli cells to communicate through the signaling molecule EDF as it participates in mazEF induction . Thus , using synthetic EDF together with an antibiotic that is an inhibitor of transcription or translation could lead to an increase in the efficiency of killing the bacterial cells , even in the initial stage of infection when the density of the bacteria is low . This implies that EDF ( or its derivatives ) increases the repertoire of antibiotic drugs . Thus , at least in E . coli and probably in other bacteria as well , bacteriostatic antibiotics could be turned into bactericidal antibiotics by using EDF to turn on the built-in mazEF system .
The following strains were used: MC410relA+ and its ΔmazEF::kan derivative [43]; E . coli strain K38 and its ΔmazEF derivative [6]; MG1655 [44] and its ΔmazEF::kan derivative , which we constructed by P1 transduction from strain MC4100relA1ΔmazEF::kan . In addition , we used E . coli strain MC4100relA1 and its ΔicdA , ΔIscS and ΔacnB derivatives , which we constructed by P1 transduction from the same strains in genetic background of strains BW25113 [3] . We used plasmid pBAD-mazF [35] . We constructed plasmids pQEkatE and pQEsodA as follows: KatE gene was PCR amplified from strain MG1655 using primers 5′-GGGGTACCCCAGTTCAATGTCGCAACATAACGAAAAG-3′ for sense sequences and 5′-AACTGCAGCCAATGCATTGGAATCCCATCAGGCAGGAATTTTGTCAATC-3′ for antisense sequences . The PCR fragment was digested with KpnI and PstI restriction enzymes and ligated into the KpnI-PstI restriction sites on the multicloning site ( MCS ) of pQE-30 plasmid ( Qiagen ) bearing an ampicillin resistance gene , downstream the T5 promoter . sodA gene was PCR amplified from strain MG1655 using primers 5′-CGGGATCCCGGATGAATATGAGCTATACCCTGCCATCCCTG-3′for sense sequences and 5′-CCCAAGCTTGGGAAATGATTATTTTTTCGCCGCAA-3′ for antisense sequences . The PCR fragment was digested with BamHI and HindIII restriction enzymes and ligated into the BamHI-HindIII restriction sites on the multicloning site ( MCS ) of pQE30 plasmid ( Qiagen ) bearing an ampicillin resistance gene , downstream from the T5 promoter . The bacteria were grown in liquid M9 minimal medium with 1% glucose and a mixture of amino acids ( 10 μg/ml each ) [45] and then plated on rich LB agar plates as described previously [4] . The following materials were obtained from Sigma: IPTG ( isopropyl-β-D-thiogalactopyranoside ) , L-arabinose , nalidixic acid , mitomycin C , trimethoprim , rifampin , serine hydroxamate , chloramphenicol , spectinomycin , 2–2′ dipyridyl , trizma-base , sodium dodecyl sulphate ( SDS ) , DNAse , and RNAse . We also used the following materials: lysosyme and glycerol ( United States Biochemical Corporation ) , and ampicillin ( Biochemie GmbH ) . Carbonylated proteins were detected using the chemical and immunological reagents from the OxyBlot Oxidized Protein Detection Kit ( Chemicon ) , nitrocelloluse membranes ( Pall Corporation ) . The chemiluminescence assay was performed using luminol , and p-cumaric acid ( Sigma ) and hydrogen peroxidase solution ( Merck ) , AnaeroGen bags ( Gamidor Diagnostics ) . Chemically synthesized EDF and iEDF peptides ( having 98% purity ) were synthesized for us by GenScript Corporation . Aerobic conditions . Cells were grown in M9 medium with shaking ( 160 rpm ) at 37 °C for 12 h . Then , cells were diluted 1:100 in 10 ml of M9 medium and were grown with shaking ( 160 rpm ) at 37 °C to mid-logarithmic phase ( OD600 0 . 6 ) . The cells were grown in 50-ml tubes . Samples of 500 μl were withdrawn into Eppendorf tubes ( 1 . 5-ml volume ) and were further incubated without shaking at 37 °C for 10 min as described below for each case . Stressful conditions were induced as described in each figure legend . The cells were centrifuged and re-suspended in pre-warmed saline , diluted , plated on pre-warmed LB plates , and incubated at 37 °C for 12 h . Cell survival was calculated by comparing the number of the colony-forming units of cells treated by stressful conditions to those of the cells that were not exposed to the treatment . Anaerobic conditions . Cells were grown in 15-ml tubes containing 10 ml of M9 medium standing without shaking in an anaerobic jar containing AnaeroGen bags at 37 °C . The cells were incubated for 10–12 h until the cultures reached an optical density of OD600 of 0 . 6 . Samples of 1 ml were withdrawn into 1 . 5 ml Eppendorf tubes and were further incubated at 37 °C by standing in the anaerobic jar for 10 min . Then , stressful conditions were induced under anaerobic conditions as described in the legend to Figure 4 . The cells were centrifuged , washed , diluted , and plated as described above and incubated in the anaerobic jar at 37 °C for 20 h . Cells survival was calculated as described above . Addition of catalase or 2–2′ dipiridyl ( iron chelator ) . Cells were grown in aerobic conditions as described above . The cells were incubated without shaking at 37 °C with or without catalase ( 20 μg/ml ) or 2–2′ dipiridyl ( 1mM ) for 10 min . Then , stressful conditions were induced as described in the figure legends . The cells were centrifuged and washed in pre-warmed saline with or without catalase as described above . The cells were diluted in pre-warmed LB , plated on pre-warmed LB plates and incubated at 37 °C for 12 h . Cells survival was calculated as described above . Over-expression of katE and sodA . Cells were transformed with plasmid pQEkatE or pQEsodA . The strains were grown in M9 medium with ampicillin ( 100 μg/ml ) in aerobic conditions as described above , and incubated for 10 min without shaking . For the rest of the experiment , see the previous paragraph . Cells were grown in M9 minimal medium containing 0 . 5% glycerol as a carbon source with ampicillin ( 100 μg/ml ) and chloramphenicol ( 50 μg/ml ) in aerobic conditions or in anaerobic conditions as described above . Then 0 . 2% arabinose was added in order to induce mazF expression and cultures were incubated at 37 °C without shaking for 1 h . Then 0 . 2% glucose was added . The cultures were incubated in M9 for additional 8 h in aerobic conditions or anaerobic conditions as described above . Cells were grown in M9 medium and the cultures were submitted to specific stressful conditions for the time required to induce the expression of the chromosomally borne mazEF module as described above . After the induction of mazEF , the agents added to cause stressful conditions were removed by centrifugation . Then the cells were washed , and re-suspended in M9 medium preheated to 37 °C and further incubated . At various times , cells were lysed as follows: 1 ml of the culture was washed with 50 mM Tris buffer ( pH 7 . 5 ) and centrifuged for 10 min at 14 , 000 rpm . The pellet was re-suspended in 150 μl lysis buffer containing 0 . 5 mg/ml lysosyme , 20 μg/ml DNAse , 50 μg/ml RNAse , 1 mM EDTA , and 10 mM Tris ( pH 8 ) . 15μl of 10% SDS solution was added and the cells were incubated at 100 °C for 5 min . To examine the level of protein carbonylation in these lysates , we used the Chemicon OxyBlot kit to derivatize the carbonyl groups in the protein side chains to 2 , 4-dinitrophenylhydrazone ( DNP-hydrazone ) by reaction with 2 , 4-dinitrophenylhydrazine . These DNP-derivative crude protein extracts were dot blotted onto nitrocellulose membrane , which was incubated with primary antibody , specific to the DNP moiety of the proteins , and subsequently incubated with secondary ( goat anti-rabbit ) horseradish peroxidase-antibody conjugate directed against the primary antibody . Carbonylation was observed by ECL . The intensity of each band was quantified using the Image Master VPS-CL ( Amersham Pharmacia Biotec ) . In the case of a poor signal , the samples were concentrated by speed-vac and tested again ( see example in Figure S3 ) . In the case of signal saturation the sample was diluted and re-tested . The intensity of each represented band was normalized to equal levels of protein which were determined using Bradford reagent ( Bio-Rad ) . E . coli strains MC4100relA+ , MC4100relA+mazEF , and MG1655 were either transformed with plasmid pQEkatE or pQEsodA or not transformed . Then , cells were grown aerobically as described above . In case of strains MG1655/pQEkatE and MG1655/pQEsodA , cells were grown with IPTG ( 1 mM ) . Mid-logarithmic cells were applied with catalase or 2–2′-dipyridyl or EDF ( 0 . 05 μg/ml ) or iEDF ( 0 . 05 μg/ml ) and incubated for 10 min without shaking at 37 °C . Rifampicim ( 20 μg/ml ) or nalidixic acid ( 1 mg/ml ) were added and cells were further incubated . For CFU/ml measurements , 100 μl of each culture was collected at different time points . The cells were centrifuged and re-suspended in pre-warmed saline , diluted in pre-warmed LB , and plated on LB plates as described above . | The modes of action of antibiotics are mainly characterized by the effect they have on their targets . Recently , it was reported that the formation of Reactive Oxygen Species ( ROS ) is a common downstream mechanism of antibiotics that leads to cell death—called bactericidal—while the bacteriostatic antibiotics—causing growth arrest—do not cause ROS formation . We uncovered complexity in how an antibiotic kills by linking antibiotic action and ROS formation with the bacterial toxin–antitoxin system called mazEF , a system by which bacteria are known to commit suicide . We show that mazEF affects antibiotics cell death through ROS-dependent and ROS-independent mechanisms . Following antibiotic treatment , the communication signaling peptide , called Extracellular Death Factor ( EDF ) , mediates cell death through the activation of the mazEF system . Our study challenges the classical division between bacteriostatic and bactericidal antibiotics and provides evidence that antibiotics' mode of action is determined by the ability of the bacteria to communicate through the signaling peptide EDF . | [
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] | [
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] | 2008 | The Communication Factor EDF and the Toxin–Antitoxin Module mazEF Determine the Mode of Action of Antibiotics |
The hormone auxin plays a crucial role in plant morphogenesis . In the shoot apical meristem , the PIN-FORMED1 ( PIN1 ) efflux carrier concentrates auxin into local maxima in the epidermis , which position incipient leaf or floral primordia . From these maxima , PIN1 transports auxin into internal tissues along emergent paths that pattern leaf and stem vasculature . In Arabidopsis thaliana , these functions are attributed to a single PIN1 protein . Using phylogenetic and gene synteny analysis we identified an angiosperm PIN clade sister to PIN1 , here termed Sister-of-PIN1 ( SoPIN1 ) , which is present in all sampled angiosperms except for Brassicaceae , including Arabidopsis . Additionally , we identified a conserved duplication of PIN1 in the grasses: PIN1a and PIN1b . In Brachypodium distachyon , SoPIN1 is highly expressed in the epidermis and is consistently polarized toward regions of high expression of the DR5 auxin-signaling reporter , which suggests that SoPIN1 functions in the localization of new primordia . In contrast , PIN1a and PIN1b are highly expressed in internal tissues , suggesting a role in vascular patterning . PIN1b is expressed in broad regions spanning the space between new primordia and previously formed vasculature , suggesting a role in connecting new organs to auxin sinks in the older tissues . Within these regions , PIN1a forms narrow canals that likely pattern future veins . Using a computer model , we reproduced the observed spatio-temporal expression and localization patterns of these proteins by assuming that SoPIN1 is polarized up the auxin gradient , and PIN1a and PIN1b are polarized to different degrees with the auxin flux . Our results suggest that examination and modeling of PIN dynamics in plants outside of Brassicaceae will offer insights into auxin-driven patterning obscured by the loss of the SoPIN1 clade in Brassicaceae .
Active transport of the plant hormone auxin provides key positional and environmental cues during plant development [1] , [2] . Of the numerous auxin transport proteins [3] , the membrane-localized PIN-FORMED ( PIN ) proteins appear to define the direction and rate of auxin movement in many contexts [4] , [5] . The angiosperm PIN family can be divided into “short” and “long” classes based on the length of the hydrophilic region [6] , [7] . Short PIN proteins are likely involved in auxin homoeostasis within the cell [8] . Long PINs show a characteristic polar localization in the cell plasma membrane that provides directionality to auxin transport [9]–[13] . The hydrophilic loop domains of long PIN proteins contain phosphorylation sites that control PIN cellular localization [14]–[16] . Thus it is likely that variation in function between PIN family members is at least in part due to differing protein domains within this region . Localization and genetic studies have identified the long PIN group member PIN1 as the major auxin transporter involved in leaf initiation , leaf margin definition , and vascular patterning in shoots [9] , [12] , [13] , [17] , [18] . The convergence point hypothesis posits that the creation of auxin maxima by convergent localization of PIN1 defines the locations of initiating leaves , serrations , lobes and vasculature [12] , [13] , [17] , [19] , [20] . Most models of how convergent localization of PIN1 facilitates formation of auxin maxima propose positive feedback regulation where PIN1 is allocated to the cell membrane adjacent to the neighboring cell with the highest auxin concentration , thus moving auxin against the concentration gradient [17] , [21]–[24] . Such up-the-gradient models are able to accurately recapitulate the initial phase of organ initiation , the formation of PIN1 convergence points and auxin maxima in the correct phyllotactic patterns . These models can also generate files of cells with aligned PIN polarities , similar to those observed during vascular development [24] , but do not reproduce localization data showing PIN1 oriented away from auxin maxima , measured using the DR5::GFP reporter , during patterning of leaf veins [12] , [13] , [17] , [19] , [20] . Complementary models based on the canalization hypothesis [25] , [26] propose an alternate positive feedback where auxin transport is facilitated in the direction of highest auxin flux [27]–[31] . Simulations of this with-the-flux type of polarization can accurately recapitulate formation of canalized traces and are useful in explaining how PIN1 mediates vein patterning [29] . While with-the-flux polarization models can create convergent PIN localization when PIN is assumed to polarize weakly with-the-flux in the epidermis and strongly with-the-flux in subepidermal layers [30] , these models predict dynamics that do not match experimental observations . Specifically , they do not predict the observed transient localization of PIN towards the convergence point in internal layers [32] . In addition , they display a transient dip in auxin concentration at the convergence point , which is not observed experimentally [22] , [32] . A model that dynamically combines up-the-gradient and with-the-flux modes according to auxin concentration is able to recapitulate the observed DR5 dynamics as well as PIN1 polarization during convergence point formation and vein canalization [32] . However , this dual polarization model requires a hypothetical signal from pre-existing veins in order for new canalization events to consistently connect to the existing vasculature , a pattern that is highly regular in vascular development [32] . Reliably connecting auxin sources and sinks is a noted problem in models of vein formation [33] . Here we describe the phylogenetic analysis of angiosperm long PIN coding sequences . We provide evidence that Arabidopsis and other members of the Brassicaceae have lost a clade of long PIN genes that is conserved in all other angiosperms sampled , a clade we designate Sister-of-PIN1 ( SoPIN1 ) . We then localize SoPIN1 along with the PIN1 clade members PIN1a and PIN1b in Brachypodium distachyon ( Brachypodium ) and maize . These two clades exhibit dramatically different expression and polarization patterns , suggesting a role for SoPIN1 in maximum formation , and for PIN1a and PIN1b in vein patterning . Our computational model shows that these patterns can emerge assuming a combined action of SoPIN1 polarizing up the gradient of auxin concentration and PIN1 members polarizing with the auxin flux . The model also shows how newly formed auxin transport axes may reliably connect to older organs without a hypothetical signal from pre-existing veins .
Our phylogenetic analysis defines four major long PIN clades within the sampled angiosperms ( Figure 1A , B ) . Because of the large body of previous work on Arabidopsis thaliana ( Arabidopsis ) PINs , we named three clades , PIN1 , PIN2 , and PIN3/4/7 , based on the previously characterized Arabidopsis proteins that nested within these clades . All sampled angiosperms have at least one member in each of these three canonical long PIN clades . However , we also found strong support for a fourth clade placed sister to PIN1 , here designated “Sister-of-PIN1” ( SoPIN1 ) , which contains sequences from all sampled angiosperms except species within the Brassicaceae , including Brassica rapa , Arabidopsis lyrata , and Arabidopsis thaliana ( Figure 1 ) . In previous smaller phylogenetic analyses SoPIN1 proteins were placed in the same clade as PIN1 members [32] , [34] , [35] . In support of our phylogeny that suggests SoPIN1 is a unique clade , we identified several conserved regions within the variable cytosolic loop of both PIN1 and SoPIN1 proteins that are unique to each clade ( Figure S2 ) . These results suggest that SoPIN1 was lost in the lineage leading to the Brassicaceae sometime after diverging from papaya . In support of this loss , we identified syntenic chromosomal regions across a subset of angiosperms and found that SoPIN1 was absent in the syntenic chromosomes of all sequenced Brassicaceae species despite overall conservation of gene order with other angiosperms that still have SoPIN1 ( Figure S3 ) . Thus , Arabidopsis and other Brassicaceae members have lost SoPIN1 , one of the four canonical long PIN clades conserved in all other sampled angiosperms . Within the grasses our phylogenies support a lineage-specific duplication in the PIN1 clade , termed PIN1a and PIN1b based on previous maize annotations ( Figure 1A ) [36]–[38] . Overall , both PIN1a and PIN1b protein sequences resemble other eudicot PIN1 proteins , but in some regions of the variable cytosolic loop PIN1a and PIN1b have grass-specific sequences ( Figure S2 ) . While several species-specific duplication events have occurred in rice , Setaria , and maize , both Brachypodium and Sorghum grasses contain single members within the PIN1a , PIN1b and SoPIN1 clades . The relationship of Brachypodium SoPIN1 , PIN1a and PIN1b to Arabidopsis PIN1 is summarized in Figure 1C . To explore the significance of the loss of SoPIN1 in the Brassicaceae and the duplication of PIN1 in the grasses , we examined expression and localization of PIN1a , PIN1b and SoPIN1 during Brachypodium spikelet development . Each Brachypodium spikelet meristem is indeterminate , and initiates two sterile bracts followed by 7 to 14 floral meristems in an alternate distichous phyllotaxy before terminating ( Figure 2A , B ) [39] . The first product of each floral meristem is the lemma , a leaf-like organ that surrounds the remaining floral organs ( Figure 2B ) . We examined PIN expression and localization during lemma initiation in the first few florets . This stage had several advantages for live imaging: the spikelet meristem is relatively exposed early during spikelet development , and the indeterminate nature of the Brachypodium spikelet meristem allows visualization of a developmental series of one leaf initiation event ( lemma ) and one axillary branch initiation event ( floral meristem ) at each node in a distichous phyllotaxy ( Figure 2A ) . To visualize each PIN , we created stable transgenic plants containing full-length Citrine ( a variant of Yellow Fluorescent Protein , YFP ) fluorescent protein fusion constructs under their native promoters . SoPIN1 , PIN1a , and PIN1b have partially overlapping but unique expression domains in the spikelet meristem ( Figure 2C–F and Video S1 in supplementary material ) . SoPIN1 expression is highest in the epidermal cell layer ( Figure 2C ) , and substantial internal expression is restricted to the sites of initiating organs ( Figure 2D ) . In contrast , PIN1a and PIN1b are primarily expressed in the internal cell layers along the presumed paths of incipient lemma veins ( Figure 2E , F ) . PIN1b is also expressed in the center of both spikelet and floral meristems at this stage ( Figure 2F ) . In the epidermis , PIN1a and PIN1b are only expressed in a few cells at the distal tips of both mid and lateral vein traces ( Figure 2E , F and Video S1 ) . To visualize the entire potential path of auxin transport in the Brachypodium spikelet , we examined DR5 expression during lemma initiation . Although indirect , DR5 is a standard reporter used to estimate relative auxin concentrations during development [13] , [22] , [37] , [40] , [41] . In the Brachypodium spikelet DR5 expression is highest in the epidermis of both spikelet and floral meristems , at the tip of each lemma primordium , along the path of each incipient vein , and in a broad column down the center of the spikelet ( Figure 2G ) . Only combined SoPIN1 , PIN1a , and PIN1b expression matches the entire DR5 expression pattern ( Figure 2C–H , Video S1 ) . These data suggest that all three PINs act in concert to create the auxin transport path in the Brachypodium spikelet meristem , but each PIN may have a unique role . The tunica-corpus theory of meristem organization divides the meristem into the tunica , where cell divisions are primarily anticlinal , or perpendicular to the meristem surface , and the corpus , which undergoes cell divisions in several planes . We examined the cell division planes in the Brachypodium spikelet meristem and found that the outer two cell layers in the meristem apex , L1 and L2 , are dominated by anticlinal divisions ( Figure S4 ) . This suggests that , similar to wheat and Arabidopsis [42] , [43] , Brachypodium has a two-layered tunica . Cell divisions in the tunica layers that are parallel to the meristem surface ( periclinal ) mark the beginning of leaf morphogenesis , and allowed us to easily identify incipient lemma primordia [44] ( Figure S4 , arrows ) . Because organ initiation in Brachypodium is distichous and the spikelet meristem indeterminate , we were able to define two stages of SoPIN1 expression prior to visible lemma morphogenesis , numbered I1 and I2 in order of their appearance ( Figure 2A box , 3A ) . At I2 , SoPIN1 expression is highest in the epidermal cell layer and polarity begins to converge , with shootward polarity in abaxial cells and rootward polarity in adaxial cells ( Figure 3A , red arrows ) . DR5 expression at this stage is highest in the apical epidermis and is limited in the internal cell layers ( Figure 3B , S5 ) . At the I1 stage , SoPIN1 expression increases in both the epidermal and internal cell layers ( Figure 3G , S6 ) . Cellular localization of SoPIN1 at I1 shows strong convergent polarization in the epidermis as well as along the presumptive midvein axis , and is coincident with an increase in DR5 expression in the epidermis as well as internally ( Figure 3G , S6 ) . In later stage I1 primordia , SoPIN1 convergence surrounds the periclinal cell divisions in both tunica layers which are a hallmark of leaf initiation in grasses [44] ( Figure 3A , H , S7 ) . SoPIN1 is polarized toward the new cell plate in the daughter cells resulting from these periclinal divisions . After morphogenesis begins , primordia are designated P1 , P2 , P3 , etc . , from youngest to oldest ( Figure 2A ) . In P1 primordia , SoPIN1 convergence narrows around a DR5 maximum present in a few cells at the midvein tip ( Figure 3B , S5 ) . As the lemma expands , SoPIN1 expression briefly persists at low levels along the axis of the midvein oriented both toward the DR5 maximum at the tip and parallel to the midvein axis ( Figure 3M , S8 ) . At the same time , SoPIN1 expression increases at two secondary convergence points equidistant around the circumference of the meristem ( Figure 2C circles ) , marking the initiation of two symmetrical secondary lemma veins ( Video S1 ) . At later stages SoPIN1 expression decreases at each midvein convergence point , and by P3 expression along the midvein is almost gone ( Figure 2D , Video S1 ) . PIN1b expression is absent from I2 but was observed at several stages during the formation of the I1 midvein . In a small proportion of spikelets , PIN1b expression was observed to varying degrees in the center of the spikelet meristem apical dome connecting to the P1 midvein trace . This expression appears initially as a small ill-defined spike protruding from the top of the P1 midvein into the apical dome ( Figure 3J , L , double arrow ) . In what is likely a later stage , PIN1b is expressed well inside the meristem apex , consistently limited to the corpus cell layers ( Figure 3D , double arrow ) . PIN1b polarity at these early stages is often unclear , and expression is relatively low . In a larger proportion of the meristems examined , presumably at an even later developmental stage after convergence point formation in I1 by SoPIN1 , PIN1b expression narrows and is loosely polarized along the presumptive path of the I1 midvein , connecting the I1 epidermis to the midvein trace of P1 ( Figure 3E , double arrow ) . In summary , we infer a likely progression of PIN1b during the I1 stage , first extending from the P1 midvein ( Figure 3J ) , into the apical dome ( Figure 3D ) , then connecting to the I1 epidermis after maxima formation ( Figure 3E ) . PIN1a expression is completely absent at the I2 stage . PIN1a expression begins in only a few cells of I1 , usually highest in the L2 layer ( Figure 3C , S9 ) . In later stages , expression is present in both the epidermis and internally ( Figure 3I ) . PIN1a polarity in I1 is consistently oriented either away from the epidermis ( Figure 3I ) or rootward ( Figure 3C ) . The area of highest PIN1a expression in I1 correlates with the periclinal divisions that mark the beginning of lemma morphogenesis ( Figure 3I ) . By P1 , PIN1a and PIN1b expression increases , overlapping in a distinct midvein trace . As the P1 primordium extends , the polarity of PIN1a and PIN1b becomes more ordered , oriented along the midvein trace axis into the center of the spikelet and then rootward ( Figure 3C , E , S9 ) . While PIN1b expression remains relatively broad and extends completely across the spikelet , connecting to the midvein trace of P2 ( Figure 2F ) , expression of PIN1a is narrow and terminates in the center of the spikelet ( Figure 3C , N , S10 ) . PIN1b expression is highest in P1 and P2 and decreases by P3 , but is continuously connected between all primordia ( Figure 2F ) . In contrast , PIN1a remains strongly expressed in a narrow path along each presumptive lemma midvein . In each successive older primordium expression extends further rootward into the center of the spikelet toward the midvein of the next older primordium ( Figure 2E ) . We observed PIN1a connecting to older primordia only in much later stages , where the tissue thickness made clear imaging difficult ( not shown ) . In general , DR5 expression follows the combined expression pattern of PIN1a and PIN1b . Significant expression along the developing midvein occurs in later-stage I1 primordia , and is maintained in each successive older vein trace ( Figure 2G ) . DR5 expression in older primordia is highest in the epidermal maximum at the midvein tip and in the central column where PIN1b is expressed ( Figure 3L ) . Remarkably , in the P1 trace both PIN1a ( Figure 3C , F large arrow , Video S1 ) and PIN1b ( Figure 3J–L , large arrow ) usually span a region of lowered DR5 expression between the central column of high DR5 and the maximum in the epidermis . To support our reporter analysis in Brachypodium , we immuno-localized SoPIN1 in maize spikelet meristems ( Figure S11A ) and found a similar expression pattern where convergent localization of SoPIN1 marked the sites of incipient primordia ( Compare Figure S11A to Figure 3A ) . We also observed a localization pattern similar to the combined expression pattern of PIN1a and PIN1b using antibodies that likely detect both PIN1 proteins in maize ( Compare Figure S11B to Figure 3E ) , suggesting that the split between SoPIN1 and PIN1 is conserved between these two grass species . We observed that the spatio-temporal patterns of SoPIN1 , PIN1a , and PIN1b expression and polarization , as well as the pattern of auxin concentration as reported by DR5 , are consistent with aspects of previous models of PIN1 polarization in response to auxin . In general , the progressive convergence of SoPIN1 is coincident with increasing DR5 expression , and thus the formation of presumed auxin maxima in the tunica layers . The resulting SoPIN1 convergence points around DR5 maxima mark the sites of initiating organs and precede PIN1a and PIN1b expression . The observed polarization of SoPIN1 is consistent with the up-the-gradient model of PIN1 polarization leading to the formation of convergence points in the tunica [21] , [22] . In contrast , both PIN1a and PIN1b are expressed mainly in the corpus . As development proceeds , they become gradually polarized away from convergence points , and along presumptive vein traces . The expression and polarization of PIN1a is consistent with the with-the-flux model of vein canalization [25]–[28] . The relatively broad expression characteristic of PIN1b was considered from a theoretical perspective by Feugier et al . [31] and Stoma et al . [30] , who observed that a weak polarizing response to auxin flux can generate broad regions of PIN1 polarization towards the sink . We postulate that PIN1b in Brachypodium behaves in a similar fashion . To verify whether this conceptual model can plausibly capture the experimentally observed spatio-temporal pattern of expression and polarities of the three PINs in Brachypodium during the initiation of convergence points and vascular strands , we constructed a computational model , described below . A longitudinal section of a Brachypodium apex was modeled as a regular 2D array of hexagonal cells ( Figure 4A ) . Associated with each cell is the concentration of auxin and the distributions of SoPIN1 , PIN1a and PIN1b proteins ( Figure 4B ) . These distributions are represented by storing concentrations of the three PINs separately for each segment of the cell membrane ( colored edge of each hexagonal cell ) . PIN production is assumed to be auxin dependent , permitting expression levels to vary from cell to cell . See the Computational Model Description S1 ( CMD S1 ) Eqs . 7 , 8 and 10 in the supplementary materials . Each cell stores a concentration of unallocated PINs ( colored circles in Figure 4B ) , which are moved to the cell membrane by exocytosis ( green arrows ) and removed from the membrane by endocytosis ( red arrows ) . For PIN1a , exocytosis is increased by total auxin flux through the membrane , and endocytosis is increased by influx through the membrane ( CMD S1 Eq . 5 ) . For PIN1b , exocytosis is increased by auxin outflux through the membrane , and endocytosis is increased by influx ( CMD S1 Eq . 4 ) . For SoPIN1 , allocation to the membrane is increased by high auxin concentration in the neighboring cell ( CMD S1 Eq . 9 ) . Cellular auxin concentration ( CMD S1 Eq . 1 ) changes based on biosynthesis , turnover , and the flux due to auxin transport between neighboring cells ( CMD S1 Eqs . 2–3 ) . Additional mathematical details of PIN production and allocation can be found in Section 3 of the Computational Model Description S1 . To capture the tissue specific differences in the Brachypodium spikelet meristem observed in our analysis of cell division patterns during lemma initiation ( Figure S4 ) , we divided the cellular array into the tunica layers , L1 and L2 , and the sub-epidermal corpus ( Figure 4A , CMD S1 Section 4 . 1 ) . Following previous work [12] , [32] , we assumed that up-the-gradient patterning is particularly strong in the tunica layers . This was implemented by increasing auxin-dependent production of SoPIN1 in the L1 and decreasing auxin-dependent production of both PIN1a and PIN1b in the L1 and L2 layers , consistent with the observed expression patterns ( Figures 2 and 3 ) . In addition , we assumed that auxin biosynthesis is increased ( two fold ) in the L1 [22] , [32] , which is consistent with observed DR5 expression in the spikelet meristem , and that communication between the L1 and inner tissues is reduced ( See CMD S1 Section 4 . 1 ) [32] , [45] , [46] . The model is limited to a segment of meristem immediately below the central zone . Growth is introduced by adding rows of cells to the top end of this segment at regular time intervals . The impact of tissues outside the scope of the model is approximated by boundary conditions ( CMD S1 Section 4 . 2 ) . The effect of older primordia at the base is simulated by withdrawing auxin from the lowest bottom left cell and four bottom right cells in the L1 ( Figure 4A ) . This asymmetry reflects the alternating distichous phyllotaxy of Brachypodium . A single sink cell , placed in the center of the bottom row , provides a target for auxin flow along the axis of the meristem ( Figure 4A ) . Primordia and the associated vascular strands are produced periodically in the spikelet meristem . Figure 5 shows the simulated dynamics of auxin and PIN distribution during a single period ( plastochron ) . Column 1 shows the state of the simulation at the beginning of this period . A single maximum of auxin concentration , resulting from the convergent polarization of SoPIN1 , is present in the L1 layer on the left side of the tissue section shown ( Figure 5 , Panel 1A ) . SoPIN1 expression is evident in the L1 and L2 layers , where the production of SoPIN1 is greater than in the sub-epidermal layers ( Figure 3A ) . The convergence point is connected to the sink at the base of the tissue with a single strand of high PIN1b ( Panel 1B ) and PIN1a ( Panel 1C ) expression . The high auxin flux in this strand makes it act as an auxin sink for cells closer to the apex . Consequently , PIN1b proteins and auxin transport are polarized towards the strand ( Panel 1B , blue arrows ) . This polarization is the strongest near the vein and gradually decreases closer to the apex . Except for near the convergence point , PIN1b and PIN1a are predominantly expressed in sub-epidermal layers , where their production was observed to be higher than in L1 and L2 ( Figure 3C , D ) . As the simulation progresses ( Figure 5 , Column 2 ) , additional rows of cells are added to the top of the tissue ( not shown ) and increase auxin supply . Transient variation in the auxin concentration triggers an emergent reinforcement of concentration differences in the L1/L2 layers by SoPIN1 , leading to the formation of a second SoPIN1 convergence point and auxin maximum opposite to and above the first one ( Panel 2A ) . The auxin that forms the maximum at the new convergence point is supplied by neighboring cells , in which auxin concentration thus decreases , and by increased local auxin production in the new convergence point ( see Section 4 . 3 in the Computational Model Description S1 in the supplementary materials ) . Auxin from the new maximum enters the sub-epidermal layers and is transported towards the vascular strand below by PIN1b . This transport is coupled with an increase in PIN1b expression in L1/L2 layers , a substantial increase in PIN1b polarization near the convergence point ( Panel 2B , region outlined in white ) and a broad strengthening of polarization in the region between the new convergence point and the previous vascular strand ( Panel 2B ) . This pattern of PIN1b expression and localization is consistent with the observed progression during the formation of the I1 midvein in the Brachypodium spikelet ( Figure 3J , D , E ) The next phase of the simulation ( Column 3 ) is marked by the onset of PIN1a expression at the convergence point , and its gradual extension towards the vascular strand below ( Panel 3C ) . This extension is guided by the broad expression of PIN1b ( Panel 3B ) . This PIN1a expression generates a high-flux strand canalizing auxin transport , and refines the broad field of PIN1b expression into a narrow strand coinciding with that of PIN1a ( Panel 3B ) . Leaving the incipient vein tip , auxin is carried by PIN1b towards the previous vein , forming an approximately triangular region of increased auxin concentration and PIN1b polarization ( Panel 3B , region outlined in white ) . This region is continually refined , both guiding the progressive extension of the incipient PIN1a vein towards the previous strand and being guided by it . Concurrently , PIN1b proteins in the nearby cells become polarized towards this vein ( Panel 3B ) . Combined , these dynamics are consistent with PIN1a and PIN1b expression and localization observed in the formation of the Brachypodium P1 primordia ( Figure 3C , E ) . The transport of auxin by SoPIN1 towards the convergence point in the L1 and L2 layers , combined with efficient transport by PIN1a and PIN1b along the emerging vein , creates a gap in auxin concentration near the convergence point ( arrow in Panel 3C ) . This gap is consistent with experimental data ( large arrow in Figure 3F , K ) . As the simulation proceeds , the strand of PIN1a expression extends until it joins the previously patterned vein ( Column 4 ) . At this point , PIN1b expression and polarization largely coincide with that of PIN1a ( compare Panel 4B and Panel 4C ) . The last two cells of the strand ( Panel 4C , white arrow ) exhibit a transient increase in auxin concentration as they progress from a low-flux to high-flux state ( compare Panel 4C to Panel 5C ) . The focused flux of auxin canalized by PIN1a completes the patterning of the incipient vein ( Column 5 ) . The increased auxin flux generated by strong PIN1a expression causes PIN1b in nearby cells to polarize towards the newly formed strand , thus indicating its transformation into an auxin sink ( Panel 5B ) . With the addition of a new primordium and the corresponding vascular strand , the pattern of SoPIN1 , PIN1a and PIN1b localization and expression , as well as auxin concentration , in the cells near the apex mirror those that existed prior to initiation of the new primordium ( compare Panel 1D and Panel 5D ) . A new convergence point and midvein trace have been added to the stem . Iteration of this process leads to the formation of convergence points and vascular strands arranged in an alternate branching pattern ( Figure 6 and Videos S2 and S3 ) , as observed experimentally ( Figure 2 ) . To examine the dependence of simulation results on the choice of cellular template we constructed a second model operating on a 2D array of square cells ( Figure S12 and Video S4 ) . With small changes to model parameters ( see Tables S2 , S3 in the supplementary materials ) , we were able to obtain qualitatively similar results to those described above for a 2D array of hexagonal cells . This indicates that the proposed model is not dependent on the choice of a hexagonal cellular template , and can generate equivalent patterns when other cellular topologies are employed .
The patterning mechanisms resulting in leaf initiation and vein patterning integrate three distinct processes: determination of organ placement and vein origins , guidance of newly forming veins towards appropriate end points , and the refinement of emerging veins into narrow canals patterning the procambial tissue . While in previous work these processes have been attributed to a single PIN protein in the shoot , PIN1 , our results suggest that outside of the Brassicaceae they are associated with different PIN proteins . In the grass species Brachypodium and maize the division of these processes between different PIN proteins appears to be particularly crisp . The observed expression and convergent localization of SoPIN1 in the tunica suggests a role for SoPIN1 in the formation of auxin maxima . The expression and localization of PIN1b indicates a broad domain of auxin transport preceding vein formation that connects to previous organs ( Figure 3D ) . Following the formation of a convergence point by SoPIN1 , PIN1a appears to refine the broad PIN1b-promoted auxin transport into a narrow auxin stream , leading to the formation of a procambial strand . Based on these expression and localization patterns we propose that each PIN has a distinct role in pattern formation in the shoot: SoPIN1 forms convergence points which determine the sites of organ initiation and position of veins , PIN1b directs the developing veins to target locations , and PIN1a transforms broad regions of polar auxin flux into narrow canals ( Figure 7 ) . We used a computer model to show that the experimentally observed spatio-temporal pattern of expression and polarization of SoPIN1 , PIN1a and PIN1b can result from distinct polarization regimes of the three proteins . Specifically , we assumed that SoPIN1 is polarized up the gradient of auxin concentration , PIN1b polarization is a relatively weak linear function of the flux , and PIN1a polarization is a stronger , non-linear ( power ) function of the flux . The concurrent operation of up-the-gradient and with-the-flux polarization modes is consistent with the previously proposed dual polarization model of primordia initiation and vein formation [32] . Our results provide another example where these two modes suffice to explain the observed localizations of PIN and concentrations of auxin reported by DR5 . At the same time , the nature of molecular mechanisms polarizing PIN remains an open problem , and we cannot preclude the possibility that we observe two facets of a single lower-level molecular mechanism . Several arguments support a functional division between the SoPIN1 and PIN1 clades . First , these two clades are conserved across most angiosperms , which suggests that both are functionally important . Second , there are significant differences in protein sequence between SoPIN1 and PIN1 proteins , which suggests that they may be functionally distinct . Most of these sequence differences are in the intervening hydrophilic loop domain ( Figure S2 ) , a region that contains phosphorylation sites involved in PIN1 localization in Arabidopsis [14]–[16] . Third , our evidence suggests that SoPIN1 and PIN1a can have opposing polarities within a single cell , consistent with the presence of different mechanisms controlling their cellular localization . The periclinal cell divisions in the I1 primordia mark a unique place and developmental time point . Our comparison of PIN1a and SoPIN1 polarization at the periclinal cell division in the L2 layer of I1 primordia shows that SoPIN1 is polarized toward the new periclinal cell plate in the L2 division ( in Figure 3H ) , whereas PIN1a is polarized away from the new cell plate , towards the center of the inflorescence meristem ( Figure 3I ) . This opposing polarization pattern persists along the axis of the midvein of P1 lemmas ( Compare Figure 3M to 3N ) . While further work is needed to clarify the functional separation between the PIN1 and SoPIN1 clades , our results support a model in which epidermal convergence point formation and internal vein patterning involve different molecular mechanisms . Our model for the overlapping roles of PIN1a and PIN1b during vein patterning further suggests that the guiding of new auxin traces to existing sinks ( sink finding ) can be mechanistically distinguished from patterning the final vascular trace ( canalization ) . In our model PIN1b provides a reliable guiding mechanism allowing new veins to consistently connect with older traces and form the regular pattern of connections observed in Brachypodium . In the context of the river network metaphor proposed for the canalization hypothesis [25] , [26] , the broad polarization of PIN1b is analogous to a broad slope that orients the overall direction of water flow towards the river mouth . In this setting , the initial flow of water is thus guided by the direction of the slope , which subsequently orients the overall direction of canals that emerge via erosion . Likewise , as PIN1b polarizes towards auxin sinks , the “slope” it provides directs emerging strands towards these sinks . The necessity of supplementing the canalization hypothesis with a guiding mechanism that directs veins towards their target locations ( sinks ) was observed by Bayer et al . [32] . However , the postulated distinction between PIN1a and PIN1b provides a mechanism for guiding developing veins towards their target locations that is different from the hypothetical guidance by a diffusing substance postulated by Bayer at al . The question arises to what degree the postulated roles of SoPIN1 in auxin maximum formation , PIN1b in sink-finding , and PIN1a in canalization ( Figure 7 ) can be generalized to diverse angiosperms . In Arabidopsis thaliana and Cardamine hirsuta , a single PIN1 appears to both form convergence points and effect canalization [12] , [47] . According to the dual polarization model , this behavior results from PIN1 combining up-the-gradient and with-the-flux polarization modes in context-dependent proportions [32] ( alternative explanations have also been proposed [24] , [30] , as reviewed in [48] ) . Such integration of polarization modes appears to be limited to Brassicaceae , since all angiosperm species sampled outside of the Brassicaceae have distinct SoPIN1 and PIN1 proteins ( Figure 1 ) . It should be noted that the dual polarization model was largely based on tomato , which has both PIN1 and SoPIN1 members , rather than Arabidopsis . In the experiments of Bayer et al . [32] , the localization of the presumed tomato AtPIN1 ortholog was inferred using the AtPIN1::AtPIN1:GFP reporter . Our phylogenetic analysis suggests that the presumed tomato PIN1 ortholog in Bayer et al . is in fact a member of the SoPIN1 clade ( Accession HQ127075 in Figure 1A ) . Remarkably , the expression of the AtPIN1::GFP reporter in tomato appears almost identical to immuno-localization using peptide antibodies targeting this member of the SoPIN1 clade [32] . Both the tomato SoPIN1 clade member and the AtPIN1::GFP reporter are expressed in convergence points as well as along the emerging veins of tomato leaf primordia . This indicates that the apparently sharp distinction between the convergence point formation by SoPIN1 and vein patterning by PIN1 in grasses may not be as precise in other species with both clades . These discrepancies could suggest that all PINs combine with-the-flux and up-the-gradient modes in some proportions , or possibly , that a so far unidentified mechanism controls PIN polarity in response to auxin . Finally , while PIN1b in Brachypodium provides a striking demonstration of sink-finding behavior , the PIN1a/b duplication was only identified in grass species . Thus the proposed division of roles between PIN1b involved in finding vein targets and PIN1a refining veins does not hold for tomato . A possible solution to the problem of finding the vein target may be a context-dependent transition between weaker ( linear ) and stronger ( non-linear ) flux-driven polarization of the so far uncharacterized tomato PIN1 member ( HQ127074 in Figure 1A ) , thus combining the functions of PIN1a and PIN1b . Alternatively , PIN1 in tomato may function similar to PIN1b in Brachypodium , while SoPIN1 in tomato would combine functions of Brachypodium SoPIN1 and PIN1a . One can envision different partitioning of functions between SoPIN1 , PIN1 and their variants , with unique solutions in diverse species . Such cross-species comparisons highlight the risks of using well established model species as representatives for large groups . It is likely that considerable variation exists in the systems of auxin self-organization , and further comparative work is essential to outline general mechanisms . The question of whether different solutions drive morphological diversity remains to be tested . While molecular mechanisms that polarize PIN remain an area of intensive research [49]–[52] , our results point to the minimal set of functions that are needed to pattern organ initiation and vasculature in the shoot: convergence point formation , sink finding , and canalization . Our computational model of the behavior of SoPIN1 , PIN1a and PIN1b shows that splitting up-the-gradient and with-the-flux modes between separate proteins can provide a robust patterning mechanism consistent with the available localization data . Further examination of the split between SoPIN1 and PIN1 is essential to the understanding of PIN function and the patterning of organs in flowering plants outside the Brassicaceae .
Coding sequences from Phytozome ( http://www . phytozome . org/ ) , NCBI ( http://www . ncbi . nlm . nih . gov/ ) , and CoGe ( http://genomevolution . org/CoGe/ ) were analyzed with Geneious ( http://www . geneious . com/ ) . We found that the third codon position was GC base rich in monocots ( 60% monocots , 45% eudicots ) and biased phylogenetic analyses , thus after MUSCLE alignment , this position was removed . Analysis of GC normalized sequences ( 33% monocots , 30 . 2% eudicots ) was performed with MrBayes 2 . 0 . 3 ( http://mrbayes . sourceforge . net/ ) on GreenButton ( geneious . greenbutton . net ) using the Jukes-Cantor model of nucleotide evolution ( selected using the AIC: MEGA 5 . 0 , http://www . megasoftware . net/ ) . Four chains , sampled every 200 generations , were run until convergence ( 1 , 013 , 000 generations , standard deviation of split frequencies below 0 . 01 ) . After examination of the likelihood scores , 25% of trees were discarded as burnin . P . patens - Pp1s10_17V6 . 1 was used as the outgroup . The final tree is available for download on Treebase: http://purl . org/phylo/treebase/phylows/study/TB2:S15020 . BLAST searches using both DNA and protein sequences of SoPIN1 members identified only PIN1 clade members in the Brassicaceae . To identify the SoPIN1 syntenic regions across the angiosperms the sequence for a gene neighboring SoPIN1 in Papaya was used in CoGe . Internal fluorescent protein fusions were generated in a similar position to [37] , [40] . Brachypodium PIN promoters and the 5′ part of each coding region were cloned into pDONR-P4-P1R . The YFP variant Citrine was cloned into pDONR221 with 5× Ala linkers . The 3′ part of each coding region and downstream sequences were cloned into pDONR-P2R-P3 . Genomic regions 3045 , 5164 , and 3147 nt upstream of the ATG , and 1652 , 1512 , 1403 nt downstream of the stop codon were cloned for PIN1a , PIN1b , and SoPIN1 respectively ( see supplementary Table S1 for primers ) . Fragments were recombined using Multisite Gateway ( Invitrogen , Grand Island , NY ) into pH7m34GW ( http://gateway . psb . ugent . be/search ) . The DR5 synthetic auxin signaling promoter driving expression of an endoplasmic reticulum-localized monomeric RFP ( DR5 in text ) was described previously [37] . Brachypodium transformation was performed as described [53] . At least three events were characterized for each PIN-Citrine reporter . Two DR5-RFP events were recovered and showed identical expression , similar to maize [37] . Images were captured on a Leica TCS-SP5 laser-scanning confocal equipped with a water-dipping 20× objective ( NA 0 . 7 ) ( http://www . leica-microsystems . com/ ) and processed with ImageJ ( http://rsbweb . nih . gov/ij/ ) . Citrine was excited at 514 nm and mRFP at 561 nm . The pinhole was set to one Airy unit . For each z-stack , transmitted light was detected and flattened using an extended-depth-of-field plugin ( http://bigwww . epfl . ch/demo/edf/ ) . Fluorescent channels were processed with a median filter to reduce noise and were recombined with the processed transmitted light image either as single z-planes or maximum projections . In most images maximum projections were limited to internal sections in order to reveal sub-epidermal localization and midvein development . Brightness and contrast were adjusted after fluorescence channels were pseudo-colored with look-up tables . PIN cellular polarity was observed through multiple confocal sections , and similar to previous work was defined by a characteristic arching shape [12] , [13] , [32] , [40] . Multiple samples were analyzed at each developmental stage , and ratios printed below each figure label reflect the number of times phenomena discussed in the text were observed out of the total images captured . FM4-64 staining was performed as described in [54] . Purified protein of residues 188–407 of maize SoPIN1 and 188–414 of maize PIN1c were created as described in [55] and injected into Guinea pigs ( Cocalico , Reamstown , PA ) . Western blots with primary serum showed preference of anti-SoPIN1 for SoPIN1 recombinant protein and anti-PIN1c for PIN1a/b/c recombinant proteins ( not shown ) . Serum was used directly for immuno-localization . Dilutions: 1∶200 anti-SoPIN1 , 1∶200 anti-PIN1c . Tissue was fixed in FAA and imbedded in Steedman's wax ( Electron Microscopy Sciences , http://www . emsdiasum . com/microscopy/default . aspx ) . 9 µm sections were mounted , dewaxed in ethanol , dried , rehydrated into PBS , blocked with 5% Donkey serum in PBS , then probed . Secondary antibody dilution: 1∶200 anti-Guinea Pig cy3 ( Jackson ImmunoResearch , http://www . jacksonimmuno . com/ ) . Washes were performed with 1% Fish Gelatin in PBS ( Sigma , http://www . sigmaaldrich . com/ ) . The computational model was implemented in C++ using the VVE system ( an extension of the Vertex-Vertex ( VV ) system [56] used in [22] , [32] ) , which provides a data-structure and libraries for representing cellular tissues . Details of the model are presented in the Computational Model Description S1 in the supplementary materials . | Computational models and functional studies using the plant Arabidopsis thaliana have led to competing models for how the PIN-FORMED1 ( PIN1 ) auxin transporter polarizes in the cell to create both the maxima required for organ initiation and the narrow streams required for vein patterning . Here we identify a previously uncharacterized PIN protein most closely related to PIN1 that is present in all flowering plants but lost in the Brassicaceae , including Arabidopsis . We localized this protein , here termed Sister-of-PIN1 ( SoPIN1 ) , along with duplicate members of PIN1 ( PIN1a and PIN1b ) , in two grass species . Our localization data provide striking evidence for a spatial and temporal split between SoPIN1 and the two PIN1s during organ initiation in grasses . Based on our localization results we created a computational model showing that the observed patterns of expression and polarization of the grass PINs can emerge assuming SoPIN1 polarizes up the gradient of auxin concentration while the PIN1 members polarize with the auxin flux . This model reveals a minimal framework of necessary functions involved in auxin-transport-mediated patterning in the shoot and demonstrates that work outside of Arabidopsis is essential to understanding how auxin-transport mediates patterning in most flowering plants . | [
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... | 2014 | A Division in PIN-Mediated Auxin Patterning during Organ Initiation in Grasses |
Trypanosoma brucei is transmitted between mammalian hosts by the tsetse fly . In the mammal , they are exclusively extracellular , continuously replicating within the bloodstream . During this stage , the mitochondrion lacks a functional electron transport chain ( ETC ) . Successful transition to the fly , requires activation of the ETC and ATP synthesis via oxidative phosphorylation . This life cycle leads to a major problem: in the bloodstream , the mitochondrial genes are not under selection and are subject to genetic drift that endangers their integrity . Exacerbating this , T . brucei undergoes repeated population bottlenecks as they evade the host immune system that would create additional forces of genetic drift . These parasites possess several unique genetic features , including RNA editing of mitochondrial transcripts . RNA editing creates open reading frames by the guided insertion and deletion of U-residues within the mRNA . A major question in the field has been why this metabolically expensive system of RNA editing would evolve and persist . Here , we show that many of the edited mRNAs can alter the choice of start codon and the open reading frame by alternative editing of the 5’ end . Analyses of mutational bias indicate that six of the mitochondrial genes may be dual-coding and that RNA editing allows access to both reading frames . We hypothesize that dual-coding genes can protect genetic information by essentially hiding a non-selected gene within one that remains under selection . Thus , the complex RNA editing system found in the mitochondria of trypanosomes provides a unique molecular strategy to combat genetic drift in non-selective conditions .
Trypanosomes are one of the most successful parasites in existence , inhabiting an incredibly wide range of hosts [1 , 2] . The dixenous members cycle between two distinct hosts and can encounter different environments with distinct metabolic constraints . These parasites are unique in that they all possess glycosomes ( where glycolysis occurs ) as well as mitochondria [3] . The salivarian trypanosomes ( e . g . T . brucei , T . vivax ) are especially interesting , because they are exclusively extracellular in their mammalian hosts , continuously replicating within the bloodstream over periods of months . During this stage of the life cycle , the mitochondrion is down-regulated , lacking both Krebs cycle enzymes and a functional electron transport chain ( ETC ) [4] . Successful transition to the fly vector , requires activation of the ETC and ATP synthesis via oxidative phosphorylation . This unique lifecycle leads to a major problem: when the mitochondrial genes are unused , they are not under selection , hence the integrity of these genes are threatened by genetic drift [5 , 6] . Exacerbating this , salivarian trypanosomes undergo a severe bottleneck as they transition through the tsetse fly and into the mammalian host , and then within the bloodstream , they undergo multiple bottlenecks at each antigenic switch , as they evade the host immune system [7] . Such bottlenecks create additional forces of genetic drift , where genes can be lost even if their deleterious fitness effect is considerable . These parasites possess several unique genetic features , including RNA editing of the mitochondrial transcripts . RNA editing creates open reading frames in “cryptogenes” by insertion and deletion of uridylate residues at specific sites within the mRNA . The U-insertions/deletions are directed by small guide RNAs ( gRNA ) and can repair frameshifts , generate start and stop codons and more than double the size of the transcript ( for review see [8] ) . While the mRNA cryptogenes are encoded on maxicircles ( 25–50 copies per DNA network ) , the guide RNAs are encoded on thousands of 1 kb minicircles , encoding 3–5 gRNA genes each [9] . This effectively means that the genetic information for the edited mitochondrial mRNAs is dispersed between the mRNA cryptogenes on the maxicircles and the thousands of gRNA coding minicircles . The extensive editing of a single transcript can require more than 40 gRNAs and hundreds of editing events [10] . While the initial gRNA can interact with the 3’ end of the pre-edited transcript , all subsequent gRNAs anchor to edited sequence created by the preceding gRNA . Hence , editing proceeds from the 3’ end to the 5’ end of the mRNA transcript with the terminal gRNA ( last one in the cascade ) often creating the start codon needed for translation . This sequential dependence means that with even high accuracy rates for each gRNA , the overall fidelity of the process is astonishingly low . A major question in the field has been why this fragile and metabolically expensive system of RNA editing would evolve and persist . Another level of complexity in the kinetoplastids RNA editing process was the detection of an alternative editing event that leads to the production of a functionally discrete protein isoform . Alternative editing of Cytochrome Oxidase III ( COIII ) is reported to generate a novel DNA-binding protein , AEP-1 , that functions in mitochondrial DNA maintenance [11 , 12] . In this transcript , one alternative gRNA generates sequence changes at two sites that links an open reading frame ( ORF ) found in the pre-edited 5’ end , to the 3’ transmembrane domains found in the COIII edited ORF . This was the first indication , that one cryptogene could contain information for more than one protein . Here , we show that as many as six additional cryptogenes also encode for more than one protein . Analyses of the terminal gRNA populations indicate that gRNA sequence variants exist that can alter the choice of the start codon and the open reading frame by alternative editing of the 5’ end of the mRNA . Mutational bias analyses indicate that six of the mitochondrial genes may be dual-coding , with RNA editing allowing access to both reading frames . Dual-coding genes are defined as a stretch of DNA containing overlapping open reading frames ( ORFs ) [13 , 14] . Of particular interest are dual-coding genes that contain two ORFs read in the same direction: a canonical protein ( normally annotated as protein coding in the literature ) and an alternative ORF . Maintaining dual-coding genes is costly , as it constrains the flexibility of the amino acid composition of both proteins . Hence , it is thought that dual-coding genes can survive long evolutionary spans only if the overlap is advantageous to the organism [15] . We hypothesize that trypanosomes use dual-coding genes to protect genetic information by essentially hiding a non-selected ( ETC ) gene within one that remains under selection . Thus , the ability to access overlapping reading frames may be added to a growing list of gene protective strategies made possible by the complex RNA editing process [5 , 6 , 16] .
T . brucei procyclic clones from IsTAR ( EATRO 164 ) , TREU 667 and TREU 927 cell lines were grown in SDM79 at 27°C and harvested at a cell density of 1-3x107 . The TREU 667 cell line was originally isolated from a bovine host in 1966 in Uganda [17] . The TREU 927 cell line was originally isolated from Glossina pallidipes in 1970 in Kenya [18] . The EATRO 164 strain was isolated in 1960 from Alcephalus lictensteini and maintained in the lab of Dr . K . Vickerman until being obtained by Dr . Ken Stuart in 1966 [19] . Dr . Stuart derived the procyclic form from the bloodstream form culture in 1979 . Mitochondrial mRNAs and gRNAs were isolated as previously described [10] . All RNAs were treated with Promega DNAse RQI . In order to isolate gRNAs from TREU 667 and TREU 927 cells , RNAs were size fractionated on a polyacrylamide gel as previously described [10] . Guide RNAs were then extracted and prepped for sequencing using the Illumina Small RNA protocol [10] . Libraries from TREU 667 and TREU 927 were deep sequenced on the Illumina GAIIx; reads were processed and trimmed as previously described [10] . In order to isolate target mRNAs , isolated TREU 667 mitochondrial RNAs were reverse transcribed using the Applied Biosystems High Capacity cDNA Reverse Transcription Kit . CR3 cDNAs were amplified via PCR using the following primers ( underlined portions are gene specific and non-underlined portions are tag regions used in deep sequencing reaction ) : CR3DS5’NEV: ACACTGACGACATGGTTCTACAAGAAATATAAATATGTGTATG CR3DS3’170: TACGGTAGCAGAGACTTGGTCTCAATAAACCCATATTAAATAAAAAACAAAAATCC After amplification , the products were purified using the QIAquick PCR Purification Kit , and paired end Illumina deep sequencing was performed on the Illumina Miseq ( 2x 250 bp paired end run ) . Low quality results were removed using FaQCs , adapters were removed using Trimmomatic and PEAR was used to merge paired end reads . Finally , Fastx was used to compile identical reads while maintaining the number of redundant reads . CR3 edited transcripts were identified by comparing sequence downstream of the 5' never edited region to the edited CR3 sequence . Guide RNAs were identified by using the mRNA sequences as queries against our existing gRNA databases , as previously described [10] . Mitochondrial pan-edited genes were categorized as potentially dual-coding based on identification of extended alternative reading frames and/or presence of identified gRNAs that generate alternative 5’ end sequences . These genes include CR3 , CR4 , ND3 , the 5’ editing domain of ND7 , ND9 and RPS12 . Nondual-coding pan-edited genes include ATPase 6 , COIII , ND8 and the 3’ editing domain of ND7 . Partially edited genes include CYb , Murf II and COII . Never edited genes include COI , ND1 , ND2 , ND4 and ND5 . For all analyses , ND7 was considered as two separate coding regions: the 5' editing domain ( ND7N ) and the 3' editing domain ( ND7C ) [20] . As we hypothesize that only the 5' editing domain of ND7 is dual-coding , mutation calculations for ND7N was pooled with the dual-coding genes and ND7C was pooled with nondual-coding pan-edited genes . T . brucei and T . vivax mRNA sequences of mitochondrial encoded genes were aligned based on protein sequence using Clustal Omega [21] . Nucleotide sequence mutations were identified and their effects on the amino acid sequence were classified as silent , missense or nonsense mutations . Missense mutations were further divided into three groups based on the PAM 250 matrix where conversions with a value <0 were considered not conserved , conversions with a value 0≤x≤0 . 5 were considered modestly conserved , and conversions with a value >0 . 5 were considered strongly conserved [22] . Mutation frequencies were normalized for each gene using nucleotide sequence length . Frequencies were compared using unpaired t-tests . The extent of editing conservation between T . vivax and T . brucei was calculated by aligning the pan-edited genes based on ACG sequence . For each alignment , each location between an A , C or G nucleotide where a U-residue was inserted or deleted in either sequence was considered an editing site . Editing sites were classified as identical in both sequences , altered in insertion or deletion length , having switched from an insertion site to a deletion site , or only occurring in one of the sequences . Percent editing conservation was based on total number of editing sites within each mRNA . Percentages were compared using unpaired t-tests . A principal component analysis ( PCA ) was performed on all three reading frames of the pan-edited genes using the scikit-learn principal component analysis tool [23] . For this analysis , the predicted protein sequences for all three reading frames were aligned using Clustal Omega [21] . Missense , nonsense , and indel mutations were quantified . Missense mutations were further divided into three groups as described above . Each mutation type was quantified and the relative frequency of each mutation calculated based on protein amino acid length . The variables used in the PCA include the protein mutation frequencies and the percentage of identical editing sites in each mRNA . The first reading frame of each gene is defined as the ORF published in the literature . CR3 sequence accession number: SAMN06318039 . TREU 927 gRNA sequence accession number: SAMN06318154 . TREU 667 gRNA sequence accession number: SAMN06318153 . NCBI’s Sequence Read Archive .
In T . brucei , analyses of the gRNA transcriptome for the pan-edited transcripts indicate that full editing involves a large number of gRNA populations [10 , 24] . In addition , most of the gRNA populations ( population defined as guiding the same or near same region of the mRNA ) contain multiple sequence classes . The sequence classes most often differ in R to R or Y to Y mutations , hence guide the generation of the same mRNA sequence ( A:U and G:U base pairs allowed ) . During these analyses , we noted that the terminal gRNA population for Cytosine-rich Region 3 ( CR3 ) ( putative NADH dehydrogenase subunit 4L [25] ) , had 3’ sequences that would extend editing beyond the previously identified translation start codon . In addition , this population had several sequence variants that would generate different edited sequences in this region . The most abundant terminal gRNA would introduce a stop codon in-frame with two alternative AUG start codons found near the 5’ end ( Fig 1A ) . Other sequence classes however , would either bring the upstream AUGs into frame , or shift the reading frame . Intriguingly , the alternative +1 reading frame ( ARF ) did not contain any premature termination codons . In order to determine if these gRNAs were utilized , we used Illumina deep sequencing to identify the most abundant forms of fully edited CR3 transcripts . Surprisingly , we identified multiple forms of the mRNA ( Fig 1A , 1B and 1C and S1 Table ) . The first was the fully edited sequence predicted by the most abundant gRNA identified ( Fig 1A ) . The other transcripts however , had unique editing patterns at the 5’ end ( Fig 1B and 1C and S1 Table ) . Use of these 5’ CR3 sequences allowed us to identify novel gRNAs . Predicted translation of these mRNA sequences indicate that they use the +1 reading frame , and that the protein generated would be the same length as the ORF previously identified . This suggests that CR3 is dual-coding , and that selection of the terminal gRNA determines which reading frame will be used . A re-examination of the terminal gRNAs for the pan-edited genes indicated that at least two other transcripts , NADH dehydrogenase subunit 7 ( ND7 ) and ribosomal protein subunit 12 ( RPS12 ) , have identified gRNA sequence variants within the terminal gRNA population that allow access to alternative reading frames ( Fig 1D and 1E and S2 and S3 Tables ) . Interestingly , the alternative gRNA for ND7 generates a +2 frameshift with a 65 amino acid open reading frame . The ND7 transcript is differentially edited in two distinct domains separated by 59 nts that are not edited in the mature transcript ( the HR3 region ) [20] . Only the 5’ domain is edited in both life cycle stages; full editing of the 3’ domain was only found in bloodstream form ( BF ) parasites . The stop codon for the +2 frameshift is found within the HR3 region , therefore this alternative protein would be generated by full editing of only the 5’ domain . While the most abundant gRNA in the Eatro BF transcriptome ( ~50 , 000 reads ) would generate a sequence utilizing the identified ND7 ORF ( Fig 1D ORF ) , the most abundant gRNA ( >100 , 000 ) in the Eatro 164 procyclic library is the +2 ARF gRNA ( Fig 1D ARF and S2 Table ) . In RPS12 , the alternative gRNA deletes an additional U-residue downstream of the existing start codon , shifting the reading frame into the +1 ARF ( Fig 1E ) . Interestingly , in Leishmania tarentolae , a gRNA , gRPS12VIIIa , has been identified that would also shift the frame of the existing start codon into the +1 ARF [26] . The identification of gRNAs that could alter the reading frame led us to re-analyze the ORFs of the edited transcripts . In addition to CR3 , we found extended ORFs in two different frames for Cytosine-rich Region 4 ( CR4 ) and NADH Dehydrogenase ( ND ) subunit 9 , while several others had shorter , but still significant ORFs in alternative frames ( Fig 2 ) . We do note that the original sequence publications for both CR4 and RPS12 ( CR6 ) had indicated that the fully edited sequence contained extended ORFs in two different frames [27 , 28] . Additionally , NADH Dehydrogenase subunit 3 ( ND3 ) was also considered to be potentially dual coding , based on mutational analysis described below . As we did find potential ARFs in the edited transcripts , we analyzed the predicted ORFs for biases in their mutational pattern . Dual-coding genes often display an atypical codon mutation bias due to constraints imposed by the need to maintain protein function in both genes . In single-coding genes , changes in the third nucleotide of a codon give rise to synonymous amino acids , so this position ( N3 ) is much less constrained . In contrast , in dual-coding genes , the N3 position in one frame is the N1 or N2 position in the alternative frame . Therefore , they have low rates of synonymous mutations [29] . This codon bias has been used to develop algorithms to detect novel overlapping genes [30 , 31] . These algorithms however , cannot be used in the analysis of our edited transcripts as the two-component genetic system ( mRNAs created by gRNA editing ) introduces another layer of mutational constraint [32] . In addition , the edited sequence of the transcripts is known for only a limited number of kinetoplastids , and only the salivarian trypanosomes have the same general life cycle; other kinetoplastids , like Leishmania and T . cruzi , have evolved different infective cycles and are under very different selective pressures [5 , 33 , 34] . Fully edited sequences are known for T . vivax , the earliest branching salivarian trypanosome [35 , 36] . T . vivax differs from T . brucei in that they complete the insect phase of their life cycle entirely within the proboscis of the fly . This parasite has been described as an intermediate stage in the evolutionary pathway from mechanical transmission ( ancestral ) to full adaptation to the midgut and salivary glands of the tsetse fly [37] . Using the T . vivax sequence , we analyzed mutation patterns in all of the mitochondrially-encoded mRNAs ( Fig 3 ) . mRNA sequences were aligned by codons based on their protein alignments ( Clustal Omega [21] ) . Mutated codons were identified and classified as silent , missense and nonsense mutations . Missense mutations were further divided into three groups based on the PAM 250 matrix [22] . These data clearly show that the RNA editing process significantly constrains the types of mutations tolerated within the mitochondrial genome . In comparison to the genes that are not edited ( ND1 , ND2 , ND4 , ND5 , COI ) or have limited editing ( CYb , Murf II and COII ) , a distinct suppression of silent mutations and strongly conserved missense mutations were observed for all of the pan-edited genes , consistent with previous observations ( Fig 3 ) [32] . A suppression of mutations that lead to moderately conserved amino acid replacements was also observed , but these were not as striking due to the low frequency of this type of mutation . No significant difference was observed in the frequency of not conserved missense mutations , though a trend towards a lower frequency of these mutations in the putative dual-coding genes ( CR3 , CR4 , ND3 , ND9 , 5’ND7 and RPS12 ) was noted . This was complemented by a significant increase in the frequency of strongly conserved missense mutations in the putative dual-coding genes in comparison to the other pan-edited genes ( 3’ND7 , ND8 , A6 and COIII ) . Surprisingly , while the overall mutational frequency of the fully edited pan-edited genes was similar , a comparison of the conservation of editing patterns did show a significant difference between the putative dual-coding and the other pan-edited genes ( Fig 4 ) . The dual-coding genes consistently had a lower conservation of their editing pattern . Upon further examination , we found that most changes in the editing pattern resulted from thymidine insertions and deletions within the maxicircle DNA sequence , which was then corrected by the editing machinery . These types of mutations do not result in a change to the final mRNA sequence once edited . The T . brucei ( Tb ) dual-coding genes appeared to consistently insert more U-residues , while T . vivax ( Tv ) had more U-residues encoded within the DNA sequence . Indeed , comparisons of the length of the coding regions of Tb and Tv cryptogenes ( unedited sequence ) show that the putative dual-coding genes are almost 10% shorter in Tb . In contrast , the non-dual coding cryptogenes are not significantly shorter ( ~2 . 5% ) . Some of the other changes in editing patterns did generate small internal frameshifts as previously described by Landweber and Gilbert [32] . However , the high prevalence of internal frameshifts reported for COIII by Landweber and Gilbert is reflected in our analysis only for COIII and A6 . Since differences in the types of amino acid mutations were observed , we performed a principal component analysis on the frequency of mutation types for all three reading frames of the pan-edited genes ( Fig 5 ) . In addition , we included the percentage of editing site conservation as a variable . This analysis clearly clustered the putative +1 dual-coding transcripts ( reading frame 2 ) . The first component ( z-axis , ) is strongly based on editing conservation , and separates the dual-coding genes from the other pan-edited genes as expected . While component 2 ( x-axis ) separated ORF1 and ORF3 from ORF2 of each gene , component 3 clearly separated the dual-coding ORF2s from nondual-coding ORF2s . The ND7N ORF3 was the only exception , and the gRNA data suggests that it is a dual-coding gene using the +2 ( ORF3 ) reading frame . This suggests that an additional layer of mutational constraint beyond that imposed by the RNA editing process can be detected for six of the extensively edited transcripts . Because dual-coding genes are often conserved in multiple species , we analyzed the available sequences of other kinetoplastids ( Leishmania tarentolae ( Lt ) , Leishmania mexicana amazonensis ( Lma ) , Phytomonas serpens ( Ps ) , Perkinsela CCAP1560/4 ( Pk ) ) to determine if they also contain multiple overlapping reading frames with homology to those found in T . brucei . Interestingly , many of the alternative reading frames did show some homology to the ARFs found in Tb . However , most of these ARFs are punctuated with stop codons ( S2 Fig ) . Extended alternative reading frames are found in CR3 , 5’ND7 and RPS12 in Ps . However , the extended ARF in the Ps CR3 is in the +2 reading frame and the ND7 and RPS12 ARFs shows very little homology with the Tb/Tv ARF ( S2A , S2D and S2F Fig ) [38] . Interestingly , while Perkinsela has lost many of the genes in the mitochondria , RPS12 was retained [39] . The Pk RPS12 ARF possesses a near full open reading frame with one stop codon three codons after an in frame start near the 5' end of the edited transcript . This pattern is reminiscent of the conventionally edited sequences of CR3 and ND7 , and could suggest that an alternative edit may remove the stop codon , allowing access to the ARF . The L . tarentolae CR4 orthologue also has two extended ORFs . Interestingly , the published sequence for Lt CR4 appears to switch between the two ORFs ( switch appears to occur in a stretch of 13 inserted Us ) [40] . This may explain why only the carboxyl half of the published Lt CR4 showed good homology with Tb and Lma [41] . Translation of the Lt ARF does generate a protein with the N-terminus showing high homology to the conventional Tb and Lma CR4 , while translation of the published ORF shows some homology to the Tb CR4 ARF ( S2B Fig ) . These data are intriguing enough that these sequences should be re-examined . While most of the other pan-edited transcripts had multiple stop codons in the +1 and +2 reading frames , many did show good homology to the Tb ARF sequences . Particularly intriguing are the ND3 , ND8 and ND9 alignments . While internal stop codons are found in Tv , Lt and Lma ND9 ARFs , they show strong homology to the Tb ND9 ARF throughout the protein ( S2E Fig ) . In ND3 , the amino ends of the ARFs show strong homology between all four of the Trypanosoma and Leishmania species ( S2C Fig ) . This homology decreases after an internal stop codon found in the same position in 3 of the 4 species . As ND8 is the only other pan-edited gene in Lt , Lam and Ps , we also examined the conservation of the ND8 ORF and ARF , even though our mutational analyses did not tag the ND8 gene as dual coding . While the ND8 ARFs were punctuated by multiple stop codons , they surprisingly also showed areas of strong homology between all 5 species , especially down stream of an internal methionine ( S2G Fig ) . We do note that we cannot rule out the possibility that alternative editing can remove stop codons observed in the ARFs . Analyses of the ARF predicted proteins suggest that they are all short transmembrane proteins with two or more predicted transmembrane alpha helices ( Fig 6 ) [42 , 43] . While functional homologues are often difficult to detect in trypanosomes , searches using the predicted protein sequence of each ARF did identify small molecule transport proteins with limited confidence . Using Phyre2 , the ND7 ARF was identified as a homolog of the bacterial sugar transporter SemiSWEET ( 61 . 5% confidence ) [44 , 45] . SemiSWEET , which forms homodimeric structures , is also a distant homolog of the yeast mitochondrial pyruvate carrier 1 ( MPC1 ) . This protein has two transmembrane alpha helices and forms a heterodimer with either of the other two pyruvate carrier proteins [46 , 47] . While still very speculative , it is intriguing that the small ARF proteins might oligomerize to form small mitochondrial membrane transporters .
The work presented here , suggests that as many as six of the extensively edited mRNAs in T . brucei are dual-coding and that it is alternative editing using different terminal gRNAs that allows access to the two different reading frames . Deep sequencing of the 5’ end of CR3 indicates fully edited transcripts that have access to both reading frames are present in the mitochondrial transcriptome and gRNA analyses indicate that three different cell lines contain gRNAs that can alternatively edit the 5’ ends of CR3 , RPS12 and ND7 . In addition , analyses of the mutational bias in pan-edited genes suggest that an additional layer of mutational constraint is observed in the putative dual-coding genes . While the overall mutational frequency observed for the fully edited mRNAs is similar for all pan-edited genes , the types of amino acid changes that appear to be tolerated are significantly different . This is consistent with these genes having to maintain functional proteins in two different reading frames . Analyses of other trypanosomes , do show that some of the ARFs have intriguing homology to the ARFs identified in T . brucei and T . vivax . However , most of the ARFs are punctuated with stop codons . These data are difficult to interpret because we cannot rule out the possibility that the stop codons are removed by alternative editing events . In addition , the other trypanosome species have evolved very different infective life cycles and are under different selective pressures . For example , P . serpens is a pathogen that infects important crops and is transmitted by sap-feeding bugs . These parasites have glucose readily available in both life cycle stages and are unique in that they lack a fully functional respiratory electron transport chain [48 , 49] . For Leishmania , all life cycle stages possess an active Krebs cycle and ETC linked to the generation of ATP [50 , 51 , 52] . These unique adaptations to different hosts suggest that they may not be under the same evolutionary pressure to maintain dual-coding genes . Overlapping reading frames are common in viruses , and are thought to persist due to strong genome size constraints [53 , 54] . More recently however , over-lapping genes have been identified in mammalian and bacterial genomes [55–58] . In these organisms , size is not an issue and the potential advantage of overlapping genes is less clear . For dual-coding genes , the need to maintain both ORFs constrains the ability of each protein to become optimally adapted [15] . As this constraint can be alleviated by gene duplication , it is thought that dual-coding regions can survive long evolutionary spans only if the overlap provides a selective advantage . In mammals , many of the identified dual-coding genes like Gnas1 and XBP1 , produce two proteins that bind and regulate each other [59–60] . For these proteins , dual-coding may be advantageous for the tight co-expression needed . An alternative model , suggests that under high mutation rates , the overlapping of critical nucleotide residues is advantageous because it may reduce the target size for lethal mutations [61] . This may be particularly important for organisms that have evolved to exist in dual-metabolic environments ( two hosts ) . We hypothesize that the trypanosome mitochondrial ARFs encode small metabolite transporters that provide a distinct growth advantage to bloodstream form parasites . The complete overlap of these small transporter genes with electron transport chain ( ETC ) genes would protect the integrity of the ETC genes that are required only in the insect host . Thus , in trypanosomes , dual-coding genes may be a mechanism to combat genetic drift during extended periods of growth in non-selective environments . In T . brucei , it is known that a number of bloodstream form essential proteins are functionally linked to Krebs cycle or ETC genes . While not a “classic” dual-coding gene in that production of the alternative protein does not involve overlapping reading frames , the pan-edited COIII gene does contain the information for two distinct proteins , COIII and AEP-1 . AEP-1 is important for kinetoplastid DNA maintenance and over-expression of the DNA-binding domain results in a dominant negative phenotype including decreased cell growth and aberrant mitochondrial DNA structure [12] . The nuclear encoded α-ketoglutarate dehydrogenase E2 ( α-KDE2 ) is known to be a dual-function protein , in that it plays important roles in both the Krebs cycle and in mitochondrial DNA inheritance [62] . RNAi knockdowns of this gene in bloodstream form ( BF ) trypanosomes also show a pronounced reduction in cell growth . Similarly , the Krebs cycle enzyme α-ketoglutarate decarboxylase ( α-KDE1 ) is also a dual-function protein with overlapping targeting signals that allow it to be localized to both the mitochondrion and glycosomes [63] . RNAi knockdowns of α-KDE1 in BF trypanosomes is lethal , suggesting that in addition to its enzymatic role in the Krebs cycle , it plays an essential role in glycosomal function in T . brucei [63] . It has been previously suggested that both alternative editing and dual-function proteins are important mechanisms for expanding the functional diversity of proteins found in trypanosomes [11 , 62–64] . We hypothesize , that in salivarian trypanosomes , an equally important role for these dual-coding/function genes may be the protection of genetic information . The “why” of the unique RNA editing process in kinetoplastids has been a long-standing paradox . The complex machinery and the sheer number of gRNAs required to direct the thousands of U-insertion/deletions indicate that this process is metabolically very costly . Initially , it was proposed that U-insertion/deletion editing ( kRNA editing ) was one of many RNA editing processes that were in fact relics of the RNA world . However , the very different mechanism of the RNA editing systems in existence , and their very limited distribution within specific groups of organisms indicate that they are more likely derived traits that evolved later in evolution [65 , 66] . The sheer complexity of the kRNA editing process , with no obvious selective advantage , led to the proposal that insertion/deletion editing arose via a constructive neutral evolution ( CNE ) pathway [67] . Indeed , RNA editing in trypanosomes is always mentioned in support of CNE as an example of how seemingly non-advantageous , complex processes can arise [68 , 69] . More recently however , it has been hypothesized that RNA editing co-evolved with G-quadruplex structures found in the pre-edited mRNAs [16] . These structures are thought to be advantageous in that they can help regulate transcription in order to promote DNA replication and prevent kinetoplast DNA loss . However , they must be removed by the RNA editing system prior to translation [16] . Another prominent hypothesis is that RNA editing is advantageous because it is a mechanism by which an organism can fragment and scatter essential genetic information throughout a genome [6 , 70] . Kinetoplast DNA is far less stable than chromosomal DNA , and loss of minicircles due to asymmetric division of the kDNA network have been frequently observed , especially in laboratory cultures of Leishmania [40 , 71] . Buhrman et al . [70] suggest that the scattering of essential guide RNA genes throughout the DNA network , would prevent fast growing deletion mutants from outcompeting more metabolically versatile parasites during growth in the mammalian host . Using a mathematical model of gene fragmentation in changing environments ( absence of functional selection ) , they showed a distinct advantage for gene fragmentation . In their model , the number of tolerable generations under periods of relaxed selective pressure was increased by more than 40% before loss of the ability to move to the next life cycle stage . If the dual-coding ARFs give BF trypanosomes a selective growth advantage similar to that observed by the COIII alternative protein AEP1 , then the number of “essential” gRNA genes would increase greatly . Currently , only AEP1 , A6 and RPS12 mitochondrial genes have been experimentally shown to be essential [12 , 72–73] . In addition , the presence of alternative editing and dual-coding genes would complement the protection provided by gene fragmentation by also shielding the genes from deleterious point mutations within critical ETC genes . This suggests that the complex RNA editing system found in the mitochondria may therefore provide multiple molecular strategies to increase genetic robustness . Protection of the mitochondrial genome during growth in the mammal would increase the capacity for successful transfer to an insect vector and maximize the parasites long-term survival and spread . | In African trypanosomes , many of the mitochondrial mRNAs require extensive RNA editing before they can be translated . During this process , each edited transcript can undergo hundreds of cleavage/ligation events as U-residues are inserted or deleted to generate a translatable open reading frame . A major paradox has been why this incredibly metabolically expensive process would evolve and persist . In this work , we show that many of the mitochondrial genes in trypanosomes are dual-coding , utilizing different reading frames to potentially produce two very different proteins . Access to both reading frames is made possible by alternative editing of the 5’ end of the transcript . We hypothesize that dual-coding genes may work to protect the mitochondrial genes from mutations during growth in the mammalian host , when many of the mitochondrial genes are not being used . Thus , the complex RNA editing system may be maintained because it provides a unique molecular strategy to combat genetic drift . | [
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... | 2017 | Mitochondrial dual-coding genes in Trypanosoma brucei |
To determine whether the distinctive features of Caenorhabditis elegans chromosomal organization are shared with the C . briggsae genome , we constructed a single nucleotide polymorphism–based genetic map to order and orient the whole genome shotgun assembly along the six C . briggsae chromosomes . Although these species are of the same genus , their most recent common ancestor existed 80–110 million years ago , and thus they are more evolutionarily distant than , for example , human and mouse . We found that , like C . elegans chromosomes , C . briggsae chromosomes exhibit high levels of recombination on the arms along with higher repeat density , a higher fraction of intronic sequence , and a lower fraction of exonic sequence compared with chromosome centers . Despite extensive intrachromosomal rearrangements , 1:1 orthologs tend to remain in the same region of the chromosome , and colinear blocks of orthologs tend to be longer in chromosome centers compared with arms . More strikingly , the two species show an almost complete conservation of synteny , with 1:1 orthologs present on a single chromosome in one species also found on a single chromosome in the other . The conservation of both chromosomal organization and synteny between these two distantly related species suggests roles for chromosome organization in the fitness of an organism that are only poorly understood presently .
The comparative analysis of the related nematodes Caenorhabditis elegans and C . briggsae offers a powerful approach toward understanding the genetic basis for the form and function of these simple animals . Studies to date have already yielded valuable insights into the evolution and role of particular sequences , genes , and pathways [1 , 2] . Morphologically , the two species are almost indistinguishable , despite the fact that their most recent common ancestor ( MRCA ) existed about 100 million years ago ( Mya ) . Both are soil-dwelling , self-fertilizing hermaphrodites , with facultative males . Both have a ~100-megabase ( Mb ) genome apportioned into six chromosomes . Genes isolated in one species will frequently rescue mutants in the other [3 , 4] . Despite these similarities , nucleotide alignments ( using the wobble-aware bulk aligner [WABA] algorithm [5] ) of the complete genome sequence of C . elegans [6 , 7] with the draft sequence of C . briggsae strain AF16 reveals that 52 . 3% of the C . elegans genome and 50 . 1% of the C . briggsae genome aligns between the two species with the bulk of this in coding sequence [8] . The substantial body of knowledge accrued about C . elegans over the past few decades will help interpret the sequence similarities and differences . Much less is known about C . briggsae . To facilitate the molecular genetic study of C . briggsae and thus enhance its utility for further comparative analysis , we sought to convert the whole genome sequence assembly into a genome map , in which the genome sequence and genetic maps are linked to each other through common markers across the chromosomes . Before our present work , the draft whole genome assembly contained 102 Mb of sequence in 142 physical map–based contigs ( fpc contigs ) , with the remaining 6 Mb in 463 supercontigs ( see Materials and Methods ) . The classical genetic map ( Bhagwati Gupta , personal communication ) has fewer than 40 mutants placed on the six linkage groups and only ten of these have a molecular assignment . The large number of contigs and the paucity of genetic mapping data did not allow meaningful merging of the two maps . We undertook the construction of a genome map by first generating a genetic map using molecularly based single nucleotide polymorphism ( SNP ) markers . This more detailed genetic map based on SNPs would be of use in its own right , for example , simplifying positional cloning of genetically defined genes . But it would also provide long-range continuity , which would in turn allow the placement of much of the assembled sequence along the chromosomes . This long-range map of the genome would in turn allow a direct comparison of chromosomal organization in C . briggsae to the distinctive features of C . elegans organization [6 , 9] . Using other wild isolates of C . briggsae , we discovered thousands of SNPs . By genotyping selected SNPs across recombinant inbred ( RI ) lines between the sequenced strain ( AF16 ) and the SNP source strains , we generated a genetic map . We then combined the resultant genetic map and the sequence assembly information to place 91 . 2 Mb of sequence onto the six linkage groups , with another 9 . 9 Mb tentatively associated ( but not ordered ) with chromosomes . The integrated map allowed us to correct several misassemblies in the initial C . briggsae sequence . Of broader interest , we were also able to explore chromosomal scale phenomena . Like in C . elegans , rates of recombination appear much higher on arms than in central regions for the autosomes . Autosome arms and centers also differ in their repeat content , coding density , and fraction of highly conserved genes , as is seen in C . elegans . Unexpectedly , the comparison also revealed an extensive conservation of synteny between the two organisms , with the vast majority of genes with 1:1 orthologs that reside on one chromosome in one species lying on a single chromosome in the other . Long-range gene order within the chromosomes has been substantially altered in the 100 million years ( Myr ) since their MRCA , but despite these rearrangements , sequences tend to remain in their respective domains of arm or center . Our findings support the emerging recognition of the importance of overall chromosomal organization in metazoans .
To find a suitable strain for SNP discovery , we investigated four independent wild isolates that grow well in culture , are interfertile with the sequenced AF16 strain , and represent both tropical and temperate groups [10 , 11] ( Table 1 ) . We initially aligned a small number of random genomic sequences against the AF16 assembled sequence to ascertain the approximate incidence of single nucleotide variation . Two temperate strains of Japanese origin ( HK104 and HK105 ) both had relatively high rates of difference ( ~1 SNP/110 bases ) while the Hawaiian ( VT847 ) and the Ohio ( PB800 ) strains ( tropical and temperate respectively ) had apparently lower rates ( see Materials and Methods for details of SNP detection ) . We selected one strain of each level ( HK104 and VT847 ) for more extensive SNP discovery . From 8 , 405 and 9 , 970 aligned sequence reads from whole genome shotgun libraries from each strain we identified respectively 32 , 246 and 14 , 183 substitutions with Phred [12] quality scores of greater than 35 , giving overall rates of 8 . 7 and 3 . 2 per kilobase . We also identified a number of candidate small insertion/deletion differences ( 7 , 118 events affecting 18 , 196 bases and 3 , 575 events affecting 8 , 315 bases , respectively ) . To construct a genetic map , 390 SNPs distributed across the sequence were tested against 93 AF16 × HK104 RI lines and the parental strains [10] using the fluorescent polarization detection ( FP-TDI ) assay ( Vieux et al . 2002; see Materials and Methods for details of SNP assay ) . To maximize the amount of sequence mapped and to provide an independent check of the assembly , the 390 SNPs were selected from the larger supercontigs , thus ensuring that the larger physical map–based contigs ( called fpc contigs for simplicity , after the program used to assemble the physical map [13] ) would contain multiple markers and thus serve to check the assembly . In about a quarter of the cases , a second SNP was selected within a single supercontig to test the assembly at this level . A SNP was declared as mapped when the assays were successful on between 80% and 100% of the 95 strains tested , with a total of 248 SNPs ( 64% ) meeting this criterion . Some 84 SNPs ( 22% ) had success rates between 0% and 40% and were deemed failures . The high rate of failures was likely caused by PCR problems due to unaccounted SNPs in primer sites , a problem faced by all investigated genotyping platforms [14] . Some five SNPs ( 1 . 3% ) were monomorphic and likely due to false SNP calls . Other SNPs failed quality control tests or had success rates of 40–80% . These same SNP assays were also tested against the VT847 strain , for which RI lines were also available . Relatively few of the AF16/HK104 SNPs were polymorphic between AF16 and VT847 , suggesting that the overlap in variation between the HK104 and VT847 is very limited . This meant that genotyping of these additional RI lines with these markers added little new map information . We tested several different parameters for map construction , using the program Map Manager QTXb20 ( http://www . mapmanager . org/ [15] ) . The different versions varied in map length per chromosome , total map length , and in the local order of markers within a chromosome , but assignment of markers to common linkage groups was a robust feature of the maps . The latter was due in part to the large number of nonrecombinant chromosomes in the RI lines ( 35–60% per chromosome ) , which allowed ready assignment to linkage groups . Based on these experiences , we adopted the following strategy to build version 3 . 0 of the genetic map: we used an initial set of 115 very high quality markers ( >95% call rates ) and a second set of slightly lower quality ( 80–95% call rates ) . We used the Haldane function and an initial probability of incorporation of a SNP into the map of 10−6 . Seven linkage groups were formed , one with only two SNPs ( cb23233 and cb23314 ) . We reduced the probability required for incorporation to 10−3 , and the latter group was incorporated into the end of chromosome CbIV . Thus the number of linkage groups matched the observed number of chromosomes ( Table 2 ) . The program provided map positions in centimorgans ( cM ) for each of the incorporated markers , with each of the chromosomes approximately 50 cM in length . Inspection of the raw data in the version 3 . 0 map in conjunction with the marker order in the sequence assembly highlighted places where markers of equivalent or nearly equivalent position in the genetic map could be shuffled to reconcile their order with that in the assembly . In addition the initial genetic map of the X chromosome ( CbX; see below for chromosome assignments ) showed a number of inconsistencies with the sequence assembly that could all be reconciled by a single inversion of the central segment of the genetic map for CbX . Additional recombinant data obtained for CbX from an experimental cross ( see Materials and Methods ) supported the revised genetic marker order . These changes were incorporated into version 3 . 1 . Finally , inspection of the raw data in conjunction with the known groupings of markers based on the assembly suggested alternate orders of markers on chromosomes CbI , CbIV , and CbV that reduced the number of multiply recombinant chromosomes . These changes reduced overall map length by over 16 cM and did not reduce logarithm of the odds scores ( logarithm of the odds score is a statistical estimate of whether two loci are likely to be near each other on a chromosome and therefore likely to be inherited together ) of any markers below the threshold; they were incorporated into the genetic map to produce version 3 . 2 . Using this framework map , inspection of the remaining markers with lower call rates indicated that 44 of them could be readily linked to chromosomes and tentatively positioned within the chromosome . These added markers sometimes helped in orienting contigs and in five cases , positioned previously unplaced contigs . However , the lower overall call rates of these markers make their placement less certain . With the genetic map in place , we examined the frequency of parental alleles within the RI lines across the chromosomes . For chromosomes CbII and CbX , there was little variation from the expected value of 50% for each marker . But for other chromosomes , there were regions of biased representation of the AF16 and HK104 alleles . For example , the AF16 allele was consistently underrepresented for most of CbIII , whereas it was overrepresented for much of chromosome CbIV ( Figure 1 ) . Chromosomes CbI and CbV also showed biased representation , but over more limited regions ( see Datasets S1 and S2 ) . The biased representation of alleles presumably reflects some selective advantage for offspring with these regions , either singularly or in combination . The selection of the first progeny at each generation in establishing the RI lines may have contributed to this bias . The relatively small number of recombinant events in these lines however precludes finer localization of such factors . The sequence-based markers used in the construction of the genetic map allowed for ready integration of the genetic and sequence maps into a genome map . The association of a genetic marker with a supercontig and , in turn , an fpc contig positioned that sequence on a specific chromosome , and when multiple , genetically separated markers were assigned to a single sequence assembly , that sequence could be oriented . Generally , multiple markers from the same supercontig or fpc contig had adjacent positions in the genetic map , confirming the assembly in these instances . However , markers assigned to 21 sequence assemblies were derived from more than one linkage group , indicating an error in either the genetic linkage assignment or in the sequence assembly . Because marker assignment to linkage groups was a robust feature of the genetic map and inspection of the raw data revealed no problems with the assignment in these discordant cases , the sequence map was probed for possible errors . Only one discrepancy was noted among 68 supercontigs with more than one marker , suggesting that misassemblies within supercontigs ( constructed by using read-pair information to link sequence contigs ) were unlikely to account for the bulk of the observed discrepancies . On the other hand , we noted that most markers with discordant linkage fell on fpc contigs ( in which supercontigs were linked based on the physical clone map information ) . Detailed inspection showed that in these cases , a join based on the physical clone map information fell between discordant markers . Once the conservation of synteny between C . elegans and C . briggsae chromosomes was established ( see below ) , the 1:1 orthology landmarks were used to delimit the region with the assembly problem , making it clear that the discrepancies arose because of false joins based on the lower resolution physical clone map ( Figure 2 ) . Inspection of the physical map in a sample of these regions revealed questionable clone overlaps often accompanied by an editor's comment to that effect , consistent with a misassembly at that point . As a result , 27 breaks were made in the fpc contigs at the site defined by the orthology landmarks ( renamed as segments a , b , etc . of the parent contig ) . The single discordant supercontig was also broken at a site bounded by the ortholog landmarks . These breaks in the sequence assembly eliminated the inconsistencies between assignment of the markers to sequence assemblies and linkage groups ( Table 3 ) . Obviously , other misassemblies may remain undetected , because misassembled regions failed to have a genetic marker . Investigation of the entire sequence for clusters of discordant orthologs suggests five regions of more than 50 kilobases ( kb ) that are likely candidates for misassembly . Further , our analysis is less sensitive to misassemblies within the same chromosome , because precise order within linkage groups is less robust , making detection harder . Nonetheless , with one exception , markers in a single sequence assembly lie adjacent to one another in the current map . In the exception ( cb25 . fpc4010 ) , a high-quality marker maps to the right end of chromosome CbIII , whereas two low-confidence markers suggest positions near the opposite end . Further , with one exception , multiple markers in a single sequence assembly fall in an order consistent with the genetic map order . In the single exception , a simple inversion of a pair of SNP markers in cb25 . fpc3752 would reconcile the maps . However , we only altered the sequence assembly where there were compelling genetic data that the assembly was in error . The integrated genetic and sequence map provide an initial genome map . The confidently placed genetic markers position 141 sequence assemblies , accounting for 89 . 4 Mb of the sequence along the chromosomes , with 42 of these oriented , accounting for 47 . 7 Mb . Inclusion of the lower-confidence markers provides tentative positions for an additional five assemblies , containing 1 . 8 Mb . By using read-pair information for assemblies adjacent in the genetic map , we were able to orient an additional 45 contigs , bringing the total oriented sequence to 67 . 3 Mb . In addition , by considering local order of 1:1 orthologs in both species ( see below ) , we could tentatively order an additional 4 . 4 Mb . This reconciled genome map is reflected in version 3 . 3 of the genetic map . In C . elegans , a distinctive feature of the genetic map is the reduced recombination per Mb of the centers of the autosomes compared with the arms [16] . We looked at the recombination rates across the C . briggsae autosomes and the putative X chromosome ( see below ) to see if the same features existed . Similar to that of C . elegans , each of the C . briggsae autosomes shows reduced recombination in the centers compared to the arms ( Figure 3A , Datasets S3 and S4 , and Figures S1–S4 ) . Indeed , no recombinant events were observed in the RI lines over several megabases of the centers of several chromosomes , even though 60–70 recombinant events were observed on the 11–16-Mb autosomes . In contrast , recombination rates are more uniform on the presumptive X chromosome ( Figure 3B ) . These observations must be interpreted with some caution , because the C . briggsae genome map contains only 85% of the sequence , and assembly biases could mean that much of the unassigned sequence belongs on the arms . Further , some biases were introduced in the recovery of the RI lines , as noted above , which might also distort recombination rates . Finally the sequence differences and perhaps even inversions between the two strains could reduce recombination rates in local regions . Nonetheless , the general features seen here seem likely to be reflected in a more comprehensive map . In addition to the marked variation in recombination rates along the autosomes in C . elegans , repeat density and gene density were found to vary by region [6] . We observed similar variation in density of these features in the C . briggsae autosomes , with the repeat density higher and intron length greater on the arms and exon density greater in the centers ( Figure 4 , Datasets S3 and S4 , and Figures S1 and S2 ) . Again , as seen in C . elegans , telomere related repeats ( TTAGGC ) show a particularly marked difference in distribution . Strikingly , 1:1 orthologs , even after accounting for the greater exon density in the centers , are more common in the centers . With the bulk of the C . briggsae genome placed along chromosomes , the conservation of synteny ( using synteny here in the originally defined sense of genes on the same linkage group or chromosome ) and colinearity ( meaning the order of genes along the chromosome ) between C . elegans and C . briggsae could be investigated directly across the whole genome . Early analyses of colinearity , using clone-based datasets of limited sequence continuity , estimated median tract lengths of <10 kb in one study [5] and 17 kb for autosomes and 41 kb for the sex chromosome in a second study [17] . The initial analysis of the C . briggsae whole-genome assembly observed a mean of 37 , 472 base pairs ( bp ) and a median 5 , 557 bp with a maximum block of 1 . 68 Mb [8] . This initial analysis used genome-wide alignment data and allowed regions to match as many as five segments in the reciprocal genome . Inspection of the junctions between the 4 , 837 candidate colinear blocks ( minimum length 1 . 8 kb ) suggested the breakpoints represented 1 , 384 inversions , 244 translocations , and 2 , 735 transpositions . To make the present analysis less sensitive to repeated sequences and to small blocks of similarity that may have arisen by the large number of transposition events , we began by identifying 9 , 767 1:1 gene pairs ( where each gene was represented only once in its genome and matched only one gene in the other genome ) using the previously defined gene set [8] . These data provide an unambiguous orthologous landmark on average about every 10 kb . For those sequence assemblies that had only one genetic marker or that had genetic markers all on a single linkage group in the initial map , we found that the 1:1 orthologs on that assembly overwhelmingly derived from a single C . elegans chromosome . The same observations held for the corrected assemblies . More remarkably , we noted for sequence assemblies assigned unambiguously to the same C . briggsae linkage group that the 1:1 orthologs assignments were consistently from a single C . elegans chromosome ( Table 4 ) . Exceptional orthologs were often isolated , singular events . This remarkable conservation of synteny between the two species allowed us to assign not just regions but each of the entire C . briggsae linkage groups to its corresponding C . elegans chromosome . To look at the colinearity of the orthologs within chromosomes , we plotted their location in each of the pairs of syntenic chromosomes ( Figure 5 , Dataset S5 , and Figure S3 ) . There have been extensive intrachromosomal rearrangements , but large colinear blocks remain , especially in the centers . More interestingly , sequences that are in the central , low-recombination segment of one species tend to be in the corresponding region in the other species . By contrast , there is mixing between the two arms . To quantify this , we established blocks of sequence with the same order of genes in the two genomes allowing minor exceptions ( see Materials and Methods ) . Our methods yielded only 851 blocks using a minimum block size of one ortholog , with only a third of these more than 50 kb long . Because our analysis excludes repeated sequences , these numbers do not reflect most transposition events , which formed the bulk of the rearrangements detected in [8] . Nonetheless , 351 of the 1:1 ortholog blocks are small enough ( <20 kb ) to be consistent with transposition events . Only 12 blocks greater than 20 kb involve nonsyntenic orthologs and might represent translocations; none of these have confirmatory genetic markers and could all represent assembly problems . Thus the only confirmed rearrangements represent intrachromosomal events . Their distribution across the chromosomes is distinctly nonrandom . As seen in Table 5 , the block size of the X chromosomes is considerably larger than for the autosomes , and similarly within the autosomes , the block size in the centers is much larger than the arms . The median for the autosomes is similar to that obtained in [17] , whereas the median for the X is considerably larger , perhaps because of the greater continuity of the sequence in our study . Given the overwhelming tendency of orthologs to remain on the same chromosome , we investigated the nonsyntenic ortholog pairs to see what features might distinguish them from syntenic pairs . To minimize the likely contamination of the nonsyntenic set with misassemblies , we excluded 12 larger clusters of nonsyntenic orthologs ( see Materials and Methods ) . The most distinctive difference between the two sets was the lower percent identity of the aligned nonsyntenic pairs ( Figure 6 ) . These differences existed among pairs regardless of whether the members of the pair lay both on chromosome arms , both in chromosome centers , or one on an arm and one in the center . One explanation for the greater divergence of the nonsyntenic ortholog pairs might be that the true ortholog is missing in the draft C . briggsae sequence . We looked for evidence of this by finding the 1:1 orthologs ( e . g . , A and C ) flanking the C . elegans member of a nonsyntenic ortholog pair , ( ABC , where B is from the nonsyntenic pair ) and then searching the region between the C . briggsae orthologs of A and C for evidence of large gaps or partial genes . Of 175 nonsyntenic ortholog pairs , we detected homology in the interval defined by the flanking orthologs for only 19 cases , and only 29 regions had 4% or more of the interval as uncalled bases ( Ns ) . Almost half the intervals had less than 1% of the sequence as Ns . Thus , while the draft nature of the C . briggsae sequence may result in incorrect assignment of 1:1 orthology , producing an apparent increased divergence , it seems unlikely to account for the bulk of our observations .
The comparison of random clone sequences from whole genome shotgun libraries from the Japanese ( HK104 ) and Hawaiian ( VT847 ) isolates with the genome assembly of AF16 provided in each case adequate numbers of widely distributed SNPs to develop markers across the genome assembly . The sequence generated also provides the opportunity for more in-depth studies of patterns of variation among the different isolates . In this study we have confined our analysis to the overall rates of differences , determined by the simple method of scoring base differences between aligned sequences with quality scores >35 . With this quality score cutoff , errors should contribute a false SNP no more than one per 3 , 200 bases , and given that most bases have quality scores well above this , the contribution is likely to be much smaller . Since the observed rates of difference considerably exceed this , errors will only slightly inflate the observed rates . Indeed , of the more than 320 SNP assays that provided data , only five ( 1 . 5% ) were monomorphic . The SNP rates we observed between these C . briggsae strains are higher than those observed between the most divergent C . elegans strains tested to date , with the HK104/AF16 differences about 8-fold higher and the VT847/AF16 differences about 3-fold higher than rates observed in similar experiments between N2 , the standard strain of C . elegans , and CB4856 , a strain from Hawaii that is among the most divergent strains yet isolated [18 , 19] . The SNP rates we observed for both VT847 and HK104 compared with AF16 are similar to those reported by studies focused on a few genes [20 , 21] . We also looked at regions of overlap of VT847 and HK104 sequences ( total 129 kb ) and noted that few differences were shared between the strains . Similarly we observed that the HK104/AF16 SNP assays were predominantly monomorphic when assayed against VT847/AF16 RI lines . These results are consistent with those of [21] , on studies of 4 . 2 kb of sequence from six genes . The authors of [21] noted that strains from temperate regions across the globe , including HK104 , HK105 , and PB800 , had little diversity among themselves , but were more variant as groups from tropical strains , which include both AF16 ( India ) and VT847 . In contrast to the temperate strains , the tropical strains contained considerable diversity . These results suggest that the effective population size of C . briggsae may be several-fold larger than that observed for C . elegans . Initial analysis suggests that the overall SNP rates may be greater on chromosome arms than in the centers . However , the differences in gene density and other features between the chromosomal regions may contribute to the apparent rate differences . A more careful parsing of the sequence reads among the features of the genome , a process now underway ( LW Hillier and RH Waterston , unpublished data ) , will be required to evaluate the different regions . The placement of 248 markers onto six linkage groups is in accord with cytogenetic estimates of chromosome number [22] . The observed length in centimorgans of the autosomes is consistent with the hypothesis that each chromosome undergoes one recombinant event per meiosis , as is thought to be the case for C . elegans . However for CbX , the total length was only 34 cM . Of course the present markers may not extend to the ends of the chromosome , although the X , at more than 20 Mb , is the largest of the chromosomes and had no additional assemblies assigned to it based on ortholog assignments . Also , the two strains used to generate the RI lines might differ significantly in some regions in the genome , reducing recombination , e . g . , through an inversion . If the X length is not artifactually short for one of the reasons given above , the genetic length of 34 cM would suggest that other mechanisms exist to ensure normal segregation of the X chromosome . Such mechanisms must exist in males , which are XO , and might be operative in XX animals in C . briggsae . Although the RI lines served adequately to generate the map , they had shortcomings that might be improved in future studies . There was clearly biased recovery of some markers , with markers from the AF16 strain underrepresented on chromosome CbII and overrepresented on CbIV . This bias might be readily corrected by a more-random selection of progeny to establish each line . In addition , the RI lines had relatively few recombinant events . As a result , central regions of low recombination often contain several successive markers at the same distance . Strategies to establish lines that allowed several rounds of interbreeding would capture more events . The long-range continuity of the genetic map served to order and in many cases orient more than 90 Mb of the sequence assemblies along the chromosomes . Combining this with linking information from read-pairs and ortholog local colinearity , additional order and orientation of the contigs was provisionally imposed on the map . By exploiting the conservation of synteny , another 9 Mb could be tentatively assigned to chromosomes , although not ordered along them . The conflicts between the genetic map and sequence assembly exposed misassemblies in the whole genome assembly . By carefully defining a set of 1:1 orthologous genes between the two species , the extensive conservation of synteny between the two species became more apparent and made clear that the problems lay in the assembly . The analysis also suggests at least another five regions of potential misassembly , each spanning more than 79 kb with a cluster of ten or more orthologs matching to a nonsyntenic chromosome . Smaller clusters of genes from nonsyntenic chromosomes also exist , but the fraction of these ( or indeed the larger clusters ) that represent assembly errors is uncertain . Positioning markers within these regions and testing them against the RI lines should distinguish misassembly from rearrangements . The integrated map revealed that organization into arms and centers for a number of features found in C . elegans is also present in C . briggsae . These include the rates of recombination as a function of physical distance ( Marey maps ) , the distribution of repeats and exons and the size of introns . Comparative analysis also shows a relative paucity of 1:1 orthologs in the arms as opposed to the centers , beyond that expected from the difference in exon density alone . The maintenance of this distinctive organization over approximately 200 My of evolution , and despite numerous intrachromosomal inversion events , strongly supports the selective advantage this organization confers . The enrichment for strongly conserved genes with yeast and for 1:1 orthologs in the centers suggests that genes are protected in this environment from the mutagenic effects of the high recombination and associated transposable element ( TE ) activity that is prevalent on the arms . By contrast , the arms are enriched for rapidly evolving gene families , where recombination , higher mutation rates , and TEs may facilitate family expansion and rapid gene adaptation [23] . The association between regions of higher recombination and more rapidly evolving genes has been reported in other species as well , including yeast [24] and Drosophila [25 , 26] . The genome map revealed a striking degree of synteny conservation . More than 95% of 1:1 orthologs remain on the same autosome despite the extensive evolutionary time since the MRCA . For the X chromosome , the conservation is even greater , with about 97% of orthologs remaining syntenic in accord with theory [27] . Even this may underestimate the extent of conservation , since misassemblies may still contribute to some of the nonsyntenic regions . The conservation of synteny in worms does not reflect a lack of overall rearrangements , however , since hundreds of rearrangements have occurred intrachromosomally . But within chromosomes , the observed breakpoints are not randomly distributed , with the block size much greater in the centers . Nor is there substantial mixing of the centers with the arms . The extensive conservation of synteny between C . elegans and C . briggsae may extend beyond the genus to more distantly related nematodes . Analyses of short stretches of Pristionchus pacifica and Brugia malayi with C . elegans genomic sequence suggest that , although local gene order may be altered over this evolutionary distance , orthologs remained overwhelmingly on a single chromosome [28 , 29] . Our results are in striking contrast to the observations in mammals . Mouse-human comparisons show extensive mixing of DNA between chromosomes [30] with the notable exception of the X chromosome . Using a simple two-hit model to account for the difference in chromosome number , and the ratio of break points attributable to translocations and intrachromosomal rearrangements in mammals to estimate the expected number of translocations in nematodes , the failure to observe any validated translocation events in nematodes is highly significant ( p < 0 . 0001 ) . Even within primates synteny is often not conserved . For example , human chromosome 2 represents a fusion of 2 smaller chromosomes present in the MRCA with chimpanzee [31] , and the gibbon branch has experienced an exceptional number of inversions and translocations [32 , 33] . Our findings are more similar to observations in the various species of the Drosophila genus . Muller recognized as early as 1940 that the chromosome arms of D . melanogaster are largely maintained as intact , though internally rearranged , units in other species of the genus . Recent analysis of genome sequences reveal just two pericentric inversions among the dozen species with a total branch length of more than 200 My [34] . The vast majority of genes remain on the same Mullerian element , as the arms have come to be called , although elements may fuse or split at centromeres and there are extensive rearrangements within elements . What accounts for the marked difference between the Caenorhabditis and Drosophila species and vertebrates ? We presume that interchromosomal translocations occur but rarely become fixed in these invertebrates . In contrast , such events appear to be fixed more frequently in vertebrates . The paucity of translocations in worms and flies might be explained , at least in part , by larger effective population sizes in invertebrates . Before fixation , the half translocations will suppress recombination and their segregation will produce aneuploid genotypes that would be selected against in both vertebrates and invertebrates . Larger effective population sizes ( Ne ) would lead to stronger selection against such unfavorable traits . Estimates of Ne for humans and for the common ancestor of humans and chimpanzees are 10 , 000 and 52 , 000–96 , 000 , respectively [35 , 36] . Estimates of Ne for C . elegans and C . briggsae are similar , 9 , 600 and 60 , 000 , respectively [21 , 37] . However , C . elegans and C . briggsae are hermaphroditic whereas their most recent common ancestor likely was dieocious [38] . C . remanei , the dieocious sister species of C . briggsae and perhaps more representative the ancestral populations , has an effective population size of approximately 1 , 000 , 000 [11] . Beyond differences in selection strength , the disruption of chromosome architecture may also contribute to the paucity of fixed translocations in the Caenorhabditis . Gene density of course is greater in Caenorhabditis so that translocations are more likely to disrupt genes . However , intrachromosomal rearrangements are abundant , making gene density less likely to be an important factor . As noted above , each of the autosomes has distinct domains with arms and centers displaying different characteristics . Most translocations would disrupt this architecture , presumably with unfavorable effects given the conservation of the structure over time . Also Caenorhabditis chromosomes are holocentric , with a kinetochore that spreads along the chromosome's entire length [39] . Perhaps these are associated with chromosome-specific sequences , with translocations producing a hybrid signal that might interfere with normal segregation . And formally , long-range interactions of genes on the same chromosome may be important , so that the particular combinations of genes on the different chromosomes may confer a selective advantage . Finally we cannot rule out that worms are more sensitive to differences in gene dosage . Compared to syntenic ortholog pairs , the small fraction of non-syntenic pairs is unusual in having a lower percent identity . Rather than arising through translocation , these small segments presumably arose by transposition-like events , creating at least temporarily duplicate genes . These events may have occurred before the time of the MRCA , with loss of the copy in one line and loss of the original gene in the other . In this case , the lower percent identity between apparent 1:1 ortholog pairs could reflect simply the longer divergence time of the genes compared to the species divergence . If , however , the duplication/loss events occurred after the MRCA , the lower percent identity might reflect rapid adaptation of the nonsyntenic gene to its new environment . The rapid evolution might be aided by the temporary presence of two gene copies . Alternatively , perhaps only weakly conserved genes tolerate a break in synteny . Either explanation would imply a strong effect of a chromosome-wide environment , since the effect is observed independent of position along the chromosome . By using SNPs and RI lines to create a dense genetic map , we have localized much of the whole genome shotgun sequence assembly to chromosomes , with the bulk of that oriented . The C . briggsae chromosomes have an organization similar to that of C . elegans , suggesting that the distinctive features of chromosome arms and centers are functionally important over evolutionary time . Further , our analysis suggests that nematodes , perhaps like insects , are strikingly different from mammals with respect to conservation of chromosome structure and the infrequent movement of genes between chromosomes , specifically with respect to chromosomal translocations . The strong conservation of synteny indicates that chromosomal levels of selection are operating , although it is unclear what functions are being selected for or against .
C . briggsae strains were obtained from the Caenorhabditis Genetics Center . AF16 was originally isolated in Gujarat , India [40] . HK104 and HK105 were derived from collections in Okayama , Japan ( H Kagawa ) . VT847 was collected in Hawaii , United States ( V Ambrose ) , whereas PB800 was isolated in Ohio , United States . AF16 and VT847 group in the tropical clade of C . briggsae , whereas HK104 , HK105 , and PB800 group in the temperate clade [10 , 11] . C . briggsae recombinant inbred lines ( RILs ) were constructed from the AF16 and HK104 parental strains and AF16 and VT847 parental strains [10] . RILs were constructed from F2 progeny of crosses between HK104 ( or VT847 ) males and sperm-depleted AF16 hermaphrodites . F2 larvae were picked as L4s and propagated through one hermaphrodite per generation from F2 to F11 . Genomic DNA was prepared from each of the strains [41] . The DNA was sheared , sized-selected , ligated into the pOT sequencing vector , and transformed into competent cells as described [42] . The resultant colonies were used to prepare plasmid DNA , which was sequenced as described [42] . The several levels of sequence assembly are defined as follows . Sequence contigs are assembled from overlapping sequence reads with no gaps . Supercontigs are constructed by linking contigs using read-pair information to span a gap . In turn , fpc contigs were constructed by aligning the supercontigs , where possible , to the clone-based physical map , and using the physical map continuity to link and orient supercontigs with respect to one another . We use “sequence assemblies” where it is not important to distinguish the different levels . The acronym “fpc” or FingerPrint Contigs is derived from the program fpc used in physical map construction ( Soderland et al . 1997 ) . SNP discovery/alignment methods: Each of the reads was initially aligned against the C . briggsae genome sequence , using WU-BLASTN ( S = 1000 , S2 = 150 , W = 13 , gapW = 4 , gapS2 = 150 , M = 5 , N = −11 , Q = 11 , R = 11 , B = 10000 , V = 10000 , hspmax = 1000 ) [43] . The alignments were then filtered for alignments over 100 bases long and greater than 96% identity . The top alignments by p-value were then re-aligned using CROSSMATCH ( P . Green , unpublished data ) using the following parameters: -masklevel 0 , –alignments , –discrep_lists . Discrepancies with quality values higher than 35 were then mapped backed to the C . briggsae genome . Marker selection and primer design: Design of FP-TDI genotyping assays was attempted for all putative SNPs in high-throughput fashion as previously described [44] . Flanking sequences were extracted from the cb25 . supercontigs . fasta assembly and masked for repetitive elements with RepeatMasker , using a customized library of C . briggsae repeats . However , the positions of nearby putative SNPs were not marked . PCR primers for the optimal melting temperature ( 54–56 ) and product size ( 80–400 bp ) were identified using Primer3 [45] . For each SNP that passed PCR primer design , Perl scripts identified the shortest extension primer of 16–40 bp with TM of 50–55 . If a suitable extension primer was not found in forward orientation , design on the reverse strand was attempted . Supercontigs in the C . briggsae cb25 . supercontigs . fasta whole-genome assembly with at least one assayable HK104 putative SNP were sorted by size from largest to smallest . One or two markers were selected for each supercontig until a total of 400 SNPs was reached . For supercontigs with more than two available SNPs , the markers with the lowest and highest contig positions were selected . FP-TDI: The SNPs were genotyped using the template-directed dye-terminator incorporation ( FP-TDI ) assay as previously described [46 , 47] . The FP-TDI assay required three unlabeled oligonucleotides for each SNP . Two served as PCR primers and the third was a SNP probe that was complementary to the template sequence with its 3′ end annealed to the target one base before the polymorphic site . The entire reaction was conducted in single reaction tube without separation or purification . The DNAs from the two RI line crosses were assembled in two 96-well trays including parental DNAs ( each duplicated as controls ) , and two no-DNA controls . The FP-TDI experiments were conducted in a 384-well plate format , typing two SNPs against the DNAs . . Kits ( AcycloPrime-FP , Perkin Elmer Life Sciences , http://www . perkinelmer . com ) were used for FP-TDI . Briefly , after a PCR step using a hot start Taq polymerase and two designed primers , Exonuclease I and shrimp alkaline phosphatase were added to digest remaining primers and inactivate deoxynucleotide triphosphates , and the enzymes were heat inactivated at the end of the digestion . For the TDI step , also called primer extension or minisequencing , the designed SNP primer , Taq polymerase from the kit , buffer , and the appropriate combination of dye terminators labeled with TAMRA or R110 dye were added and the samples were subjected to a thermocycling program . We detected incorporation of the dyes by measuring fluorescent polarization ( EnVision , Perkin Elmer Life Sciences ) . We further used quenching properties of the dyes to aid in scoring genotypes [48] . Genetic map construction: After quality control for genotyping , the genotypes , classified by SNP and RI line , were assembled in a text file . Using this text file , the genetic map was assembled as described in results using the program Map Manager QTXb20 ( http://www . mapmanager . org/ ) [15] . To confirm the order of chromosome CbX , single F2 worms , which were provided to us by Bhagwati Gupta ( McMaster University ) , were isolated from an AF16 x HK104 cross and placed in lysis buffer . We performed whole-genome amplification on each sample using a kit containing Phi 29 DNA polymerase according to the manufacturer's instructions ( GenomiPhi , GE Healthcare , http://www . gehealthcare . com ) . Some 95 animals typed with 11 markers on CbX were used to generate a new version of the genetic map using Map Manager QTXb20 . The results were consistent with version 3 . 3 ( unpublished data ) . Details of the genetic map are available ( Dataset S1 , http://snp . wustl . edu/ , and http://www . wormbase . org/ ) . Comparison to other genetic maps: Genes with molecular correlates in the current classical genetic map ( Bhagwati Gupta , personal communication ) were identified and placed on the C . briggsae integrated assembly . In turn , the C . elegans ortholog was identified along with its chromosomal location . No significant differences arose in comparison of these maps with the integrated map derived here . Methods for breaking sequence assemblies: For each assay the three markers were independently aligned to the genome sequence using WU-BLASTN , selecting the site with all three markers at expected intervals . For those sequence assemblies assigned to multiple linkage groups , we identified the interval where a transition occurred in the chromosomal assignment in groups of genes identified by 1:1 orthology ( see Methods below ) . We located any gaps between supercontigs in the interval ( usually only one ) and split the sequence assembly at that point , assuming there had been a false join . In the few instances where more than one gap lay in the interval , other alignments were used to determine the most likely site of the false join . Sequence assemblies were only broken when genetic mapping data dictated the break . Defining order/orientation: Sequence assemblies were localized to chromosomes and then to locations along those chromosomes based on the genetic positions of the assigned markers . Similarly , sequence assemblies were oriented based on the genetic position of multiple assigned markers . For adjacent ultracontigs where the genetic markers had identical genetic map positions , read-pairing data from the underlying whole-genome shotgun assembly were used where possible to assign order . Also for ultracontigs where the genetic markers did not establish orientation , we used read-pairing data with neighboring ultracontigs where possible to orient them . Rules for placing on Chr*_random: For those sequence assemblies remaining unlocalized after using the genetic mapping data , we assigned them to a specific chromosome in the Chr*_random bin if that assembly had at least six 1:1 orthologs ( defined as below ) on the majority chromosome and no more than four and less than 15% assigned to the secondary chromosome . The remainder were left on “chrUn” . Center versus arm boundaries in C . elegans and C . briggsae: We created recombination plots ( genetic versus physical location ) for both C . elegans and C . briggsae . From those data , we identified the inflection points that delineate central cluster region from the arms for both species ( Table 6 ) . Gene sets: We used both the C . briggsae hybrid gene set [8] obtained from WormBase ( versions brigpep2 . pep/cb25 . hybrid . gff ) and a set of genes based on homology with C . elegans confirmed genes ( L . Hillier and R . Waterston , unpublished data ) in our analyses . We mapped these genes onto the coordinates of our modified assembly and integrated genome sequence . For the exon and intron density plots and for all 1:1 ortholog calculations reported here , we used the hybrid gene set , whereas we used the alternative set for refining breakpoints in the fpc contigs as described . We obtained the C . briggsae integrated hybrid gene set [8] from WormBase ( versions brigpep2 . pep/cb25 . hybrid . gff ) and mapped that set to the new C . briggsae coordinates . For C . elegans , we created a nonredundant set of C . elegans genes from WormBase release 137 by retaining the longest gene per transcript for those with multiple transcripts per gene . Defining the C . elegans:C . briggsae orthologs and ortholog blocks: To define the C . elegans:C . briggsae 1:1 orthologs , for both the C . elegans gene set and the C . briggsae hybrid gene set we searched each gene set against itself and against each other using WU-BLASTP in two rounds first using ( filter = seg , V = 10000 , B = 10000 , hspmax = 10000 , -topcomboN = 1 ) and then rerunning the analyses removing filter = seg . Using the results from the WU-BLASTP with filter = seg , a gene was labeled as unique ( “1” ) if the best hit against its own protein set had a P-value exponent at least 29 larger than the P-value of the next best hit . We then examined the between-species matches . To qualify a match as a C . elegans:C . briggsae 1:1 ortholog , we required ( a ) that a p-value be at least as significant as 1× 10−09 between the sets , ( b ) that the gene be a “1” in C . elegans and a “1” in C . briggsae , ( c ) that the proteins be mutual best similarities , ( d ) that the top match was better by 10−29than the second best match and ( e ) at least 50% of the C . briggsae protein must align to at least 50% of the C . elegans protein . For requirements a , b , c , and d , the WU-BLASTP results using filter = seg were used . For requirement ( e ) , the WU-BLASTP results that were obtained not using filter = seg . We defined a syntenic ortholog as one localized to the same chromosome in both C . elegans and C . briggsae; a nonsyntenic ortholog was defined as one localized to different chromosomes . For a subset of the analyses , we removed clusters of more than three nonsyntenic orthologs . To define an ortholog block , we identified stretches of C . briggsae sequence where the C . elegans genes were on the same chromosome and in the same order as those in C . briggsae allowing only a single “out of order” C . elegans gene to interrupt a block and allowing no more than two C . elegans genes to be “missing”/moved . Repeats Repeatmasker [49] was run using the C . briggsae repeat library [8] to identify repeats in C . briggsae . For C . elegans , the repeat boundaries were downloaded from WormBase ( release 137 ) .
This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) under the project accession CAAC00000000 . The version described in this paper is the first version , CAAC01000000 . Accession numbers for the C . briggsae chromosomal sequences are: CU457376 , CU457377 , CU457378 , CU457379 , CU457380 , and CU457381 . The chromosomal assembly is also available at http://www . wormbase . org as “C . briggsae build CB3 . ” | The importance of chromosomal organization in the fitness of a species is only poorly understood . The publication of the C . elegans genome sequence in 1998 revealed features of higher level organization that suggested its chromosomes were organized into distinct domains . Chromosome arms were accumulating changes more rapidly than the centers of chromosomes . In this paper , we have compared the organization of the nematode C . briggsae genome with that of C . elegans . By building a genetic map based on DNA variations between two strains of C . briggsae , and by using that map to organize the draft genome sequence of C . briggsae published in 2003 , we found the following: ( 1 ) Intrachromosomal rearrangements are frequent within and even between arms but are less common within central regions and between arms and centers . ( 2 ) Genes have remained overwhelmingly on the same chromosomes . ( 3 ) The distinctive features that distinguish C . elegans arms from centers also are seen in C . briggsae chromosomes . The conservation of these features between these two species , despite the approximately 100 million years since their most recent common ancestor , provides clear evidence of the selective advantages of the domain architecture of chromosomes . The continuing association of genes on the same chromosomes suggests that this may also be advantageous . | [
"Abstract",
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] | 2007 | Comparison of C. elegans and C. briggsae Genome Sequences Reveals Extensive Conservation of Chromosome Organization and Synteny |
Type 1 diabetes ( T1D ) is a chronic multi-factorial disorder characterized by the immune-mediated destruction of insulin-producing pancreatic beta cells . Variations at a large number of genes influence susceptibility to spontaneous autoimmune T1D in non-obese diabetic ( NOD ) mice , one of the most frequently studied animal models for human disease . The genetic analysis of these mice allowed the identification of many insulin-dependent diabetes ( Idd ) loci and candidate genes , one of them being Cd101 . CD101 is a heavily glycosylated transmembrane molecule which exhibits negative-costimulatory functions and promotes regulatory T ( Treg ) function . It is abundantly expressed on subsets of lymphoid and myeloid cells , particularly within the gastrointestinal tract . We have recently reported that the genotype-dependent expression of CD101 correlates with a decreased susceptibility to T1D in NOD . B6 Idd10 congenic mice compared to parental NOD controls . Here we show that the knockout of CD101 within the introgressed B6-derived Idd10 region increased T1D frequency to that of the NOD strain . This loss of protection from T1D was paralleled by decreased Gr1-expressing myeloid cells and FoxP3+ Tregs and an enhanced accumulation of CD4-positive over CD8-positive T lymphocytes in pancreatic tissues . As compared to CD101+/+ NOD . B6 Idd10 donors , adoptive T cell transfers from CD101−/− NOD . B6 Idd10 mice increased T1D frequency in lymphopenic NOD scid and NOD . B6 Idd10 scid recipients . Increased T1D frequency correlated with a more rapid expansion of the transferred CD101−/− T cells and a lower proportion of recipient Gr1-expressing myeloid cells in the pancreatic lymph nodes . Fewer of the Gr1+ cells in the recipients receiving CD101−/− T cells expressed CD101 and the cells had lower levels of IL-10 and TGF-β mRNA . Thus , our results connect the Cd101 haplotype-dependent protection from T1D to an anti-diabetogenic function of CD101-expressing Tregs and Gr1-positive myeloid cells and confirm the identity of Cd101 as Idd10 .
Type I diabetes ( T1D ) is a complex autoimmune disease driven by multiple genetic traits and facilitated by various immune cells infiltrating the pancreatic islets . In rodent models such as the frequently studied non-obese diabetic ( NOD ) mouse , myeloid cells including macrophages , dendritic cells ( DCs ) and neutrophils are the first cells to accumulate in the pancreas [1–4] and contribute to the initiation and perpetuation of the T cell-driven pancreatic islet destruction [5] . Myeloid-derived suppressor cells ( MDSCs ) and regulatory T cells ( Tregs ) , in contrast , suppress diabetogenic immune cells and hinder the development of T1D [6 , 7] . While the transcription factor FoxP3 identifies Tregs , the delineation of MDSCs from other myeloid cell subsets is challenging and requires a scrutinized investigation . Tregs are pivotal for the maintenance of immune homeostasis . Their ability to suppress other immune cells maintains tolerance to self-antigens and prevents autoimmune disease . The depletion of Tregs promotes the development of T1D while their transfer or therapeutic enhancement exhibits protective effects [8–10] . Compared to other reference strains , some studies reported primary deficits in Treg numbers of NOD mice [8 , 11–13] , whereas others did not [14–19] . Although Treg frequencies in T1D patients appear normal in most studies , defects in the phenotype and the suppressive capacity of Tregs have been reported [20–23] . In mice , induced NOD-derived Tregs are also less effective in standard in vitro suppression assays and reveal subtle defects in the expression of distinct genes [18] , although the relevance of these genes on Treg function and on the induction of T1D in NOD mice remains to be determined . An age-related decline in Treg function of NOD mice over time is also pivotal for T1D development in NOD mice [14–16] . Various myeloid cell populations and myeloid cell responses are frequently altered and impeded in NOD mice [24] . Thus , the development of precursors to dendritic cells ( DCs ) and macrophages , for example , is hampered in NOD mice [25–27] as well as the maturation of myeloid DCs [28] . In addition , the recruitment of neutrophils to sites of infection is severely impaired [24] . The molecular signals underlying these phenotypes , however , have rarely been identified . CD101 is a negative costimulatory molecule expressed on subsets of myeloid and lymphoid cells [29–33] . Upon engagement of CD101 by agonistic antibodies myeloid cells are induced to be immunosuppressive in vitro [34] . In vitro , CD101+ Tregs are more suppressive than their CD101− counterparts [30] . In vivo , CD101 perpetuates the suppressive function of Tregs and reduces the development of T1D and chronic colitis [30 , 31 , 35] . Furthermore , CD101+ myeloid cells release more IL-10 than CD101− myeloid cells [35] . In addition , reduced CD101 expression is observed in T1D and Inflammatory Bowel Disease patients [35 , 36] . Rare polymorphisms in the Cd101 gene have been suggested to underlie the reduced CD101 expression in some T1D patients [36] . We have identified Cd101 as a T1D candidate gene within the Idd10 region using multiple Idd10 congenic strains [37] . Susceptibility to T1D was correlated with genotype-dependent CD101 expression on multiple cell subsets , including Foxp3+ Tregs , CD11c+ dendritic cells , and Gr1+ myeloid cells [31] . To evaluate the impact of CD101 on T1D , we introgressed the Idd10 and Idd10/Idd18 regions from a B6 CD101 KO strain onto the NOD background and observed that T1D protection mediated by the B6-derived Idd10 and Idd10/Idd18 regions was lost in CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/Idd18 mice . The loss of CD101 expression reduced the frequency of Tregs and transformed anti-inflammatory Gr1-expressing myeloid cells into an inflammatory , disease-promoting subset . Thus , our data further confirm the identity of Cd101 as Idd10 and provide cellular mechanisms by which the molecule mediates its protection from T1D .
Auto-reactive T cells are initially primed in the pancreatic lymph nodes of NOD mice beginning at the age of three weeks [5] and attracted by an infiltration of innate immune cells into the pancreatic islets one to three weeks later [38] . As we had observed that CD101 expression was genotype-dependent in bone marrow and spleen in NOD Idd10 congenic strains [31] , we assessed the influence of Cd101 gene variation on the distribution of immune cells in peripheral lymph nodes as compared to spleen . In both lymph nodes and spleen CD101 expression was observed on a portion of T cells and myeloid cells ( Fig 1A , S1A and S1B Fig ) . T cells constituted the majority of CD101-expressing cells in peripheral lymph nodes but not in the spleen ( S1C Fig ) . A small portion of the CD101-expressing cells were neither T cells nor CD11b+ ( S1C Fig ) . We observed an increased proportion of CD101-positive T cells and of CD101-positive Tregs which represent the largest CD101-positive subset within the T lymphocyte population [29–33] in the pancreatic lymph nodes of 4-8-week-old NOD and NOD . B6 Idd10 mice compared to other peripheral lymph nodes ( Fig 1A and 1B; S2 Fig , S3 Fig ) . The percentages of CD101-positive T cells between NOD and NOD . B6 Idd10 mice were comparable ( S3B Fig ) while the mean fluorescence intensity for CD101 was often enhanced on T cells from NOD . B6 Idd10 mice ( S3C Fig ) , a phenotype observed previously in splenic T cells [31] . CD101-expressing Tregs in pancreatic , but not popliteal , lymph nodes of NOD . B6 Idd10 mice were more frequent at the two later time points assessed compared to NOD mice ( Fig 1C and 1D; S4A Fig ) , while Treg frequencies themselves in both organs remained comparable ( Fig 1C and 1E; S4B Fig ) . The proportion of other CD101-expressing myeloid and lymphoid subsets was also similar between NOD and NOD . B6 Idd10 mice ( S5 Fig ) . Thus , the introgression of the B6 Idd10 region not only promotes the expansion of Gr1-positive myeloid cells in the bone marrow [31] but also favors at later time-points an accumulation of CD101-expressing Tregs in the pancreatic lymph nodes . Based on sequence comparisons of four Idd10 regions tested for T1D susceptibility and the observation that susceptibility to T1D correlated with genotype-dependent CD101 expression on multiple immune cell subsets , Cd101 is the prioritized gene candidate for the Idd10 region [31] . We therefore reasoned that if Cd101 is Idd10 , elimination of CD101 protein expression should alter T1D susceptibility , a finding that would further strengthen our hypothesis that allelic variation in the structure or expression of CD101 influences T1D frequency in the context of the NOD background . Following deletion of a portion of the B6 Cd101 gene required for protein expression [31] the T1D-protective Idd10 and Idd10/18 regions carrying the Cd101 modification were introgressed onto the NOD background by genotype-selected backcrossing to generate congenic strains ( Fig 2A ) . We wanted to examine the effect of eliminating CD101 expression both in the context of the B6-derived protective Idd10 region , but also in the context of the more complex Idd10/18 region where multiple Idd subregions have been defined [39 , 40] and are depicted on Fig 2A . Therefore , as backcrossing of the B6 CD101−/− strain to the NOD strain occurred , we screened progeny for recombination events that most closely resembled those defining the boundaries of the regions in previous studies in order to later characterize the strains versus the congenic regions with intact B6 Cd101 alleles . As was observed on the B6 background [31] , the engineered deletion within the Cd101 gene led to a lack of CD101 protein expression on all myeloid and T cells in CD101−/− NOD . B6 Idd10 ( Fig 2B; S2 Fig ) and CD101−/− NOD . B6 Idd10/18 mice ( Fig 2C ) . We have recently reported a correlation of CD101 expression on immune cells from four independent Idd10 haplotypes with the development of T1D [31] . Thus , to further establish the causative role of Cd101 in the pathogenesis of T1D , we evaluated T1D frequencies in our newly generated CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/18 mice compared to parental CD101-expressing NOD . B6 Idd10 and NOD . B6 Idd10/18 controls . We observed that CD101-expressing NOD . B6 Idd10 and NOD . B6 Idd10/18 mice developed T1D substantially slower and with a reduced incidence than their CD101-deficient counterparts in two different animal facilities ( Fig 3 ) . Indeed , CD101−/− NOD . B6 Idd10 mice and CD101−/− NOD . B6 Idd10/18 mice had frequencies of diabetes equivalent to that of NOD mice housed in the same colony ( Fig 3A , 3C and 3D ) . Contemporaneously , cohorts of CD101+/+ and CD101−/− NOD . B6 Idd10 mice as well as CD101+/+ and CD101−/− NOD . B6 Idd10 progeny from heterozygous CD101+/− NOD . B6 Idd10 intercross breeders were monitored for diabetes ( Fig 3A and 3B ) . In the latter comparison CD101-replete and CD101 KO Idd10 homozygous progeny are part of the same litters and therefore exposed to the same micro-environment . Once again , mice homozygous for the CD101 KO Idd10 region had a higher frequency of diabetes than those having two doses of the intact B6-derived Idd10 region ( Fig 3B ) ; however , the difference in diabetes occurrence was less significant between the homozygous genotypes derived from heterozygous breeders than when the mice had been bred from homozygous parents ( Fig 3A ) . CD101+/− NOD . B6 Idd10 heterozygous progeny had a diabetes frequency intermediate between those of CD101+/+ and CD101−/− NOD . B6 Idd10 progeny ( S6 Fig ) . Protection from diabetes was associated with a significantly reduced infiltration of pancreatic islets by immune cells in CD101-expressing congenic mice at 8–10 weeks of age ( Fig 3E , S7 Fig ) . Thus , these data confirm the protective role of the B6 Cd101 allele within the Idd10 region and strongly suggest that the gene encoding CD101 is Idd10 . In order to define the CD101-expressing cell subset ( s ) promoting protection from T1D , we assessed the distribution of myeloid and lymphoid cells in the organs of CD101-expressing and CD101-deficient NOD . B6 Idd10 and NOD . B6 Idd10/18 mice . The inflammatory infiltrate in the pancreata of CD101−/− NOD . B6 Idd10/18 and CD101−/− NOD . B6 Idd10 mice and respective CD101-expressing controls consisted mainly of T lymphocytes ( Fig 4A ) and few Gr1-expressing myeloid cells ( Fig 5A and 5B ) . There was an enhanced proportion of CD4- over CD8-positive T lymphocytes within the TCRβ+ population in both CD101−/− strains compared to their CD101-expressing counterparts ( Fig 4B–4D ) . While the NOD versus B6 Idd10 haplotypes revealed no differences in Treg percentages in the pancreatic lymph nodes ( Fig 1E ) , Tregs were significantly reduced in the pancreatic lymph nodes ( Fig 4E ) , but not the spleens ( Fig 4F ) of CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/18 mice compared to CD101-expressing NOD . B6 Idd10 and NOD . B6 Idd10/18 mice . Popliteal lymph nodes revealed also comparable Treg percentages ( S8 Fig ) . Thus , together with the improved function of the B6 over the NOD Cd101 allele the increased T1D frequency in CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/18 mice as compared to their CD101+/+ counterparts is associated with a reduction of Tregs in pancreatic lymph nodes suggesting that CD101 acts locally at the site of T cell priming . We have recently reported a correlation between the CD101-dependent distribution of Gr1-positive myeloid cells and the susceptibility to T1D [31] . Thus , we evaluated the distribution of different Gr1-expressing myeloid cell populations in CD101-expressing and CD101-deficient NOD . B6 Idd10 and NOD . B6 Idd10/18 mice . Similar to B6 CD101−/− mice [31] , CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/18 mice have a reduction of Gr1-positive cells in the bone marrow compared to CD101-expressing congenic controls ( S9A Fig ) . A similar tendency was also observed in the spleen ( S9B Fig ) . To further characterize the Gr1-positive population , we used additional markers and also investigated its distribution in the pancreatic islets and pancreatic lymph nodes . As previously reported for NOD mice [4] we observed myeloid immune cells in the infiltrates of the pancreatic islets of CD101-expressing NOD . B6 Idd10 and CD101-deficient NOD . B6 Idd10 mice . Strikingly , however , cells infiltrating the pancreatic islets of CD101−/− NOD . B6 Idd10 mice contained substantially smaller proportions of both CD11b+ Gr1− and CD11bhigh Gr1+ cells than their CD101-expressing counterparts ( Fig 5A ) . In contrast , the CD11b+ Gr1− population was detected equivalently in the CD101−/− and CD101+/+ NOD . B6 Idd10 strains in the pancreatic lymph nodes ( Fig 5B ) . The distribution of CD11c and F4/80 within this CD11b-positive subset in the pancreatic lymph nodes was also comparable in these two strains ( S10 Fig ) . Similar to the islet infiltrating cells , the proportion of CD11bhigh Gr1+ cells , consisting of neutrophils and myeloid-derived suppressor cells ( MDSCs ) [41–43] , were significantly reduced in the pancreatic lymph nodes of CD101−/− as compared to CD101+/+ NOD . B6 Idd10 mice ( Fig 5B ) . To study the function of CD101 on T cells in the T1D model we purified T cells from the spleens of CD101−/− and CD101-expressing NOD . B6 Idd10 mice and transferred the CD4- and CD8-positive T cell population into lymphopenic NOD scid recipients . The combined transfer of CD4- and CD8-positive T lymphocytes increased T1D frequency in recipients of donor T cells originating from CD101−/− NOD . B6 Idd10 mice compared to donor T cells originating from CD101-expressing NOD . B6 Idd10 controls ( Fig 6A ) . The increased frequency was modest , just reaching significance ( p = 0 . 038 ) . The higher frequency of T1D was accompanied by a more rapid expansion of T cells from CD101−/− NOD . B6 Idd10 donors ( Fig 6B and 6C ) . Furthermore , similar as observed in CD101−/− NOD . B6 Idd10 mice ( Fig 4E ) , less FoxP3+ Tregs in relation to CD4-positive T cells accumulated in the pancreatic lymph nodes of NOD scid recipients from CD101−/− NOD . B6 Idd10 donors than from CD101-expressing NOD . B6 Idd10 donors ( Fig 6D ) . Thus , these data support the hypothesis that CD101 expression on T cells reduces effector T cell expansion , a conclusion also reached in our T cell transfer colitis studies [35] . We had observed that CD101-expressing myeloid cells decreased upon transfer of naïve CD4+ T cells [35] . Thus , we evaluated the composition of myeloid cells and the distribution of CD101 expression in pancreatic islets of NOD scid recipient mice upon combined CD4+/CD8+ T cell transfer from CD101-expressing and CD101-deficient NOD . B6 Idd10 donors . Interestingly , significantly fewer Gr1-positive myeloid cells derived from NOD scid recipients accumulated in the pancreatic lymph nodes upon T cell transfer from CD101−/− NOD . B6 Idd10 donors as compared to T cells transferred from CD101+/+ NOD . B6 Idd10 donors ( Fig 7A and 7B ) . Furthermore , when CD101−/− NOD . B6 Idd10 donor T cells were transferred fewer of the accumulating Gr1-positive myeloid cells expressed CD101 ( Fig 7A and 7C ) and the Gr1-positive myeloid cells produced less TGF-β ( Fig 7D ) and IL-10 ( Fig 7E ) than their CD101-expressing counterparts , as observed previously for CD101-expressing myeloid cells in the gut [35] . To further characterize the role of the B6 Cd101 allele on myeloid cells for the protection from T1D , we generated NOD . B6 Idd10 scid mice and assessed T1D incidence in these recipients as compared to NOD scid recipients upon T cell transfer from CD101+/+ NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10 donors . The development of T1D was significantly ameliorated ( p = 0 . 007 ) when T cells from CD101-expressing NOD . B6 Idd10 donors were transferred into NOD . B6 Idd10 scid as compared to NOD scid recipients indicating that in the presence of the CD101 protein encoded by the B6 Cd101 allele in the T cell compartment , the status of the Cd101 allele expressed by myeloid cells determines the level of protection from T1D ( Fig 8A ) . When CD101−/− NOD . B6 Idd10 donor T cells were used in the adoptive transfer , fewer NOD . B6 Idd10 scid recipients than NOD scid recipients developed T1D but the difference was not significant ( p = 0 . 2; Fig 8B ) . In contrast to the results in Fig 6A where CD101−/− NOD . B6 Idd10 T cells mediated a modest increase in T1D upon transfer into NOD scid recipients as compared to CD101+/+ NOD . B6 Idd10 T cells , no difference ( p = 0 . 35 ) was observed in a repeat of the same transfer combination ( Fig 8A and 8B ) . A consideration of both results supports the conclusion that the effect of CD101 expression in T cells for influencing T1D progression is marginal when CD101 expression in the myeloid compartment is encoded by the NOD Cd101 allele rather than the B6 Cd101 allele . Overall our data support the hypothesis that the expression of the B6 CD101 molecule is not only important for limiting the aggressiveness of diabetogenic T cells , but also promotes the function of disease-limiting myeloid cell subsets .
Allelic variations within Cd101 have been previously associated with susceptibility to T1D [31 , 37] . Here , we provide further evidence that Cd101 is Idd10 as the genetic deletion of CD101 within the introgressed B6 Idd10 region abolishes protection from T1D . Significantly reduced Treg frequencies in the pancreatic lymph nodes in these newly generated CD101−/− NOD . B6 Idd10 mice and an enhanced T cell expansion upon adoptive transfer accompanied the loss of protection from T1D observed in the CD101−/− NOD . B6 Idd10 strain . Furthermore , the proportion of CD101-expressing CD11b-positive myeloid cells in recipient scid mice was reduced upon transfer of CD101−/− donor T cells correlating with a reduced IL-10 and TGF-β mRNA . Myeloid cells from CD101-expressing NOD . B6 Idd10 mice also accumulated more frequently in pancreatic tissues than myeloid cells from CD101−/− NOD . B6 Idd10 mice . Thus , the interplay of CD101-expressing Tregs [30 , 35] with CD101-positive myeloid cells appears to perpetuate an anti-inflammatory cytokine profile and limits the onset of T1D . CD101 exhibits intrinsic effects on Treg differentiation and function and promotes the production of IL-10 by myeloid cells [35] . Here , we confirmed the pivotal role of simultaneous expression of the B6 Cd101 allele within the myeloid cell and T lymphocyte compartment for the most complete protection from T1D . Based on similar T1D frequencies in NOD and CD101−/− NOD . B6 Idd10 mice we hypothesize that the NOD CD101 allotype present in NOD mice may not function properly due to the 10 amino acid differences from the B6 CD101 protein [37] , and thus , resembles the CD101 knockout . Furthermore , the B6 Idd10 allele enhances the expression of CD101 protein on Tregs and Gr1-expressing myeloid cells [31] . A greater understanding of the signaling and cellular interactions mediated by the CD101 protein is required to determine how the B6 and NOD Cd101 alleles mediate , or fail to mediate , their functions . As Gr1-expressing myeloid cells in the bone marrow are precursors for multiple myeloid lineages in the periphery , we investigated the myeloid cell composition and the distribution of myeloid surface markers in pancreatic tissues and lymph nodes in more detail . However , with the exception of CD11b , we did not detect significant differences in the expression of F4/80 , Ly6C or Ly6G between Gr1-expressing myeloid cells from CD101-expressing and CD101-deficient NOD . B6 Idd10 mice . As significantly more Gr1-positive CD11b-positive myeloid cells accumulated in CD101+/+ NOD . B6 Idd10 mice than in CD101−/− NOD . B6 Idd10 mice , CD101 might promote the maturation and function of this myeloid subset . In accordance with the anti-inflammatory cytokine profile , these Gr1-expressing cells might reflect MDSCs , which have been reported to suppress T1D [44] . However , the origin of this myeloid subset and its classification into granulocytes or MDSCs need to be assessed . Alternatively , these Gr1-expressing myeloid cells might consist of a plastic MDSC and neutrophil mixture which exert an anti-inflammatory cytokine profile dependent on the expression of CD101 . Alternatively , CD101 might represent a functional marker separating inflammatory neutrophils from immunoregulatory MDSCs . Further studies are needed to delineate whether alterations in the development and/or generation of additional myeloid subsets are promoted by CD101-deficiency since NOD mice have been reported to have multiple alterations in DC subsets compared to B6 mice , for example [45 , 46] . Our observation that CD101 affects Gr1-expressing cells is also of clinical relevance as reduced neutrophil counts in the peripheral blood and enhanced neutrophil activity have been reported in T1D patients [47–49] . Neutrophil infiltration and neutrophil extracellular trap formation are also detected in the islets of NOD mice as early as two weeks after birth , well before the onset of overt diabetes . The blockade of neutrophil activities or neutrophil depletion at these early stages reduces the development of insulitis and diabetes in NOD mice [4] . Thus , the reduced neutrophil counts in the periphery of CD101−/− NOD . B6 Idd10 mice might be a consequence of one or more of the following reasons: 1 ) impairment in the output of neutrophils from the bone marrow and/or the differentiation of neutrophils; 2 ) increase in peripheral consumption/destruction; 3 ) tissue sequestration . Based on our previous studies [31] , we suspect that a bone marrow defect affects either the egress or the generation of myeloid cell precursors . Decreased T1D frequency has been shown to be associated with enhanced CD101 expression on splenic Tregs of congenic NOD . B6 Idd10 mice compared to parental NOD mice [31] . In the current study , we observed that significantly more Tregs accumulated in the pancreatic lymph nodes of CD101+/+ NOD . B6 Idd10 compared to CD101−/− NOD . B6 Idd10 mice , confirming the positive effect of CD101 on Treg differentiation and function [30 , 35 , 50] . Thus , although NOD mice , similar to T1D patients , do not have a primary deficit in Treg percentages or numbers compared to other reference strains [17 , 18 , 51–53] , our data imply that the B6 Cd101 allele primarily affects Tregs within the T cell compartment . These animal studies are also in line with a recent report on distinct T1D patient cohorts raising the possibility of CD101 being a susceptibility gene for human T1D [36] . In addition , since IL-2 administration acts via pancreatic Tregs [54] and CD101 sensitizes Tregs to IL-2 signals [35] , the accumulation of CD101+ Tregs in NOD mice carrying the B6 Cd101 allele likely contributes to the protection from T1D . CD101 expression restrains the accumulation and expansion of diabetogenic CD4- and CD8-positive T lymphocytes and reduces T1D frequency in lymphopenic recipient mice upon mixed CD4/CD8 T cell transfers from CD101+/+ NOD . B6 Idd10 mice . In particular , CD8-positive T cells might be interesting to study further since recent reports claimed altered functions between CD101-expressing CD8-positive T lymphocytes and their CD101-negative counterparts [55 , 56] . Thus , in summary , our data clearly indicate that CD101 promotes the accumulation of anti-inflammatory lymphoid and myeloid cells and slows or halts disease in an autoimmune-prone background when sufficiently expressed .
The experiments were conducted according to the Institutional Animal Care and Use Committee guidelines of the Cincinnati Children’s Hospital ( IACUC protocol number Protocol 8D02011 ) and approved by the Animal Welfare Committee of the local government ( Regierung von Mittelfranken , Ansbach , Germany; protocol: 54–2532 . 1-30/10 ) . Daily inspections were performed to minimize animal suffering . Mice with signs of discomfort or disease were euthanized immediately by C02 and cervical dislocation . NOD/MrkTac ( NOD ) and NOD scid mice were obtained from Taconic Farms ( Germantown , NY , USA ) . The development of the NOD . B6 Idd10 ( N16 ) ( Taconic line 3538 ) and NOD . B6 Idd10/18 ( N10 ) ( Taconic line 7754 ) strains were described previously [31 , 39] . NOD . B6 Idd10 and NOD scid mice were intercrossed and F2 mice homozygous for the Idd10 and scid-containing regions selectively bred to develop the NOD . B6 Idd10 scid strain . B6 CD101−/− mice [31] were backcrossed onto the NOD background to develop the CD101−/− NOD . B6 Idd10 ( N11 ) and CD101−/− NOD . B6 Idd10/18 strains ( N10 ) . Polymorphic markers near and within the Idd10 and Idd18 regions were used to define recombination events as similar as possible to the boundaries of these regions ( S1 Table ) as previously defined . The CD101−/− NOD . B6 Idd10 and CD101−/− NOD . B6 Idd10/18 strains were free of B6-derived genetic segments outside of the selected areas as defined by screening with a 1449 polymorphic marker panel as described previously [31] . All mice were raised and kept in a specific pathogen-free environment and used at 3–15 weeks of age for cellular and molecular analyses and for T1D frequency studies until the age of 200 days . The appropriate institutional review committee approved the T1D frequency studies performed at Taconic Farms . Mice were euthanized and pancreata were perfused with a 1 . 5 mg/mL solution of collagenase P ( Roche Molecular Biochemicals , Mannheim , Germany ) , dissected from surrounding tissues and cut into small pieces . The digestion buffer was supplemented with 1 mM PMSF , 100 μM leupeptin and 1 μM pepstatin A ( Sigma-Aldrich , Taufkirchen , Germany ) . Pancreata were digested at 37°C for 10 min in a shaking water bath . The digestion was stopped by adding HBSS containing 5% FCS . The tissue suspension was washed three times and centrifuged through a discontinuous Ficoll gradient ( 23 , 20 . 5 and 11%; Sigma-Aldrich , Taufkirchen , Germany ) at room temperature . The purified islets were disrupted by adding 1 mL of cell dissociation buffer ( GIBCO/Thermo Fisher Scientific , Waltham , MA , USA ) for 10 minutes at 37°C . The obtained cells were washed , resuspended and used for analyses . 5x107 spleen cells enriched in TCRβ-positive T cells ( consisting of about 2/3 CD4-positive T cells and of about 1/3 CD8-positive T cells ) from 6-8-week-old donor female mice were transferred intraperitoneally into 6-8-week-old NOD scid or NOD . B6 Idd10 scid recipients . B cells , NK cells , DCs , granulocytes or macrophages were depleted using Auto-MACS ( Miltenyi Biotec , Bergisch Gladbach , Germany ) and PE- or APC-fluorescence coupled beads against CD19 , NKp46 , Gr1 , CD11c or F4/80 from NOD mice before transfer following the manufacturer's instructions with purity control by FACS . For cell division studies donor T cells were labeled with 5 μM CFSE prior to transfer according to the manufacturer's instructions ( Molecular Probes/Thermo Fisher Scientific , Waltham , MA , USA and BD Pharmingen/BD Biosciences , San Diego , CA , USA ) . Single-cell suspensions were prepared from the spleen , lymph nodes , pancreas and bone marrow . Red blood cells were not removed . Cell-surface expression of CD45 . 1 ( clone A20 ) , CD45 . 2 ( clone 104 ) , CD11c ( clone N418 ) , CD11b ( clone M1/70 ) , Ly6C ( clone HK1 . 4 ) , Ly6G/Gr1 ( clone RB6-8C5 ) , F4/80 ( clone BM8 ) , B220 ( clone RA3-6B2 ) , the β-chain of the TCR ( clone H57-597 ) , CD3 ( clones 145-2C11 and 17A2 ) , CD4 ( clone GK1 . 5 ) and CD8a ( clone 53–6 . 7 ) was detected using fluorescently labeled mAbs obtained from eBioscience ( San Diego , CA , USA ) . PE-labeled anti-CD101 ( clone 307707 ) was obtained from R&D Systems . Intracellular Foxp3 was detected with a staining kit following the manufacturer´s instructions ( eBioscience ) . Cells were analyzed on an LSR II or a BD FACS Canto II ( BD Biosciences , San Diego , CA , USA ) with FlowJo software ( Tree Star , Ashland , OR , USA ) . Gr1-expressing cells were purified on a FACS Aria II ( BD Biosciences , Franklin Lakes , NJ , USA ) ( purity of > 98% ) . All diabetes cumulative frequency studies were conducted using female mice . The presence of T1D was tested every 14 d beginning at 84 d of age by the detection of urinary glucose >500 mg/dl using Diastix ( Miles , Elkhart , IN , USA ) . Overt diabetes was confirmed by a blood sugar level of >200 mg/dl . Studies were terminated at 196 d of age . Kaplan–Meier survival curves were plotted for each mouse strain , and these were compared using the log rank test ( Prism4 software; GraphPad ) . Pancreatic tissue was fixed in 10% buffered formalin , embedded in paraffin , and cut into 5 μm thick sections . Pancreas sections were deparaffinized , stained with H&E by the Department of Pathology and the Medical Department I of the FAU Erlangen-Nürnberg , and evaluated microscopically in a double-blinded manner . H&E–stained sections were scored for insulitis . At least 10 islets per mouse present on two or three non-adjacent pancreas sections were scored as either 0 , no infiltration; 1 , peri-insulitis; 2 , mild-invasive insulitis; or 3 , severe invasive insulitis . The average score of each pancreas was calculated and used for statistical analysis . cDNA was synthesized using a High-capacity cDNA Reverse Transcription Kit ( Applied Biosystems/Thermo Fisher Scientific , Waltham , MA , USA ) following the manufacturer’s instructions . Quantitative ( q ) -PCRs were performed as described [33] using specific primers ( Thermo Scientific ) and pre-designed probes ( Roche , Basel , Switzerland ) : TGF-β ( forward: 5´- gtggtgtccccacacagg-3´; reverse: 5´-ccagggctgtaaccacttg-3´ ) and IL-10 ( forward: 5´- cagagccacatgctcctaga-3´; reverse: 5´-gtccagctggtcctttgttt-3´ ) . Gene expression was calculated relative to the house keeping gene HPRT ( forward: 5´-tcctcctcagaccgctttt-3´; reverse: 5´-cctggttcatcatcgctaatc-3´ or Applied Biosystems assay Mm00446968_m1 ) using the ΔΔCt algorithm . Samples were analyzed for normal distribution by a Kolmogorov-Smirnov test . According to the results , statistical significance in normal distributed samples were analyzed by one-way ANOVA with posthoc test ( Bonferroni ) and Student’s t-test , and samples failing the normal distribution test by Kruscal-Wallis Test with posthoc ( Dunn’s multiple comparison ) or Mann-Whitney U test as indicated in the respective experiments . A sample size of at least three ( n = 3 ) was used for each sample group in a given experiment , and a p value of 5% ( *; p 0 . 05 ) , 1% ( **; p 0 . 01 ) , 0 . 1% ( ***; p 0 . 001 ) or 0 . 01% ( ****; p 0 . 0001 ) was considered significant to accept the alternate hypothesis . GraphPad Prism software was used for statistical analysis . | The complex interplay of environmental factors and genetic traits determines the susceptibility of an individual to autoimmune disease such as type 1 diabetes ( T1D ) . Despite T1D being one of the most common and most studied polygenic autoimmune disorders , the mechanisms underlying the immune-mediated destruction of the insulin-producing pancreatic beta cells are still largely unknown . Genetic association studies identified many DNA sequence variants that confer risk to or protect from autoimmune disease . In this regard , we have identified a single gene , Cd101 , as a T1D susceptibility locus . In accordance with our previous studies in which we reported an association of allelic Cd101 variants on T1D prevalence , we observed here that deletion of Cd101 perpetuated the expansion of pathogenic , pancreas-infiltrating immune cells and subsequently enhanced T1D incidence . The mechanisms by which Cd101 variants interfere with autoimmune responses will allow us to understand the regulation of molecules in autoimmunity in general as diabetes susceptibility loci have been associated with other autoimmune diseases . Consequently , our work will help to identify therapeutic approaches that can be used to guide the development of effective therapies for T1D , but also allows the identification of common targets in autoimmune disease for clinical intervention in the future . | [
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... | 2019 | Genetic and functional data identifying Cd101 as a type 1 diabetes (T1D) susceptibility gene in nonobese diabetic (NOD) mice |
Polycomb group ( PcG ) proteins are part of a conserved cell memory system that conveys epigenetic inheritance of silenced transcriptional states through cell division . Despite the considerable amount of information about PcG mechanisms controlling gene silencing , how PcG proteins maintain repressive chromatin during epigenome duplication is still unclear . Here we identified a specific time window , the early S phase , in which PcG proteins are recruited at BX-C PRE target sites in concomitance with H3K27me3 repressive mark deposition . Notably , these events precede and are uncoupled from PRE replication timing , which occurs in late S phase when most epigenetic signatures are reduced . These findings shed light on one of the key mechanisms for PcG–mediated epigenetic inheritance during S phase , suggesting a conserved model in which the PcG–dependent H3K27me3 mark is inherited by dilution and not by de novo methylation occurring at the time of replication .
The genes of the Polycomb group ( PcG ) prevent changes in cell lineage identity by maintaining silenced transcription patterns throughout cell division via chromatin structure [1] . To date , four PcG-encoded protein complexes have been isolated from different organisms: Pho Repressive Complex ( PhoRC ) , Polycomb Repressive Deubiquitinase Complex ( PR-DUB ) , Polycomb Repressive Complex 1 ( PRC1 ) and 2 ( PRC2 ) . Biochemical studies revealed that the zinc finger protein Pleiohometic ( PHO ) of PhoRC is required for PRC2 targeting [2]–[4] while Enhancer of zeste ( E ( Z ) ) , the Histone Methyl Transferase ( HMTase ) subunit of PRC2 , marks lysine 27 of histone H3 [5]–[8] . This chromatin mark is specifically recognized by PRC1 complex through the chromo-domain present in the Polycomb protein ( PC ) [5] . PRC1 complex has several catalytic functions believed to be important for transcriptional repression . By electron microscopy , it has been shown that PRC1 induces compaction of defined nucleosomal arrays in vitro [9] . Components of PRC1 can also function as E3 ligase for H2A ubiquitylation [10] . On other hand , PR-DUB complex is able to deubiquinate H2A [11] and , interestingly , both activities are required for proper gene silencing in vivo . In Drosophila , PcG function is mediated by specialized epigenetic DNA modules called Polycomb Response Elements ( PREs ) , which organize repressed PcG target genes at a distance via chromatin structure and nuclear architecture [12]–[16] . Notably , similar cis-elements were recently reported in mammals [17] , [18] . The characteristic feature of the PcG memory system is the mitotic inheritability of gene expression patterns . However , the mechanism by which PcG proteins maintain repressive chromatin during cell division is poorly understood . In mammals , it has been proposed that PRC2 binds to its own methylation mark H3K27me3 to re-establish epigenetic signatures after replication [19] , [20] . In Drosophila , by in vitro and partially in vivo assay , it has been observed that PSC , a chromatin compacting subunit of PRC1 complex , remains bound to chromatin during replication [21] . Such an association suggests that , in principle , epigenetic players could be transferred from maternal to daughter strands . To date however , direct evidence for existence of these models in vivo is still lacking . In particular , the time at which the parental marks are imposed and how tightly the process of PcG epigenetic inheritance is coupled to replication have not been determined . To address these questions we used the D . melanogaster embryonic Schneider 2 cell line ( S2 ) to analyse replication timing , PcG proteins binding , , H3K27me3 mark deposition , dynamics of PRE mediated higher order structures and transcriptional repression during S phase . Our data suggest a putative conserved mechanism for epigenetic inheritance , identifying a critical time window before replication , during which the PcG memory system sets the stage for subsequent epigenome duplication .
We first measured BX-C PRE replication timing in S2 embryonic cell line where the homeotic genes of Bithorax Complex ( BX-C ) are silenced . The relative abundance of nascent DNA synthesised during different fractions of the S phase was determined by bromodeoxyuridine triphosphate ( BrdU ) labelling and FACS ( fluorescence-activated cell sorting ) sorting [22] . DNA was prepared from an equal number of cells representing the first and last stages of the S phase , hereafter referred to as “early” and “late” ( Figure 1A ) . BrdU-labelled DNA was immunoprecipitated from these S-phase specific fractions to enrich for genomic sequences that replicate during the labelling period . We then performed quantitative real-time PCR ( qRT-PCR ) , using primers specific for Fab-7 , Mcp , bxd , bx PREs , and control regions , the latters consisting of CG108735 gene locus and dodeca repeats , which are early and late replicating sequences , respectively [22] . Ratios between the amounts of amplified products in early and late S phase showed that repressed PREs replicate late during S phase ( Figure 1B ) , in agreement with the Drosophila genome wide replication timing database [23] . We then repeated the experiment using synchronized S2 cells to confirm that BX-C PRE replication timing was comparable to the FACS-sorted cells . Upon release from hydroxyurea ( HU ) block ( Figure S1A ) , cells synchronously proceeded through S phase over the next 2 hours ( end of S phase ) . Cells representative of early S phase were pulse-labelled with BrdU at the start of S phase and collected after 1 h from the HU block release , while cells representing late S phase were pulse-labelled after 1 h and collected after 2 h from HU block release . Quantification of the relative amount of PRE sequences after BrdU immunoprecipitation ( Figure S1B ) confirmed that repressed PREs are late replicating in synchronized S2 cells . We have previously demonstrated by Chromosome Conformation Capture ( 3C ) and fluorescent in situ hybridization ( FISH ) that all major elements bound by PcG proteins , including PREs and core promoters , interact at a distance in the repressed state , resulting in a topologically complex structure necessary for the maintenance of BX-C silencing [16] . Similar results were obtained in mammals [24] , [25] . In order to investigate whether PRE-mediated BX-C higher order structures are disrupted during replication , we used 3C analysis to monitor DNA/DNA interactions between PcG targets during S phase ( Figure 2 ) . We used synchronized cells collected 1 h and 2 h after the release from the HU block as representative of early and late S phase , respectively ( Figure S1A ) . In comparing crosslinking frequencies of different fractions , we found that BX-C promoters were interacting with all PREs during early S phase , while during replication ( late S phase ) most PRE/promoters interactions were impaired ( Figure 2B and 2C ) . These data may partially explain the previously reported dynamic nature of higher order interactions [13] , [16] and suggest that during replication epigenetic higher order structures are altered and need to be reconstituted at each cell cycle . Interestingly , the frequencies of interaction between analysed PRE elements were stable throughtout DNA replication ( Figure 2D ) , suggesting that PRE-PRE clustering may serve as the scaffolding template for PcG inheritance . To dissect the dynamics of PcG proteins binding during S phase , we performed Chromatin Immunoprecipitation ( ChIP ) in synchronized S2 cells . Chromatin collected from G1/S , early and late S phase was immunoprecipitated with antibodies against PHO ( Figure 3A ) , PC ( Figure 3B ) and E ( Z ) ( Figure 3C ) , which are members of PhoRC , PRC1 and PRC2 complexes , respectively . Notably , all three PcG proteins were present at PREs during S phase , in agreement with and further corroborating previous reports [19]–[21] . However , we found that the amount of PcG proteins bound to target sites varied over S phase progression . In particular , we observed a striking increase , up to 10 fold , in early S phase ( Figure 3A–3C ) , followed by a dramatic drop in PcG binding in late S phase , returning to G1/S basal levels . Thus PcG complexes engagement is uncoupled from and precedes target sites replication . To analyse PcG dependent HMTase function on chromatin we measured the levels of histone lysine methylation during S phase with antibodies that recognize total H3 and H3K27me3 . Although total H3 levels at PREs did not change between G1/S and early S phase fractions ( Figure S2A ) , the ratio between H3K27me3 and H3 peaked in early S phase ( up to 10 fold; Figure 3D ) following PcG protein loading onto PREs ( Figure 3A–3C ) . Moreover , we observed a consistent drop of H3K27me3 from early to late S-phase while total H3 showed only a mild increase during PRE replication , suggesting that H3K27me3 trend during replication depends on mark deposition and not on replication dependent histone fluctuation . Little is known about in vivo dynamics of chromatin proteins during S-phase . Thus , as a further control , we looked at Topoisomerase II ( TOPO II ) an enzyme that plays a crucial role in DNA replication and binds PREs in Drosophila [26] . As shown in Figure S2B , we found that the amount of TOPO II at PREs did not change during S-phase , proving that the observed dynamic is specific for PcG complexes . As an additional control we performed ChIP experiments for repressive H3K9me3 mark that is also present on PREs [27] , [28] . Interestingly , H3K9me3 , also controlled by PcG proteins [28] , showed a trend similar to H3K27me3 ( Figure S2C ) during S phase , suggesting that PcG epigenetic signatures are inherited at the same time during replication . Recently , phosphorylation of Serine 28 on histone H3 ( H3S28ph ) via mitogen and stress activated kinases , has been proposed as a novel mechanism that induces PcG chromatin displacement , counteracting the H3K27me3 docking site [29] , [30] . To explore the contribution of the H3Ser28ph mark in S phase dependent PcG protein binding , additional ChIP experiments were performed . We found a progressive increase in H3Ser28ph mark from G1/S to late S phase ( Figure S2D ) both on PREs and the bw negative control , likely due to its role in mitosis [31] . Although we cannot completely exclude that H3K27me3 mark recognition by specific antibodies may be partially influenced by histone phosphorylation , the trend of H3Ser28ph mark did not correlate with the observed PcG and H3K27me3 . This suggests that , at least during PRE replication , a putative signal dependent mechanism for PcG protein displacement by phosphorylation of Ser28 of histone H3 does not appear to be required . Next we examined whether global levels of PcG proteins and H3K27me3 would be S phase regulated . Western blot and RT-PCR analysis revealed that both E ( Z ) and PC reached their maximum peak of expression during early S phase ( Figure 3E and Figure S2E ) , while PHO showed only a slight increase . These dynamics were not observed for TOPO II , used as control ( Figure 3E ) . The same trend was observed for total H3K27me3 while H3 levels remained constant throughout S phase ( Figure 3F ) . We conclude that PcG proteins quantitatively engage their target sites and enrich for H3K27me3 epigenetic mark in early S phase , preceding PRE replication . Despite the repression imposed on BX-C by PcG proteins in S2 cells it is possible to detect basal transcription levels of homeotic genes . To measure the correlation between the amount of PcG bound to its repressive function , we performed transcriptional analysis in synchronized S2 cells ( Figure S3A ) . Different primer pairs were used to discriminate the mature and the primary transcripts of two homeotic genes , Ubx and abdA . We found a slight transcriptional increase in late S phase when Polycomb proteins are reduced on their targets ( Figure S3A ) and the analysed sequences are replicated ( Figure S3B ) . However , we observed the same trend also with the late replicating bw negative control ( Figure S3A and S3B ) , indicating that this effect is not dependent on PcG protein levels . We performed additional transcriptional analysis on synchronized cells treated with dsRNA against Pho , PC and Ez , which give rise to homeotic gene derepression ( Figure S3C ) , and against Gfp as a control . The reproducible transcriptional trend found in PcG depleted cells lends support to the view that it is not dependent of PcG binding . These results suggest that , although continously repressed during S phase , some transcripts escape the restraint at the moment of DNA replication . As PcG proteins form discrete bodies in the nucleus [32] , [33] , we followed PC and H3K27me3 localization pattern during replication . To identify S phase , a S2 population of cells was pulse-labelled with BrdU and then analyzed by immunofluorescence ( Figure 4A–4D and Figure S4A ) . As expected , PC does not colocalize with constitutive heterochromatin and it is excluded from replication foci ( Figure 4A ) . This is in agreement with data in mammals showing no colocalization of the PRC1 subunit CBX8 with BrdU foci [19] . In order to perform a deeper analysis of PC dynamics , we then followed the time-lapse of the S phase by measuring nuclei dimensions . First , we used FACS to measure the mean cell size of two fractions representing early and late S phase ( Figure 1A ) . We found that cells belonging to the early S phase were smaller compared with cells of the late S phase ( Figure S4B ) , indicating that as S phase progresses , cell dimension increases . This allowed us to study protein distribution throughout S phase by immunofluorescence . Second , we quantified PC protein levels by measuring the intra-nuclear mean fluorescent intensity in BrdU positive cells . We plotted these values , classifying cells by dimension , and we found that PC amount decreased with S phase progression ( Figure 4E ) . As a control , we repeated the experiment to study TOPO II dynamics and we found a more diffuse distribution of the protein in the cell ( Figure 4B ) and a slight decrease in overall levels in late S phase compared to early ( Figure 4F ) , in agreement with Western blot results . In parallel , we followed the activity of PRC2 complex by analysing the distribution and the amount of H3K27me3 mark in BrdU positive cells . As observed for PRC1 , we found that the repressive mark is excluded from constitutive heterochromatin and replication foci ( Figure 4C ) and that the relative nuclear intensity of H3K27me3 fluorescence decreased with a similar trend ( Figure 4G ) . In H3 immunostaining , used as control , we found a strong signal in heterochromatin foci ( Figure 4D ) and relative nuclear fluorescence remained constant throughout S-phase ( Figure 4H ) . Taken together , these data confirm results from ChIP and Western blot analysis , clearly showing specific dynamics of PC and histone H3K27me3 mark during replication . Previous reports indicated that PRC1 and 2 complexes are localized at silent INK4/ARF locus in proliferating mouse embryonic fibroblasts ( MEF ) and that this locus is replicated during late S phase [34] . This evidence was further supported by large scale analysis in Hela cells in which the presence of PcG H3K27me3 repressive mark positively correlated with late-replicating genomic regions , suggesting that PcG targets replicate during late S phase [35] , [36] . To check if early S-phase PcG dynamics observed in Drosophila cells would be evolutionary conserved , we analysed the levels of H3K27me3 mark in synchronised mammalian cells . Three different human cell lines ( 293 , Hela S3 and Hela B ) were treated with HU and , after block release , several fractions from G1/S ( HU block ) to late S/G2 phase were collected ( Figure 5A ) . H3K27me3 total amounts were measured by western blot analysis ( Figure 5B ) . Strikingly , all three cellular systems revealed a trend similar to Drosophila S2 cells , showing H3K27me3 enrichment in the first part of the S phase and then a gradual reduction along with S phase progression . Interestingly , it has been previously shown that Hela cells collected 6 hours after G1/S block release can be considered representative of late replication [35] , indicating that H3K27me3 deposition precedes duplication of potentially silenced , late replicating sequences . In the same samples , to correlate the H3K27me3 mark deposition with PcG dependent HMTase function on chromatin we analysed the human homologue of Drosophila Ez protein , EZH2 ( Figure 5C ) . As for Drosophila , western blot analysis indicate that increase in H3K27me3 was accompanied by increase in EZH2 levels . These findings suggest that a characteristic timing of PRC2 activity and H3K27me3 deposition preceding PcG target replication may be a key and evolutionary conserved mechanism for epigenetic inheritance of gene silencing . Interestingly , the H3K27me3 drop in late S phase was not followed by a comparable decrease of EZH2 levels , suggesting that other cell-cycle coupled mechanisms could be involved in EZH2 HMT activity regulation .
At each cell cycle , the integrity of genetic and epigenetic information is challenged during DNA replication , when chromatin undergoes a wave of disruption and subsequent restoration in the wake of the passage of the replication machinery . It is well described that assembly of core histones is coincident with DNA replication and takes place at the replication fork [37]–[41] . However , temporal re-establishment of epigenome structure during cell division remains a key question in epigenetic research . In mammals , mechanisms of heterochromatin formation involve the sequential recruitment of HMT , deposition of histone mark and binding of HP1 chromodomain protein at the replication fork [42] . In yeast , generation of short interfering RNAs from centromeric repeats in S-phase allows the loading of heterochromatin factors that , in turn , restore the H3K9me2 mark after replication [43] , [44] . Despite the considerable amount of information about PcG catalytic and repressive functions , by now mechanisms of PcG mediated epigenetic inheritance at cell division are not fully understood . Increasing evidences suggest that PcG mediated epigenetic signatures are cell cycle regulated , being controlled in S and M phases , when cells are subjected to profound modifications of chromosomal components and nuclear structure [45]–[48] . The key question , still open , is how PcG dependent epigenetic marks are inherited when the genome is replicated . Studies in mammalian cells suggest that all three proteins of PRC2 in the trimeric complex are required to form a combined binding surface that can recognize the H3K27me3 modification , thus generating a positive feedback loop that helps to propagate H3K27me3 mark through DNA replication . Since also PRC1 can recognize H3K27me3 , via chromodomain proteins , this could also be a mechanism for recruiting new PRC1 complexes following DNA replication [19] , [20] . Notably , it has been shown that in Drosophila chromatin histone proteins turn over faster than cell cycle suggesting that in principle they may be loaded on DNA not only during S phase [49] . Indeed , the ability of PcG proteins to bind their own mark , occurring during all phases of cell cycle and reinforcing the epigenetic repressed status of target genes , could partially explain the stability of epigenetic signatures despite their high turnover in the cell [19] , [20] . On other hand , during replication , stability of epigenetic marks is challenged by the replication fork passage . Hence , specific mechanisms of epigenetic inheritance in S-phase must be provided in order to preserve cell identity . We addressed this issue by analyzing the in vivo , cell cycle dependent dynamics of PcG proteins and their role in maintaining BX-C homeotic gene silencing . By using different experimental approaches , we show that components of the three major PcG complexes follows a characteristc dynamics in S-phase and , notably , it is uncoupled from replication timing . We found that , in early S phase , endogenous PcG protein levels increase and PcG complexes chromatin loading and enrichment for characteristic H3K27me3 mark are strongly enhanced ( Figure 3 ) . All these events precede late S-phase when PREs are replicated ( Figure 1 ) and most of epigenetic signatures , such as looping between regulatory sequences ( Figure 2 ) , PcG binding and H3K27me3 mark levels ( Figure 3 and Figure 4 ) , appear to be challenged . These results are supported by experiments on synchronized mammalian cells showing a conserved dynamics of PcG proteins and H3K27me3 mark through S phase ( Figure 5 ) . Of note , our data are in line with previous findings based on mass spectrometry quantification on parental versus newly deposited histones showing that the establishment of H3K27me3 patterns during cell cycle takes place to large extent before replication [47] . Further , the conclusions that can be drawn from these data are strongly reminiscent of centromeric heterochromatin duplication in which epigenetic inheritance of histone variant CENP-A ( centromeric protein A ) is restricted to a brief interval in G1 and subsequent dilution occurs during S phase [50] , [51] . Thus , we propose a mechanism for PcG epigenetic signature inheritance in which H3K27me3 mark is actually inherited by dilution and not by de novo methylation occurring at the time of replication . Overexpression of PcG proteins and consequent changes in specific chromatin landscapes have been extensively documented in human cancer [52] , where control on the cell cycle is lost , and cells constantly enter S phase . We suggest that higher levels of PcG proteins characteristic of cancer cells might be needed to maintain transcriptional repression on differentiation genes and oncosuppressors through S phase . The identification of PcG regulated epigenetic inheritance time window may be relevant for cell reprogramming by allowing the modulation of cell memory function [48] , [53] .
Drosophila embryonic S2 cells were grown at 25°C in serum-free insect culture medium ( HyQ SFX; Hyclone , Logan , UT ) . 293 , Hela B , and Hela S3 were cultured in Dulbecco Modified Eagel's Medium ( DMEM ) supplemented with penicillin/streptomycin and 10% fetal bovine serum ( Euroclone ) . Exponentially growing S2 cells ( 1×106 cells/ml ) were cultured in presence of 50 µM Bromodeoxyuridine ( BrdU ) for 60 min . For sorting , cells were divided into aliquots containing 5×106 cells per tube , washed with cold PBS , resuspended in 0 . 5 ml of cold PBS , fixed with drop by drop addition of 5 ml of 70% cold ethanol and incubated for 1 h on ice . Cells were then washed with PBS , resuspended in PBS/RNase A ( 1 mg/ml ) 30 min at 37°C followed by addition of Propidium Iodide ( 20 µg/ml ) and incubated 30 min in the dark at 4°C . On the basis of DNA content , cells were sorted into different S phase fractions using two selective gates representing roughly the first and the last thirds of S phase . Equal numbers of cells from each cell cycle fraction ( 150 , 000 ) were sorted ( using a Becton Dickinson or a Moflo , Coulter ) into microcentrifuge tubes containing lysis buffer ( 50 mM TrisHCl pH 8; 10 mM EDTA; 0 , 8% SDS; supplemented with 0 . 2 mg of proteinase K per ml ) . For analysis after HU synchronization , cells representing early S phase were pulse-labelled with BrdU at the beginning of S phase and collected 1 h from HU block release , while cells representing late S phase were pulse labelled with BrdU after 1 h and collected after 2 h from HU block release and resuspended in lysis buffer . The aliquots , collected either by FACS or after synchronization , were incubated at 50°C for 2 h in lysis buffer and then stored at −20°C . Lysates were then extracted once with phenol-chloroform , and phenol was extracted again with an additional volume of TE1X . DNA was precipitated with sodium acetate and ethanol and resuspended in 500 µl of TE . DNA was sonicated to an average size of 0 . 5 kb , and an aliquot of 100 µl was checked on agarose gel . DNA was heat denatured 10 min at 95°C and cooled on ice . Then 50 µl of 10× phosphate buffer ( 1 M Sodium phosphate [pH 7 . 0] , 1 . 4 M NaCl; 0 . 5% Triton X-100 ) and 40 µl of mouse anti-BrdU DNA monoclonal antibody ( 25 mg/ml Becton Dickinson ) were added to each tube . After 2 h of constant rocking at room temperature , protein AG plus agarose beads ( Santa Cruz Biotechnology ) were added and incubation continued for an additional hour at room temperature with rocking . DNA-protein complexes were pelleted by microcentrifuging for 5 min at 4°C . After washing with 750 µl of 1× phosphate buffer , pellets were resuspended in 200 µl of digestion buffer ( 50 mM Tris HCl pH 8 , 10 mM EDTA , 0 , 5% SDS , 250 µg/ml proteinase K ) . Digestion was allowed to proceed overnight at 37°C and then for 1 h at 50°C after the addition of 100 µl of fresh digestion buffer . DNA was extracted and precipitated as above , briefly dried and resuspended in 40 µl of TE . RT PCR was performed using 1 µl of each nascent strand sample as template . Primer sequences: CG10873-f 5′agcttgctgcgcagcgag3′ , CG10873-r 5′tctccaggcagaagactaagg3′; dodeca-f 5′actggtcgcgtactggtcc3′ , dodeca-r 5′gtctcgtactctgtcccgtatt3′; fab-7-f 5′gaaaatgcccaacaaaatgc3′ , fab-7-r 5′cgctgtctcgcctcttcttc3′; mcp-f 5′tgcggacgccatttgacac3′ , mcp-r 5′gagccacgcagcgagttc3′; bxd-f 5′tcgtcgcttgtttggataattact3′ , bxd-r 5′tgcggtgataaggtccataatc3′; bx-f 5′ttattgttgctacaccgctg3′ , bx-r 5′agtaggtgccgcgtatgtg3′; CG3436–f 5′atcgctaacagccatgtcgg3′ , CG3436-r 5′cttaccgattcaaggagcgc3′; CG4345-f 5′ttcccgagtctctcaccgc3′ , CG4345-r 5′acaggaacccacaccactgac3′; Ubxpr-f 5′tcagccctcctccatgatg3′ , Ubxpr-r 5′ccaaatcgcagttgccagtg3′; abdApr-f 5′ttgagtcagggagtgagcc3′ , abdApr-r 5′cgctttgagtcgttggagac3′; bwpr-f 5′tgatgagcgacaattagctgg3′ , bwpr-r 5′tgtccgtctgtctgtctgtc3′ . The 3C assay was performed as previously described [16] . Antibodies against PC were kindly provided by R . Paro , antibodies against Topoisomerase II by D . Arndt-Jovin , and antibodies against Pho and E ( z ) by J . Muller . Commercial rabbit polyclonal antibodies against methylated Lysine 27 of histone H3 ( Upstate , 07-449 ) , methylated Lysine 9 of histone H3 ( Abcam , ab8898 ) , phosphorilated Serine 28 of histone H3 ( Upstate , 07-145 ) , histone H3 ( Abcam , ab1791 ) and EZH2 ( Diagenode pAb-039-050 ) were used . ChIP experiments were performed as previously described [27] with minor modifications . After synchronization , cells of different phases of cell cycle were fixed in 1% formaldehyde for 15 min at room temperature and quenched by addition of glycine at 125 mM final concentration for 5 min at room temperature before being placed on ice . Cells were washed once with ice-cold PBS , resuspended in ice-cold cell lysis buffer ( 5 mM Pipes pH 8; 85 mM KCl; 0 . 5% NP40; 1 mM PMSF; 1× Protease Inhibitors ) and left on ice for 10 min . After centrifugation at 2000 rpm for 5 min , nuclei were resuspended in ice-cold nuclear lysis buffer ( 50 mM TrisHCl pH 8 . 0; 10 mM EDTA; 0 . 8% SDS; 1 mM PMSF; 1× Protease Inhibitors ) and left for 10 min on ice . Chromatin was sonicated in the presence of glass beads ( 150–200 mm , Sigma ) , spun for 10 min at maximum speed at 4°C , diluted to 0 . 2% SDS with dilution buffer ( 10 mM Tris–HCl pH 8 . 0 , 0 . 5 mM EGTA , 1% Triton X-100 , 140 mM NaCl ) , then split into aliquots and processed immediately for IP . For pre-clearing and antibody recovery , Protein A/G Plus-agarose beads ( Santa Cruz Biotechnology ) were used . After washing , samples and control chromatin ( input ) were incubated in the presence of 2 µl of Rnase cocktail ( DNase-free , Ambion ) overnight at 65°C . Then , samples were adjusted to 0 . 5% SDS and 0 . 5 mg/ml proteinase K and incubated for additional 2 h at 55°C . The DNA was phenol–chloroform extracted and precipitated . The final pellet was resuspended in 30 µl of TE and stored at 4°C for RT-PCR analysis . Primer sequences are indicated above . Total proteins were prepared by resuspending 2×106 S2 or 1×106 mammalian cells in extraction buffer ( 50 mM TrisHCl pH 7 . 6; 0 . 15 M NaCl; 5 mM EDTA; 1× Protease Inhibitors; 1% Triton X-100 ) . Three pulses of 10 sec sonication at 30% amplitude were performed to allow dissociation of protein from chromatin and solubilization . For histone extraction , 8×106 S2 cells were washed in cold 1× PBS and resuspended in 800 µl of extraction buffer ( 10 mM Hepes pH 8; 0 . 1 mM MgCl2; 0 . 1 mM EDTA; 2 mM PMSF; 1× Protease Inhibitors; 1 mM NaF; 1 mM Na3VO4 ) and passed through a needle on ice . After incubation of 10 min on ice , cells were centrifuged for 10 min at 4°C at 2000 rpm . Pellets were washed with 400 µl of extraction buffer , resuspended in 100 µl of 0 . 2 N HCl and incubated overnight at 4°C with constant rocking . After 10 min of centrifugation at 13000 rpm 4°C , the supernatant was run on 12% SDS-PAGE . Alternatively , 30 µg of mammalian or Drosophila protein extracts were treated for 1 h with 4 units of DNAse ( Turbo DNAse Ambion ) at 37°C . For Western blot analysis , the densities of protein bands were measured using Image J software program . Total RNA was isolated with Trizol reagent ( Invitrogen ) . 1 µg of RNA from each sample was subjected to cDNA synthesis using a Quantitect reverse transcription kit ( Qiagen ) . DNA from ChIP , 3C or cDNA preparation was amplified in 20 µl reaction mixtures in the presence of 10 µl 2× QuantiTect SYBR Green master mix ( Qiagen ) and 0 . 5 µM of corresponding primers . Real-time PCR was performed with the DNA Engine Opticon 2 ( MJ ) . Copy number was determined using the cross-point ( Cp ) value , which is automatically calculated using the Opticon Monitor 2 software ( MJ ) . Primer sequences: rtgapdh-f 5′aagggaatcctgggctacac3′ , rtgapdh-r 5′accgaactcgttgtcgtacc3′; rtpho-f 5′tcagttggttcacaccggtg3′ , rtpho-r 5′gaggtatcttcactctggctg3′; rtpc-f 5′ttcaagactcaagtgctgcc3′ , rtpc-r 5′ccatgggaaataagcaggag3′; rtez-f 5′ctgtggctgagatcaactcc3′ , rtez-r 5′gacaggtcttggtcagcatg3′; rtbw-f 5′tcgctgtgcctcgagtgg3′ , rtbw-r 5′aatcgccgccagcagcg3′; rtUbx-f 5′agtgtcagcggcggcaac3′ , rtUbx-r 5′agtctggtagaagtgagcccg3′; rtabdA-f 5′caaatacaacgcaacccgagac3′ , rtabdA-r 5′agcgatcgtgttgctgctg3′; utrgapdh-f 5′cgaactgaaactgaacgagag3′ , utrgapdh-r 5′ttgacatcgatgaagggatcg3′; utrUbx-f 5′gttcgatggcaacggattgg3′ , utrUbx-r 5′tgacggatttcctcgaatctg3′; utrabdA-f 5′aactcactgtgtgcggttcg3′ , utrabdA-r 5′tcaagtgcgtgagtgtgtgtg3′; utrbw-f 5′agtcggcacatcacatagcc3′ , utrbw-r 5′gttccagaaactgtagttgctc3′ . For BrdU labelling , exponential S2 cells were grown for one hour in the presence of 50 µM BrdU . 106 cells were centrifuged , resuspended in 0 . 4 ml of medium and placed at room temperature ( RT ) for 30 min on a Poly-Lysine coated slide ( 22 mm×22 mm ) . Fixation was performed in 4% paraformaldehyde 1× PBS for 10 min at RT . Cells were washed 3 times with PBT ( PBS 1× , 0 . 1% Tween 20 ) , incubated for 1 h at RT with RNAseA ( 100 µg/ml in PBT ) and for 10 min at RT with PBS , 0 . 5% Triton . After washing cells again in PBS , they were incubated for 2 min at RT in 0 . 07N NaOH , briefly rinsed twice in PBS and blocked in PBS/1%BSA . All antibody hybridizations were carried out in a humid atmosphere at 37°C . Anti-PC and anti-H3K27me3 antibodies were incubated for 12–16 h while anti-BrdU antibody was incubated for 1 h . Washes were done in PBT . DNA was counterstained with DAPI , and glasses were mounted in Vectashield Antifade ( Vector Laboratories ) . Images were taken with a Nikon ECLIPSE 90i microscope ( 100× objective ) that was equipped with a digital camera ( Nikon Coolpix 990 ) and NIS-Element software . Fluorescence quantification was done by determining the intranuclear mean fluorescence intensity using an Image J software program that computes area , mean , and grey values . Exonic fragments of 600 bp , 1400 bp , 658 bp or 810 bp , respectively , from Gfp , Pc , Pho or E ( z ) genes , were amplified by PCR , creating T7 polymerase binding sites for the transcription of both strands . RNAi was performed as described previously [54] . Primer sequences: Gfp 5′acgtaaacggccacaagttc3′-5′tgctcaggtagtggttgtcg3′; Pc 5′attggcaagttaagcacgggca3′-5′acatcctggatcgccgcctca3′; Pho 5′acagtacgatgaagatataggc3′-5′tgatctgaactgagcttatagg3′; E ( z ) 5′tcgaaggcattatgaatagcac3′-5′atccgcatcttcagtctcc3′ . | During embryonic development , pluripotent cells divide and use their potential to differentiate into a variety of cells with identical genomes but different phenotypes . The emerging concept suggests that the DNA sequence information is not the sole determinant of cell identity . Indeed , epigenetic mechanisms , acting via chromatin organization , control transcriptome complexity and contribute to maintain cell fate . Polycomb-group proteins ( PcG ) are epigenetic transcriptional regulators that maintaining gene silencing programs through cell division . During S phase , in addition to DNA , the entire epigenome needs to be duplicated . A key question that remains to be addressed is how epigenetic marks are transmitted to subsequent generations . In this study we propose a model for PcG epigenetic inheritance during replication . We found that , during S phase , PcG engagement and characteristic H3K27me3 histone mark deposition on target sites are restricted to a brief interval occurring before DNA replication of the same regions . By increasing the dose of PcG binding the system would prevent potential weakening of silencing control , which is challenged at the time of replication , allowing proper transmission of epigenetic marks to the next generation and preservation of cell identity . | [
"Abstract",
"Introduction",
"Results",
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] | [
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] | 2011 | PcG Complexes Set the Stage for Epigenetic Inheritance of Gene Silencing in Early S Phase before Replication |
Transposable elements ( TEs ) are obligate genetic parasites that propagate in host genomes by replicating in germline nuclei , thereby ensuring transmission to offspring . This selfish replication not only produces deleterious mutations—in extreme cases , TE mobilization induces genotoxic stress that prohibits the production of viable gametes . Host genomes could reduce these fitness effects in two ways: resistance and tolerance . Resistance to TE propagation is enacted by germline-specific small-RNA-mediated silencing pathways , such as the Piwi-interacting RNA ( piRNA ) pathway , and is studied extensively . However , it remains entirely unknown whether host genomes may also evolve tolerance by desensitizing gametogenesis to the harmful effects of TEs . In part , the absence of research on tolerance reflects a lack of opportunity , as small-RNA-mediated silencing evolves rapidly after a new TE invades , thereby masking existing variation in tolerance . We have exploited the recent historical invasion of the Drosophila melanogaster genome by P-element DNA transposons in order to study tolerance of TE activity . In the absence of piRNA-mediated silencing , the genotoxic stress imposed by P-elements disrupts oogenesis and , in extreme cases , leads to atrophied ovaries that completely lack germline cells . By performing quantitative trait locus ( QTL ) mapping on a panel of recombinant inbred lines ( RILs ) that lack piRNA-mediated silencing of P-elements , we uncovered multiple QTL that are associated with differences in tolerance of oogenesis to P-element transposition . We localized the most significant QTL to a small 230-kb euchromatic region , with the logarithm of the odds ( LOD ) peak occurring in the bruno locus , which codes for a critical and well-studied developmental regulator of oogenesis . Genetic , cytological , and expression analyses suggest that bruno dosage modulates germline stem cell ( GSC ) loss in the presence of P-element activity . Our observations reveal segregating variation in TE tolerance for the first time , and implicate gametogenic regulators as a source of tolerant variants in natural populations .
Transposable elements ( TEs ) are omnipresent and abundant constituents of eukaryotic genomes , comprising up to 80% of genomic DNA in some lineages ( reviewed in [1] ) . The evolutionary footprint of TEs is extensive , including dramatic genome size expansions [2 , 3] , acquisition of new regulatory networks [4 , 5] , structural mutations [6] , novel genes [7–9] , and adaptive insertions [10–12] . However , the charismatic and occasionally beneficial impact of TEs over evolutionary time masks their fundamental identity as intragenomic parasites and mutagens . In addition to causing deleterious mutations [13 , 14] , TEs can exert lethal , genotoxic effects on host cells by producing abundant double-stranded breaks ( DSBs ) during insertion and excision [15 , 16] . TEs are therefore intragenomic parasites . Host developmental and evolutionary responses to parasites , pathogens , and herbivores are broadly delineated into two categories: resistance and tolerance ( reviewed in [17 , 18] ) . Mechanisms of resistance prevent—or limit the spread of—infection or herbivory . By contrast , mechanisms of tolerance do not affect propagation but rather limit the fitness costs to the host . With respect to TEs , resistance by eukaryotic genomes is enacted by small-RNA-mediated silencing pathways [19] and Kruppel-associated box zinc-finger proteins ( KRAB-ZFPs ) [20] , which regulate the transcription and subsequent transposition of endogenous TEs . However , it remains unknown whether genomes can also evolve tolerance of TEs by altering how host cells are affected by TE activity . Tolerance therefore represents a wholly unexplored arena of the evolutionary dynamics between TEs and their hosts . Germline tolerance of TEs is predicted to be of particular importance because of the significance of this cell lineage in ensuring the vertical transmission of the parasite and the reproductive fitness of the host . The absence of research on tolerance is at least partially due to the primacy of resistance: endogenous TEs are overwhelmingly repressed by host factors in both germline and somatic tissues [19 , 21 , 22] . However , the invasion of the host genome by a novel TE family , which happens recurrently over evolutionary timescales ( reviewed in [23] ) , provides a window of opportunity through which tolerance could be viewed , both empirically and by natural selection . The absence of evolved resistance in the host against a new invader could reveal differential responses of germline cells to unrestricted transposition . A classic example of genome invasion by a novel TE is provided by P-elements , DNA transposons that have recently colonized two Drosophila species . P-elements first appeared in genomes of D . melanogaster around 1950 ( reviewed in [24] ) and later colonized its sister species D . simulans around 2006 [25 , 26] . Particularly for D . melanogaster , a large number of naïve strains collected prior to P-element invasion are preserved in stock centers and laboratories , providing a potential record of ancestral genetic variation in tolerance [27 , 28] . Furthermore , 15 of these naïve strains were recently used to develop the Drosophila Synthetic Population Resource ( DSPR ) , a panel of highly recombinant inbred lines ( RILs ) that serve as a powerful tool kit for discovering the natural genetic variants influencing quantitative traits [29–31] . Here , we harness the mapping power of the DSPR to screen for phenotypic and genetic variation in the tolerance of the D . melanogaster female germline to unrestricted P-element activity . We developed a novel screen for phenotypic variation in host tolerance by taking advantage of the classic genetic phenomenon of hybrid dysgenesis , in which TE families that are inherited exclusively paternally can induce a sterility syndrome in offspring because of an absence of complementary maternally transmitted regulatory small RNAs ( Piwi-interacting RNA [piRNAs] , reviewed in [24 , 32 , 33] ) . The dysgenesis syndrome induced by P-elements in the female germline is particularly severe and can be associated with a complete loss of germline cells [15 , 34 , 35] . P-element hybrid dysgenesis is directly related to genotoxic stress , as apoptosis is observed in early oogenesis in dysgenic females [15] , and the DNA damage response factors checkpoint kinase 2 and tumor protein 53 ( p53 ) act as genetic modifiers of germline loss [35] . Variation in the sensitivity of the DNA damage response to DSBs therefore represents one potential cellular mechanism for tolerance . By phenotyping the germline development of >32 , 000 dysgenic female offspring of RIL mothers , we uncovered substantial heritable variation in female germline tolerance of P-element activity . We furthermore mapped this variation to a small 230-kb quantitative trait locus ( QTL ) on the second chromosome and associated it with the differential expression of bruno , a well-studied developmental regulator of oogenesis with no known function in TE repression or DNA damage response [36–39] . We further demonstrate that bruno loss-of-function alleles act as dominant suppressors of germline loss , and relate these effects to the retention of germline stem cells ( GSCs ) in dysgenic females . Our findings represent the first demonstration of natural variation in TE tolerance in any organism . They further implicate regulators of gametogenesis , such as bruno , as a source of tolerant variants that could be beneficial when new TEs invade the host .
We first sought to uncover phenotypic variation in germline tolerance of P-element activity among a panel of highly recombinant RILs derived from eight founder genomes [29] . To quantify tolerance , we performed dysgenic crosses between RIL females and males from the P-element containing strain Harwich ( Fig 1A ) . Harwich is a strong paternal inducer of hybrid dysgenesis , producing filial 1 ( F1 ) females with 100% atrophied ovaries in crosses with naïve females at the restrictive temperature of 29 °C [28] . We therefore performed our crosses at 25 °C , a partially permissive temperature at which intermediate levels of ovarian atrophy are observed [40 , 41] . Because P-element dysgenic females may recover their fertility as they age , through zygotic production of P-element–derived piRNAs [42] , we assayed both 3-day-old and 21-day-old F1 female offspring from each RIL . In total , we documented the incidence of atrophied ovaries among 17 , 150 3-day-old and 15 , 039 21-day-old F1 female offspring , and estimated the proportion of F1 atrophy within broods of ≥20 3-day-old and 21-day-old offspring from 592 and 492 RILs , respectively ( Fig 1B and 1C ) . Notably , because we phenotyped F1 offspring , our phenotypic variation will be determined only by variants in which one of the RIL alleles is at least partially dominant to the Harwich allele , or maternal effects that reflect only the RIL genotype . We observed continuous variation in the proportion of F1 atrophy among broods of both 3-day-old and 21-day-old offspring of different RIL genotypes , capturing the full range of proportional values from 0 to 1 ( Fig 1B and 1C ) . After accounting for the effects of experimenter and experimental block , the incidence of ovarian atrophy is strongly correlated among 427 RILs for which we sampled broods of both 3-day-old and 21-day-old F1 females ( Pearson’s R = 0 . 56 , p > 10−15 , Fig 1D ) . Because broods of different age classes were sampled from separate crosses and experimental blocks , this correlation strongly implies that phenotypic differences are explained by the maternal genotype . Indeed , based on the F1 atrophy proportions measured in broods of different ages , we estimate that the broad sense heritability of F1 ovarian atrophy among the RIL offspring was 40 . 35% ( see Materials and methods ) . Furthermore , we saw greater reproducibility across a small sample of 14 RILs , for which we phenotyped two independent 21-day broods ( Pearson’s R = 0 . 97 , p > 10−8 ) , suggesting even higher heritability among offspring of the same age class . Despite the previous observation of developmental recovery from hybrid dysgenesis [42] , the relationship between age and the proportion of F1 atrophy is only marginally significant in our data ( F1 , 1037 = 3 . 57 , p = 0 . 058 ) . Furthermore , 21-day-old dysgenic females exhibited only a 0 . 63% decrease in the proportion of F1 atrophy when compared to 3-day-old females , indicating that the overall effect of age in our crosses was very modest . Finally , we did not observe a group of RILs in which ovarian atrophy is much more common among 3-day-old as compared with 21-day-old F1 females ( Fig 1D ) , as would be predicted if there were genetic variation for developmental recovery across the RIL panel . The absence of developmental recovery in our experiments could reflect differences in developmental temperature between our two studies ( 22 °C in [42] and 25 °C here ) . Alternatively , the causative variant that allows for developmental recovery could be absent from the founder RILs . To identify the genomic regions that harbor causative genetic variation in tolerance , we performed a QTL analysis using the published RIL genotypes [29] . In these data , the founder allele ( A1–A8 ) carried by each RIL is inferred probabilistically for 10-kb windows along the euchromatic regions of the major autosomes 2 and 3 , and the X chromosome [29] . The fourth chromosome is ignored because the absence of recombination makes it uninformative for QTL mapping ( reviewed in [43] ) . Consistent with the strongly correlated phenotypes of 3-day-old and 21-day-old offspring ( Fig 1D ) , we identified a single major effect QTL associated with phenotypic variation at both developmental time points ( Fig 2A ) . Additionally , the Δ2-LOD drop confidence intervals ( Δ2-LOD CIs ) of the logarithm of the odds ( LOD ) peak from each analysis are both narrow ( <300 kb ) and highly overlapping ( Table 1 ) . The peak explains 14 . 2% and 14 . 8% of variation in ovarian atrophy among 3-day-old and 21-day-old F1 females , indicating it is a major determinant of heritable variation . Multiple minor peaks close to the centromere on chromosome 3 left ( 3L ) may represent another source of heritable variation in 3-day-old females . To further narrow the location of genetic variation in tolerance , we took advantage of the striking concordance in QTL mapping for the 3-day-old and 21-day-old data sets and performed a combined analysis , including all 660 RILs whose F1 offspring were sampled at either developmental time point . F1 female age was included as a covariate ( see Materials and methods ) . From this analysis we obtained a final Δ2-LOD CI for the major QTL peak , which corresponds to a 230-kb genomic region containing 18 transcribed genes—15 protein-coding and 3 noncoding ( Fig 2B ) . Simulation testing of the statistical properties of the DSPR indicates that a causative variant explaining 10% of phenotypic variation lies within the Δ2-LOD drop CI 96% of time for sample sizes of 600 RILs [30] . Furthermore , overestimation of variance explained by a QTL ( i . e . , the Beavis effect ) is rare in DSPR studies sampling greater than 500 RILs , particularly for variants explaining ≥10% of variation [48] . Therefore , given our sample ( 660 RILs ) and effect ( about 14% ) sizes , the Δ2-LOD CI we infer for our major peak should be conservative . Indeed , Bayesian credible intervals , an alternate approach for identifying QTL windows [44] , are even narrower than those estimated by Δ2-LOD CI ( Table 1 ) . Of the 18 genes within the QTL peak , only bruno and crooked are highly expressed in the D . melanogster ovary [47] . While bruno is a translational repressor whose major essential role is in oogenesis [37 , 49 , 50] , crooked is a more broadly expressed component of septate junctions , which is essential for viability [51] . The LOD peak resides within the 138-kb bruno locus , which , in addition to its function , makes it the strongest candidate for the source of causative variation . We next sought to partition founder alleles at the QTL peak into phenotypic classes , in order to better understand the complexity of causative genetic variation . First , we identified all sampled RILs whose genotype was probabilistically assigned ( p > 0 . 95 ) to a single founder ( A1–A8 ) at the LOD peak , and estimated the phenotypic effect associated with each founder allele ( Fig 3A and 3B ) . We then used stepwise regression ( see Materials and methods ) to identify the minimum number of allelic classes required to explain phenotypic variation associated with the founder alleles . For both age classes , we found strong evidence for two allelic classes , one sensitive and one tolerant , which were sufficient to explain phenotypic variation in tolerance . This implies that the major QTL peak could correspond to a single segregating genetic variant . Furthermore , with the exception of founder A8 , founder alleles were assigned to the same allelic class for both age cohorts , revealing that allelic behavior is highly biologically reproducible . To further study phenotypic differences between tolerant and sensitive alleles , we identified three pairs of background-matched RILs , which exhibited a tolerant ( founder A4 ) or a sensitive ( founder A5 ) haplotype across the QTL window but otherwise shared a maximal number of alleles from the same founder across the remainder of the genome . Consistent with our QTL mapping , these RIL pairs differed dramatically in the incidence of ovarian atrophy they displayed in crosses with Harwich males ( Fig 4A ) . While we did not detect a significant effect of genetic background ( drop in deviance = 3 . 57 , df = 2 , p = 0 . 17 ) , the QTL haplotype was strongly associated with the incidence of ovarian atrophy ( drop in deviance = 52 . 01 , df = 1 , p = 5 . 36 × 10−13 ) . RILs carrying the tolerant haplotype exhibited 39% less F1 ovarian atrophy than those carrying the sensitive haplotype . To determine whether reduced ovarian atrophy conferred by tolerant alleles increases female reproductive fitness , we examined the presence and number of filial 2 ( F2 ) adults produced by young ( 0–5-day-old ) F1 female offspring of tolerant and sensitive dysgenic crosses ( Fig 4B ) . The proportion of F1 sterility was somewhat lower than the proportion of F1 atrophy for the same dysgenic cross ( Fig 4A versus 4B ) , consistent with loss of germline cells in early adult stages . Equivalent to ovarian atrophy , there was no significant effect of genetic background ( drop in deviance = 5 . 26 , df = 2 , p = 0 . 07 ) , but the QTL haplotype was strongly associated with F1 sterility ( drop in deviance = 65 . 787 , df = 1 , p = 5 . 55 × 10−16 ) . F1 females carrying the tolerant haplotype exhibited a 54% reduction in sterility as compared with those carrying the sensitive haplotype . Interestingly , when we examine the number of F2 offspring produced by fertile F1 females from resistant and tolerant crosses ( Fig 4C ) , we detect dramatic effects of genetic background ( F2 , 110 = 29 . 05 , p = 7 . 48 × 10−11 ) but no significant effect of the tolerant allele ( F1 , 110 = 1 . 01 , p = 0 . 31 ) . Therefore , while tolerant alleles enhance female reproductive fitness by increasing the odds of fertility , other genetic factors likely determine the number of offspring produced by those fertile females . In light of the simple biallelic behavior of our phenotype , we sought to identify polymorphisms within the founder strains whose genotypic differences matched their phenotypic classifications ( i . e . , “in-phase” polymorphisms [29 , 31] , Fig 3C ) . We excluded A8 from these analyses because of the ambiguity of its allelic class . In total , we identified 36 in-phase single nucleotide polymorphisms ( SNPs ) , which potentially affect the function of only seven transcribed genes in the QTL interval ( [46] S1 Table , Figs 2B and 3C ) . We did not identify any in-phase , segregating TE insertions , although a recent reassembly of the founder A4 genome based on long single-molecule real-time sequencing reads suggests many TE insertions remain unannotated [52] . Focusing on bruno and crooked , the two genes in the QTL window that are highly expressed in the ovary [47] , 22 of the in-phase SNPs are within bruno introns , while none are found in the gene body or upstream of crooked . Furthermore , none of the 36 in-phase SNPs are nonsynonymous , implying a regulatory difference between the tolerant and sensitive alleles . To determine if tolerance is associated with bruno regulation , we compared ovarian expression of bruno in young ( 3-day-old ) females from our background-matched RIL pairs carrying a tolerant or sensitive haplotype across the QTL locus . While we observed only modest effects of genetic background on bruno expression ( likelihood ratio test = 6 . 57 , df = 2 , p = 0 . 04 ) , we observed dramatic effects of founder haplotype at the QTL window ( likelihood ratio test = 29 . 47 , df = 1 , p = 5 . 67 × 10−8 ) . Across genetic backgrounds , tolerant alleles were associated with a 20% reduction in bruno expression ( 95% CI: 14%–26% ) , suggesting that bruno function reduces germline tolerance of P-element activity . Given the differences in bruno expression between sensitive and tolerant alleles , we wondered whether bruno loss-of-function alleles affect the atrophy phenotype . For comparison , we also considered available alleles from three other ovary-expressed genes that are located within the Δ2-LOD CI of the 21-day-old or 3-day-old female analyses , but not in the combined analysis: ced-12 , Rab6 , and Threonyl-tRNA synthetase ( ThrRS ) . We reasoned that , because the causative variant is almost certainly not recessive , being found only in the maternal RIL genotype , mutant alleles might also exhibit non-recessive effects . We therefore used balanced heterozygous females as mothers in dysgenic crosses with Harwich males and compared the incidence of F1 ovarian atrophy among their 3–7-day-old F1 females ( mutant/+ versus balancer/+ ) . Strikingly , while ced-12 , Rab6 , and ThrRS alleles had no effect on the incidence of ovarian atrophy ( Fig 5A ) , two different bruno alleles ( brunoRM and brunoQB ) acted as dominant suppressors ( Fig 5B ) . In contrast to their balancer control siblings , who exhibited 64%–75% ovarian atrophy , brunoRM/+ and brunoQB/+ offspring exhibited only 13% and 20% atrophy , respectively—a dramatic reduction . We further observed that an independently derived bruno deficiency [53 , 54] suppresses ovarian atrophy to a similar degree ( Fig 5B ) , indicating these effects cannot be attributed to a shared linked variant on the brunoRM and brunoQB chromosomes [49] . Notably , the non-recessive , fertility-enhancing effects of bruno loss-of-function alleles in dysgenic females contrasts with their effects on the fertility of non-dysgenic females , in which they act as recessive female steriles [49] . Our observations therefore suggest that a novel phenotype of bruno alleles is revealed by the dysgenic female germline . Furthermore , our observation that reduced bruno dosage suppresses ovarian atrophy is fully consistent with our observation that tolerant alleles are associated with reduced bruno expression ( Fig 4D ) . bruno is a translational regulator with three known functions in D . melanogaster oogenesis . At the start of oogenesis in the ovarian substructure called the germaria , bruno is required to promote the differentiation of cystoblasts ( CBs ) , the immediate daughters of GSCs [37 , 55] . In mid-oogenesis , Bruno protein blocks vitellogenesis if not properly sequestered by its mRNA target oskar [38 , 39] . Finally , in late oogenesis , Bruno repression of oskar translation is required to establish dorsoventral ( DV ) patterning in the subsequent embryo [36] . This final role of bruno affects only the morphology of egg chambers , but not their production , suggesting that it cannot account for bruno’s effects in dysgenic germlines . We therefore focused on bruno’s earlier roles in GSC differentiation and vitellogenesis , which are distinguishable by their dependency on oskar mRNA . While bruno’s functions in GSC differentiation are independent of oskar mRNA [36 , 37 , 56] , bruno and oskar’s impact on vitellogenesis are interdependent , because of the requirement for oskar mRNA to sequester Bruno protein [39] . To determine if the effect of bruno alleles on hybrid dysgenesis are independent of oskar mRNA , we examined whether two oskar mRNA null alleles , osk° and oskA87 , as well as an oskar deficiency , affected the incidence of ovarian atrophy when compared with a balancer control ( Fig 5B ) . We observed that osk° and deficiency on chromosome 3R over oskar ( Df ( 3R ) osk ) exhibited no effect on the atrophy phenotype , while oskA87 was associated with only a marginal increase in ovarian atrophy ( p = 0 . 07 ) . If bruno suppression of ovarian atrophy reflects reduced sequestration by oskar mRNA in the dysgenic female germline , atrophy should be enhanced by oskar mRNA mutants [57] . Comparison of single and double heterozygotes of bruno and oskar also do not strongly suggest that bruno suppression is dependent on oskar mRNA dosage ( Fig 5B ) . In comparisons involving two separate balancer third chromosomes , Df ( 2L ) bru/+; oskA87/+; double heterozygotes did not differ from their single Df ( 2L ) bru/+;balancer/+ heterozygous siblings with respect to ovarian atrophy . A second double heterozygote Df ( 2L ) bru/+;Df ( 3R ) osk/+; was associated with significantly increased atrophy when compared with the single heterozygote ( Df ( 2L ) bru/+; balancer/+ ) . However , because this behavior was unique to the deficiency chromosome and was not also exhibited by the oskA87 mRNA null mutant that specifically eliminates oskar function [38] , we suspect it is a synthetic consequence of hemizygosity in both deficiency regions , rather than evidence for a genetic interaction between oskar and bruno , with respect to hybrid dysgenesis . Consistent with this , we also did not detect any Bruno mislocalization in the developing egg chambers of wild-type dysgenic females ( S1 Fig ) , nor did we see any evidence of an arrest in mid-stage oogenesis in wild-type dysgenic ovaries , as occurs when Bruno is not sequestered by oskar mRNA [38 , 39] . To evaluate whether bruno suppression of ovarian atrophy could be explained by its oskar-independent role in GSC differentiation [55 , 58] , we directly examined and compared GSCs between brunoQB/+ and CyO/+ dysgenic ovaries ( Fig 5C and 5D ) . While we did not observe any direct evidence of delayed GSC differentiation in brunoQB/+ germaria , we did observe that ovarioles containing developing egg chambers overwhelmingly retained all oogenic stages , including GSCs ( Fig 5D ) . In contrast , atrophied ovaries lacked any developing egg chambers including GSCs ( Fig 5C ) . These observations are consistent with recent evidence that GSC retention is a key determinant of P-element–induced ovarian atrophy [35] . They further suggest that reduced bruno signaling for differentiation may stabilize GSCs in their niche , allowing them to be retained despite the genotoxic effect of P-elements .
While the evolution of TE resistance through small-RNA-mediated silencing is a topic of immense research interest , the existence and evolution of tolerance factors that may reduce the fitness costs of TEs on the host remain undocumented . By opportunistically employing a panel of genotyped RILs , which uniformly lack small-RNA-mediated silencing of the recently invaded P-element , we have here uncovered the first example of segregating variation in host-TE tolerance in any organism . The natural variation in tolerance that we uncovered is unlikely to be exceptional . Population A RILs were generated from only eight genotypes , sampling but a small subset of alleles that were present in the ancestral population . Furthermore , major QTL peak that we identified here in Population A explains only about 35% of the heritable variation in tolerance . Therefore , other segregating variants in the Population A RILs must also affect female germline response to P-element activity . Our inability to map these variants likely reflects the fact that they are rare , exhibit small effects , or both [30] . Finally , because our phenotyping scheme involved crossing genetically variable RILs to the same paternal strain , zygotic recessive alleles that are not found in the paternal Harwich genotype would remain undetected . While differences in tolerance may be masked by resistance after small-RNA-mediated silencing evolves , our study reveals that in the absence of silencing , tolerance can be a major determinant of fitness . While the major peak we identified is modest in its functional effects , explaining about 14% of variation in ovarian atrophy , variation in fertility of this scale would be dramatic in the eyes of natural selection . Segregating tolerance alleles , such as the one we have detected here , could therefore be subject to strong positive selection during genome invasion by a novel TE . Tolerance may therefore be an important feature of the host evolutionary response to invading TEs . Indeed , the correlation between P-element dosage and the severity of hybrid dysgenesis is poor at best , causing many to suggest that other genetic factors , such as tolerance alleles , may also be important determinants of the dysgenic phenotype [59–61] . Furthermore , the hybrid dysgenesis induced by recent collections of D . melanogster tends to be mild when compared to collections from the 1970s and 1980s , providing circumstantial evidence that tolerance factors may have increased in frequency over time [61] . Once the causative variant responsible for the tolerance phenotype we uncovered here is identified , we will be poised to ask whether its increase in frequency has enhanced tolerance in extant populations . Based on its high expression in the Drosophila female ovary [47] , the presence of 22 SNPs that are in phase with founder QTL alleles , its differential expression between tolerant and sensitive alleles , and the dominant suppressive effect of classical loss-of-function alleles on dysgenic ovarian atrophy , bruno is a very strong candidate for the source of causative variation in P-element tolerance that we mapped on chromosome 2L . Identifying the causative variant ( s ) within the very large ( 138 kb ) bruno locus and understanding how its altered function relates to hybrid dysgenesis present exciting challenges for future work . On the surface , it is not obvious how bruno function could be related to P-element activity . Because Bruno physically interacts with the piRNA pathway component Vasa [62] and localizes to nuage [63] , the multifunctional germline organelle in which piRNA biogenesis occurs ( reviewed in [64 , 65] ) , a straightforward explanation is that bruno function is unrelated to tolerance but rather suppresses piRNA-mediated resistance of P-elements . However , resistance suppression is inconsistent with several important aspects of piRNA biology and bruno function . First , piRNA-mediated silencing of P-elements is short-circuited in the absence of complementary maternally deposited piRNAs ( absent from the RILs ) , and P-element–derived piRNAs are exceptionally rare in the ovaries of young dysgenic females [42 , 66] . Thus , the dramatic suppression of ovarian atrophy exhibited by bruno alleles in young dysgenic females ( Fig 5B ) is developmentally inconsistent with piRNA-mediated silencing , which can occur only in older female offspring of dysgenic crosses [42] . Additionally , germline knock-down of bruno does not significantly affect TE expression and , if anything , is associated with increased expression of some TE families [67] . If bruno suppressed piRNA silencing , reduced TE expression would be predicted upon knock-down . We propose that our results are best explained by bruno’s function in promoting GSC differentiation [37 , 55] , which could determine the tolerance of GSCs to DNA damage resulting from P-activity . GSC maintenance is dependent on a balance between self-renewal and differentiation ( reviewed in [68] ) , and is disrupted by the presence of DNA damage , leading to GSC loss [69] . We recently have discovered that the DNA damage response factor p53 is ectopically activated in the GSCs and CBs of dysgenic germlines [35] , which explains why GSCs are frequently absent from dysgenic ovaries ( Fig 5C , [15 , 34 , 35] ) . bruno loss-of-function alleles could therefore stabilize damaged GSCs in their niche in dysgenic germaria by reducing signals for differentiation . Indeed , loss-of-function mutations in two other GSC differentiation factors , bag of marbles and benign gonial cell neoplasm , have been associated with enhanced retention of GSCs in the niche of non-dysgenic germaria [70] . This model is fully consistent with our observation that bruno suppression of ovarian atrophy is accompanied by a rescue of oogenesis at all stages , including enhanced maintenance of GSCs ( Fig 5C ) . Our observations with bruno suggest an unexpected and novel role for developmental regulators of gametogenesis as determinants of germline tolerance of transposition . Interestingly , multiple regulators of female GSC maintenance and differentiation in D . melanogaster exhibit recent or ongoing signatures of positive selection [71–73] . Tolerance to the selfish bacterial endosymbiont Wolbachia has already been implicated in driving some of this adaptive evolution [74] . The fact that bruno alleles act as strong repressors of P-element hybrid dysgenesis suggests that another class of parasites , TEs , may also contribute to the adaptive evolution of stem cell determinants .
RILs from Population A were generously provided by Stuart Macdonald . Harwich ( #4264 ) , Ced-12c06760/CyO ( #17781 ) , Rab6GE13031/CyO ( #26898 ) , Rab6D23D/CyO ( #5821 ) , and ThrRSK04203/CyO ( #10539 ) were obtained from the Bloomington Drosophila stock center . Harwich was sibmated for one generation to increase homozygosity . Bruno and oskar mutants and deficiencies , in single and double heterozygous combinations , were generously provided by Paul MacDonald . Canton-S was obtained from Richard Meisel . All flies were maintained in standard cornmeal media . All experimental flies were maintained at 25 °C . Virgin RIL females were crossed to Harwich males and flipped onto fresh food every 3–5 days . Resulting F1 offspring were maintained for 3 days or 21 days , at which point their ovaries were examined using a squash prep [60] . Twenty-one-day-old females were transferred onto new food every 5 days as they aged to avoid bacterial growth . For the squash prep , individual females were squashed in a food-dye solution allowed to incubate for ≥5 minutes . After incubation , the slide was examined for the presence of stage 14 , chorionated egg chambers , which preferentially absorb the dye . In the interest of throughput , we assayed F1 females for the presence or absence of mature egg chambers: females who produced ≥1 egg chambers were scored as having non-atrophied ovaries , and females producing 0 egg chambers were scored as having atrophied ovaries . A phenotyping schematic is provided in Fig 1A . Crosses and phenotyping were performed for 656 RILs across 24 experimental blocks for 3-day-old F1 females and 606 RILs across 21 experimental blocks for 21-day-old F1 females . If fewer than 20 F1 offspring were phenotyped for a given cross , it was discarded and repeated , if possible . In total , we phenotyped ≥20 3-day-old and 21-day-old F1 female offspring for 592 RILs and 492 RILs , respectively , and 660 RILs were assayed for at least one of the age groups . For age-class-specific QTL mapping ( 3-day- and 21-day-old ) , the arcsine transformed proportion of F1 females ( S2 Fig ) with atrophied ovaries produced by each RIL ( S1 and S2 Data ) was used as the response variable in a random effects multiple regression model that included experimental block and undergraduate experimenter . For the combined analysis of both age classes , we used the full set of arcsine transformed proportions , and accounted for female age as an additional fixed effect in the regression model . All models were fit using the lmer function from the lme4 package [75] and are described in S2 Table . The raw residuals of the regression models above were used as the phenotypic response for QTL analysis ( S3–S5 Data ) , implemented with the DSPRqtl package [29] in R 3 . 02 [76] . The output yields a LOD score for the observed association between phenotype and genotype at 11 , 768 10-kb intervals along the euchromatic arms of the X , second , and third chromosomes . The LOD significance threshold was determined from 1 , 000 permutations of the observed data , and the confidence interval around each LOD peak was identified by a difference of −2 from the LOD peak position ( Δ2-LOD ) , as well as a Bayesian credible interval [44] . Maternal genotype was added as a random effect to the models above , in order to determine the genetic variance in the phenotype ( VG ) . VG was obtained by extracting the variance component for maternal genotype using the VarCorr ( ) function from the nlme package [77] . Broad sense heritability ( H2 ) was then the estimated proportion of overall variance ( VG/VP ) . To estimate the phenotypic effect of each founder at the QTL peak , we considered the residual phenotype for each used in QTL mapping and then determined the founder allele carried by the RIL at the LOD peak position [29] . RILs whose genotype at the LOD peak could not be assigned to a single founder with >0 . 95 probability were discarded . Founder alleles were phased into phenotypic classes by identifying the minimal number of groups required to describe phenotypic variation associated with the QTL peak [29] . Briefly , founder alleles were sorted based on their average estimated phenotypic effect , which was provided by the sampled RILs . Linear models containing all possible sequential partitions of founder alleles were then fit and compared to a null model in which all founder alleles are in a single partition , using an extra-sum-of-squares F-test . The two-partition model with the highest F-statistic was retained and fixed only if it provided a significantly better fit ( p < 10−4 ) than the null model . The two partitions of founder haplotypes were then fixed , and all possible three-partition models were explored . This process was continued until the model fit could not be improved . Founders were assigned a “hard” genotype for all annotated TEs [78] and SNPs [29] in the QTL window if their genotype probability for a given allele was greater than 0 . 95 [29] . We then looked for alternate alleles ( SNPs and TEs ) that were in phase with our inferred allelic classes [29 , 31]: the sensitive class ( A3 and A5 ) and the tolerant class ( A1 , A2 , A4 , A6 , and A7 ) . A8 was excluded because its assignment to the sensitive or tolerant class differed between the data sets from 3-day-old and 21-day-old females . To identify RILs containing either the A4 ( “tolerant” ) or A5 ( “sensitive” ) haplotypes for the QTL window , we took advantage of the published , hidden Markov model–inferred genotypes for the Population A RIL panel [29] . We first identified RILs that carried a contiguous A4 or A5 haplotype for the Δ2-LOD confidence interval for the combined analysis with a genotype probability of greater than 0 . 95 ( Table 1 ) . Then , for all possible RIL pairs ( A4 and A5 ) , we calculated the number of 10-kb genomic windows for which they carried the same RIL haplotype , also with a genotype probability of greater than 0 . 95 . We selected three pairs of background-matched RILs , which carry the same founder haplotype for 67% ( 11374 and 11120 ) , 64% ( 11131 and 11200 ) , and 60% ( 11435 and 11343 ) of genomic windows but alternate haplotypes for the QTL window . Virgin female offspring of dysgenic crosses between tolerant ( 11120 , 11200 , 11343 ) and sensitive ( 11374 , 11131 , 11435 ) RILs and Harwich males were collected daily and placed individually in a vial with two ywF10 males . Females were allowed to mate and oviposit for 5 days , and adults were discarded when the females reached 6 days of age . The presence and number of F2 offspring were quantified for each dysgenic female . The effects of genetic background and QTL haplotype on the presence and number of F1 offspring were assessed by logistic and linear regression models , respectively . Models were fit using the glm ( logistic ) and lm ( linear ) functions in R 3 . 02 [76] . Ovaries were dissected from young , 3-day-old females from tolerant ( 11120 , 11200 , 11343 ) and sensitive ( 11374 , 11131 , 11435 ) RILs and homogenized in TRI-reagent ( Sigma-Aldrich ) . RNA was extracted according to manufacturer instructions . Purified RNA was treated with DNAse , reverse transcribed using oligo ( dT ) 15 primers and M-MLV RNAseH− , and then treated with RNAse H , according to manufacturer instructions ( Promega ) . Synthesized cDNA was diluted 1:125 for qRT-PCR . Abundance of bruno and rpl32 transcript was estimated using SYBR green PCR mastermix ( Applied Biosystems ) according to manufacturer instructions . Three biological replicates were evaluated for each genotype , with three technical measurements for each replicate , for a total of nine measurements of each genotype . Bruno expression was estimated relative to rpl32 for each replicate , according to a five-point standard curve . Primers were as follows: bruno-F: 5′-CCCAGGATGCTTTGCATAAT-3′ , bruno-R: 5′- ACGTCGTTCTCGTTCAGCTT-3′ , rpl32-F: CCGCTTCAAGGGACAGTATC , and rpl32-F: GACAATCTCCTTGCGCTTCT . The relationship between genetic background and QTL haplotype with bruno expression was evaluated with mixed-effects linear regression , accounting for the biological replicate as a random effect . The regression model was fit with the lme4 package [75] in R 3 . 02 [76] . Single and double heterozygote mutant virgin females ( mutant/balancer ) were crossed to Harwich males at 25 °C . Because the vast majority of Drosophila lab stocks are P-element–free , with the exception of any P-element–derived transgenes , these crosses are dysgenic . Resulting F1 dysgenic female offspring were collected and aged at 25 °C for 3–7 days , when their ovaries were assayed using the squash prep described above [60] . The incidence of ovarian atrophy was then compared between mutant/+ and balancer/+ siblings from the same cross . Ovaries from 3–7-day-old female offspring of dysgenic crosses were dissected and immediately fixed with 4% EM-grade methanol-free paraformaldehyde ( Thermo Scientific ) . Ovaries were washed with 0 . 1% Triton X-100 in PBS and blocked with 5% goat serum albumin ( Sigma-Aldrich ) . Primary antibody concentrations were as follows: anti-Hts 1B1 1:4 ( DSHB [79] ) , anti-Vasa 1:40 ( DSHB ) , anti-Bruno 1:1 , 000 ( provided by Paul MacDonald [50] ) , and anti-Orb 4H8 and 6H4 1:20 ( DSHB [50] ) . Secondary antibody concentrations were 1:500 . Ovaries were visualized with an SP8 Upright Confocal DM6000 CFS ( Leica ) Microscope , outfitted with a 60× oil immersion lens . Images were collected using an EM-CCD camera ( Hamamatsu ) and LAS-AF software ( Leica ) . | Transposable elements ( TEs ) , or “jumping genes , ” are mobile fragments of selfish DNA that leave deleterious mutations and DNA damage in their wake as they spread through host genomes . Their harmful effects are known to select for resistance by the host , in which the propagation of TEs is regulated and reduced . Here , we study for the first time whether host cells might also exhibit tolerance to TEs , by reducing their harmful effects without directly controlling their movement . By taking advantage of a panel of wild-type Drosophila melanogaster that lack resistance to P-element DNA transposons , we identified a small region of the genome that influences tolerance of P-element activity . We further demonstrate that a gene within that region , bruno , strongly influences the negative effects of P-element mobilization on the fly . When bruno dosage is reduced , the fertility of females carrying mobile P-elements is enhanced . The bruno locus encodes a protein with no known role in TE regulation but multiple well-characterized functions in oogenesis . We propose that bruno function reduces tolerance of the developing oocyte to DNA damage that is caused by P-elements . | [
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"dro... | 2018 | QTL mapping of natural variation reveals that the developmental regulator bruno reduces tolerance to P-element transposition in the Drosophila female germline |
The bacterial pathogen Neisseria gonorrhoeae ( Gc ) infects mucosal sites rich in antimicrobial proteins , including the bacterial cell wall-degrading enzyme lysozyme . Certain Gram-negative bacteria produce protein inhibitors that bind to and inhibit lysozyme . Here , we identify Ng_1063 as a new inhibitor of lysozyme in Gc , and we define its functions in light of a second , recently identified lysozyme inhibitor , Ng_1981 . In silico analyses indicated that Ng_1063 bears sequence and structural homology to MliC-type inhibitors of lysozyme . Recombinant Ng_1063 inhibited lysozyme-mediated killing of a susceptible mutant of Gc and the lysozyme-sensitive bacterium Micrococcus luteus . This inhibitory activity was dependent on serine 83 and lysine 103 of Ng_1063 , which are predicted to interact with lysozyme’s active site residues . Lysozyme co-immunoprecipitated with Ng_1063 and Ng_1981 from intact Gc . Ng_1063 and Ng_1981 protein levels were also increased in Gc exposed to lysozyme . Gc lacking both ng1063 and ng1981 was significantly more sensitive to killing by lysozyme than wild-type or single mutant bacteria . When exposed to human tears or saliva , in which lysozyme is abundant , survival of Δ1981Δ1063 Gc was significantly reduced compared to wild-type , and survival was restored upon addition of recombinant Ng_1981 . Δ1981Δ1063 mutant Gc survival was additionally reduced in the presence of human neutrophils , which produce lysozyme . We found that while Ng_1063 was exposed on the surface of Gc , Ng_1981 was both in an intracellular pool and extracellularly released from the bacteria , suggesting that Gc employs these two proteins at multiple spatial barriers to fully neutralize lysozyme activity . Together , these findings identify Ng_1063 and Ng_1981 as critical components for Gc defense against lysozyme . These proteins may be attractive targets for antimicrobial therapy aimed to render Gc susceptible to host defenses and/or for vaccine development , both of which are urgently needed against drug-resistant gonorrhea .
Neisseria gonorrhoeae ( Gc ) is a Gram-negative diplococcus and the causative agent of the sexually transmitted infection gonorrhea . The World Health Organization ( WHO ) estimates 78 million cases of gonorrhea occur each year , with over 800 , 000 cases reported annually in the United States [1–3] . The lack of a protective vaccine , widespread prevalence of antibiotic-resistant Gc , and treatment failures with last line therapeutics have prompted the United States Centers for Disease Control to label antibiotic-resistant Gc as an urgent threat to public health [1 , 4–6] . Likewise , the WHO lists the control and elimination of Gc infection as a high priority [7] . Dissecting Gc pathogenesis and virulence is critical for the development of novel therapeutics and vaccines . Gc colonizes mucosal sites , including the cervix , urethra , pharynx , conjunctiva , and rectum . Colonization initiates an inflammatory response , culminating in the robust recruitment of neutrophils , an innate immune cell with antimicrobial killing activities [8] . Thus , Gc survival during human infection requires defenses against the antimicrobial molecules made both by the mucosal epithelium and neutrophils . For instance , Gc evades killing by cationic antimicrobial proteins by modifying surface lipooligosaccharide and producing multidrug efflux pumps , and these activities are important for Gc survival from human neutrophils , survival in the mouse model of Gc cervicovaginal colonization , and/or survival in a human male urethral-infection model [9–11] . The antimicrobial protein lysozyme is ubiquitous at the mucosal sites colonized by Gc , reaching concentrations as high as 2 mg/mL in the conjunctiva and 1 mg/mL in the cervix [12 , 13] . Lysozyme is also abundant in phagocytes like neutrophils [14 , 15] . Lysozyme hydrolyzes the glycan backbone of bacterial cell wall peptidoglycan , causing lysis and death . Because lysozyme is highly cationic , it can also kill bacteria through an enzymatic-independent mechanism , purportedly via pore formation on bacterial membranes [16–19] . Bacteria have evolved numerous , non-redundant mechanisms to thwart the killing activities of lysozyme , which in many cases contribute to enhanced survival and virulence in vivo ( reviewed in [20] ) . These observations highlight lysozyme as a critical player in host defense , and , in turn , underscore the importance for lysozyme resistance to a pathogen’s success . Lysozyme resistance in Gc is mediated by envelope integrity , peptidoglycan modifications , and protein inhibitors of lysozyme . In Gram-negative bacteria like Gc , the outer membrane ( OM ) restricts periplasmic access of lysozyme [21] . Consequently , maintenance of envelope integrity is vital for Gram-negative resistance to lysozyme . We recently reported that two cell wall turnover proteins , the lytic transglycosylases LtgA and LtgD , contribute to envelope integrity in Gc , and ΔltgAΔltgD mutant Gc is more sensitive to killing by lysozyme and human neutrophils [22] . Another mechanism of lysozyme resistance in Gc involves O-acetylation of peptidoglycan , which can sterically hinder lysozyme’s hydrolytic activity [23 , 24] . O-acetylation , however , only contributes to lysozyme resistance in Gc if the envelope is also compromised , as when LtgA and LtgD are lost [22 , 24] . Recently , we identified a third mechanism of lysozyme resistance in Neisseria , the production of a protein inhibitor of lysozyme [25] . Protein inhibitors have only been identified in Gram-negative bacteria , and their expression contributes to bacterial survival within mucosal secretions , survival from phagocytes , and/or survival in vivo [26–30] . Inhibitors against c-type lysozymes , like human lysozyme , are classified as Ivy ( inhibitor of vertebrate lysozyme ) , PliC ( periplasmic lysozyme inhibitor of c-type lysozyme ) , or MliC ( membrane-bound lysozyme inhibitor of c-type lysozyme ) . PliC- and MliC-type inhibitors share structural similarity and conserved sequence motifs that are distinct from Ivy-type inhibitors [31] . All function by insertion of one or more protein loops into the active site of lysozyme to interfere with its peptidoglycan-hydrolyzing activity [31] . We found that purified recombinant Ng_1981 ( also known as the Ng-Adhesin Complex Protein , Ng-ACP ) from Gc inhibits the enzymatic activity of lysozyme in vitro and contributes to gonococcal tolerance to lysozyme [25] . The homolog of Ng_1981 in Neisseria meningitidis , NMB_2095 ( Nm-ACP ) , is 94% identical to Ng_1981 and binds directly to lysozyme with micromolar affinity [25] . Although the structure of NMB_2095 exhibits overall similarity with PliC/MliC-type inhibitors , NMB_2095 and Ng_1981 lack the conserved PliC/MliC sequence features and therefore were classified as a novel type of lysozyme inhibitor [25 , 31] . Gram-negative bacteria often produce multiple , non-redundant inhibitors of lysozyme [26–28 , 32–35] , prompting us to investigate whether Gc also produces more than one inhibitor . In this work , we report that the open reading frame ng1063 ( KEGG GENOME , Gc strain FA1090 ) encodes for a protein that shares sequence and structural homology with MliC-type inhibitors . We tested the hypothesis that Ng_1063 functions as a lysozyme inhibitor in Gc and further defined its biological activity in the context of Ng_1981 . Our findings suggest that Ng_1063 functions as a bona fide inhibitor of lysozyme and that Gc employs both Ng_1063 and Ng_1981 , which exhibit distinct properties and localizations , for optimal defense against lysozyme .
Ng_1063 shares overall amino acid sequence similarity with MliC from P . aeruginosa ( 23% identity , 40% similarity ) and E . coli ( 16% identity , 32% similarity ) ( Fig 1A ) . S89 and K103 of P . aeruginosa MliC interact with the active site residues of lysozyme and are required for MliC inhibitory function [36]; the corresponding residues , S83 and K103 , are conserved in Ng_1063 ( Fig 1A ) . Like other MliC inhibitors , Ng_1063 is predicted to be a lipoprotein as determined by signal sequence analysis using lipoP 1 . 0 ( http://www . cbs . dtu . dk/services/LipoP/ ) . We characterized the potential for Ng_1063 as an MliC-type inhibitor of lysozyme using PHYRE2 ( www . sbg . bio . ic . ac . uk/phyre2 ) to predict the Ng_1063 three-dimensional structure . PHYRE2 predicted Ng_1063 to have an MliC-type fold with 99 . 6% confidence . Alignment of the Ng_1063 predicted structure with P . aeruginosa MliC in PyMOL shows overlap of S83 and K103 in Ng_1063 with the corresponding residues of P . aeruginosa MliC ( Fig 1B ) . Threading of Ng_1063 through the P . aeruginosa MliC-lysozyme co-crystal structure ( PDB 3f6z ) positions the S83 and K103 residues of Ng_1063 in close proximity to the active site residues of lysozyme , E53 and D70 ( Fig 1C ) . Based on these in silico analyses , we hypothesized that Ng_1063 is a protein inhibitor of lysozyme . To test this hypothesis , we first examined if Ng_1063 protein could rescue the lysozyme-mediated lysis of Micrococcus luteus , a Gram-positive bacterium that is intrinsically sensitive to lysozyme . We generated purified , recombinant Ng_1063 ( r1063 ) protein ( see Materials and methods ) and found it prevented the lytic activity of lysozyme on M . luteus in a concentration-dependent manner ( Fig 2A ) . As Gc is the relevant context for Ng_1063 activity , we next investigated the effect of r1063 on survival of Gc after exposure to lysozyme . In contrast to M . luteus , WT Gc is relatively resistant to lysozyme . Thus , we used an ΔltgAΔltgD Gc mutant , which we previously demonstrated has increased sensitivity to lysozyme , as a tool to assess r1063 inhibitory activity with Gc [22] . As expected , ΔltgAΔltgD mutant Gc was markedly reduced in survival in the presence of lysozyme , compared to WT ( strain MS11 ) ( Fig 2B ) . Preincubation of lysozyme with r1063 , but not an unrelated Neisserial OM protein ( macrophage infectivity potentiator ( MIP ) [25] ) , restored survival of the ΔltgAΔltgD mutant to WT levels ( Fig 2B ) . Overexpression of ng1063 in ΔltgAΔltgD mutant Gc was also sufficient to rescue bacterial survival to WT levels ( Fig 2C ) . Although the ΔltgAΔltgD mutant has reduced envelope integrity that correlates with lysozyme sensitivity [22] , overexpression of ng1063 in this mutant did not affect bacterial susceptibility to vancomycin , an indicator of cellular permeability ( S1 Table ) . These observations strongly suggest that Ng_1063 protects Gc by inhibiting lysozyme , rather than enhancing envelope integrity . Together , these results identify Ng_1063 as a new lysozyme inhibitor made by Gc . Besides Ng_1063 , Gc produces another inhibitor of lysozyme , Ng_1981 , which we recently characterized [25] . Ng_1981 shares limited sequence similarity with Ng_1063 ( 19% identity , 35% similarity ) ( S1A Fig ) yet is predicted to share a similar overall three-dimensional structure [25] . Recombinant Ng_1981 ( r1981 ) inhibits the lytic activity of lysozyme against M . luteus [25] , and we found that pretreatment of lysozyme with r1981 or overexpression of ng1981 restored ΔltgAΔltgD mutant survival after exposure to lysozyme to WT levels ( S1B and S1C Fig ) . Overexpression of ng1981 did not affect the sensitivity of ΔltgAΔltgD mutant Gc to vancomycin ( S1 Table ) . We next tested whether both Ng_1063 and Ng_1981 interact with lysozyme in the physiological context of the bacterial cell . We generated a Δ1981Δ1063 mutant , where ng1063 was replaced with a null allele and ng1981 was disrupted by insertion-deletion . Δ1981Δ1063 mutant Gc was then complemented with C-terminal FLAG-tagged versions of either ng1063 or ng1981 at an ectopic chromosomal location under IPTG regulation ( see Materials and methods ) . As a negative control , we used Gc expressing C-terminal FLAG-tagged ltgA or ltgD , also under IPTG regulation ( see Materials and methods ) [37] . These strains were induced for FLAG-protein expression and incubated with lysozyme or vehicle control . FLAG-tagged proteins and their interacting partners were then immunoprecipitated from bacterial cell lysates . Lysozyme co-precipitated with Ng_1063-FLAG and Ng_1981-FLAG , but not with LtgA-FLAG , LtgD-FLAG , or anti-FLAG resin alone , showing the lysozyme inhibitors both interact with lysozyme in intact bacteria ( Fig 3A ) . In some bacteria , exposure to lysozyme induces a signaling cascade that results in upregulation of lysozyme resistance factors [38–41] . Therefore , we next examined whether treating Gc with lysozyme under sublethal conditions ( see Materials and methods and Fig 4A ) affected Ng_1063 and Ng_1981 protein abundance . To do so , we engineered a Gc strain carrying 1063 ( WT ) -FLAG at its native locus to probe for Ng_1063 using the anti-FLAG antibody . There was a significant increase in Ng_1063 protein from Gc treated with lysozyme for 3 hrs , compared with vehicle ( Fig 4B and 4C ) . Similarly , using anti-r1981 antisera , Ng_1981 protein was significantly increased upon lysozyme treatment , compared to vehicle ( Fig 4D and 4E ) . On average , Ng_1063 and Ng_1981 protein were increased 2-fold and 3 . 5-fold , respectively , in Gc treated with 1 mg/mL lysozyme ( Fig 4C and 4E ) . Taken together , these findings indicate that both Ng_1063 and Ng_1981 interact with lysozyme in intact Gc , and Gc alters expression of Ng_1063 and Ng_1981 proteins after exposure to lysozyme . We next took a genetic approach to define how Ng_1063 and Ng_1981 , singly and in combination , affect Gc resistance to killing by lysozyme . To our surprise , survival of Δ1063 mutant Gc was equivalent to WT after exposure to lysozyme ( Fig 5A ) . One possible explanation for this phenotype is that the outer membrane barrier of Gc limits the access of lysozyme to the periplasm where Ng_1063 could be functioning; however , loss of ng1063 had no effect on bacterial survival compared with parent Gc under the following envelope-compromising conditions: subinhibitory concentrations of the pore-forming cationic antimicrobial peptide LL-37 ( S2A and S2B Fig ) ; membrane-destabilizing treatment with EDTA ( S2C Fig ) ; and genetically-induced loss of envelope integrity using the ΔltgAΔltgD mutant ( S3 Fig ) . In contrast , at the same concentrations of lysozyme used for the Δ1063 mutant , Δ1981 mutant Gc was significantly compromised for survival compared to WT bacteria , reaching a near 5-fold decrease in survival when exposed to 1 mg/mL lysozyme ( Fig 5A ) . Complementation with ng1981 restored Δ1981 mutant survival to WT levels ( Fig 5A ) . Loss of ng1981 also reduced survival of the ΔltgAΔltgD mutant after exposure to lysozyme , an observation that highlights how multiple , mechanistically distinct factors contribute to lysozyme resistance in Gc ( S3 Fig ) . Thus , Ng_1981 is confirmed as an important factor for Gc defense against lysozyme . Importantly , deletion of both ng1063 and ng1981 markedly reduced Gc survival in the presence of lysozyme in a concentration-dependent and time-dependent manner ( Fig 5A and 5B ) . The sensitivity to lysozyme in Δ1981Δ1063 mutant Gc was greater than either single mutant alone , and this effect was particularly apparent at higher concentrations of lysozyme ( Fig 5A and 5B ) . As seen in Fig 5B , recoverable CFU from Δ1981Δ1063 mutant Gc was reduced 100-fold compared to Δ1981 single mutant Gc after 4 hrs exposure to lysozyme . Similarly , loss of both ng1063 and ng1981 significantly impaired the survival of ΔltgAΔltgD mutant Gc after lysozyme exposure , in comparison to loss of ng1981 alone ( S3 Fig ) . Complementation with ng1063 in Δ1981Δ1063 mutant Gc restored survival to the level of the single Δ1981 mutant ( Fig 5A ) . In the absence of lysozyme , single and double mutants grew similarly to WT Gc ( Fig 5B ) . These findings indicate that in addition to Ng_1981 , Ng_1063 is also important for Gc defense against lysozyme . Moreover , these findings shed light on the biological activities of Ng_1063 and Ng_1981 in Gc , where Ng_1981 can fully compensate for loss of ng1063 , yet Ng_1063 cannot fully compensate for loss of ng1981 . Lysozyme can kill bacteria through its hydrolase activity and through an enzyme-independent mechanism that relies on its cationicity [16 , 17] . We investigated if Ng_1063 and Ng_1981 , like other proteinaceous inhibitors of lysozyme , defend Gc from the enzymatic activity of lysozyme . To this end , we exposed Δ1981Δ1063 mutant Gc to lysozyme that had been boiled , eliminating its enzymatic activity [22 , 42] . Survival of the Δ1981Δ1063 mutant was unaltered by boiled lysozyme compared to WT Gc ( Fig 5C ) , confirming that Ng_1063 and Ng_1981 inhibit the enzymatic activity of lysozyme . We next tested the ability of Ng_1063 and Ng_1981 to defend Gc against other peptidoglycan-degrading enzymes ( i . e . , peptidoglycan muramidase activity ) ( S4A Fig ) . Δ1981Δ1063 mutant Gc was significantly reduced over 6-fold in survival after incubation with the chicken egg white c-type lysozyme , compared to WT , and survival was rescued by overexpression of ng1063 ( S4B Fig ) . At the concentrations tested , the single Δ1981 mutant was unaffected by chicken egg white lysozyme ( S4B Fig ) . In contrast , in the presence of mutanolysin , a distinct bacteria-derived muramidase ( S4A Fig ) , survival of Δ1981 and Δ1981Δ1063 mutant Gc was equivalent to WT ( S4C Fig ) . Mutanolysin was active in this setting , as evidenced by the reduced survival of ΔltgAΔltgD mutant Gc ( S4C Fig ) [22] . These findings indicate that Ng_1063 and Ng_1981 can inhibit some but not all muramidases , possibly owing to co-evolution of Gc with humans , which possess one c-type lysozyme . Residues S89 and K103 of P . aeruginosa MliC interact with the active site of lysozyme and are required for lysozyme inhibition ( see Fig 1 ) [36] . Thus , we evaluated if the corresponding residues in Ng_1063 , S83 and K103 , similarly contributed to lysozyme inhibition by complementing Δ1981Δ1063 Gc with C-terminal 3X-FLAG-tagged Ng_1063 , in which each of these residues was replaced with an alanine . Immunoblotting with a FLAG antibody showed equivalent expression between Ng_1063 WT , S83A , and K103A variants in the Δ1981Δ1063 mutant upon IPTG induction ( S5 Fig ) . In the presence of lysozyme , Δ1981Δ1063 mutant Gc expressing WT Ng_1063-FLAG exhibited a significant , greater than 60-fold increase in survival , which was not reproduced with expression of either S83A or K103A Ng_1063-FLAG ( Fig 5D ) . This finding suggests that these residues are important for the lysozyme inhibitory activity of Ng_1063 . A MUSCLE alignment between Ng_1063 and Ng_1981 shows possible conservation of the MliC inhibitory serine ( S76 in Ng_1981 ) and lysine residues ( K99 in Ng_1981 ) ( S1A Fig ) . We previously found that the N . meningitidis homolog to Ng_1981 , NMB_2095 , exhibits structural homology to MliC/PliC inhibitors , yet computational docking models between NMB_2095 and lysozyme predicts a mode of binding that differs from MliC/PliC inhibitors [25] . Because mutation of residues in NMB_2095 that were predicted to interact with lysozyme ( i . e . , Asn79 , Tyr84 , and Gly95 ) failed to alter its inhibitory activity [25] , we investigated the role of S76 and K99 in Ng_1981 inhibition of lysozyme . We complemented Δ1981 mutant Gc with C-terminal 3X-FLAG-tagged Ng_1981 , where S76 and K99 were replaced with an alanine residue . We observed equivalent expression of Ng_1981 ( WT ) -FLAG , Ng_1981 ( S76A ) -FLAG , and Ng_1981 ( K99A ) -FLAG by immunoblot upon IPTG induction ( S1D Fig ) . Complementation with either WT , S76A , or K99A versions of ng1981 was sufficient to significantly increase Δ1981 mutant survival in the presence of lysozyme ( S1E Fig ) . This finding supports our previous conclusions that Ng_1981 and its homolog NMB_2095 behave differently from MliC/PliC inhibitors and are thus novel inhibitors of lysozyme . Mucosal sites that are colonized by Gc are bathed in fluids with high concentrations of lysozyme , including tears ( 2 mg/mL ) and saliva ( 0 . 12 mg/mL ) [13] . We therefore tested the possibility that Ng_1063 and Ng_1981 contribute to Gc survival when exposed to pooled human tears or pooled human saliva . Compared to WT Gc , Δ1981Δ1063 mutant Gc was significantly reduced in survival in the presence of tears as well as saliva ( Fig 6A and 6B ) . Survival of the single Δ1981 mutant was equivalent to WT at the dilutions tested ( Fig 6A and 6B ) . We confirmed the secretions contained active lysozyme by their ability to lyse M . luteus ( S6A and S6B Fig ) . Pretreatment of the secretions with r1981 was sufficient to inhibit the lytic activity of secretions against M . luteus ( S6A and S6B Fig ) , and pretreatment with r1981 restored Δ1981Δ1063 mutant survival to WT levels ( Fig 6A and 6B ) . Neutrophils are another abundant source of lysozyme and are heavily recruited to sites of Gc infection [14 , 15] . Thus , we tested if Ng_1063 and Ng_1981 are important for Gc survival from adherent , interleukin 8-treated primary human neutrophils , a model to recapitulate the physiological state of tissue-migrated neutrophils during gonorrheal disease [43] . Δ1981 mutant Gc exhibited a modest but statistically significant reduction in survival in the presence of human neutrophils , compared to WT and ng1981 complemented Gc ( Fig 6C ) . In contrast , survival of Δ1063 Gc was equivalent to WT after exposure to neutrophils , and Δ1981Δ1063 mutant Gc was equally sensitive to neutrophils as the single Δ1981 mutant ( Fig 6C ) . Under the conditions tested , Gc resides in a phagosome that exhibits limited fusion with neutrophil primary granules , which contain lysozyme [14 , 15 , 44 , 45] . In comparison with Δ1981Δ1063 mutant Gc , ΔltgAΔltgD mutant Gc is markedly sensitive to even small amounts of lysozyme . Because we previously linked lysozyme sensitivity of the ΔltgAΔltgD mutant with increased killing by human neutrophils [22] , we next tested whether overexpression of ng1063 or ng1981 enhanced ΔltgAΔltgD survival in the presence of human neutrophils , as was the case in vitro with purified human lysozyme ( see Fig 2C and S1C Fig ) . However , neither overexpression of ng1063 nor ng1981 was sufficient to rescue the survival defect of ΔltgAΔltgD mutant Gc in the presence of human neutrophils , pointing to factors in addition to lysozyme for the sensitivity of ΔltgAΔltgD mutant to neutrophils ( S7 Fig ) . Together , these findings suggest that Ng_1063 and Ng_1981 help defend Gc from physiologically relevant sources of lysozyme that would be encountered in its obligate human host . Although Ng_1063 and Ng_1981 both interact with lysozyme and display a similar ability to inhibit the enzymatic activity of lysozyme , their biological activities in Gc are distinct . To gain insight into the mechanism underlying these differences , we examined the localization of Ng_1063 and Ng_1981 in Gc . Ng_1063 is a putative lipoprotein predicted to be extracellularly exposed on the surface of Gc [46] , whereas Ng_1981 is predicted to be a soluble , periplasmic protein , according to LipoP 1 . 0 and CELLO bioinformatic analyses . To test these predictions , we assessed the surface exposure of each protein , using Δ1063 and Δ1981 mutant Gc complemented with IPTG-inducible WT copies of C-terminal FLAG-tagged Ng_1063 and Ng_1981 , respectively . FLAG-complemented Gc were incubated with anti-FLAG antibody , and fluorescence intensity per individual bacterium was quantified by imaging flow cytometry . We detected strong surface labeling of FLAG protein from ng1063-FLAG complemented Gc ( Fig 7A–7C ) . In contrast , ng1981-FLAG complemented Gc displayed negligible surface expression of FLAG protein , which was no different from the negative control ( Fig 7A–7C ) . As a complementary approach , we visualized FLAG-tagged protein expression using immunofluorescence microscopy . We observed staining with the FLAG antibody in ng1063-FLAG complemented Gc , but not in ng1981-FLAG complemented Gc under non-permeabilizing conditions , in agreement with the imaging flow cytometry data ( Fig 7D ) . Surface expression of Ng_1981-FLAG was also not detectable using polyclonal anti-r1981 antisera ( S8 Fig ) . When Gc was permeabilized with methanol and Triton-X-100 prior to incubation with the FLAG antibody , both ng1063-FLAG and ng1981-FLAG complemented Gc exhibited peripheral staining , suggestive of localization to the bacterial envelope ( Fig 7D ) . Together , these findings indicate that Ng_1063 , but not Ng_1981 , is exposed extracellularly on the surface of Gc . The lack of Ng_1981 surface staining was unexpected because its homolog in N . meningitidis , NMB_2095 , was found to be surface-exposed , where it contributes to bacterial adhesion to epithelial cells [47] . A clue to resolving this discrepancy came from the results in Fig 3 , where we observed less FLAG-tagged Ng_1981 protein than FLAG-tagged Ng_1063 protein in lysates prepared from pelleted bacteria , despite using the same overexpression system . We thus considered the possibility that a fraction of Ng_1981 is released from Gc . To directly test this possibility , we evaluated the presence of Ng_1981 and Ng_1063 protein in bacterial whole cell lysates and supernatants . We used Gc carrying 1063 ( WT ) -FLAG at its native locus to simultaneously detect Ng_1063-FLAG using the anti-FLAG antibody and Ng_1981 using anti-r1981 antisera . Conditioned supernatants from this strain contained Ng_1981 , but not Ng_1063 , while both proteins were present in the whole cell lysates ( Fig 7E ) . In addition , we did not detect the cytoplasmic protein ZWF in conditioned supernatants ( Fig 7E ) , providing further evidence that Ng_1981 is not released as the result of autolysis . To test whether Ng_1981 in Gc supernatants contains functional inhibitory activity against lysozyme , we co-cultured Δ1981Δ1063 mutant Gc with Ng_1981-complemented Gc in the Δ1981Δ1063 background . Under these conditions , the presence of the ng1981 complement significantly increased the survival of the non-complemented Δ1981Δ1063 mutant after exposure to lysozyme ( Fig 7F ) . This observation suggests that extracellularly released Ng_1981 can protect Gc from lysozyme , in keeping with our initial findings with ectopically added recombinant protein ( S1 Fig ) . Taken together , these results indicate that Gc produces two distinct inhibitors of lysozyme: Ng_1981 is both released extracellularly and is in the bacterial envelope , whereas Ng_1063 is OM-anchored and surface-exposed .
The success of Gc as a human pathogen requires that it defends itself against antimicrobial components found at mucosal surfaces . In this work , we identified Ng_1063 as a new functional homolog of the MliC-type lysozyme inhibitors in Gc . We then interrogated its functions alongside another , recently characterized lysozyme inhibitor , Ng_1981 [25] . We found that Ng_1063 and Ng_1981 interact with lysozyme in the physiological context of the bacterium , and both proteins’ expression was increased upon exposure to lysozyme . Gc lacking both ng1063 and ng1981 was markedly reduced in survival after exposure to human lysozyme and lysozyme-containing human secretions , and this effect was greater than either single mutant alone . Ng_1063 was exposed on the surface of Gc , while Ng_1981 was not; however , a fraction of Ng_1981 protein was released into the extracellular milieu . Based on these results , we conclude that Gc produces two distinct inhibitors of lysozyme that together confer full resistance to this abundant antimicrobial defense protein . MliC-type inhibitors of lysozyme are periplasmic-facing lipoproteins that form an eight-stranded antiparallel β-barrel structure [31] . The structure of P . aeruginosa MliC in complex with lysozyme revealed the protrusion of two loops from the inhibitor into the active site of lysozyme , each loop contributing one key , conserved inhibitory residue ( S89 and K103 ) [31 , 36] . Our in silico analyses predicted that Ng_1063 is a lipoprotein with sequence and structural similarity to MliC-type inhibitors . We obtained biological support for this prediction by showing that the corresponding inhibitory residues in Ng_1063 , S83 and K103 , were required for its inhibitory activity . In contrast , S83 and K103 were dispensable for the interaction of Ng_1063 with lysozyme in vivo ( Fig 3B ) . This finding implicates a second binding site in Ng_1063 for lysozyme , which has been shown for a shallow binding pocket consisting of Y92 and T98 in P . aeruginosa MliC [36] . Intriguingly , we found that C-terminal FLAG-tagged Ng_1063 is on the surface of Gc . Since Ng_1063 has no predicted membrane-spanning regions , the entire Ng_1063 protein is likely exposed extracellularly , where it could bind and inhibit lysozyme . This observation contrasts with most MliC-type inhibitors , which are predicted to reside within the inner leaflet of the OM , facing the periplasm [31] . Since most studies have not experimentally verified localization of MliC proteins , we anticipate other microbes produce MliC-type proteins that are also surface-exposed . It remains unclear how Ng_1063 becomes surface exposed , but a likely candidate involves the recently identified surface lipoprotein assembly modulator ( Slam ) [48] . Several bacterial species , including E . coli , P . aeruginosa , Yersinia pestis , and Edwardsiella tarda , produce more than one inhibitor of lysozyme , and often these proteins are non-redundant [26–28 , 32–35] . Here , we found that Gc produces two proteins , Ng_1063 and Ng_1981 , which both bind to and inhibit lysozyme . Each protein’s expression is increased upon exposure to lysozyme . While other bacteria regulate lysozyme resistance factors via alternative sigma factors or two component systems [38–41 , 49] , such regulatory networks are limited in Gc; thus how this regulation is occurring in Gc is not yet clear . Despite their shared ability to inhibit lysozyme , Ng_1063 and Ng_1981 have several distinguishable properties . First , even though Ng_1063 and Ng_1981 share overall structural similarity with MliC/PliC-type inhibitors [25] , these proteins share little sequence similarity ( 19% identity; 35% similarity ) . In fact , Ng_1981 lacks conserved MliC/PliC sequence motifs [25 , 31] . Further , we found that mutation of the Ng_1981 residues S76 and K99 , which align with the key inhibitory residues of Ng_1063 , failed to alter Ng_1981 inhibitory activity against lysozyme . Ng_1981 protein is also predicted to be soluble whereas Ng_1063 is a predicted lipoprotein . In contrast to Ng_1063 that is surface-exposed , we found evidence that Ng_1981 is present both in the Gc envelope and in the extracellular milieu . It remains unclear whether extracellular Ng_1981 is present as a soluble protein or resides within or associates with outer membrane vesicles; however , we found evidence that ng1981 expressing Gc could rescue survival of lysozyme-susceptible Gc in trans , suggesting that extracellular Ng_1981 retains inhibitory activity . Future studies will investigate how Ng_1981 is released by Gc . Gc employs both Ng_1063 and Ng_1981 for optimal defense against lysozyme , but in a non-redundant manner . Loss of ng1981 significantly reduced Gc survival when exposed to lysozyme or human neutrophils . This finding extends upon our previous work , which showed sensitivity of the Δ1981 mutant in a different genetic background ( strain FA1090 ) to lysozyme [25] , and suggests that the presence of Ng_1063 is insufficient to fully compensate for loss of ng1981 . In contrast , survival of Gc lacking ng1063 was equivalent to WT after exposure to lysozyme . Even though OM-permeabilization has been used to reveal a protective role for lysozyme inhibitors in other bacteria [34 , 35] , survival of Δ1063 mutant Gc remained unaltered from WT when the Gc envelope was compromised . Although our reductionist conditions did not reveal a phenotype for the single Δ1063 mutant , it is possible that this protein contributes to Gc survival in vivo , warranting future investigation . This finding implies that Ng_1981 sufficiently compensates for loss of ng1063 under our in vitro conditions . The differential ability of Ng_1981 and Ng_1063 to protect Gc from lysozyme may be explained by their differential localization , as well as differences in their expression or affinity for lysozyme , all of which are subjects for future investigation . Nevertheless , the role of Ng_1063 as a barrier to lysozyme in Gc was revealed in the absence of ng1981 , where Δ1981Δ1063 mutant Gc exhibited increased sensitivity to lysozyme over either single mutant . Importantly , ng1063 and ng1981 are expressed during human infection in female patients , implying that both proteins contribute to colonization and/or pathogenesis in vivo [50] . Moreover , pathogenic and non-pathogenic Neisseria carry homologs for both Ng_1063 ( S2 Table ) and Ng_1981 [25] , and in the Ng_1063 homologs , the S83 and K103 equivalent residues are 100% conserved ( S3 Table ) . Since commensal Neisseria , like the pathogens , colonize mucosal sites , conservation of Ng_1063 and Ng_1981 amongst Neisseria may be important for residence at these lysozyme-rich surfaces . Together , these findings support a role for both Ng_1063 and Ng_1981 in Gc lysozyme resistance and implicate these inhibitors as important virulence determinants in Gc . In addition to Ng_1063 and Ng_1981 , Gc O-acetylates its peptidoglycan to directly block lysozyme-mediated hydrolysis [22 , 24] . Gc also expresses two cell wall turnover proteins , LtgA and LtgD , that maintain envelope integrity , a physical barrier to lysozyme [22] . Thus , lysozyme resistance in Gc is multifactorial . Given the diverse mechanisms employed by Gc to resist lysozyme , if and how these factors are connected at the level of gene regulation or protein-protein interactions remains an open question . However , it is noteworthy that each factor contributes unequally to lysozyme resistance . For example , O-acetylation is dispensable for Gc survival in the presence of lysozyme , as long as envelope integrity is also maintained [22 , 24] . In contrast , both an ΔltgAΔltgD mutant and an Δ1981Δ1063 mutant exhibit sensitivity to low concentrations of lysozyme , where the ΔltgAΔltgD mutant is the most sensitive [22] . Together , these mechanistically distinct approaches underscore that lysozyme resistance in Gc is vital to the infectivity of this pathogen . By extension , these observations open the possibility of targeting such factors to enhance bacterial susceptibility to lysozyme in the human host . For instance , chemicals that interfere with Ng_1063 and Ng_1981 could help to treat infections with strains exhibiting increased resistance to traditional antibiotics . Ng_1063 and Ng_1981 are also potential vaccine candidates , based on their extracellular localization , expression during human infection , and relative conservation among Gc strains ( S2 Table ) [25 , 50 , 51] . A vaccine against Ng_1063 and Ng_1981 is attractive because antibodies raised against them could promote Gc killing by multiple mechanisms . For instance , antibody deposition on surface Ng_1063 could promote bacterial killing via opsonophagocytosis and serum bactericidal activity , while function-neutralizing antibodies against both extracellular Ng_1063 and Ng_1981 could sensitize Gc to lysozyme in mucosal secretions and in neutrophils [51] . Thus , we propose that Ng_1063 and Ng_1981 are worthy targets for consideration in the context of revitalized global efforts to develop new antibiotics and vaccines against the urgent threat of drug-resistant gonorrhea .
Human Subjects: Venous blood was collected from adult healthy human subjects with their signed informed consent . All materials collected were in accordance with a protocol ( #13909 ) approved by the University of Virginia Institutional Review Board for Health Sciences Research . Animals: Healthy , specific pathogen-free rabbits were immunized with r1981 by Davids Biotechnologie GmbH ( Regensburg , Germany ) . Davids Biotechnologie GmbH has a permit from the Veterinäramt Regensburg for housing specific-pathogen free , healthy rabbits according to §11 TierSchG ( Az31 . 4 . 4/ScP1 ) . The company is registered for immunization of animals under the Aketenzeichen: AZ 2532 . 44/14 at the approving authority Umweltamt Regensburg/Veterinärwesen . All immunizations were done in accordance with National Institute of Health standards for animal welfare ( NIH animal welfare number A5646-01 ) . All Gc used in this study are in the MS11 background and are listed in S4 Table . WT Gc is a RecA+ MS11 strain nonvariably expressing the VD300 pilin [22] , and ΔltgAΔltgD mutant Gc was used from previous work [52] . A null allele of ng1063 was made by introducing a stop codon in-frame into the coding region of the gene . A 5’ flank was PCR amplified using the forward primer 5’AAAAATTTACATTCCTCCGGGCGGGC3’ and the mutagenesis reverse primer 5’GCACAGGCCTACAATCTAGAAACCGATACG3’ ( in-frame stop codon; XbaI site ) . A 3’ flank was amplified by PCR using the mutagenesis forward primer 5’TTTCTAGATTGTAGGCCTGTGCCGTG3’ ( XbaI site; in-frame stop codon ) and the reverse primer 5’AGCAGGTTTAAAGTTGGCATTGAGCCG3’ . The 5’ and 3’ flanks were combined via overlap extension PCR , and the resulting product was introduced into Gc by spot transformation [53] . Bacteria from the transformation were screened by PCR followed by digestion with XbaI , and positive transformants were confirmed by sequencing the ng1063 allele . The ng1981 mutant was made by transforming Gc with pGEM-Δ1981 , where 1981 is disrupted with a kanamycin cassette [25] . Transformants were selected on 50 μg/mL kanamycin and positive transformants confirmed by PCR . To make the ng1981 complementation construct , ng1981 was PCR amplified using the forward primer 5’CCCCCGGGCGCCTTTTTACAAAC3’ ( SmaI site ) and reverse primer 5’GGAACCGCGGAAAAACAGCGTTTTCAG3’ ( SacII site ) . The resulting product was digested with SmaI and SacII and ligated into the isopropyl β-D-1-thiogalactopyranoside ( IPTG ) -inducible complementation plasmid pKH35 [54] . The resulting plasmid ( pKH35_1981 ) was used to spot transform Gc , where ng1981 was integrated between the lctP and aspC chromosomal loci . Bacteria were selected with 8 μg/mL chloramphenicol and confirmed by DNA sequencing . To make the ng1063 complementation construct , ng1063 was PCR amplified using the forward primer 5’ACATAGCCGCGGGCTTTAATGTG3’ ( SacII site ) and the reverse primer 5’ GACAGGAGATATCCAGAACGAAACG3’ ( EcoRV site ) . The resulting product was digested with SacII and EcoRV and ligated into the anhydrotetracycline ( AT ) -inducible complementation plasmid pMR68 [55] . The resulting plasmid ( pMR68_1063 ) was used to spot transform Gc , where ng1063 was integrated between the iga and trpB chromosomal loci . Transformants were selected for using 10 μg/mL erythromycin and confirmed by DNA sequencing . To make the ng1063 ( S83A ) point mutant , a 5’ flank was PCR amplified with the forward primer 5’ACATAGCCGCGGGCTTTAATGTG3’ ( SacII site ) and the reverse primer 5’GTTCGCCCGCTGCGGCAACGTC3’ ( codon for alanine ) . A 3’ flank was PCR amplified with the forward primer 5’GACGTTGCCGCAGCGGGCGAAC3’ ( codon for alanine ) and the reverse primer 5’GACAGGAGATATCCAGAACGAAACG3’ ( EcoRV site ) . The 5’ and 3’ flanks were combined via overlap extension PCR . The resulting product was digested with SacII and EcoRV , and ligated into pMR68 to make pMR68_1063 ( S83A ) . To make the ng1063 ( K103A ) point mutant , a 5’ flank was PCR amplified with the forward primer 5’ACATAGCCGCGGGCTTTAATGTG3’ ( SacII site ) and the reverse primer 5’ CTTCGCCGCCCGCCTGGTGCCAC3’ ( codon for alanine ) . A 3’ flank was PCR amplified using the forward primer 5’GTGGCACCAGGCGGGCGGCGAAG3’ ( codon for alanine ) and the reverse primer 5’ GACAGGAGATATCCAGAACGAAACG3’ ( EcoRV site ) . The 5’ and 3’ flanks were combined via overlap extension PCR . The resulting product was digested with SacII and EcoRV , and ligated into pMR68 to make pMR68_1063 ( K103A ) . To make the ng1981-3XFLAG construct , ng1981 was PCR amplified using the forward primer 5’CCCCCGGGCGCCTTTTTACAAAC3’ ( SmaI site ) and the reverse primer 5’TTGAATTCACGTGGGGAACAGTCTTTG3’ ( EcoRI site ) . The resulting product was digested with SmaI and EcoRI , and then ligated into pMR100 , a C-terminal 3XFLAG vector [56] , to make pMR100_1981 ( WT ) . For additional 3’ homology , the 3’ region for ng1981 was PCR amplified using the forward primer 5’ GGCAAGCTTAAACAGCGTTTTCATTTCTG3’ ( HindIII site ) and the reverse primer 5’ GGCTCGAGGCCGCGGTCATTAAAAAAGAC3’ ( XhoI site and SacII site ) . The resulting product was digested with HindIII and XhoI , and then ligated into pMR100_1981 ( WT ) . The resulting plasmid , pMR100_1981 ( WT ) _3’homology , was digested with SmaI and SacII to release the 1981 ( WT ) -3XFLAG-3’homology fragment , which was subsequently ligated into pKH35 to make pKH35_1981 ( WT ) _3’homology . pKH35_1981 ( WT ) _3’homology was used to spot transform Gc . Transformants were selected with chloramphenicol and were confirmed by DNA sequencing . To make the ng1063 ( WT ) -3XFLAG , ng1063 ( S83A ) -3XFLAG , and the ng1063 ( K103A ) -3XFLAG constructs , ng1063 was PCR amplified from MS11 genomic DNA , pMR68_1063 ( S83A ) plasmid DNA , and pMR68_1063 ( K103A ) plasmid , respectively , with the forward primer 5’ACATAGCCCGGGGCTTTAATGTG3’ ( SmaI site ) and the reverse primer 5’TTGAATTCACGGGCGCGGCAGGAAGTTTC3’ ( EcoRI site ) . The resulting products were digested with SmaI and EcoRI and each ligated into pMR100 to make pMR100_1063 ( WT ) , pMR100_1063 ( S83A ) , and pMR100_1063 ( K103A ) . For additional 3’ homology , the 3’ region of ng1063 was PCR amplified using the forward primer 5’AAAAAGCTTAGCCTGTTTGAACCGCCG3’ ( HindIII site ) and the reverse primer 5’ GACTCGAGCCCGCGGACTTTAGGC3’ ( XhoI site and SacII site ) . The resulting product was digested with HindIII and XhoI , and then ligated into pMR100_1063 ( WT ) , pMR100_1063 ( S83A ) , and pMR100_1063 ( K103A ) . The resulting plasmids , pMR100_1063 ( WT ) _3’homology , pMR100_1063 ( S83A ) _3’homology , and pMR100_1063 ( K103A ) _3’homology , were digested with SmaI and SacII to release the 1063-3XFLAG-3’homology fragments , which were subsequently ligated into pKH35 to make pKH35_1063 ( WT ) _3’homology , pKH35_1063 ( S83A ) _3’homology , and pKH35_1063 ( K103A ) _3’homology . Gc were spot transformed with these plasmids . Transformants were selected with chloramphenicol and were confirmed by DNA sequencing . To make the ng1981 ( S76A ) -3XFLAG construct , a 5’ flank of ng1981 was PCR amplified from MS11 genomic DNA using the forward primer 5’CCCCCGGGCGCCTTTTTACAAAC3’ ( SmaI site ) and the reverse primer 5’ GTCCATATTGTCCGCTTTATCCAAATTG3’ ( codon for alanine ) . A 3’ flank of ng1981 was PCR amplified using the forward primer 5’CAATTTGGATAAAGCGGACAATATGGAC3’ ( codon for alanine ) and the reverse primer 5’TTGAATTCACGTGGGGAACAGTCTTTG3’ ( EcoRI site ) . The 5’ and 3’ flanks were combined using overlap extension PCR . The resulting amplicon was digested with SmaI and EcoRI for replacement of WT ng1981 in the pMR100_1981 ( WT ) _3’homology backbone to make pMR100_1981 ( S76A ) _3’homology . pMR100_1981 ( S76A ) _3’homology was subsequently digested with SmaI and SacII as above for insertion into pKH35 to make pKH35_1981 ( S76A ) _3’homology . To make the ng1981 ( K99A ) -3XFLAG construct , a 5’ flank of ng1981 was PCR amplified from MS11 genomic DNA using the forward primer 5’CCCCCGGGCGCCTTTTTACAAAC3’ ( SmaI site ) and the reverse primer 5’GTTTGCGGTAGGACGCGCTGTCCATTG3’ ( codon for alanine ) . A 3’ flank of ng1981 was PCR amplified using the forward primer 5’CAATGGACAGCGCGTCCTACCGCAAAC3’ ( codon for alanine ) and the reverse primer 5’TTGAATTCACGTGGGGAACAGTCTTTG3’ ( EcoRI site ) . Overlap extension PCR was used to combine the 5’ and 3’ flanks , and the resulting amplicon was digested with SmaI and EcoRI to make pMR100_1981 ( K99A ) _3’homology as above . pMR100_1981 ( K99A ) _3’homology was digested with SmaI and SacII and inserted into pKH35 to make pKH35_1981 ( K99A ) _3’homology . Gc were transformed with the pKH35 constructs via spot transformation , and transformants were selected for with chloramphenicol and were confirmed by DNA sequencing . To replace the native ng1063 gene with ng1063 with a C-terminal 3X-FLAG tag , the 5’ flank of ng1063 was PCR amplified from MS11 genomic DNA with the forward primer 5’AAAAATTTACATTCCTCCGGGCGGGC3’ and the reverse primer 5’CGGTCAGCGCGAAAAACCTGGTATT3’ . The 3’ flank was PCR amplified from the pKH35_1063 ( WT ) _3’homology plasmid with the forward primer 5’AATACCAGGTTTTTCGCGCTGACCG3’ and the reverse primer 5’TCTTGCAAGCGTTGGCAAACAGC3’ . The 5’ and 3’ flanks were combined via overlap extension PCR , and the resulting product was spot transformed into Gc . Transformants were screened by PCR using the forward primer 5’AATACCAGGTTTTTCGCGCTGACCG 3’ and the reverse primer 5’AGCAGGTTTAAAGTTGGCATTGAGCCG3’ , and positive transformants confirmed by DNA sequencing . Piliated , opa-negative Gc were grown on Gonococcal Medium Base ( GCB , Difco ) plus Kellogg’s supplements [57] at 37°C with 5% CO2 ( v/v ) . Gc was inoculated into liquid medium ( GCBL with Kellogg’s supplements and NaHCO3 ) and repeatedly diluted until Gc reached mid-logarithmic stage , as described [58] . For experiments using human neutrophils , the absence of Opa expression in Gc was confirmed by Western blot with the 4B12 pan-Opa antibody . For IPTG- and AT-inducible constructs , 1 mM IPTG and 10 ng/mL AT , respectively , were added to Gc growing in liquid culture for at least 5 hr . To compare protein sequences , the MUltiple Sequence Comparison by Log-Expectation ( MUSCLE , http://www . ebi . ac . uk/Tools/msa/muscle/ ) tool was used . For alignment of ng1063 alleles , the Clustal omega tool was used . To determine percent identity and percent similarity between proteins , the Sequence Manipulation Suite ( Ident and Sim; http://www . bioinformatics . org/sms2/ident_sim . html ) feature was used on the MUSCLE alignment between two proteins in question , with signal sequences included . Protein signal sequences were predicted using the LipoP 1 . 0 Server ( http://www . cbs . dtu . dk/services/LipoP/ ) , and envelope localization was predicted using CELLO ( subCELlular LOcalization predictor; http://cello . life . nctu . edu . tw/ ) . The protein sequence of Ng_1063 ( MS11 ) , excluding the predicted signal sequence , was used for structure prediction via the PHYRE2 server ( www . sbg . bio . ic . ac . uk/phyre2 ) . The predicted Ng_1063 structure was aligned with the known structure of MliC from P . aeruginosa in complex with hen egg white lysozyme ( PDB 3f6z , [36] ) using PyMOL Molecular Graphics System . For allelic sequence comparisons across Neisseria , the NEIS1425 ( ng1063 ) allele was analyzed on December 1 , 2017 using the PubMLST database ( http://pubmlst . org/perl/bigsdb/bigsdb . pl ? db=pubmlst_neisseria_isolates ) . The ng1063 gene sequence , optimized for E . coli expression and encoding the entire coding sequence for ng1063 ( NEIS1425 , http://pubmlst . org/neisseria/ , 381 bp ) , was synthesized in vitro ( GeneArt , Invitrogen ) . The ng1063 gene was cloned into the pET22b ( + ) system ( Novagen ) and inserted between the NdeI and XhoI restriction sites fused to a C-terminal hexa-histidine tag . The resulting recombinant plasmid , pET22b::1063 ( WT ) , was transformed into E . coli DH5α cells for plasmid amplification , and subsequently transformed into competent E . coli BL21 ( DE3 ) pLysS ( NEB ) cells for protein expression . The transformants were cultured in LB broth at 37°C to mid-logarithmic phase , and expression of recombinant protein was induced by addition of IPTG to a final concentration of 1 mM . After growth at 37°C for an additional 4 h , the cells were harvested and insoluble recombinant Ng_1063 ( r1063 ) protein was purified by nickel iminodiacetic acid ( Ni-IDA ) affinity chromatography under denaturing conditions . Bound protein was eluted using 100 mM NaH2PO4 , 10 mM Tris-HCl , 6 M GuHCl and 250 mM imidazole buffer , pH 8 . 0 , precipitated with 5% v/v Trichloroacetic acid ( TCA ) , and subsequently resuspended in phosphate buffered saline ( PBS ) , with 0 . 5% w/v SDS for solubilization . Protein concentration was determined using the BCA™ Protein Assay ( Pierce ) . The molecular mass of mature r1063-His-tag without the leader peptide sequence ( predicted by SignalP 4 . 1 Server ) is 12 . 5 kDa , as confirmed by SDS-PAGE . Recombinant MIP ( rMIP ) and recombinant Ng_1981 ( r1981 ) were prepared as described in [25 , 59] . Rabbits ( n = 2 ) were hyper-immunized subcutaneously with r1981 using the services of David Biotechnologie GmbH , Regensburg , Germany . Rabbits were immunized with r1981 ( 100 μg per dose per rabbit ) emulsified in Freund’s Complete Adjuvant for the primary injection ( day 0 ) and Freund’s Incomplete Adjuvant for a subsequent four injections at ~14 day intervals , with terminal bleeding on day 63 . All sera were stored at -20°C until needed . Lysis kinetics of freeze-dried Micrococcus luteus cells ( ATCC 4698 ) were performed as previously described [25] . M . luteus cells were exposed to 2 μg/ml human lysozyme ( Sigma ) in the absence or presence of increasing concentrations of PBS-diluted r1063 . Bacterial lysis was measured by the change in optical density ( OD595 ) at 25 °C over time , using a spectrophotometer ( microplate reader ) . Pooled human tears ( filter sterilized , LEE Biosolutions ) were diluted to 0 . 1X in H2O and pretreated with 200 μg/mL recombinant 1981 ( r1981 ) [25] , or vehicle ( H2O ) , for 20 min at 37°C . Pooled human saliva ( Lee Biosolutions ) was similarly diluted to 0 . 5X with 125 μg/mL r1981 , or vehicle ( H2O ) , for 20 min at 37°C . M . luteus , which had been prepared as previously described [22] , was exposed to pretreated secretions for a final concentration of 0 . 01X tears ( 20 μg/mL r1981 ) and 0 . 05X saliva ( 12 . 5 μg/mL r1981 ) . Bacterial lysis was measured over time at OD450 at 37°C using a Victor3 multilabel plate reader ( Perkin-Elmer ) . Mid-logarithmic phase Gc was suspended in 0 . 5X GCBL ( diluted with H2O; Kellogg’s supplements and NaHCO3 were also at 0 . 5X ) , with IPTG and AT , if necessary , prior to exposure to the antimicrobial protein at 37°C with 5% CO2 ( v/v ) . The final concentration of Gc with lysozyme was 5x105 CFU/mL , unless otherwise indicated . Gc survival is expressed as the percent of Gc surviving after exposure to the antimicrobial divided by the percent of Gc surviving in vehicle , and normalized to the vehicle control ( = 100% ) . Human Lysozyme: Human lysozyme was prepared as in [22] . Gc was incubated with lysozyme for 3 hr unless otherwise indicated . Lysozyme with inactivated hydrolase activity was prepared by boiling as in [22] . Ethylenediaminetetraacetic acid ( EDTA ) treatment with lysozyme was performed as previously described [22] where the final concentration of Gc with EDTA and lysozyme was 5x107 CFU/mL . For lysozyme pretreatment with recombinant proteins , vehicle ( PBS ) , lysozyme alone ( 3 μg/mL ) , lysozyme + r1063 ( 3 μg/mL lysozyme + 1 . 5 μg/mL r1063 ) , lysozyme + rMIP [25 , 59] ( 3 μg/mL lysozyme + 1 . 5 μg/mL rMIP ) , and lysozyme + r1981 ( 3 μg/mL lysozyme + 1 . 9 μg/mL r1981 ) were pretreated for 20 min at 37°C . Because r1063 was solubilized in a PBS buffer with SDS , SDS was also added for an equivalent final concentration for all conditions . Gc were exposed to pretreated samples for 5 hr at the following final concentrations: vehicle ( PBS ) , lysozyme + r1063 ( 1 . 5 μg/mL lysozyme + 0 . 75 μg/mL r1063 ) , lysozyme + rMIP ( 1 . 5 μg/mL lysozyme + 0 . 75 μg/mL rMIP ) , and lysozyme + r1981 ( 3 μg/mL lysozyme + 0 . 97 μg/mL r1981 ) . For mixed Gc experiments , Δ1981Δ1063 mutant Gc was mixed with Δ1981Δ1063::1981 ( WT ) -FLAG complemented Gc at a ratio of 1:1 for a total of 5 x 105 CFU/mL final . Gc were incubated together or separately for 1 hr prior to exposure to lysozyme for 3 hr . In mixed infection , Δ1981Δ1063 mutant Gc was differentiated from Δ1981Δ1063::1981 ( WT ) -FLAG based on chloramphenicol resistance . LL-37: For the LL-37 antimicrobial assay , Gc was incubated with LL-37 ( from Dr . William Shafer , Emory University ) as described [22] . For LL-37 pretreatment , 5x105 CFU/mL Gc was pretreated with 0 . 4 μg/mL LL-37 for 25 min at 37°C . The bacteria were centrifuged , supernatant removed , and the bacterial pellet resuspended in equivalent volume of 0 . 5X GCBL , prior to exposure to human lysozyme for 3 hr . Chicken Egg White lysozyme: Chicken Egg White lysozyme ( Sigma ) was reconstituted in 10 mM Tris-HCl , pH 8 . 0 , and incubated with Gc for 3 hr at 1 , 000 μg/mL . Mutanolysin: Gc was incubated with mutanolysin ( Sigma ) as previously described [22] . Human Secretions: Pooled human tears and pooled human saliva were pretreated with vehicle or r1981 exactly as in the M . luteus assay above . Gc was incubated with untreated or treated secretions for 3 hr at the indicated final concentration . Gc was exposed to vancomycin Etests ( bioMérieux ) as described [22] except that Gc was first grown to mid-logarithmic phase in liquid culture , and , if needed , induced with IPTG or AT for 5 hr prior to testing . Mid-logarithmic phase Gc ( 7 . 5x108 CFU of Δ1981Δ1063 , ΔltgA::ltgA-FLAG , ΔltgD::ltgD-FLAG , Δ1981Δ1063::1981 ( WT ) -FLAG , Δ1981Δ1063::1063 ( WT ) -FLAG , and Δ1981Δ1063::1063 ( S83A ) -FLAG , Δ1981Δ1063::1981 ( K103A ) -FLAG ) in 0 . 5X GCBL with IPTG were exposed to vehicle or 1 , 000 μg/mL human lysozyme for 3 hr at 37°C with 5% CO2 . Gc was subsequently centrifuged , washed once with PBS , and resuspended in 1 mL ice cold lysis buffer ( 1% v/v Triton-X-100 , 150mM NaCl , 2mM EDTA , 20mM Tris-HCl pH 7 . 5 , 1X v/v protease inhibitors ( Calbiochem Set V ) ) for 30 min with end-over-end rotation at 4°C . Insoluble debris and unlysed Gc were pelleted at 10 , 000xg for 15 min at 4°C . The supernatant ( “whole cell lysate” ) was removed and incubated with lysis buffer-equilibrated M2 FLAG Affinity Gel ( Sigma ) for 2 hr with end-over-end rotation at 4°C . The resin was washed six times with ice cold wash buffer ( 1% v/v Triton-X-100 , 150mM NaCl , 2mM EDTA , 20mM Tris-HCl pH 7 . 5 ) , and subsequently resuspended in 1X sample buffer . Western blots were probed with anti-lysozyme ( Abcam 108508 ) , stripped ( 200mM glycine , 0 . 1% w/v SDS , 1% v/v Tween-20 ) , and reprobed with anti-FLAG ( M2 , Sigma ) . For sublethal conditions of lysozyme , 7 . 5x107 CFU/mL ( as opposed to 5x105 CFU/mL used for antimicrobial assays ) of Gc , which had been grown to mid-logarithmic phase , was exposed to vehicle or increasing concentrations of human lysozyme in 0 . 5X GCBL at 37°C with 5% CO2 ( v/v ) for 3 hr . Gc survival was assessed at 0 hr and 3 hr under these conditions , as described above . Otherwise , Gc exposed to vehicle or lysozyme were centrifuged , supernatant removed , and pellet resuspended in sample buffer ( 12 mM Tris-HCl pH 6 . 8 , 0 . 4% w/v SDS , 5% v/v Glycerol ) without β-mercaptoethanol ( BME ) or bromophenol blue . Protein concentration was determined by BCA™ Protein Assay , followed by addition of BME and bromophenol blue . 5 μg of total protein was separated by SDS-PAGE . Rabbit anti-r1981 antisera and mouse anti-FLAG antibody ( M2 , Sigma ) were used to visualize native Ng_1981 ( from WT Gc ) and Ng_1063-FLAG ( from Gc with 1063 ( WT ) -FLAG at its native locus ) protein , respectively . Blots were stripped and re-probed with rabbit anti-Zwf antibody to confirm equivalent loading . ImageJ was used to measure the percent area for each protein band . The relative band density at each concentration of lysozyme was determined by dividing the vehicle-normalized band density of Ng_1981/Ng_1063-FLAG protein by the vehicle-normalized band density of Zwf protein . Vehicle-normalized band densities were determined by dividing the band density of Ng_1981/Ng_1063-FLAG/Zwf at one concentration of lysozyme by the band density for the corresponding vehicle-treated sample . Neutrophils were isolated from the venous blood of healthy human subjects as described [60] , and used within 2 hr of isolation . Neutrophils were adhered to plastic coverslips in the presence of 10 nM human Interleukin 8 ( R&D ) in Roswell Park Memorial Institute 1640 medium ( RPMI ) with 10% v/v FBS at 37°C with 5% CO2 ( v/v ) for at least 30 min prior to infection . Mid-logarithmic phase Gc at a multiplicity of infection of 1 was exposed to neutrophils in a synchronous manner , as previously described [60] . Gc ( WT , Δ1063::1063 ( WT ) -FLAG , and Δ1981::1981 ( WT ) -FLAG ) was grown to mid-logarithmic phase in the presence of IPTG to induce protein expression . Gc ( 7 . 5x107 CFU ) was centrifuged , resuspended in 30 μg/mL 5- ( and-6 ) -carboxylfluorescein diacetate , succinimidyl ester ( CFSE ) in Dulbecco’s phosphate-buffered saline ( DPBS ) + 5 mM MgSO4 , and incubated for 15 min at 37°C . Bacteria were washed once with DPBS + 5 mM MgSO4 and subsequently resuspended in RPMI ( no phenol red ) with 10% v/v FBS for 10 min at 37°C . Bacteria were centrifuged , resuspended in 8 μg/mL anti-FLAG ( M2 , sigma ) in 100 μL RPMI ( no phenol red ) with 10% v/v FBS , and incubated for 30 min at 37°C . Bacteria were washed twice with RPMI with 10% v/v FBS , resuspended in 100 μL of 1:400 goat anti-mouse coupled to Alexa Fluor 647 ( Thermo ) in RPMI ( no phenol red ) with 10% v/v FBS , and incubated for 30 min at 37°C . Bacteria were washed twice with RPMI with 10% v/v FBS and resuspeded in 2% w/v paraformaldehyde . Data were acquired with an ImageStreamX Mark II cytometer operated by INSPIRE software ( Amnis ) and analyzed using IDEAS Application v6 . 2 software ( Amnis ) . For analysis , focused cells were gated as determined by a high gradient root mean square for image sharpness . From the focused cells , single cells were gated as defined by a high intensity of CFSE and low side scatter . Focused , single cells were used for analysis of intensity of anti-FLAG/AlexaFluor647 . 1 mL of induced , mid-logarithmic phase Gc ( Δ1063:1063 ( WT ) -FLAG and Δ1981:1981 ( WT ) -FLAG ) was centrifuged and processed as described [37] . For cells that were not permeabilized , PBS with 5% v/v normal goat serum was used as a blocking agent . For FLAG staining , the anti-FLAG antibody was used at 27 μg/mL in PBS with 5% v/v normal goat serum , and goat anti-mouse coupled to Alexa Fluor 647 ( Thermo ) was used at 1:400 in PBS with 5% v/v normal goat serum . For 1981 staining with antisera , rabbit anti-r1981 antisera was diluted 1:100 in PBS with 5% v/v normal goat serum , and goat anti-rabbit coupled to Alexa Fluor 647 ( Thermo ) was used at 1:400 in PBS with 5% v/v normal goat serum . Bacterial DNA was stained with 18 μM DAPI ( Sigma ) . Cells were visualized using a Nikon E800 with Hamamatsu Orca-ER camera using Nikon Elements software and processed using Adobe Photoshop CS3 . Using 1063 ( WT ) -FLAG native Gc , 7 . 5x107 CFU of mid-logarithmic phase Gc was centrifuged and resuspended in 1 mL Hanks’ balanced salt solution ( HBSS ) with 10 mM 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid and 5 mM sodium bicarbonate . Bacteria were incubated at 37°C with 5% CO2 ( v/v ) for 3 hr and subsequently centrifuged . Supernatants were collected from the bacterial pellet , passed through a 0 . 2 μm filter , concentrated to a ~60 μL volume using a 3 kDa centrifugal filter unit ( Amicon ) , and brought up to a final volume of 100 μL with sample buffer . Bacterial pellets were washed once with 1X PBS and subsequently lysed ( “whole cell lysates” ) with 100 μL sample buffer . Equivalent volumes were loaded in an SDS-PAGE gel , and gel transferred for Western blot analysis . Blots were probed with rabbit anti-r1981 antisera to detect native Ng_1981 protein , and then stripped and reprobed with mouse anti-FLAG ( M2 , sigma ) to detect native Ng_1063 protein . Blots were also probed with rabbit anti-Zwf ( Aleksandra Sikora , Oregon State University [61] ) as a cell lysate loading control . Experimental values presented display the mean ± the standard error of the mean ( SEM ) of at least three independent replicates . For experiments using neutrophils , at least 3 independent donors were used . Unless otherwise indicated , a two tailed student’s t-test was performed , and significance was determined as a p-value less than 0 . 05 . | The mucosal pathogen Neisseria gonorrhoeae has acquired resistance to almost all recommended antibiotics , and no gonorrhea vaccine currently exists . Attractive targets for therapeutic discovery include bacterial factors that , when inactivated , enhance bacterial susceptibility to host-derived antimicrobial components . The bacterial cell wall-degrading enzyme lysozyme is abundant in mucosal secretions and innate immune cells . To resist killing by lysozyme , some bacteria produce proteins that bind to and directly inhibit the activity of lysozyme . Here , we demonstrate lysozyme inhibitory activity in the N . gonorrhoeae protein Ng_1063 . We found that both Ng_1063 and a second , recently described lysozyme inhibitor , Ng_1981 , contribute to full resistance of N . gonorrhoeae to lysozyme , including resistance to lysozyme-rich mucosal secretions and human neutrophils . Although Ng_1063 and Ng_1981 are both inhibitors of lysozyme , they are distinct in their sequences , biological activities , and cellular localizations . Because both Ng_1063 and Ng_1981 are extracellular , we propose they can be targeted for vaccines and drugs that sensitize Gc to human antimicrobial defenses . | [
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"a... | 2018 | Neisseria gonorrhoeae employs two protein inhibitors to evade killing by human lysozyme |
In bacterial genomes composed of more than one chromosome , one replicon is typically larger , harbors more essential genes than the others , and is considered primary . The greater variability of secondary chromosomes among related taxa has led to the theory that they serve as an accessory genome for specific niches or conditions . By this rationale , purifying selection should be weaker on genes on secondary chromosomes because of their reduced necessity or usage . To test this hypothesis we selected bacterial genomes composed of multiple chromosomes from two genera , Burkholderia and Vibrio , and quantified the evolutionary rates ( dN and dS ) of all orthologs within each genus . Both evolutionary rate parameters were faster among orthologs found on secondary chromosomes than those on the primary chromosome . Further , in every bacterial genome with multiple chromosomes that we studied , genes on secondary chromosomes exhibited significantly weaker codon usage bias than those on primary chromosomes . Faster evolution and reduced codon bias could in turn result from global effects of chromosome position , as genes on secondary chromosomes experience reduced dosage and expression due to their delayed replication , or selection on specific gene attributes . These alternatives were evaluated using orthologs common to genomes with multiple chromosomes and genomes with single chromosomes . Analysis of these ortholog sets suggested that inherently fast-evolving genes tend to be sorted to secondary chromosomes when they arise; however , prolonged evolution on a secondary chromosome further accelerated substitution rates . In summary , secondary chromosomes in bacteria are evolutionary test beds where genes are weakly preserved and evolve more rapidly , likely because they are used less frequently .
As the number of completely sequenced bacterial genomes has grown , the once surprising discovery of multiple chromosomes has become commonplace . Setting aside the issue of nomenclature ( i . e . chromosome or megaplasmid[1] ) , why some bacterial genomes are divided into multiple , large replicons and others comprised of only a single DNA molecule is largely unknown [2] . Understanding the origin of secondary replicons helps frame the question . Chromosomes may originate by three different mechanisms: by the split of a single chromosome , by chromosome duplication , or by acquisition of a large plasmid with essential genes , which ensures its prolonged maintenance . Of these processes , the last has the greatest support because some secondary chromosomes have plasmid-like origins of replication [2] . However , it is the potential effects of genome subdivision that require further investigation and may explain variation in chromosome number and evolution in bacteria . One advantage of a divided genome is the potential for faster replication and growth because of multiple origins of DNA replication . For example , Vibrio spp . with two chromosomes have among the fastest rates of cell division measured . Yet in all bacteria , the single origin of replication per chromosome means that growth may occur faster than replication , a problem solved by the ability to initiate new cycles of replication before the completion of previous cycles . As a result , daughter cells may be born with multiple partially replicated genomes that are enriched near the origin of replication [3] . Bacteria with multiple chromosomes face the additional challenge of maintaining synchronous replication; if chromosomes are of different sizes , either their timing or their rates of replication must vary . In Vibrio , it has been demonstrated that the replication of the smaller , second chromosome is delayed during the cell cycle [4] , [5] , [6] . This delayed replication in effect reduces the dosage ( copy number ) of genes on the second chromosome during periods of rapid growth [7] , but does not alter the final heredity of each chromosome . Each cell ultimately has one and only one copy of each chromosome ( absence of a chromosome would cause it be reassigned as a plasmid ) , and no evidence yet suggests that this varies . Therefore , variation in how bacterial chromosomes evolve is not , at least given current knowledge , an effect of variation in their effective numbers , as in the sex chromosomes of animals [8] . However , variation in gene dosage during the bacterial cell cycle can have profound effects on the expression of these genes as well as their evolutionary rates . In bacteria with a single chromosome , genes distant from the origin of replication tend to be expressed less than those nearby , and thus distant genes evolve more rapidly [9] . In bacteria with multiple chromosomes , delayed replication of the smaller replicon could produce a similar effect on its expression and thus its evolution . A recent report confirms this effect on expression in fast-growing cells: genes on the late replicating small chromosome of V . parahaemolyticus are expressed significantly less than those on the large chromosome , though expression varies more than would be expected from measured dosage effects [4] . In slow growing cells , overlapping replication cycles are unnecessary and hence no dosage and expression bias is found between chromosomes [4] . Replication bias within divided genomes ( and particularly those of fast growing species ) could therefore accelerate evolution on secondary chromosomes . This variation in expression caused by genome location , either relative to the origin of replication or on different chromosomes , can in principle exert selection for gene position . Genes that must be expressed frequently should be near the origin of replication and on the primary chromosome [7] , [10] . It therefore follows that in Vibrio , a significantly greater fraction of growth-essential and growth-contributing genes are found i ) on the large , primary chromosome than on the small chromosome , ii ) near the origin relative to the terminus of the large chromosome , and even iii ) near the terminus of the large chromosome relative to the small chromosome [4] . When grown under optimal conditions , the dosage bias of these genes and hence their expression is exaggerated , but under more limiting conditions dosage bias and expression do not vary with gene position [4] , [5] . Moreover , the growth rate of V . cholerae slows significantly when the replication rate of the second chromosome is genetically amplified [5] , [6] . These findings imply that selection has shaped Vibrio genomes to contain genes whose functions benefit from higher dosage during rapid growth on the first chromosome and genes that should be expressed less on the second chromosome [4] , [7] . Comparing related genomes with multiple chromosomes also suggests that their content has been segregated by priority and dispensability . In general , the major chromosome tends to have significantly more conserved housekeeping genes , greater overall synteny , and greater conservation of content [7] , [11] , [12] . Together , these patterns support a general theory that secondary chromosomes are evolutionary test beds subject to reduced purifying selection and thus greater rates of change . The key prediction of this theory is that genes found on secondary chromosomes should evolve faster and more variably than those on the primary chromosome . Furthermore , if genes on secondary chromosomes have been less needed or used over long periods of time , then they should exhibit less bias towards the use of favored synonymous codons ( codon usage bias ) . We tested this theory by studying the evolutionary rates of ‘panorthologs , ’ defined as orthologous genes present in single copy and , for a subset , obeying the consensus species phylogeny , among two sets of monophyletic , completely sequenced genomes with more than one chromosome ( Burkholderia and Vibrio ) . We then compared the rates of ortholog families found on primary chromosomes with those on secondary chromosomes , calculated the codon bias of these genes , and evaluated their evolutionary patterns in the context of orthologs from sister taxa with only a single chromosome ( Bordetella and Xanthomonas , respectively ) . We found that orthologs on secondary chromosomes indeed evolved faster and displayed less skew towards purifying selection than those on primary chromosomes . These increased rates of evolution appear to be a consequence of reduced selection for the use of specific codons and translational efficiency because of less frequent expression or necessity [13] , [14] , [15] , [16] . Each prediction of the general theory that secondary chromosomes serve as evolutionary test beds for accessory genes was therefore met .
Bacterial genomes with multiple chromosomes were selected from two genera: Burkholderia ( Beta-Proteobacteria , Burkholderiales , Burkholderiaceae ) , which have three chromosomes , and Vibrio ( Gamma-Proteobacteria , Vibrionales , Vibrionaceae ) , which have two chromosomes . Genomes were selected to span a range of evolutionary distance within each set , from isolates within the same named species to distinct species within the same genus ( Fig . 1 ) . This enabled comparisons spanning three different evolutionary distances: i ) among strains within the species B . cenocepacia , ii ) among species within the genus Burkholderia , and iii ) among more divergent species within the genus Vibrio . “Panorthologs , ” or orthologs conserved in all genomes within the genome group , were identified by the stringent analysis pipeline described in Methods , based on prior work [16] , [17] , and discussed in greater detail below . For the remainder of this report we refer to chromosome 1 as c1 , chromosome 2 as c2 , and chromosome 3 as c3 . In each genome collection , panorthologs comprised a lesser fraction of the total genes on secondary chromosomes than on primary chromosomes , and in Burkholderia , panorthologs comprised a lesser percentage still on c3 than on c2 ( Fig . 2 , column 1 ) . This general trend was mostly unaffected by changes in the chromosome position of orthologs within each group . Within B . cenocepacia , only 484 of 3848 ( 12 . 6% ) panorthologs varied in chromosome position , most of which resulted from a large contiguous rearrangement exclusive to the AU1054 genome . The same rearrangement also explained most of the limited variation among Burkholderia panorthologs ( 409 of 2992 varied in chromosome position , or 13 . 7% ) . Chromosome positions of panorthologs were also well conserved within Vibrio: only of 59 of 1647 ( 3 . 6% ) varied in chromosome location . In summary , the fraction of panorthologs varied significantly among chromosomes and this finding could not be explained by varied chromosome position among orthologs . We began our analysis by quantifying the evolutionary rates of orthologs shared by multiple strains of the same species , B . cenocepacia . This group ( analysis group A in Fig . 1 ) provided arguably the most stringent test of our model because minimal evolutionary distance should have accumulated within these gene families; on the other hand , more panorthologs were found in these closely related genomes . Our prediction that evolutionary distance would be greater among orthologs found on secondary chromosomes was met ( Fig . 2 , Table S1 ) . The distributions of both evolutionary rate parameters , dN and dS , differed among chromosomes , with panorthologs from c2 evolving more quickly than those on c1 , and those on c3 more divergent still than those on c2 ( Table S1 ) . We observed the same overall patterns with even greater resolution among different species of Burkholderia ( Fig . 2 , Table S2 ) , even as the total number of panorthologs decreased and as the noise inherent to dN and dS estimates [18] increased . ( For this and subsequent analyses , we acknowledge the unreliability of estimates of dS>1 from more divergent homologs; for the different Burkholderia species , mean dS only approaches or exceeds 1 on chromosomes 2 and 3 . ) However , given that these patterns were limited to a particular genus of Beta-Proteobacteria , we sought to test whether chromosome location affected ortholog evolution in different genomes . We chose the genus Vibrio , a Gamma-Proteobacteria clade that was one of the first described to harbor multiple chromosomes [19] . Further , we chose more divergent species within Vibrio than we had within Burkholderia as an additional test ( Fig . 1 ) . In studying more divergent genomes we increased the leniency of our ortholog alignments to allow as many as eight consecutive unaligned amino acids instead of the five-site cutoff used within Burkholderia ( Methods ) . This produced much greater estimates of dN and dS for Vibrio orthologs , the latter being too large to be considered reliable . Nevertheless , we observed essentially the same , statistically significant patterns when comparing the distributions of rates from the two Vibrio chromosomes ( Fig . 1 , Table S3 ) . We note that the fraction of panorthologs on secondary Vibrio chromosomes is substantially less than in our Burkholderia sets , despite the well-described variability among Burkholderia genomes [20] . One of the greatest challenges in phylogenetics is defining orthology [17] , [21] and it is possible that our method introduced a systematic bias , so we conducted an even more stringent test of our pipeline . Previously , we included only genes sharing a single , reciprocally best match in all other genomes and whose translated alignment was highly conserved . Here , we also tested whether the panortholog families identified within the five B . cenocepacia strains also shared the same strict phylogeny based on branching pattern . Although the number of panortholog families declined substantially due to ambiguous branching ( a polytomy ) among the J2315 , PC184 and MCO-3 genomes , we found the same general patterns ( Fig . S1 , Table S4 ) . However , this test introduced additional uncertainties because of the number of potential alternative trees ( Table S5 ) and it could be too stringent because different phylogenies could be produced by varying evolutionary rates among lineages . As a result we did not require that all families share the same phylogeny in subsequent analyses , which leaves open the possibility that panortholog families may include genes that vary in their rates of homologous recombination and are not panorthologs in the strictest sense . We return to this issue in Discussion . We also tested whether using different single genomes within groups to assign panortholog chromosome positions affected our findings . Among the B . cenocepacia genomes , using gene positions from the MCO-3 annotation instead of the HI2424 annotation did not alter any interpretations ( Table S1 ) . However , the B . cenocepacia AU1054 genome contains a unique set of rearrangements between chromosomes 1 and 3 relative to the other B . cenocepacia genomes , so we queried the evolutionary rates of these 482 genes in particular . The means and distributions of dN and dS values of these gene families strongly resemble those of their consensus location in the other genomes; that is , genes found on chromosome 1 in all other genomes but found on chromosome 3 in AU1054 are indistinguishable from the other genes on chromosome 1 ( F = 0 . 092 , p = 0 . 762 ) . This suggests that these rearrangements may have occurred recently enough that the chromosome position in AU1054 did not influence the evolutionary rates of their ortholog families . Perhaps the most telling differences among the rate distributions of each chromosome are their shapes ( Fig . 2 ) . In all genome sets , c1 rates exhibited greater positive skew ( median < mean ) and greater kurtosis than c2 rates , which in turn were more skewed than c3 rates in Burkholderia ( Table S6 ) . Positive skew and greater kurtosis ( observed as greater volume and greater width in the lower half of the shapes in Fig . 2 ) of rate distributions demonstrate that fast-evolving genes are rarer on c1 than on c2 and c3 , even for a given average rate . These properties of the rate distributions are both consistent with purifying selection and suggest that c1 panorthologs are under the greatest selective constraint and those on c2 and c3 are less conserved . In theory , genes may face weaker purifying selection and thus evolve more quickly because they are i ) less frequently expressed , which generates less selection for translational efficiency [13] , [22] , [23] ii ) less essential , which should also influence dispensability [16] iii ) less connected to multiple functions or pathways [24] or iv ) more robust to mutations [25] , [26] . Of this incomplete list of explanations , the first has garnered the most comprehensive support [13] . If genes are less frequently expressed and selection for translational accuracy is diminished , then the incorporation of suboptimal codons should be better tolerated . In general , codon usage bias [10] , [15] , [27] is positively correlated with gene expression [28] , although exceptions exist [10] , [29] . We estimated codon usage bias using a method based on the Shannon informatics theory and the entropy theory that describes the orderliness of synonymous codon usage ( SCUO ) [27] , [30] . This method facilitates the comparison of codon usage biases both within and across genomes . We tested whether genes on secondary chromosomes exhibited systematically less codon usage bias than genes on the primary chromosome in our three genome groups ( Fig . 3 ) , and in 11 other genomes with multiple chromosomes ( Table 1 ) . Remarkably , in all of these genomes SCUO was significantly less on c2 than on c1 , and if applicable , lesser still on c3 than on c2 . The distributions of gene codon usage bias also reflected decreased purifying selection on secondary chromosomes; values from c1 genes were significantly more negatively skewed ( reflecting stronger bias ) than those on c2 or c3 ( Fig . 2; skewness of B . cenocepacia HI2424 SCUO: c1: −1 . 02± . 044 , c2: −0 . 895± . 047 , c3: −0 . 579± . 081 ) . Overall codon usage bias varied substantially among genomes and these values associated strongly with their %G+C nucleotide content [27] , [31]; the AT-rich Vibrio species demonstrated low codon preference values as a result . To verify our findings that codon bias varied significantly among chromosomes , we also calculated codon usage bias with another set of tools provided by the INteractive Codon usage Analysis ( INCA ) package [32] . We found that other measures such as the codon adaptation index ( CAI ) [15] agreed well with SCUO and supported this conclusion ( Fig . S2 , Table S7 ) . Other metrics ( e . g . MELP [32] ) that have been shown to reliably infer gene expression as a function of codon usage also predicted that genes on primary chromosomes are expressed more than those on secondary chromosomes ( Fig . S2 ) . For V . cholerae in particular , the mean CAI was even greater on c1 than c2 that reported by the SCUO method ( Table S7 ) . Further , the predicted overall expression levels of V . cholerae c1 genes were significantly greater than those on c2 ( MELP , c1: 0 . 495 , c2: 0 . 439 , F = 25 . 6 , p<0 . 0001 ) . Therefore , reduced codon usage bias appears to be an intrinsic attribute of genes on secondary chromosomes , which experience reduced selection for translational efficiency perhaps because of their reduced expression [4] or greater protein dispensability . Relaxed selection on genes found on secondary chromosomes could result from properties of the genes themselves or from general effects of the chromosome , such as delayed replication or reduced copy number that could reduce their likelihood of expression . To discriminate between these possibilities , we identified orthologs shared by multi-chromosome genomes and single-chromosome genomes and quantified their taxon-specific evolutionary rates . We define shared orthologs found on the primary chromosome in the multi-chromosome genomes as “primary panorthologs” and those found on secondary chromosomes as “secondary panorthologs ( Fig . 4 ) . Thus , for Burkholderia genomes we identified common orthologs in four Bordetella genomes ( analysis group D , Fig . 1 ) and for Vibrio we found orthologs shared by five Xanthomonas genomes ( analysis group E , Fig . 1 ) . If relaxed selection is specific to the genes themselves , then secondary panorthologs should evolve more rapidly and demonstrate lesser codon bias than other genes found on the same chromosome , either in Bordetella or Xanthomonas ( Fig . 4 ) . However , if relaxed selection occurs only when orthologs are segregated to a secondary chromosome , then no differences will be found within single-chromosome genomes but significant differences will be found in multi-chromosome genomes . Finally , if both patterns occur but with a greater rate increase within multi-chromosome genomes , then both gene-specific and chromosome-specific processes likely occur . Among 619 genes shared by Burkholderia and Bordetella that met the cutoffs of our pipeline ( Methods ) , 583 were primary panorthologs and 36 were secondary panorthologs . The vast difference in their abundances reflects both the dispensability and uniqueness of most genes on secondary chromosomes . We calculated the evolutionary rates of these two groups and found that dN was significantly greater among secondary panorthologs than primary panorthologs in both Burkholderia and Bordetella ( Table S8 ) . Further , fast-evolving orthologs within Bordetella were more frequently found on chromosome 2 of Burkholderia ( Mann-Whitney U = 8348 , p = 0 . 039 ) , and the dN estimates of these genes were less positively skewed ( Fig . 4 ) . ( We do not present values for dS in this comparison owing to their unreliability ( mean dS for Bordetella >1 . ) Together these results suggest that secondary panorthologs inherently evolve faster even when found on the same chromosome , but this effect is magnified by presence on a secondary chromosome . Of the two forces , the effect of chromosome position appears slightly stronger based on our limited evidence . Over the relatively short evolutionary scale separating the Burkholderia genomes ( Fig . 1 ) , both gene- and chromosome-specific processes could have produced the 56 . 2% increase in mean dN among secondary panorthologs than primary panorthologs . Among the more divergent Bordetella genomes , only gene-specific effects could have generated the 26% increased dN among secondary panorthologs over primary panorthologs ( Fig . 4 ) . We explored the orthologs shared between Burkholderia and Bordetella for other systematic differences associated with chromosome location . Representatives of the panortholog families found in B . cenocepacia HI2424 were used for these analyses . As expected , SCUO was lower among secondary panorthologs , although not significantly so ( c1 mean: 0 . 465 , c2 mean: 0 . 444 , F = 2 . 60 , p = 0 . 11 ) . In addition , the skewness but not the means of the codon adaptation index ( CAI ) [9] differed between the collections of primary and secondary panorthologs ( a negative skew illustrates greater distribution towards highly biased genes; c1 skewness: −0 . 458±0 . 11 , a significant value , c2 skewness: 0 . 051± . 403 , not significant ) . The most intriguing difference between these two gene sets , however , was their inferred levels of expression ( MELP ) : primary panorthologs were predicted to be expressed significantly more than secondary panorthologs ( F = 4 . 87 , p = 0 . 028 ) . However , the COG annotation of primary and secondary panorthologs did not differ in any obvious manner ( Table S9 ) , which suggests that the increased evolutionary rates and lesser expression of secondary panorthologs are not artifacts of an unusual subset of the complete genomes . Only 99 ortholog families survived our initial filters of orthologs shared between Vibrio and Xanthomonas ( analysis group E , Fig . 1 ) , and only four of these were secondary panorthologs . This group , comprised only of essential genes , was too small to allow us to discriminate between effects of gene or chromosome position . We suspected that the small group resulted from relatively high-quality ortholog alignments within each genus failing to produce a consensus alignment between genera that was not compromised by gaps . To overcome this problem , we included the V . fischeri ES114 genome as an intermediate between Vibrio and Xanthomonas to facilitate more tolerant alignments and to include more panortholog families for analysis . Following this step , we identified 237 orthologs shared between Vibrio and Xanthomonas , only 13 of which are on the second Vibrio chromosome . As we found previously , both dN and dS were significantly greater on the second Vibrio chromosome and dN was greater among Xanthomonas secondary panorthologs ( mean dN = 0 . 048 ) than primary panorthologs ( mean dN = 0 . 032 ) , although this difference was not statistically significant ( p = 0 . 089 , Table S10 ) . Together , these findings also suggest that evolutionary rate differences are inherent to the genes but are more obviously an effect of chromosome position .
Why some bacterial genomes are composed of multiple chromosomes and others only a single chromosome is a mystery , thought to be a legacy of past plasmid acquisition , entrapment , and genome reshuffling . Yet how bacterial genomes evolve and become subdivided in the aftermath of these events may be quantified using the large number of completely sequenced and annotated bacterial genomes and a well-defined phylogenetic history . With these resources , we tested the theory that secondary chromosomes in bacteria are accessory genomes for specific niches or conditions [10] , [12] , [33] , [34] and thus are evolutionary test beds . The central prediction of this theory is that genes on secondary chromosomes should be subject to weaker purifying selection because of their reduced necessity or usage . Weak purifying selection is manifest as increased evolutionary rates among orthologs ( dN and dS ) , reduced positive skewness of rate distributions from ortholog sets , and reduced codon usage bias . We found that each of these patterns was strongly associated with genes found on secondary chromosomes in three different , phylogenetically independent genome collections from Burkholderia and Vibrio . Moreover , reduced codon usage bias among genes on secondary chromosomes appears to be a general phenomenon of all multi-chromosome bacteria . We propose four potential mechanisms that would explain these patterns . First , secondary chromosomes are smaller and so to maintain synchronous replication with the primary chromosome they may be replicated later , as in Vibrio [5] , [6] . Delayed replication could limit gene copy number within growing cells and systematically minimize expression [7] . Decreased expression should in turn weaken selection for optimal codon usage and increase the synonymous substitution rate , dS , and also reduce selection against protein misfolding because translation events will be fewer and thus increase the nonsynonymous substitution rate , dN [13] . Although we do not measure expression in this study , it has been shown recently that genes on the second chromosome of V . parahaemolyticus ( a genome included in this study ) are expressed less because of delayed replication and reduced dosage [4] , and another computational analysis predicts this expression bias in many multi-chromosome genomes [7] . Second , a defining feature of secondary chromosomes is their relative rarity of orthologs conserved among related genomes ( Fig . 2 ) , which implies that these genes are more dispensable . This dispensability is not the property referred to in previous studies of the correlates of evolutionary rates ( e . g . [16] ) , effects of experimental gene knockouts , but rather their likelihood to be lost following speciation . Genes that are more dispensable should be under weaker purifying selection , in general , and both dS and dN should increase . Further , if selection against protein misfolding is as strong as has been argued [13] , the deleterious effects of misfolded proteins could generate positive selection for their deletion . Exactly why these genes are or become more dispensable has prompted much speculation: secondary chromosomes have been thought to be niche-specific and thus only conditionally useful in dynamic environments [12] , which could cause genes on secondary chromosomes to be lost frequently by drift ( because they are useless ) or by antagonistic pleiotropy ( because they now reduce fitness ) [35] . Of these two forces , gene loss driven by selection is almost certainly more rapid . When we inspect the evolution of the content of divided genomes over a relatively short time span ( e . g . closely related strains of B . cenocepacia and species of Burkholderia ( Fig . 2 ) ) , we find that most differences occur on secondary chromosomes . Given that such species likely have very large effective population sizes that minimize effects of drift relative to selection [36] , we suggest that selection for the loss of orthologs explains why such genes are weakly preserved on secondary chromosomes . The differential gene preservation among primary and secondary chromosomes could also shed light on the relative roles of selection and drift in gene rearrangement . Those orthologs that persist on secondary chromosomes for long evolutionary periods become noteworthy given their generally high loss rate . If these remaining orthologs have been preserved by selection and not just by chance , then their initial rearrangement to a secondary chromosome could have been favored . Our analysis of orthologs shared by genomes with multiple chromosomes and those with one chromosome supports this model , as genes that relocated to the secondary chromosome evidently already evolved more rapidly ( Fig . 4 ) , were less codon-adapted , and are predicted to be expressed less even when confined to a single chromosome . We acknowledge , however , that gene relocation to secondary chromosomes is a chicken-and-egg problem: which came first , selection for reduced expression or an increase in dispensability that caused relocation to be selectively neutral ? We speculate that differential expression among genome locations presents a means for selection to tune the activity of individual genes by relocating them either nearer the replication terminus of the primary chromosome , or when they are present , to secondary chromosomes . As such gene rearrangements are probably more rare than other mutations that alter expression ( e . g . SNPs in regulatory sequences ) , however , positive selection for rearrangement is also likely rare . Regardless , the long-term effect of these rearrangements , driven initially by either drift or selection , is greater evolutionary rates . A third mechanism that could explain the patterns presented here is that secondary chromosomes may be inherently more tolerant and/or more prone to recombination of homologous alleles . Increased homologous recombination of divergent alleles would generate many of the patterns reported here and offers an alternative interpretation of our findings . We disfavor this interpretation because recombination should reduce similarity and greatly decrease the probability that genes in different lineages will meet our stringent tests for homology and orthology ( Methods ) . However , to test this alternative , we recognized that recombination should create incongruent phylogenies among genomes and analyzed only those ortholog families sharing the consensus phylogeny . Of the genome sets presented here , the collection of different strains of B . cenocepacia provides the most rigorous test , as lineages within the same species are expected to have undergone recombination more frequently than different species . Thus we analyzed only those panorthologs that conformed to the strict consensus phylogenetic topology within the B . cenocepacia genomes , and this subset still demonstrated both significantly increased and less skewed rates of evolution among genes on secondary chromosomes . However , we did not subject the other genome sets to this analysis and acknowledge that their panorthologs could demonstrate effects of recombination on inferred evolutionary rates . A fourth possible mechanism is that secondary chromosomes could experience inherently higher mutation rates . Although mutation rates are known to vary among genome locations , such a widespread and systematic difference would be exceptional . The delayed replication of secondary chromosomes could potentially produce such an effect if nucleotide pools vary or become limiting as a function of the cell cycle [37] or if the replication apparatus tends to require reassembly in later replication stages , which is mutagenic [38] . The probable origin of secondary chromosomes as plasmids could also lead to increased mutation rates as a consequence of their greater supercoiling , which has been associated with greater rates of mutation [39] . Of the four potential explanations that we suggest for why secondary chromosomes evolve more quickly , this one ( a systematically greater mutation rate ) is the most speculative but also the most experimentally tractable . It is inevitable that even more powerful studies of the effect of multiple chromosomes on evolutionary rates of bacterial genes will be possible as more complete genomes become available . It may be possible to compare evolutionary rates among distinct taxa of equivalent internal phylogenetic distance , which may allow us to better isolate the effect of chromosome addition . Implementation of more systematic studies of phylogenetic branch length as well as topology could also improve ortholog detection . Our design here was optimized for the genomes available at the time and we compared evolutionary rates of orthologs shared by neighboring taxa ( e . g . between Burkholderia and Bordetella ) with caution , given the many factors that could influence relative rates . However , if the generally increased evolvability of secondary chromosomes holds true for most or all multipartite bacterial genomes , we may be able to better understand how genomes evolve and function . First , simply finding that genes are located on smaller secondary chromosomes may indicate their selection for reduced use or their dispensability . If orthologs of these genes are found in related genomes and in a conserved location , then their products may be optimally expressed at lower levels; if absent , then they are more likely dispensable . Second , reduced purifying selection on secondary chromosomes should accelerate divergence among multipartite genomes in general . Given current species definitions based on empirical measures of DNA similarity or average nucleotide identity [20] , [40] , bacterial taxa comprised of multiple chromosomes will apparently be more prone to speciate because of the greater divergence of secondary chromosomes . These predictions are confirmed within the Burkholderiacae , which display unusually high genomic diversity for a given level of divergence in 16S rDNA sequence [20] , [41]; further , most of this genome divergence is found on secondary chromosomes ( Fig . 2 ) . We anticipate the need for more focused analyses of the nature of highly evolvable genes and chromosomes , including their associations with certain functions , their levels of expression during the cell cycle , and their broader membership within homologous gene families . If one way for bacteria to control the magnitude of gene expression is related to gene location , then genes that should be expressed minimally or late in the cell cycle could be selected for relocation distant from the replication origin or on secondary chromosomes . However , we speculate that this could introduce a life-history tradeoff within the genome for such functions , as they would be expected to evolve more rapidly owing to weaker purifying selection for efficient translation . Such a tradeoff is analogous to the origins of senescence , in which genes required early in life and concurrently with reproduction are under strong selection whereas those used past the age of reproduction are more prone to decay and are more dispensable . In summary , secondary chromosomes in bacteria appear to occupy a netherworld between the conserved , core genome found mostly on primary chromosomes and the transiently necessary accessories found on plasmids , offering the benefits and costs of both .
Annotations of bacterial genomes were downloaded from the Integrated Microbial Genome database ( IMG; http://img . jgi . doe . gov ) in FASTA nucleotide and amino acid formats for each chromosome . Chromosomes were defined as primary or secondary based on their annotation; in all genomes studied but one , chromosome number is defined in decreasing order of size . The one exception was the V . cholerae O395 , in which c2 and c1 definitions were reversed relative to the annotations of all other Vibrio . We calculated codon preference using a method based on Shannon information theory and entropy theory described by Wan et al . [27] , [30] . The metric , SCUO , was calculated using the CodonO software [30] . Gene annotations for each chromosome were analyzed using this method and values for each gene were retrieved . Codon bias measures for each chromosome were then compared by ANOVA and by Kruskal-Wallis tests as described . To calculate CAI [15] and MELP[42] , we downloaded genes encoding ribosomal proteins for each analyzed genome to serve as a reference for codon preference . This reference file and the complete annotations for each chromosome were uploaded into the INCA software [42] , codon preference was calculated for each gene , and then the measures for each chromosome were compared by ANOVA . We began computation of putative panorthologs for each set of genomes using NCBI BLASTP ( release 2 . 2 . 16 ) to analyze all genes in all genomes for sequence similarity . We kept for later processing all BLAST hits within an E-value threshold of 1 . These hits include each gene's self hit . We stored the E-value , bit score and alignment length for each hit . When running BLASTP , we used default parameters except for setting the E-value threshold and for setting the maximum number of hits to keep . We next identified homologs as those gene pairs that had BLAST hits in both directions within a given scaled bit score threshold . We scaled the bit scores by the bit score of the self hit of the query gene . That is , scaledBitScore ( A->B ) = bitScore ( A->B ) /bitScore ( A->A ) . This method has been used previously to identify conserved homologs among bacterial genomes and has been shown to be more stringent than criteria based solely on reciprocal best matches using E values [17] . We then formed homolog families by including two genes in a family if they had been identified as homologs . Note that not all pairs of genes in a family need to be identified as homologs . For example , if A and B are homologs , and B and C are homologs , then A and C will be in the same family even if A and C have not been identified as homologs . Finally we identified the putative panorthologs as being the genes from homolog families with exactly one gene from each genome . For each set of genomes we kept the largest set of panorthologs found by computing the putative panorthologs while varying the scaled bit score threshold from . 1 to . 9 in . 1 increments . The following scaled bit score thresholds were used for genome sets A–E depicted in Fig . 1 , followed by the number of putative panorthologs identified at that threshold: group A: threshold = 0 . 7 , 4141 panorthologs; group B: 0 . 7 , 3758 , group C: 0 . 4 , 2203 , group D: 0 . 3 , 902 , group E: 0 . 2 , 581 . To produce groups d and e , the five Bordetella genomes were first analyzed by this method ( 0 . 5 , 1592 ) as well as the five Xanthomonas genomes ( 0 . 5 , 2450 ) . The intersections of these Bordetella and Xanthomonas panortholog sets with groups b and c were used to produce groups d and e , respectively . We developed a pipeline analogous to the one described by Wall et al [16] . The amino acid sequences of each putative panortholog family was first aligned using ClustalW2 [43] . Next , we used the codon boundaries to align the nucleotide sequences . The leading and trailing edges of each amino acid sequence in every family was trimmed to generate consensus edges , and then the nucleotide sequences were trimmed to match . From this trimmed file , a consensus sequence for the family was found , using the cons utility from the EMBOSS suite . Each sequence in the family was compared against the consensus sequence and if any gene in the family differed from the consensus by more than the specified threshold number of amino acid differences , the family was discarded from further analysis . The following are the amino acid alignment thresholds used for each genome group: group A: five amino acids , group B: five , group C: eight , group D: eight , group E: eight . Phylogenetic trees were then constructed for each family using DNAML ( maximum likelihood ) in PHYLIP [44] using default settings and the Newick formatted trees were saved . Finally , dN and dS were calculated from the trimmed nucleic acid alignment and the DNAML tree as a guide using codeml in the PAML package [45] . Codeml model 0 , which allows for a single dN and dS value throughout the phylogeny , was used . In calculating evolutionary rates of panorthologs shared by two sets of organisms ( e . g . Burkholderia and Bordetella ) , we aligned all taxa in both families , trimmed their edges and discarded families with excessive gaps , but then separated these genes back into their genus groups for analysis by PHYLIP/dnaml and PAML/codeml . This produces dN and dS values for each group within these larger panortholog sets rather than just a single value . | Why many bacteria have multiple chromosomes is largely unknown , but a leading hypothesis is that secondary chromosomes evolved from plasmids and now serve as accessory genomes . We tested a key prediction of this theory that genes on secondary chromosomes should evolve faster because they are under less selective constraint . Indeed , orthologous genes shared within two groups of bacteria ( Burkholderia or Vibrio ) with multiple chromosomes were less conserved and evolved more rapidly when found on secondary chromosomes . Much of these patterns could stem from the tendency of secondary chromosomes to be replicated later in the cell cycle , which reduces their gene dosage , their potential for expression , and selection for their optimal translation . However , the content of secondary chromosomes appears to be predisposed to evolve faster , because these same genes still evolve more rapidly in single-chromosome genomes . In summary , the evolution of divided genomes therefore appears to allow for the long-term segregation of genome content by their rates of expression and dispensability , placing some genes at increased risk of mutational decay and greater turnover . | [
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"microbiolo... | 2010 | Why Genes Evolve Faster on Secondary Chromosomes in Bacteria |
We conducted a systematic literature review with indirect comparison of studies evaluating therapeutic efficacy and toxicity associated to visceral leishmaniasis ( VL ) therapy among HIV infected individuals . The outcomes of interest were clinical and parasitological cure , mortality , and adverse events . PRISMA guidelines for systematic reviews and Cochrane manual were followed . Sources were MEDLINE , LILACS , EMBASE , Web of Knowledge databases and manual search of references from evaluated studies . We included all studies reporting outcomes after VL treatment , regardless of their design . Study quality was evaluated systematically by using the Newcastle-Ottawa Scale ( NOS ) for assessing the quality of nonrandomized studies in meta-analyses . Comprehensive Meta-Analysis software v . 2 . 2 . 048 was used to perform one-group meta-analysis of study arms with the same drug to estimate global rates of success and adverse events with each drug . These estimates were used , when possible , to indirectly compare treatment options , adjusted for CD4 count . Direct comparison was pooled when available . Seventeen studies reporting five treatment regimens and outcome of 920 VL episodes occurring in HIV infected individuals were included . The main outstanding difference in outcome among the treatment regimens was observed in mortality rate: it was around 3 times higher with high-dose antimony use ( 18 . 4% , CI 95% 13 . 3–25% ) , indirectly compared to lipid formulations of amphotericin B treatment ( 6 . 1% , CI 95% 3 . 9–9 . 4% ) . It was observed , also by indirect comparison , higher rates of clinical improvement in study arms using amphotericin B than in study arms using pentavalent antimonial therapy ( Sbv ) . The parasitological cure , an outcome that presented some degree of risk of selection and verification bias , had rates that varied widely within the same treatment arm , with high heterogeneity , hampering any formal comparison among drugs . One direct comparison of amphotericin and antimoniate was possible combining results of two studies and confirming the superiority of amphotericin . Available evidence suggests that amphotericin is superior to antimony treatment . Death rate using antimoniate high dose is unacceptably high . Randomized controlled trials are necessary to compare different formulations and doses of amphotericin , alternative therapies and drug combinations .
In recent years , several reports have emphasized the increasing importance of visceral leishmaniasis ( VL ) as an opportunistic infection among HIV-positive patients in areas where both infections are endemic [1] . Chemotherapeutic agents with efficacy against VL include amphotericin B , pentavalent antimonial drugs , paramomycin ( a parenteral aminoglycoside ) and miltefosine ( the first oral drug for treatment of VL ) . The pentavalent antimonial drugs ( Sbv ) , sodium stibogluconate ( SSG ) and meglumine antimoniate have been used for past decades as the first line drugs for treatment because of their low cost and availability in most countries . Currently this option has been sidelined since more efficacious and less toxic alternatives exist [2] . Amphotericin B deoxycholate has high antileishmanial efficacy but it is associated with high risk of renal toxicity and other side effects and has been replaced in recent years in countries with sufficient financial resources by lipidic formulations of the drug . Pentamidine isethionate , a second-line alternative treatment is rarely used due to suboptimal efficacy and toxicity [3] . Although VL is treated similarly in patients with and without HIV infection , co-infected patients generally have low cure and high mortality rates [4] , [5] . Furthermore , HIV-infected patients are more likely to suffer treatment-related adverse events than the HIV-negative population [6] , [7] . Despite the prevalence , clinical implications and epidemiological impact of Leishmania/HIV co-infection , surprisingly scarce data is available regarding the treatment of leishmaniasis in HIV-infected patients . Major challenges include widespread resistance to pentavalent antimonial compounds , high treatment failure , toxicity and relapse rates [8] . The optimal therapy , including duration and dosages remain to be established . Indirect comparisons of nonrandomized studies are useful in the absence of randomized controlled trials , knowing its limitations and its assumptions [9] , [10] , adjusting for important covariates such as CD4 lymphocyte count . The aim of this study is to perform a systematic literature review regarding therapeutic efficacy and toxicity associated with visceral leishmaniasis therapy among HIV infected individuals , making comparisons of treatment options when possible .
Comprehensive Meta-Analysis software v . 2 . 2 . 048 was used to perform one-group meta-analysis of study arms with the same drug to estimate pooled rates of success and adverse events with each drug . These estimates were used , when possible , to indirectly compare treatment options . These unadjusted indirect comparisons were confronted with direct comparisons , when available . Also , the indirect comparisons were adjusted for CD4 lymphocyte count by using meta-regression . Meta-regression was used to explore the relationship between event rate and CD4 count by using mixed effects regression ( unrestricted maximum likelihood ) . We used the Inconsistency ( I2 ) statistic to evaluate heterogeneity . If significant heterogeneity was found , the results from the random effects model were emphasized and summary measures were analyzed as limited information , looking for differences in studies . Random effects model is a strategy that allows the heterogeneity inter-study would be incorporated through a broad confidence interval , generating a more conservative estimate of the measure of the effect . Clinical cure , global response and death rates analysis were performed according to the intention-to-treat analysis: the analysis was based on the total number of randomly assigned participants , irrespective of how the original study investigators analyzed the data . Publication bias was assessed by Egger's test [14] .
Our search identified 342 articles from PubMed . EMBASE , LILACS and WEB OF KNOWLEDGE databases added 10 , 2 and 3 papers respectively . After exclusions by title and abstracts , 63 potentially relevant papers were selected for full text evaluation ( Figure 1 ) . Two other titles were identified from references of the primary manuscripts . Of these 65 studies , 47 papers were excluded due to incomplete information about treatment and/or outcomes , including two congresses abstracts . One paper [15] was excluded because same patients were described elsewhere [16] . In a study [17] , one treatment arm was also excluded by overlapping publication [18] . Thus , we included 17 articles [6] , [16]–[31] involving 920 VL episodes among HIV-infected patients . Although in 4 studies the percentage of VL primary and relapse was not informed , most included patients had first VL episodes ( 76 . 1% ) . From 17 studies , only four were randomized trials [6] , [22] , [23] , [27] involving 279 participants . However , these trials compared different treatment arms . We found only two studies [29] , [30] , nonrandomized , compared the same treatment regimens ( amphotericin versus low dose antimony ) and thus allow direct comparison . The methodological characteristics of studies , namely inclusion and exclusion , VL disease , clinical improvement and TOC criteria are shown in Tables 1 and 2 . Three papers were prospective non comparative studies and other nine were historic cohort studies with one or more treatment regimen arms reported . VL was diagnosed in 10 studies exclusively if patients had a compatible clinical illness and positive Giemsa-stained smears or culture for Leishmania in samples taken in most cases from the bone marrow , spleen or liver . Few diagnoses were established by the finding of Leishmania spp . in biopsy of an unusual site such skin , tongue and gingival mucosae [29] or after staining and/or culture of the buffy coat [20] . Five authors [6] , [23] , [25] , [26] , [30] accepted VL diagnosis based on a positive serological result ( Western Blot , direct agglutination or rK39 dipstick tests ) and in some studies [6] , [26] [32] , diagnosis was based on clinical grounds alone ( negative serological tests , strong clinical suspicion and parasitological test contraindicated ) . The efficacy of therapy was assessed by clinical and/or microbiologic criteria . The clinical response definition varied among studies . The remission of fever , improvement of hematological values and regression in the size of the spleen were the main signs observed . A complete resolution of all clinical and hematological parameters [16] , [28] , [30] or the absence of recurrence in subsequent months after VL treatment [17] , [32] was required by some authors to establish clinical cure while all the others defined response as the improvement of the signs and symptoms attributed to disease at the end of treatment , even partial . A VL episode was considered microbiologically cured when the organ used at inclusion and obtained again usually between days 25 and 45 after initiation of therapy yielded no demonstrable amastigotes by direct visualization or culture . A parasitological control study was planned for all patients in three prospective trials [21] , [22] , [27] . Only two studies did not report parasitological test of cure [18] , [29] . In several centers [6] , [23] , [25] , [26] parasitological test of cure have been routinely performed after treatment , except in those patients without splenomegaly and lymphadenopathy or contraindication to the procedure . In another study [16] , the procedure was carried on patients who showed any persistent clinical or hematological alterations after treatment and finally there were those where TOC was performed in some patients after completion of therapy without any disclaimed selective criteria . The percentage of VL episodes tested for parasitological cure ranged from 7 . 4 to 100% among studies . Overall , 62 . 3% ( 456 ) of the 732 treated episodes with available information about TOC were evaluated with a parasitological test at the end of treatment . Fourteen studies reported number of patients lost to follow up: in 13 ( 93% ) of them it was less than 10% . The follow up length ( mean or median reported by fifteen studies ) varied between 5 and 14 months ( Table 3 ) . Table 3 shows also the baseline characteristics of included patients . The mean or median age varied between 28 and 36 years among the studies , most patients were male ( 87 . 4% of the 748 patients with gender information available ) and 49 . 6% of the patients ( 269/542 ) had AIDS criteria before VL diagnosis . Antiretroviral use was reported in 10 studies and in four of them , no patient was on HIV treatment at VL diagnosis . The median or mean baseline CD4 cell counts range from 25 to 204 cells/ml . Five treatment regimens were reported in 17 studies included in this review . There were 13 studies with 457 VL episodes evaluating antimony compounds: five studies assessing “low antimonial dose” and eight evaluating “high antimonial dose” schedules . Therefore , we assume that a low pentavalent antimony dose was administered when the length of treatment was shorter than 28 days and/or less than 20 mg Sbv/kg/day was administered . High antimonial dose was defined as treatment with ≥20 mg Sbv/Kg/day for at least 28 days . In some studies , the antimonial treatment was combined with allopurinol , or recombinant human interferon-gamma in few patients [20] , [28] , [29] , [31] . Three studies evaluated the response to amphotericin B deoxycholate and 6 studies described the response to one of the lipid formulations of amphotericin B ( L-Lip-AmB ) , which includes liposomal and lipid complex amphotericin , most of them ( 5 of 6 ) by using total doses above 25 mg per kg . Only one study evaluated the response to treatment with miltefosine [23] . The Newcastle-Ottawa Scale ( NOS ) for Assessing the Quality of Nonrandomized Studies is show in Table 4 and the scores ranged from 5–7 . As shown in Table 2 , there was adequate selection of patients in included studies , as almost all were parasitological confirmed cases who were largely representative of source populations . In ten studies , however , patients were reviewed retrospectively for inclusion , with some risk of bias in the case selection . Seven studies were non comparative and reported only one arm treatment outcome . The summarized measures for initial clinical improvement , global cure and death , according to the intention-to-treat analysis , are shown in Table 5 . Relapse was assessed including treated patients who were considered cured . To assess parasitological cure rate only patients who underwent test of cure were included . Clinical improvement rate using amphotericin in lipid formulation ( L-Lip-AmB ) was superior compared to both antimony therapy groups ( Figure 2 ) . The unique study herein included using deoxycholate amphotericin B also exhibited a clinical response rate ( 85% , 95%CI 41 . 7–97 . 8% ) similar to L-Lip-AmB group ( 91 . 6% , 95%CI 74 . 7–97 . 6% ) . Therefore , it was not possible to attest the presence of a difference in performance among several amphotericin B formulations . The global and parasitological cure rates varied widely within the same treatment arm , which hampered any indirect comparison between them ( Table 5 ) . This fact probably reflects different criteria used by the studies to perform test of cure . However , it is worth mentioning that the difference by indirect comparison from 76% to 56% ( wide confidence intervals ) in global cure rate between deoxycholate amphotericin B and Sbv treatment groups , respectively , although heterogeneous , was confirmed by direct comparison of the studies [29] , [30] which actually compared these two treatment arms ( OR 6 , 08 for amphotericin superiority 95%CI 1 , 99–18 , 5; I2 0% ) . Regarding tolerance , the difference in adverse event rate between high dose of Sbv ( 23 . 3; 95%CI 17 . 4–30 . 4 ) and lipid formulation of amphotericin B ( 9 . 5; 95%CI 3 . 5–23 . 3 ) seems to be relevant , despite the overlap between the confidence intervals observed ( Table 5 ) . In agreement with this , the rate of early discontinuation of therapy due to toxicity also seems to be higher with Sbv than with lipid formulation of amphotericin B . All these outcomes were adjusted for CD4 lymphocyte count , which had no influence on treatment effect as evaluated by meta-regression . The most outstanding difference in outcome between the treatment regimens was observed in early mortality rate: about 3 times higher in high-dose antimony ( 18 . 4 . % , 95%CI 13 . 3–25% ) in comparison to L-Lip-AmB ( 6 . 1% , 95%CI 3 . 9–9 . 4% ) treated patients , without overlap of confidence intervals ( Table 5 and Figure 3 ) . Meta-regression revealed no influence of CD4 lymphocyte count in death rate related to different treatments ( Figure 4 ) . A variety of adverse effects were depicted , such as vomiting , diarrhea , anemia , eletrolic disturbs , pancreatic , cardiac , hepatic and renal dysfunction ( Table 6 ) . The events reported were not sufficiently similar to allow a meta-analysis of adverse effects . In two studies [22] [27] adverse reactions were scored according to the World Health Organization ( WHO ) scale for toxicity . Six studies did not report on the occurrence of reactions to VL therapy and one study [17] reported the occurrence of adverse events without discriminating the type of treatment received . Ten out seventeen studies reported the VL relapse rate without secondary prophylaxis and it ranged from 26 to 50% . VL relapse was diagnosed if parasites were observed in tissue samples after initial clinical cure . It was not possible to attest the presence of any difference in relapse among different treatments .
The main limitation of this review is the paucity of quality evidence . On the other hand , four literature databases were searched , making it a comprehensive review . Clinical decisions must be made . To aid in this task we presented indirect comparisons , including non-randomized studies , in the same way others have done [40] , as a tool to synthesize the available information . By adjusting indirect comparison for CD4 lymphocyte count , we have evaluated an important confounder factor for mortality rate . To our knowledge , no systematic review has investigated the comparative efficacy of the several treatment options for VL-HIV patients . When there is no or insufficient direct evidence from randomized trials , the adjusted indirect comparison may provide useful or supplementary information on the relative efficacy of competing interventions . The validity of the adjusted indirect comparisons depends on the internal validity and similarity of the included trials [41] . Ideally , direct and indirect estimates should be combined in mixed treatment comparisons only after adequate assessment of the consistency of the evidence [42] . In this case , evidence of consistency is the correlation observed between indirect comparison performed ( comparison among patients treated with different schemes in different studies ) and the only direct comparison that could be made ( two studies comparing the same two treatment arms ) . Other important qualitative features include the degree of similarity of populations , interventions , outcomes , study objectives and study designs that incorporate both clinical and biological plausibility . Many studies used selective criteria excluding patients with more severe clinical conditions or with high risk of toxicity , such as those with renal , pancreatic or heart dysfunction . This methodological choice could have influenced toward a lower rate of adverse events and a higher percentage of therapeutic success . However , the more stringent studies were also comparative and randomized studies [22] , [23] , [27] , so , such selection affected equally all treatment arms . Similarly , studies with highly demanding criteria of cure , as those requiring complete resolution of symptoms [16] , [30] may have had the therapeutic success rates underestimated . In all studies , the outcome ascertainment was not blinded . Indeed , either the participants or the researchers who collected disclosure information may have been aware of participants' disease status at the time of data extraction . However , most studies have clearly defined criteria for establishing cure , and in many of them parasite clearance was required . The time to cure assessment and the follow-up time was relatively uniform and adequate in all studies . So we performed the summary measures of effectiveness for each treatment regimen to perform an indirect efficacy comparison . To carry out a clinically sound analysis , we used a conservative approach and imputed outcomes for the missing and discontinued participants assuming that they did not respond to treatment . Therefore , no response includes the intrinsic lack of efficacy and toxicity limiting the completeness of the treatment . In fact , in this review there was an inverse association between adverse events rate and clinical response , as expected . Although parasitological cure rate could theoretically provide reliable information about treatment efficacy , in most studies post treatment TOC was performed only in patients with uncertain clinical response , which represents a selection bias that could underestimate response rates . In conclusion , these indirect comparisons suggest higher clinical response rate with amphotericin B than with antimony treatment , which appears to be related to less toxicity than with higher effectiveness of lipid formulations of amphotericin . Antimonial therapy carries a higher rate of drug discontinuation and a significantly higher mortality indirectly compared to treatment with amphotericin B . A relatively large body of non-comparative cohort studies supports , at this time , the use of amphotericin B as the first choice for VL treatment in HIV-infected patients . The optimal dose of amphotericin and the difference in efficacy between its various formulations remain to be established . | In co-infection with HIV/AIDS , visceral leishmaniasis ( VL ) most often results in an unfavorable response to treatment , frequent relapses , and in premature deaths . Scarce data is available regarding the treatment of leishmaniasis in HIV-infected patients ( VL-HIV ) . Despite this , clinical decisions must be made . To aid in this task we reviewed comprehensive and systematically the available literature about efficacy and toxicity of therapeutic options for VL-HIV . PRISMA guidelines and Cochrane manual for systematic reviews were followed . Direct and indirect comparisons of nonrandomized studies were used , adjusting for CD4 count . Seventeen studies reporting five treatment regimens and outcome of 920 VL episodes occurring in HIV infected individuals were included . Results suggest higher survival and clinical response rate with amphotericin B than with antimony treatment . Antimonial therapy carries a higher rate of drug discontinuation and a significantly higher mortality indirectly compared to treatment with amphotericin B . Randomized controlled trials are needed to compare doses and formulations of amphotericin and alternative treatments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"sexually",
"transmitted",
"diseases",
"aids",
"leishmaniasis",
"neglected",
"tropical",
"diseases"
] | 2013 | Efficacy of Anti-Leishmania Therapy in Visceral Leishmaniasis among HIV Infected Patients: A Systematic Review with Indirect Comparison |
Host immune responses against infectious pathogens exert strong selective pressures favouring the emergence of escape mutations that prevent immune recognition . Escape mutations within or flanking functionally conserved epitopes can occur at a significant cost to the pathogen in terms of its ability to replicate effectively . Such mutations come under selective pressure to revert to the wild type in hosts that do not mount an immune response against the epitope . Amino acid positions exhibiting this pattern of escape and reversion are of interest because they tend to coincide with immune responses that control pathogen replication effectively . We have used a probabilistic model of protein coding sequence evolution to detect sites in HIV-1 exhibiting a pattern of rapid escape and reversion . Our model is designed to detect sites that toggle between a wild type amino acid , which is susceptible to a specific immune response , and amino acids with lower replicative fitness that evade immune recognition . Through simulation , we show that this model has significantly greater power to detect selection involving immune escape and reversion than standard models of diversifying selection , which are sensitive to an overall increased rate of non-synonymous substitution . Applied to alignments of HIV-1 protein coding sequences , the model of immune escape and reversion detects a significantly greater number of adaptively evolving sites in env and nef . In all genes tested , the model provides a significantly better description of adaptively evolving sites than standard models of diversifying selection . Several of the sites detected are corroborated by association between Human Leukocyte Antigen ( HLA ) and viral sequence polymorphisms . Overall , there is evidence for a large number of sites in HIV-1 evolving under strong selective pressure , but exhibiting low sequence diversity . A phylogenetic model designed to detect rapid toggling between wild type and escape amino acids identifies a larger number of adaptively evolving sites in HIV-1 , and can in some cases correctly identify the amino acid that is susceptible to the immune response .
Intra-host HIV evolution is characterized by very rapid escape from immune responses [1]–[5] . Such host immune selection pressures are typically mediated by neutralizing antibodies [6] , T-helper cells [7] or Cytotoxic T Lymphocytes ( CTLs ) [1] , [8] , [9] . Escape mutations associated with neutralizing antibodies [10] , [11] tend not to have a significant effect on the fitness of the virus [10] , or rate of disease progression [12] . Many examples of CTL escape mutants are known , however , that affect both viral replication ability , and thus viral load [13]–[16] , and rate of disease progression [17]–[20] . CTLs recognize viral epitopes bound by human leukocyte antigens ( HLAs ) at the surface of infected cells , causing cell death . The cellular processes by which CTL epitopes are cleaved and presented at the cell surface provide numerous opportunities for escape from the immune response . Escape can occur through viral mutations that affect proteosome processing , affinity for transport antigen processing ( TAP ) proteins , translocation of peptides to the endoplasmic reticulum , antigen processing prior to presentation , binding of MHC class I molecules and finally recognition by cytotoxic T cells [1] . Much of the work on immune escape from CTL responses in HIV-1 has focused on identifying escape mutations which either prevent MHC binding or recognition by CTLs [1] , [2] , [9] , [19] , [21] , [22] . The effect of within-host HIV-1 evolution and immune escape on viral genetic variation at the host population level is highly topical . Early research indicating a strong association between HLA type and viral polymorphisms across individuals [23] was criticized for not adequately addressing population founder effects [4] , [24] . Nonetheless , more recent studies , which account for spurious association of HLA alleles with viral polymorphisms resulting from shared ancestry , confirm widespread association between HLA alleles and polymorphisms in the viral amino acid sequence , illustrating the extent to which the virus adapts to the host-specific CTL response [4] , [24]–[26] . Escape mutations from specific HLA-mediated immune responses that incur a cost to the virus in terms of replication fitness are thought to come under selective pressure to revert to wild-type upon transmission to a host lacking the immune response [2] , [21] , [25] , [27]–[31] . Whether and how rapidly reversion occurs depend on both the fitness cost of the escape mutation [30] , [32]–[35] , and the occurrence of compensatory mutations that offset this cost [15] , [22] . Escape from a host-immune response that occurs at a substantial fitness cost to the virus and thus reverts rapidly , can result in a pattern of switching or toggling [30] , [31] between the amino acid which is most fit in the absence of the immune response ( we refer to this as the wild type state ) and amino acids that prevent CTL recognition ( the escape state ) . Models of coding sequence evolution have frequently been applied to HIV-1 sequences with the aim of identifying adaptively evolving sites [9] , [36]–[40] . These models are designed to detect an elevation in the rate of non-synonymous substitution ( dN ) over the rate of synonymous substitution ( dS ) , the latter being assumed to occur at the neutral rate of evolution . This is referred to as diversifying selection and it occurs when , on average , non-synonymous mutations result in an increase in fitness and thus have a higher fixation probability and a shorter fixation time than neutral mutations . In the idealized scenario , assumed by models of diversifying selection , all non-synonymous substitutions benefit from this increased rate , resulting in rapid diversification from the ancestral amino acid . Amino acid toggling , driven by positive selection to escape from immune responses and to revert to wild type in their absence is not specifically envisaged by these models . We propose a model of positive selection associated with immune escape and reversion , which we call the toggling selection model . Our motivation is twofold . Firstly , the toggling model is significantly more realistic than the diversifying selection model in the context of selection associated with immune escape and reversion and , consequently , it is likely to have more power to detect positive selection associated with this process than a model of diversifying selection . Since immune escape and reversion are likely to be a common source of adaptive evolution in viral sequences , we hypothesized that the toggling model may have greater power , overall , to detect adaptively evolving sites . Second , although many previous studies have identified adaptively evolving sites in HIV-1 [9] , [36]–[40] , most have not been concerned with the patterns of sequence changes found at these sites , and none has attempted to distinguish systematically between adaptively evolving sites consistent with immune escape and reversion and sites evolving under diversifying selection . Using simulation as well as publicly available HIV-1 data we compared the power of the toggling model and standard models of adaptive evolution to detect adaptively evolving sites . Because the real sequences were obtained from individuals with known HLA in which associations between HLA alleles and sequence polymorphisms had been established [4] , we could investigate the relationship between the sites detected and sites that are putatively involved in adaptation to the host HLA type . We also used model comparison techniques to compare the fit of the toggling selection model to the fit of standard selection models in order to determine which model provided a better explanation of the HIV-1 data .
Probabilistic models of codon sequence evolution [41] , [42] use a continuous-time Markov process , described by a rate matrix , Q , with element qij denoting the instantaneous substitution rate from codon i to codon j . Here we introduce a novel variant of the codon model , designed to describe host-mediated immune escape from and reversion to a wild type amino acid W . The instantaneous rate matrix describing this model is ( 1 ) where κ is the transition-transversion rate ratio , ω is the non-synonymous substitution rate relative to the rate of synonymous substitution ( dN/dS ) for substitutions not involving the wild type amino acid W , and ρ is the relative substitution rate for non-synonymous substitutions involving wild type amino acid W . For a given wild type state the 61 sense codons are divided into classes , c , depending on whether they encode the wild type amino acid ( c = x; Figure 1 ) , are separated from the wild type amino acid by a single nucleotide substitution ( c = y; Figure 1 ) , or are separated from the wild type by multiple substitutions ( c = z; Figure 1 ) . These codon classes are introduced to allow us to model the case in which immune escape and reversion involves repeated mutation from the wild type to an escape state , accessible to the wild type by a single nucleotide substitution , and back again . We introduce parameters , tc , to model the proportion of time the site spends in each class . The equilibrium frequency of a specific codon , belonging to class c , is then ( 2 ) where πj is the alignment-wide frequency of codon j , estimated using the F3x4 model [41] . This formulation allows us to retain terms accounting for general codon usage bias , assumed to be shared across all sites in the model . The model is fitted to the data one codon site at a time . Given a phylogenetic tree , the likelihood of the data at a site can be calculated using Felsenstein's pruning algorithm [43] . Because the identity of the wild type amino acid is unknown , we sum over all twenty amino acids such that ( 3 ) where L ( D|MW ) is the likelihood of the data given that W is the wild type . As an alternative to summing over the uncertainty in the wild type state we can assume that the most common amino acid at a given site is the wild type . We tested both of these approaches and refer to them as the toggling and consensus toggling models , respectively . Our test of positive selection comprises the comparison of log likelihoods between a null model , in which both the rate of amino acid toggling ( ρ ) , and non-synonymous to synonymous substitution rate ( ω ) are constrained to be less than one , to an alternate model in which the constraint on the rate of amino acid toggling is removed . An alternative test involves the removal of both constraints ( on ρ and ω ) , which we designate the unconstrained toggling model . The performance of the toggling models for detecting positive selection was evaluated and compared to existing approaches . In the latter , a site-specific diversifying selection model is defined in which the test of positive selection involves the comparison of log-likelihoods between a null model , for which ω is constrained to be less than one , to an alternate model in which the constraint is removed [44] . Null and alternate models are compared in all cases with a site-wise likelihood ratio test . The above models require phylogenetic trees as input . In all cases the trees were estimated using phyml [45] under a general time-reversible model [46] with substitution rates modeled as a 4-category gamma distribution [47] . Branch lengths were fixed to maximum likelihood estimates obtained from the optimization of a nucleotide model using the entire alignment , but scaled with a nucleotide to codon scaling parameter , R , which was estimated separately for each site thus allowing site-wise variation in synonymous rates . To prevent spurious signals of selection resulting from recombination [48]–[50] we identified recombination breakpoints using GARD [51] , and estimated phylogenies independently for each partition defined by these breakpoints . We used simulations to evaluate the performance of the toggling model compared to the diversifying selection model to detect both diversifying selection and toggling selection . A phylogenetic tree inferred from a randomly selected subset of 100 taxa from a previously published nef gene alignment [4] was estimated as above . We simulated amino acid toggling and diversifying selection ( 200 codons each ) for each of five parameter sets ( Table 1 ) , using custom scripts written in the HyPhy [52] batch language . Amino acid toggling ( Figure 1 ) was simulated with variable values of ρ ( Table 1 ) , ω = 0 . 05 for substitutions not involving the wild type state W , tx = 0 . 5 , ty = 0 . 475 , tz = 1− ( tx+ty ) = 0 . 025 , such that most time was spent either in the wild type ( codon class x ) or codons separated from the wild type by a single nucleotide substitution ( codon class y ) ( Figure 1 ) . Diversifying selection was simulated across a range of different values of ω ( Table 1 ) . We determined the effect of both tree length and tree shape on the detection of positive selection and toggling using simulations . We increased the size of the simulated data set to 200 randomly drawn taxa from a previously published nef alignment [4] , effectively doubling the total tree length ( Table 2 ) . Trees inferred from HIV-1 sequences tend to have longer terminal branches . To investigate the effect of tree shape on power to detect selection we simulated data along a tree for which branch lengths were drawn randomly from an exponential distribution with mean = 0 . 05 , using previously developed HyPhy code [44] . Power was calculated as the number of sites at which positive selection was correctly inferred as a proportion of all sites for which positive selection ( either diversifying selection or toggling ) was simulated . False positive rates , estimated as the number of sites at which positive selection was incorrectly inferred as a proportion of all sites not evolving under positive selection , was evaluated with simulations of 800 neutral and purifying selection codons ( 75% purifying , ω = 0 . 05; 25% neutral , ω = 1 ) . We obtained gag , nef and pol sequence alignments from previously published studies [4] , [53] , and an env alignment of HIV-1 subtype C sequences from the Los Alamos HIV databases ( http://www . hiv . lanl . gov/content/index ) . Because we wished to make comparisons between results obtained on different genes it was important that the number of sequences in each alignment be approximately the same . However , we note that power is dependent on both the number of sequences and the amount of variation in those sequences . Since it is not possible to control both variables simultaneously in the real data we report the tree lengths in order to facilitate the comparison of results from different genes . We randomly sampled 100 sequences from each of the large env , nef , and pol genes and took all of the 98 sequences in the gag alignment [53] . The toggling selection model is more computationally intensive than the diversifying model , requiring 20 optimizations per codon site ( one optimization for each wild type amino acid ) , for both the null and the alternative models . Use of a subset of the sequences in the larger alignments also helped to reduce running times . Sequences with stop codons within genes were pruned from the alignments . We used both a site-wise diversifying selection model and toggling selection model to identify adaptively evolving sites . For each gene we compared the fit of a toggling model versus diversifying selection model at each site using AIC [54] . Alignments used are available from the authors on request . Both the model and simulation scripts have been implemented in the HyPhy [52] batch language and are available from the authors on request .
We have developed a model ( Figure 1 ) , designed to detect positive Darwinian selection associated with host-mediated immune response and reversion acting on viral protein-coding genes . Existing models of positive selection acting on coding sequences are sensitive to an overall elevation in the rate of non-synonymous to synonymous substitutions ( we refer to this situation as diversifying selection here ) . Such a situation occurs when , on average , the effect on viral fitness of any amino acid-changing mutation is positive . For a class of viral sites that mediate escape from host immune responses at a cost to the virus in terms of replicative fitness , we do not necessarily expect an overall elevation in the rate of non-synonymous substitutions . Instead we expect to see switching between a wild type amino acid associated with high replicative fitness and susceptibility to the immune response , and an escape state with lower fitness . This model is motivated by the fact that , firstly the identification of sites that switch between wild type and escape states is of interest , because at these sites escape mutations are likely to be common , despite having a deleterious impact on viral fitness . Secondly , some sites with a rate of substitution between specific amino acids , which is higher than expected under neutrality may not have an overall rate of non-synonymous substitution greater than the neutral rate when we average over all possible non-synonymous substitutions . Our test of positive selection involves the use of model comparison techniques to compare a null model in which the parameters ρ ( describing the rate of immune escape and reversion relative to the synonymous substitution rate ) and ω ( the relative rate of all other non-synonymous substitutions ) are constrained to be less than one , to an alternate model where ρ is unconstrained . Simulations ( Figure 2 ) indicated improved power of the toggling model ( T ) over a standard diversifying selection model ( D ) ( Table 1A , Figure 3 , Table S1 ) to detect positive selection involving switching between a wild type state and escape states , consistent with host-mediated immune response . A diversifying selection model showed improved power to detect positive selection when data were simulated under a diversifying selection model ( Table 1B , Figure 3 , Table S1 ) . We accounted for uncertainty in identifying the wild type state by averaging the likelihoods over all possible wild type amino acids at each codon ( see Equation 3 in Methods ) . This would result in the loss of some power over a model in which the wild type state is known a priori . Given this loss in power associated with averaging over all potential wild type states in the toggling model , a model in which the wild type state at a site is assumed to be the same as the consensus amino acid at that site might be expected to have greater power . However , simulations indicated this model to have equivalent or less power ( results not shown ) , suggesting that the consensus is not always a good approximation of the wild type state ( the amino acid with highest replicative fitness in the absence of a specific immune response ) . Furthermore , a model in which the wild type state is taken from the data runs the risk of bias in model comparisons , because the model is defined using the data ( wild type state taken to be the consensus in the actual data ) and evaluated on the same data . As an alternative to averaging over all amino acids or drawing the wild type state from the consensus , we weighted the likelihoods for each potential wild type state by the observed alignment-wide amino acid frequency . However , this approach resulted in no increase in power and therefore we used equal weights on amino acids for the remainder of the analyses . We also compared power when both the non-synonymous to synonymous rate ratio associated with the wild type state ( ρ ) and that of other non-synonymous substitutions ( ω ) are unconstrained ( Tu ) . Simulations confirmed that the model with both parameters unconstrained has greater power to detect diversifying selection ( Table 1B ) than the toggling model in which only ρ is unconstrained , but less power than a standard diversifying selection model ( Table 1B ) . This loss of power against a diversifying selection model is due to the extra degree of freedom in the Tu model , compared to both the diversifying selection and toggling models . For the same reason , the test in which both ρ and ω are unconstrained , has lower power to detect amino acid toggling ( Table 1A ) than the model in which only ρ is unconstrained . False positive rates at the 5% significance level were low for all models evaluated on a dataset consisting of a mixture of neutral and purifying selection sites ( D = 0 . 88%; T = 1 . 63%; Tu = 1 . 25% ) , and approximately equal to the expected rate of false positives when only neutral sites ( ω = 1 ) were simulated ( D = 3 . 3%; T = 5 . 6%; Tu = 4 . 7% ) . We found that the power to detect toggling increased dramatically with larger data sets and with trees with exponentially distributed branch lengths ( Table 2 , Figure S1A , Figure S2 ) . Typical phylogenetic trees inferred from HIV-1 sequences have long terminal branches and pose a challenging problem for the toggling selection model . This is because escape and reversion events that occur on the same branch are not observed . The power of a diversifying selection model to detect positive selection involving toggling was much lower when data were simulated along a random tree and did not show much improvement with a larger dataset ( Table 2 ) . We also evaluated the power of the toggling selection model to recover the amino acid used as the wild type in simulation , and the proportion of time spent in each of the three codon classes . The amino acid which maximized the likelihood of the toggling selection model , was inferred to be the wild type . In simulations the wild type state was always identified correctly ( 100% success rate ) for both intermediate ( ρ = 2 ) and rapid ( ρ = 5 ) rates of toggling , and the inferred time spent in each state was also estimated accurately ( tx: simulated 0 . 5; inferred 0 . 494±0 . 128 , ty: simulated 0 . 475; inferred 0 . 471±0 . 128 ) . We used the toggling model developed above to detect putative escape-and-reversion sites in four HIV-1 datasets ( see Methods; tree lengths: nef = 10 . 8 , gag = 9 . 1 , env = 17 . 3 , pol = 8 . 6 ) . Amino acid toggling is evident at sites at which multiple mutations away from the wild type and reversions back to wild type are observed ( Figure 4 ) . For all genes evaluated , the toggling selection model provided a better fit than a diversifying selection model for the majority of positively selected sites ( Table 3 ) . By contrast , when all sites are considered , the toggling model provides a better fit for a smaller proportion of sites ( Table 3 ) . The toggling model detected significantly more positively selected sites in nef and env ( Binomial test , nef: P = 0 . 001374; env: P = 0 . 001028 ) , than a standard diversifying selection model ( Table 3 , Figure S3 ) . Neither diversifying selection sites nor toggling selection sites occurred more frequently than expected by chance within optimal HLA epitopes ( Figure S4 ) . Similarly there was no clear association between CTL-reactive peptides identified in gag [16] and amino acid toggling ( P = 0 . 055; Figure S4 ) . Because the mapping of optimal CTL epitopes to positively selected sites ignores population specific HLA frequencies and selective pressures , we evaluated our results for nef against HLA-associated polymorphisms detected in the same patient cohort [4] . The HLA associations detected on the full dataset ( n = 684 ) , and in this study , are shown in Table 4 . Fifteen of the 84 codon sites for which a significant association between HLA allele and polymorphism was previously detected [4] , were detected with the toggling selection method ( using just 100 of the 684 sequences from which the associations were inferred ) . We found significant enrichment for HLA-associated polymorphisms among sites detected with the toggling model ( P = 0 . 001544 ) , or with a diversifying selection model ( P = 0 . 008397 ) . Since our model evaluates each of twenty amino acids as the candidate wild type state we were able to infer both the identity of the wild type amino acid at each site showing evidence of positive selection , and the proportion of time spent in the wild type versus escaped states ( Table 4 ) . More than half of the sites detected as toggling spend the majority of time at codons that either code for the wild type amino acid , or are a single nucleotide substitution away from the wild type . Sites with multiple wild type amino acids ( e . g . sites 10 and 15 ) may be associated with multiple overlapping immune responses or with multiple equally fit amino acids . These sites were frequently also detected using a diversifying selection model . Many of the inferred wild type amino acids ( at sites 33 , 50 , 54 , 82 , 83 , 100 , 101 , 126 , 178 ) were consistent with previously identified escape and reversion mutations identified from association with HLA alleles [4] , [24] .
HIV-1 evolves rapidly and under strong selective pressure . Although purifying selection acting on coding regions is important for preserving protein functions [55] , positive selection has been shown [9] , [36]–[40] to play an important role in shaping HIV-1 genetic diversity . In particular , sites involved in escape from host immune responses , either due to CTLs or neutralizing antibodies , have frequently been reported to be under strong selection pressure [1] , [6] , [10] , [21] , [56] . Because the host immune response is highly polymorphic , viral sequences sampled from multiple hosts reflect an ongoing history of adaptation to successive host immune responses and to successive responses mounted by the adaptive immune system within individual hosts . Some mutations that facilitate escape from immune responses occur at sites that are functionally constrained [2] . These typically incur a fitness cost to the virus , and thus come under selection to revert to the wild type amino acid upon transmission to a host without the immune response [32]–[35] , [57] . Escape mutations that occur at sites that are functionally unconstrained , or for which compensatory mutations [22] fully offset the fitness cost , will not experience selection for reversion . In this case the escape state may persist over time , and become fixed [58] , in the absence of further immune responses targeting the same site . In general , escape mutations in a viral epitope could be classified according to whether they prevent recognition of the epitope by a specific clonal population of immune cells or whether they interfere with the processing or presentation of the epitope . The latter can cause permanent escape from immune responses targeted against a specific epitope within an individual , while the former may be targeted again by a future immune response in the same individual . Successive escape mutations , particularly when they occur at no great cost to the virus in terms of fitness are likely to result in a pattern of diversifying selection , where all of the possible non-synonymous substitutions at a site are affected by positive selection . By contrast , mutations that prevent epitope presentation and which revert in the absence of the immune response would be likely to fit a pattern of amino acid toggling . Much of the adaptive evolution in viral coding sequences is likely to be a consequence of the adaptive immune response , but a priori there is no way to know whether this primarily involves immune escape and reversion at functionally constrained sites or diversifying selection at less constrained sites . We introduce a model of toggling selection that seeks to model the process of immune escape and reversion at constrained sites . Our model differs from standard codon models in the manner in which equilibrium codon frequencies are included . Typically , codon frequencies are estimated from the alignment and the relative rates of substitution between codons are a product of these alignment-wide codon frequencies and exchangeability parameters that depend on the nature of the mutation . Advances in phylogenetic modeling have allowed frequency parameters to vary between sites [59] . Our model of immune escape and reversion similarly allows sites to have independent codon frequency parameters; however , we do this through the introduction of just two frequency parameters ( rather than the 19 or 60 free parameters required per site if the frequency of each amino acid or codon was free to vary independently at each site ) . Using simulation we find , unsurprisingly , that this model is more efficient at detecting toggling than a model of diversifying selection , while the opposite is the case for sites evolving under diversifying selection . Interestingly , when we apply both models to coding sequence data from HIV-1 , the toggling model detects a larger number of positively selected sites . Furthermore , positively selected sites detected using either or both models more often provide a better fit , using AIC [54] , to the toggling selection model than to the diversifying selection model ( Table 3 ) . Taken together , these observations suggest that a large proportion of positive selection in HIV-1 consists of escape and reversion at functionally constrained sites , and that the toggling model is a better description of adaptive evolution in HIV-1 than standard models of diversifying selection . Because diversifying selection and toggling selection are consistent with distinct biological scenarios , models that attempt to fit the specific characteristics of each scenario are of value . This is of particular relevance since the toggling selection model displays significantly improved power to detect escape and reversion , a process which is important because it characterizes immune responses that are effective in controlling viral replication . The significance of sites that display rapid immune escape and reversion is evident from the fact that these sites are often the targets of HLA alleles that confer improved disease prognosis [13] , [18] , [35] , [60] . Some overlap occurs between sites detected with a diversifying selection and toggling selection models ( Figure 5 , Figure S3 , Figure S4 ) ; however , in all genes , approximately as many or more sites are detected using the toggling model than the diversifying selection model ( Figure S3 ) . The fact that many sites detected using the toggling selection model are not detected using the diversifying selection model ( and vice versa ) suggests that the models are not redundant and are sensitive to different trends in the data . Both nef and env have significantly more sites detected with a toggling model than with a diversifying selection model . Amino acid diversity at positively selected sites ( detected with either the diversifying or toggling selection models ) is generally lower ( for example nef , Figure 5 ) than one would expect for diversifying selection . The toggling model detects positive selection at a greater proportion of these low amino acid diversity sites , although some sites with low diversity are detected with the diversifying selection model and not the toggling model , for example nef site 16 . At this site toggling selection is not detected when averaging over all potential wild-type amino acids ( Equation 3 ) , but there is significant evidence for selection with one wild type amino acid ( valine , P<0 . 001 ) , exemplifying the loss in power associated with summing over the uncertainty of the wild-type amino acid . Interestingly , some sites at which no non-synonymous substitutions are observed ( 6 , 103; Figure 3 ) show significant evidence of toggling . Serine is encoded by islands of codons ( TCN and AGY ) . At both site 6 and 103 serine is encoded by TCN and AGY codons , implying the occurrence of non-synonymous substitutions from serine to another amino acid and back to serine , despite the fact that no other amino acids are observed at this site . This is consistent with immune escape and reversion , in which escape occurs from serine to another amino acid followed by reversion to an alternative encoding of serine , at a remove of two substitutions from the original codon . However , it is also possible that mutations between these sets of serine codons could occur as a single event through doublet mutations [61] , which are not considered in our model and are a potential source of false positive results with most models of positive selection . The toggling selection that we observed in nef is consistent with previous studies demonstrating a high density of HLA-associated polymorphisms [4] , [25] , and a high level of immunogenicity and density of epitopes [62] in this gene . Several of the sites identified as toggling ( Table 4 ) map to within HLA epitopes for which there was a significant association between the presence of a viral polymorphism and presence or absence of an HLA allele , consistent with immune escape and reversion [4] . For example , site 83 has significant association with several HLA alleles ( Table 4; [4] ) . We find evidence for toggling with either of the two previously identified reversion mutations as wild type , and overall a general pattern of escape and reversion across the phylogenetic tree ( Figure S5 ) . Furthermore , the inferred times spent in the wild type and single step escape states are consistent with toggling , and indicate either the strength of selection or frequency of the host immune response . Several sites spend a high proportion of time in the wild type state ( Table 4 ) , consistent with a low frequency of the immune response in the population , or strong selection to revert upon transmission to a new host . Sites at which time is equally distributed between the wild type and escape states ( Table 4 ) suggest either an intermediate frequency of the immune response , or reduced selection pressure to revert . Finally , sites with little time in either the wild type or escaped states are likely to indicate either misidentification of the wild type state or that the evolution at the site does not fit well with a model of escape and reversion from a single immune response with a fixed wild type , or most fit state , in the absence of the immune response . In Table 4 , sites such as 83 , where the same amino acid occurs as an escape and a reversion , can be explained as resulting from multiple overlapping epitopes , such that an amino acid can be a wild type or an escape state , depending on the HLA genotype of the host [4] . This is particularly common in nef ( Figure S4 , [4] ) . Although our model , and the simulations we conducted , assumes a single wild type amino acid at each site , we still detect selective pressure at sites that have multiple potential wild type states ( Table 4 ) . In env , which is targeted by both cellular and humoral immune responses [10] we detect significant evidence for toggling with multiple potential wild type states ( P = 0 . 02 ) , at an N-linked glycosylation site ( N392A ) , but no evidence for diversifying selection at this site ( P = 0 . 92 ) . N-linked glycosylation sites are associated with binding of carbohydrates that may either be recognized by specific antibodies [63] or assist in the evasion of host antibodies through the formation of a glycan shield [6] . In particular , asparagine ( N392A ) facilitates the binding of the monoclonal antibody , 2G12 , to gp120 [63] , [64] , which suggests asparagine ( N ) is a susceptible state , but only in the presence of 2G12 . This site provides a good example of conflicting selective pressure , since asparagine is susceptible in the presence of 2G12 , but may represent an escape state in its absence , by contributing to evasion of antibody responses through the formation of a glycan shield [6] . We find a smaller proportion of positively selected sites favoring the toggling selection model for gag than for other coding regions . This is somewhat surprising since broad gag CTL responses control viremia [16] , [65] , several known protective HLA alleles target gag [13] , and fitness costs of many of the escape mutations in gag are substantial [15] , [33]–[35] . A recent study identified escape and reversion mutations through the mapping of polymorphisms ( observed longitudinally within acutely-infected individuals ) to epitopes in a pre-defined list of HLA-associated polymorphisms [25] . Results indicate an early CTL response biased towards protective HLA alleles ( B*13 , B*51 , B*57 , B*5801 ) , and for which mutations in gag were reverting most rapidly [25] . Similarly , the detection of escape and reversion mutations through HLA-associated polymorphisms [26] also found strong evidence for reverting mutations in gag . To understand the lack of support for toggling selection in gag we investigated a well-characterized gag epitope ( TW10 ) which is targeted by a protective HLA allele ( B*57 ) [17] , [34] . We detected only diversifying selection at the site of the common TW10 escape mutation ( T242N ) . To determine why this well-characterized site of escape and reversion is detected by a diversifying selection model and not by the toggling selection model we mapped the occurrence of wild type , neighboring and multi-step codon states for T242N to the phylogeny estimated from gag sequences ( Figure 4 ) , taking threonine , which is known to be the susceptible amino acid [17] , [34] as the wild type state . Escape mutations are evident at terminal branches; however there is no example on the tree of a case in which the wild type amino acid appears within a clade of escape amino acids ( which would point to reversion of an escape mutant to wild type ) . The likely reason for this is that escape and reversion happen sufficiently rapidly that the amino acids observed in neighboring sequences on the tree are uncorrelated . In such a case , we are unlikely to infer reversion to wild type , particularly because a given HLA allele occurs in only a small minority of individuals and most clades of sequences will be dominated by sequences from individuals without the HLA allele . Consequently multiple independent escapes will be a more parsimonious explanation of the data than escape followed by reversion . This is consistent with the much lower power to detect high rates of toggling simulated along typical HIV-1 trees with long terminal branches compared to the power to detect the same rate of toggling on trees with exponentially distributed branch lengths ( Table 2 , Figure S1 ) . Thus failure to detect toggling selection at site 242 of gag is likely the result of both rapid reversion at this site [25] , and long terminal branches . Sites that exhibit such rapid escape and reversion are likely to be relatively easily detected through association of HLA alleles with viral sequence polymorphisms [4] , [24] . However , we note that we compare previously detected HLA associations using a substantially larger dataset [4] , to sites detected as toggling in this analysis , in which only 100 sequences were used . Association methods will perform well when the HLA allele tested is common , but will lose power when multiple conflicting rare HLA alleles exert conflicting selective pressures . The use of a phylogenetic model to detect positive selection associated with host-immune response allows for the identification of sites at which multiple contrasting selective pressures are exerted by immune responses of low to intermediate frequencies . We found evidence of a large number of coding sites in HIV-1 where the amino acid diversity is limited , yet there is strong positive selection pressure . Intuitively , it is easy to see why a model that takes account of this should have more power to detect selection than models of diversifying selection . With a diversifying selection model a mutation away from the wild type amino acid followed by a reversion to wild type is treated the same as any other pair of non-synonymous substitutions . Whenever this pattern is observed , the toggling model accumulates much more evidence for positive selection pressure , because in the absence of selection the second mutation should be far more likely to result in a different amino acid than a reversion to the original ( random point mutations in a codon can result in any one of approximately eight different amino acids , depending on the specific codon ) . Using the synonymous substitution rate as a proxy for neutrality , we have set up a model that can detect when this toggling occurs at a greater rate than we would expect for a site that is neutral or evolving under purifying selection . We expect this model to be applicable to other host-pathogen systems in which escape from immune responses can occur at a cost to the pathogen . There is some evidence that this is likely to apply in the context of influenza A , for example , which is targeted by both cellular [66] and humoral [67] , [68] host immune responses , and appears to toggle between alternate states at some sites [67] . Furthermore , mutations that confer drug resistance to pathogens may do so at a cost to the pathogen [69] , [70] . Samples derived from drug treated and untreated individuals are likely to exhibit a pattern of toggling if the drug resistant pathogen reverts to a stable most fit state , in the absence of the drug . In the case of drug resistance mutations , models that take account of the treatment status of the sequence are likely to provide more power to detect the evolution of resistance mutations [71] . Sites that experience strong selection but limited diversity point to the limits of viral evolution . Despite coming under pressure to change at these sites , the virus continuously returns to a single or small number of fit states , which at many such sites appear to remain relatively stable over the course of viral evolution . Distinguishing sites at which there is strong positive selection to revert to the wild type is relevant for vaccine design . Vaccines targeting these sites may allow for better control of viremia by reducing replicative fitness of the viral population resulting in slower disease progression [5] , [72] . | Viruses , such as HIV , are able to evade host immune responses through escape mutations , yet sometimes they do so at a cost . This cost is the reduction in the ability of the virus to replicate , and thus selective pressure exists for a virus to revert to its original state in the absence of the host immune response that caused the initial escape mutation . This pattern of escape and reversion typically occurs when viruses are transmitted between individuals with different immune responses . We develop a phylogenetic model of immune escape and reversion and provide evidence that it outperforms existing models for the detection of selective pressure associated with host immune responses . Finally , we demonstrate that amino acid toggling is a pervasive process in HIV-1 evolution , such that many of the positions in the virus that evolve rapidly , under the influence of positive Darwinian selection , nonetheless display quite low sequence diversity . This highlights the limitations of HIV-1 evolution , and sites such as these are potentially good targets for HIV-1 vaccines . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"computational",
"biology/evolutionary",
"modeling",
"virology/immune",
"evasion"
] | 2008 | Frequent Toggling between Alternative Amino Acids Is Driven by Selection in HIV-1 |
Salinity is a major factor limiting crop productivity . Rice ( Oryza sativa ) , a staple crop for the majority of the world , is highly sensitive to salinity stress . To discover novel sources of genetic variation for salt tolerance-related traits in rice , we screened 390 diverse accessions under 14 days of moderate ( 9 dS·m-1 ) salinity . In this study , shoot growth responses to moderate levels of salinity were independent of tissue Na+ content . A significant difference in root Na+ content was observed between the major subpopulations of rice , with indica accessions displaying higher root Na+ and japonica accessions exhibiting lower root Na+ content . The genetic basis of the observed variation in phenotypes was elucidated through genome-wide association ( GWA ) . The strongest associations were identified for root Na+:K+ ratio and root Na+ content in a region spanning ~575 Kb on chromosome 4 , named Root Na+ Content 4 ( RNC4 ) . Two Na+ transporters , HKT1;1 and HKT1;4 were identified as candidates for RNC4 . Reduced expression of both HKT1;1 and HKT1;4 through RNA interference indicated that HKT1;1 regulates shoot and root Na+ content , and is likely the causal gene underlying RNC4 . Three non-synonymous mutations within HKT1;1 were present at higher frequency in the indica subpopulation . When expressed in Xenopus oocytes the indica-predominant isoform exhibited higher inward ( negative ) currents and a less negative voltage threshold of inward rectifying current activation compared to the japonica-predominant isoform . The introduction of a 4 . 5kb fragment containing the HKT1;1 promoter and CDS from an indica variety into a japonica background , resulted in a phenotype similar to the indica subpopulation , with higher root Na+ and Na+:K+ . This study provides evidence that HKT1;1 regulates root Na+ content , and underlies the divergence in root Na+ content between the two major subspecies in rice .
Salinity is a widespread limitation for agricultural productivity , especially for irrigated agriculture and coastal lowlands prone to seawater ingress [1 , 2] . By definition , salinity occurs when there is a high concentration of soluble salts in soil [3] . More than 800 million hectares worldwide is affected by salt , which accounts for 6% of the total land area [3] . Besides natural causes such as rising sea levels during the dry and wet cropping seasons , the poor quality of irrigation water and improper drainage , also collectively increases soluble salt concentration in the root zone [2 , 4] . Rice ( Oryza sativa L . ) is one of the most important crop species and is a staple food for more than half of the world’s population . Salinity is a major impediment to increasing production in many rice growing regions , including temperate and tropical environments , around the world [5 , 6] . Rice is the most salt-sensitive species among major cereal crops [3] . The susceptibility of rice to salinity stress varies with growth stages [7 , 8] . Rice is less sensitive to saline conditions at germination , active tillering and maturity stage [7 , 9 , 10] . Vegetative growth during the early seedling stage is highly sensitive to saline conditions , and often translates to reduced stand density in salt-affected fields [11 , 12] . Some rice varieties are most sensitive to salt stress during early tillering and panicle initiation stages of growth [8] . This developmentally-dependent salt-sensitivity , in context of yield reduction , was associated with a significant decrease in tiller number per plant , spikelet number per panicle , fertility , panicle length and primary branches per panicle [7 , 8 , 13 , 14] . Despite the overall high salt-sensitivity of rice , several studies have demonstrated that considerable natural variation for salinity tolerance exists in rice germplasm [15 , 16] . Traditional landraces or cultivars such as ‘Pokkali’ , ‘Nona Bokra’ , ‘Cheriviruppu’ and ‘SR26B’ have originated or have been selected in coastal regions and are more tolerant to saline conditions [5 , 12 , 17 , 18] . Quantitative trait loci ( QTL ) underlying salinity tolerance have undergone intensive investigations [16 , 18–24] . Although many QTL have been identified across the rice genome , the most well-characterized QTL is Saltol/SKC1 , which harbors HKT1;5 , on the short arm of chromosome 1 [18 , 20–22] . The SKC1 gene ( HKT1;5 ) was subsequently cloned from a salt-tolerant indica landrace , ‘Nona Bokra’ , and encodes a Na+ transporter that regulates shoot Na+:K+ homeostasis during salt stress [25] . Salinity tolerance is a complex polygenic trait , and several physiological mechanisms , including tissue tolerance , sodium exclusion , osmotic stress tolerance , and tissue-specific sodium sequestration can be utilized for improving salinity tolerance [3] . While many QTL have been reported for salinity tolerance in rice , few studies have identified the causal genes and confirmed the importance of these resources for improving salinity tolerance . Hence , the genetic resources ( QTL and genes ) available to rice breeders for improving salt tolerance are limited . Identification of loci that regulate salt accumulation and/or distribution will enable the introgression of favorable genic combinations and greatly accelerate the development of robust salt-tolerant rice varieties . Genetic variation within the rice germplasm collection can be utilized to identify important loci controlling variation for salinity tolerance through genome-wide association ( GWA ) analysis , which provides greater mapping resolution and evaluates greater allelic diversity compared to linkage mapping strategies [16 , 24 , 26 , 27] . In this study , we used GWA to investigate the genetic architecture of salinity tolerance using the Rice Diversity Panel 1 ( RDP1 ) [28–30] . RDP1 is comprised of 421 accessions collected from 85 countries and was developed to identify alleles associated with morphological , physiological and agronomic traits [28–30] . RDP1 captures much of the diversity in the rice germplasm collection worldwide [28–30] . We used several quantitative measures to characterize the rice diversity panel for physiological and morphological responses to salinity stress . Here , we show that allelic variants of a sodium transporter ( HKT1;1 ) underlie natural variation for root Na+ content in rice . Using a multifaceted approach , we demonstrate that variants within HKT1;1 alter Na+ transport and can explain the basis of divergence in root Na+ content between the indica and japonica subspecies of cultivated rice .
To examine the relationships between each of the eight traits , Pearson correlation analysis was performed across all accessions . No significant relationship was observed between shoot biomass and ion traits ( S3 Table ) . Moreover , root and shoot ion content showed no significant relationship when the analysis was performed with all accessions ( S3 Table ) . Due to the deep population structure in rice , correlation analysis was also performed for each of the five major subpopulations in RDP1 ( here , admixed accessions were considered a separate subpopulation; S4–S8 Tables ) [29 , 31] . Root growth response ( the ratio of root biomass in salt to control ) showed a weak negative correlation with shoot Na+:K+ in admix and tropical japonica ( trj ) accessions ( S4 and S5 Tables , respectively ) . In trj , aus , and tej subpopulations significant , albeit weak , positive correlations were observed between shoot Na+ and root Na+:K+ ( S5–S7 Tables ) . Comparisons between each of the subpopulations showed significant differences for shoot and root Na+ , K+ and Na+:K+ ( Fig 1 ) . Indica accessions exhibited significantly higher root Na+ content and Na+:K+ compared to the other four subpopulations ( Fig 1A and 1B ) . Significantly lower shoot Na+ and Na+:K+ were observed in indica and aus subpopulations compared to temperate japonica ( tej ) , tropical japonica ( trj ) and admix accessions ( Fig 1C and 1D ) . These results suggest that there are inherent differences in root and shoot ion homeostasis between subpopulations , with indica accessions generally displaying higher root Na+ and Na+:K+ , and indica and aus accessions exhibiting lower shoot Na+ content and Na+:K+ . To identify loci associated with salt tolerance-related phenotypes , GWA mapping was conducted using 397 , 812 SNPs and eight salinity-related phenotypes collected on 365 rice accessions ( Fig 2; S1–S4 Figs ) [32] . A linear mixed model implemented in EMMA was used for the association analysis [33] . A total of 90 highly significant QTL ( 245 SNPs; p < 10−5 ) were identified for salinity-related traits with the strongest associations detected for root Na+ content followed by root Na+:K+ ( Fig 2A and 2B , respectively ) . A region located at ~30 . 6 Mb on chromosome 4 was found to have the largest effect and explained 15% of the phenotypic variation beyond that explained by population structure for root Na+:K+ ( S2 File ) . An additional 25% of the phenotypic variance for root Na+:K+ was explained by population structure suggesting that this trait may be heavily influenced by the differences between the major subpopulations in rice . For each trait , the number of significant QTL ranged from 3–24 , with the highest number of QTL identified for root biomass ratio ( 24 QTL ) . Many of these QTL had small effects , explaining ~4 . 7–7 . 5% of phenotypic variation for root growth response . These results indicate a polygenetic architecture for root growth responses to salinity . A large number of QTL with minor effects ( explaining < 7% phenotypic variation ) were identified for shoot Na+ content and Na+:K+ , suggesting a polygenic architecture for these traits in rice . This trend was observed for all traits , with the exception of root Na+ and Na+:K+ , suggesting that salinity tolerance in terms of growth and shoot ion homeostasis in rice is regulated by many loci with small effects . Twenty QTL were commonly detected for two or more traits . Shoot Na+ and Na+:K+ showed the largest number of shared QTL ( 12 QTL ) , however much of this similarity is likely driven by the strong phenotypic and genetic correlation observed between these traits within tissues ( Table 1; S2 Table ) . The most significant QTL for root Na+ content and Na+:K+ , named Root Na+ Content 4 ( RNC4 ) , spans a region of ~575 Kb ( 30 , 481 , 871–31 , 057 , 205 ) on chromosome 4 ( Fig 3A ) . To characterize this region further and identify candidate genes that may be underlying natural variation for this trait , this region was segmented into haplotype blocks and the contributions of each block to root Na+ content and Na+:K+ were determined using ANOVA . A total of 36 blocks were identified in this 575 Kb region ( S5–S8 Figs ) . A single 9 . 7 Kb block from 30 , 727 , 920–30 , 737 , 580 bp was found to have the largest contribution to root Na+ and Na+:K+ , with approximately 16% of phenotypic variation explained for root Na+ and 17 . 5% explained for root Na+:K+ ( Fig 3B; Table 2 ) . The region spanning from the 5’ boundary of block 2 to the 5’ boundary of block 3 harbored only two genes , both of which were annotated as sodium transporters , HKT1;1 and HKT1;4 ( LOC_Os04g51820 and LOC_Os04g51830 respectively; Fig 3C ) . To further characterize HKT1;1 and HKT1;4 , the expression patterns of both genes were examined in twelve tissues at three developmental time points ( early seedling , early tillering and anthesis ) . The expression of both HKT1;1 and HKT1;4 were higher in leaf tissue compared to root tissue during the seedling stage ( Fig 4 , S9 Fig ) . However , the expression of HKT1;1 and HKT1;4 within aerial tissues differed across developmental stages . HKT1;1 was highly expressed in the leaf blade and leaf sheath during the early seedling stage ( Fig 4A ) . HKT1;4 , on the other hand displayed the highest expression during reproductive stage , specifically in culm tissue at ~7 days after anthesis ( Fig 4B ) . To examine whether transcript abundance may be a component of the phenotypic differences observed between allelic groups at RNC4 , RNA sequencing was performed on shoot tissue of 32 accessions in control and saline conditions , and the expression of both genes was compared between allelic groups at RNC4 ( Fig 4C and 4D; S3 File ) . For both genes , accessions that showed higher root Na+ content ( T allele at SNP-4-30535352 ) , also showed higher expression in both control and saline conditions compared to accessions with low root Na+ content ( G allele at SNP-4-30535352 ) . The expression of HKT1;1 was approximately 92% higher in high root Na+ lines in control conditions compared to low root Na+ lines , while a 44% higher expression was observed in saline conditions ( Fig 4C ) . While the overall expression level was much lower for HKT1;4 compared to HKT1;1 , a similar trend in gene expression was also observed between the two allelic groups of HKT1;4 ( Fig 4D ) . A 46% and 57% higher expression was observed in lines with high root Na+ content compared to lines with low root Na+ content in control and saline conditions , respectively ( Fig 4D ) . These results suggest that differences in expression of HKT1;1 and/or HKT1;4 may be a component underlying variation in root Na+ content at RNC4 . To determine if these two HKTs within RNC4 regulate Na+ content during salinity stress at the early tillering stage , three independent RNA-interference ( RNAi ) lines were generated for both genes . Transcript levels in the leaf tissue was reduced by approximately 2 . 9–6 . 2 and 2–2 . 2 fold in HKT1;4RNAi and HKT1;1RNAi lines compared to wild-type ( WT ) ‘Kitaake’ , respectively ( S10 Fig ) . A 9 dS·m-1 ( ~90 mM NaCl ) was gradually imposed at 10 DAT for 14 days to replicate the stress treatment for the large-scale screening . Reduced expression of HKT1;1 had severe phenotypic effects on shoot and root ion homeostasis as well as shoot and root growth under salinity . Shoot Na+ and Na+:K+ were 31–41% and 27–41% higher , respectively , in HKT1;1RNAi lines compared to WT ( p < 0 . 0001 , p < 0 . 05 respectively; Fig 5A–5C ) . A 21–27% reduction in root Na+ was observed in HKT1;1RNAi and 31–33% lower root Na+:K+ was observed in HKT1;1RNAi compared to WT ( p < 0 . 05 and p < 0 . 0001 , respectively; Fig 5D–5F ) . In RNAi plants , shoot and root growth was reduced by 44–55% and 78–72% respectively in salt treated plants relative to those in control conditions , while in WT a 26% and 45% reduction in shoot and root growth , respectively was observed in WT plants ( S11 Fig ) . No differences were observed between HKT1;4RNAi and WT plants ( Fig 5 , S11 Fig ) . These results suggest that HKT1;1 may influence the shoot and root Na+ content during the early tillering stage , and is likely the causal gene underlying RNC4 . To determine whether there were sequence differences between allelic groups at RNC4 , sequencing data was mined for variants in HKT1;1 ( S4 File ) . Nine variants were detected in the coding region of HKT1;1 with four SNPs resulting in non-synonymous amino acid substitutions in HKT1;1 ( Fig 6; S12 Fig ) [34] . Of the nine variants , only M4 displayed a significant deviation from the expected frequency in the minor allelic group , indicating that it is unlikely to be important for the high root Na+ phenotype exhibited by accessions in the minor allelic group ( Pearson’s chi squared test , p < 1 . 26 x 10−5 ) . The remaining three non-synonymous mutations ( M3 , M5 and M8 ) were detected in thirteen accessions all belonging to the minor allelic group , which is characterized by high root Na+ content , at the most significant SNP for root Na+ content ( SNP-4-30535352 ) . The higher frequency of these three non-synonymous mutations observed in minor allele accessions ( T ) suggests that allelic variation in HKT1;1 could be a component in the genetic basis of the observed difference in root Na+ content between major and minor alleles . No sequence differences in HKT1;4 were observed between allelic groups at RNC4 . To characterize the biophysical properties of the two major isoforms identified between allelic groups at RNC4 , HKT1;1 was isolated from two representative accessions , ‘Nipponbare’ and ‘Zhenshan 2’ , which have the reference and the three non-synonymous mutations at the three locations ( M3 , M5 and M8 ) , respectively ( S12 Fig ) . At the transporter structure level , two non-synonymous SNPs ( M8 and M5 ) lead to amino acid substitutions in cytosolic regions of HKT1;1: proline to leucine within the N-terminal cytosolic region , phenylalanine to serine in the cytosolic loop between the first and second transmembrane segment-pore region-transmembrane segment ( MPM ) domains ( Fig 6A and 6B; S12 Fig ) . The third non-synonymous SNP results in an asparagine to serine substitution in the external part of the pore-forming region of the second MPM ( Fig 6A and 6B; S12 Fig ) . Functional analysis was performed by voltage-clamp electrophysiology using Xenopus oocytes for the two variants of HKT1;1 ( Fig 6 ) . The amount of expressed transporters targeted to the oocyte membrane was similar for the two variants , as indicated by the mean GFP fluorescence intensity emitted by either of the tagged transporters at the membrane ( Fig 6C and 6D ) . In agreement with previous reports , both isoforms of HKT1;1 displayed low affinity , high Na+ versus K+ selectivity , inward rectifying activity and no time-dependent kinetics ( Fig 6E; S13 and S14 Figs ) [34] . However , the two allelic variants displayed considerable differences in Na+ transport activity . The variant from the accessions with high root Na+ , HKT1;1-Zh , exhibited higher inward ( negative ) currents compared to that from ‘Nipponbare’ , HKT1;1-Ni ( Fig 6E and 6F ) , essentially due to a less negative voltage threshold of inward rectifying current activation by 20–25 mV in all ionic conditions ( Fig 6E and 6F , S13 and S14 Figs ) . This latter feature was especially expected to favor transport activity of HKT1;1-Zh compared to HKT1;1-Ni during salinity stress where the high concentration of Na+ in the apoplast results in a depolarization of the plasma membrane [35 , 36] . Thus , at a weak negative voltage , the current could be more than six-fold higher in HKT1;1-Zh , compared to HKT1;1-Ni ( Fig 6G ) . To determine if these differences in transport activity have physiological effects in vivo , native overexpression lines were generated for each variant ( HKT1;1Ni , HKT1;1Zh ) . A ~4 . 3 kb genomic region was isolated from ‘Nipponbare’ and ‘Zhenshan 2’ , which included the entire CDS of HKT1;1 and a 1 . 9 kb promoter , and was expressed in ‘Kitaake’ . The endogenous HKT1;1 in ‘Kitaake’ , at the protein level , is identical to HKT1;1-Ni , and thus lacks the three non-synonymous variants . Two independent transformants for each variant ( HKT1;1Ni , HKT1;1Zh ) , each containing only a single copy of the transgene , were evaluated under a 9 dS·m-1 salt stress for a period of two-weeks . The expression of HKT1;1Zh resulted in an increase in root Na+ and Na+:K+ compared to HKT1;1Ni , while no differences were observed between variants for root K+ ( Fig 7 ) . A considerable increase in both root Na+ and Na+:K+ , as well as a reduction in root K+ was observed in both native overexpression lines ( HKT1;1Ni and HKT1;1Zh ) compared to ‘Kitaake’ , which is opposite to the root phenotype observed in the HKT1;1RNAi lines ( Fig 7 ) . However , expression under the native promoter had no effects on shoot Na+ or Na+:K+ ( Fig 7 ) . Together , these results provide further evidence that HKT1;1 is responsible for the higher root Na+ phenotype , and that the difference in Na+ content between the allelic groups at RNC4 is likely due to functional differences in Na+ transport by HKT1;1 alleles , with the three non-synonymous SNPs in HKT1;1-Zh resulting in higher Na+ transport activity . A difference in allele frequencies of the three non-synonymous mutations in HKT1;1 was observed between the major subpopulations in the 32 sequenced accessions of RDP1 . However , since it was difficult to examine subpopulation differentiation with this small of a sample size , the differences were explored in more depth using resequencing data from a larger diversity panel of 3 , 024 accessions [37] . A total of 206 SNPs spanning a ~38 Kb region around HKT1;1 was used for haplotype analysis . In agreement with the allele frequency observed in the 32 accessions of RDP1 , a clear differentiation could be observed between indica ( ind1A , ind1B , ind2 , ind3 and indx ) and japonica ( temp , trop1 , trop2 and japx ) subspecies in the larger diversity panel ( Fig 8 ) . Haplotypes H1 , H5 and H8 harbored the three non-synonymous alleles and were found in nearly 85% of the indica accessions . The sequence similarity between high root Na+ haplotypes was very high , ranging from ~88–94% identity . Haplotypes containing high root Na+ alleles of HKT1;1 were also found in the japonica ( temp , trop1 , trop2 and japx ) , aus and aromatic subpopulations , albeit at a much lower frequency ( 0–3% ) . In contrast , haplotypes H2 , H3 , H4 , and H7 were found predominately in the japonica accessions and lacked the high root Na+ allelic form of HKT1;1 . Within the low root Na+ group , haplotypes exhibited high sequence similarity ( ~65–94% ) . Given the clear divergence between indica and japonica for HKT1;1 haplotypes and the effects of HKT1;1 isoforms on root Na+ content , collectively these results strongly suggest that a significant proportion of the difference between rice subpopulations in root Na+ in RDP1 is due to differences in frequency of HKT1;1 variants . Given the contrasting haplotype frequencies of high and low root Na+ variants of HKT1;1 between subpopulations of cultivated rice , we explored the origins of these haplotypes by examining their frequencies in a collection of 446 Oryza rufipogon accessions collected throughout South and Southeast Asia [38] . These accessions represent three major populations ( Or-I , Or-II and Or-III ) and provide an adequate representation of the ancestral populations of cultivated rice [38] . Two haplotypes ( H1 and H5 ) were identified that harbored the high root Na+ variants of HKT1;1 , and were found in nearly 70% of the O . rufipogon accessions . The H1 haplotype displayed the highest frequency in the Or-II clade and was also found in the majority of indica accessions , suggesting that the indica allele is likely derived from Or-II . In contrast , two haplotypes ( H2 and H6 ) were identified with the low root Na+ variant and were present in only 19% of the O . rufipogon accessions . The H6 haplotype was the most frequent and present in 18% of the O . rufipogon accessions , but absent from the japonica cultivated rice accessions . In contrast , H2 occurred at high frequency ( 44% ) in cultivated japonica , particularly the tropical japonica subpopulation , suggesting that H2 is potentially the ancestral haplotype for the japonica subspecies . Interestingly , the haplotypes found at high frequencies in the japonica subspecies were present at considerably lower frequencies in wild rice accessions ( the highest frequency observed was 0 . 16 ) , indicating that these haplotypes in japonica subspecies may be derived from a relatively small population of wild progenitors .
RNC4 harbors two Na+ transporter genes , HKT1;1 and HKT1;4 . HKTs are well-known components of salinity tolerance in several plant species including rice ( HKT1;5 is likely the causal gene in the SalTol QTL ) , wheat and Arabidopsis [25 , 43–51] . Although both HKT1;1 and HKT1;4 displayed significant differences in expression between allelic groups at RNC4 , several key findings suggest that HKT1;1 is more important for root Na+ content during the early tillering stage and for the salinity level imposed in our experimental set-up . First , the genes are expressed at different developmental stages . HKT1;1 was expressed at the highest levels in blade and leaf sheath tissues of seedlings , while HKT1;4 showed the highest expression in culms of mature plants ( Fig 4B ) . Second , reduced expression of HKT1;1 in transgenic RNAi lines resulted in a greater sensitivity to salinity compared to WT , while HKT1;4RNAi and WT plants displayed similar phenotypes under salinity ( Fig 5 ) . In a recent report , Suzuki et al showed that HKT1;4 is primarily expressed in peduncles during flowering ( 14 week old plants ) and , through RNAi , showed that HKT1;4 is primarily involved in Na+ homeostasis only during the reproductive phase [51] . Since the current study was conducted during the early tillering stage ( < 1 month old plants ) , it is unlikely that this gene would have an impact on salinity tolerance in this developmental window . Finally , increased expression of HKT1;1 with the native promoter resulted in higher Na+ in root tissue , which is identical to the phenotype associated with RNC4 . Together , these data suggests that HKT1;1 is the causal gene underlying RNC4 and contributes to root Na+ content during the early tillering stage . The differences in Na+ content observed between allelic groups at RNC4 is likely due to functional differences in Na+ transport by HKT1;1 alleles , with the three non-synonymous SNPs in HKT1;1-Zh resulting in higher Na+ transport activity . Na+ transport occurred at less negative voltages in the isoform found in accessions with high root Na+ compared to that isolated from accessions with low root Na+ . During salt stress , the accumulation of Na+ in the apoplastic space increases HKT1;1 Na+ transport activity , the apparent affinity for Na+ of this transporter type is particularly low ( Km ~ 80 mM; S12 Fig ) , but in the meantime , uptake of Na+ from the apoplast results in membrane depolarization , which reduces HKT1;1 conductance due to inward rectification property [34] . In the high root Na+ isoform , a higher ( less negative ) voltage threshold of current activation was observed , for instance in the presence of 10 mM external Na+ noticeable Na+ transport was observed between -75 and -90 mV , while in the low root Na+ isoforms , activation occurred at more negative voltages ( Fig 6C and 6D ) . Thus , lower Na+ concentrations are required to induce Na+ uptake in the high root Na+ isoform of HKT1;1 . In summary , the enhanced ability to transport Na+ in accessions harboring the high root Na+ isoform of HKT1;1 is likely due to the early activation of Na+ transport . Indica varieties have long been recognized to as a source of salt tolerance , largely due to Na+ exclusion from leaf tissue . The most widely used QTL , SalTol , was identified by Lin et al . using a biparental population derived from the salt tolerant indica landrace ‘Nona Bokra’ and sensitive japonica variety ‘Koshihikari’ [21] . Tolerance mediated by SalTol is associated with the exclusion of Na+ from shoot tissue , through the removal of Na+ from the xylem and sequestration in xylem parenchyma cells in the root tissue [18 , 25] . While several studies have demonstrated that the indica subspecies harbors many varieties exhibiting high shoot Na+ exclusion ability , tolerant alleles in SalTol have only been utilized from a few indica landraces , and it is likely that other loci are contributing to Na+ exclusion in the indica subspecies [12 , 52] . In agreement with previous studies , a considerable difference among the five subpopulations was observed in root and shoot Na+ content and Na+:K+ , with indica accessions generally displaying higher root Na+ content and Na+:K+ , as well as slightly lower shoot Na+ and Na+:K+ . The relationship between root and shoot ion traits ( specifically Na+ and Na+:K+ ) differed considerably within each of the subpopulations . For instance , positive correlations were observed between tissues for Na+ and Na+:K+ in the tej , trj and aus subpopulations . However , in the indica and admix subpopulations no relationships were observed between tissues for Na+ and Na+:K+ . The moderate positive genetic correlation observed between tissues across all accessions of RDP1 indicates that these traits may be regulated in part by common genes . However , this may be highly dependent on the subpopulation . The high frequency of the Na+ accumulating isoform for of HKT1;1 in the indica and admix subpopulations may “uncouple” the relationship between tissues for Na+ and Na+:K+ . The contrasting root Na+ content observed between indica and japonica accessions of RDP1 is consistent with the differences in transport activity and the frequencies of the high and low root Na+ isoforms of HKT1;1 . The haplotypes of HKT1;1 could be clearly separated into two distinct groups , corresponding to the japonica ( H2 , H3 , H4 and H7 ) and indica predominate forms ( H1 and H5 ) . The high root Na+ haplotypes ( H1 , H5 and H8 ) were most frequent in Oryza rufipogon , while the low root Na+ haplotypes were identified in only ~31% of the Oryza rufipogon accessions and were nearly fixed in japonica accessions . The two major subspecies of Oryza sativa were domesticated from two geographically isolated populations of Oryza rufipogon [38 , 53] . The low diversity in japonica germplasm reported by several studies is consistent with a bottleneck during domestication , and suggests that the japonica subspecies may be derived from a relatively small founding population of Oryza rufipogon [38 , 54–56] ( S15 Fig ) . Although the high root Na+ isoform was found in ~30% of the Or-III subpopulation , the founding subpopulation of Oryza rufipogon , it is plausible that the bottleneck experienced during domestication may have resulted in the loss of the high root Na+ HKT1;1 variant from japonica subspecies . Like many other HKT members , HKT1;1 is well-expressed in the vascular tissue of the shoot , and to a lesser extent in the root [34 , 48 , 50] . In the current study , HKT1;1RNAi lines were more sensitive to salt stress , and exhibited higher shoot Na+ content and lower root Na+ content compared to WT plants . The expression patterns of HKT1;1 , as well as the phenotypes exhibited by HKT1;1RNAi lines are in agreement with those reported by Mäser et al for AtHKT1;1 in Arabidopsis , suggesting that the genes may have similar physiological functions [57] . Like HKT1;1RNAi , athkt1;1 knockout mutants are hypersensitive to salt stress and exhibit higher shoot Na+ and lower root Na+ [57 , 58] . In rice , Wang et al showed that hkt1;1 knockout mutants accumulate Na+ in xylem sap and display a reduction in Na+ in phloem sap compared to WT [50] . These observations together with the observed accumulation of Na+ in shoot tissue prompted Wang et al to suggest that HKT1;1 may regulate sodium exclusion from the shoot of seedlings possibly through xylem-to-phloem or parenchyma-to-xylem transfer of Na+ [50] . Such xylem-to-phloem transfer of Na+ by a HKT member has been debated in Arabidopsis [44 , 45 , 58] . In agreement with hkt1;1 mutant phenotype reported by Wang et al , athkt1;1 knockout mutants also exhibit higher xylem Na+ and lower phloem Na+ [44 , 45 , 50] . Although AtHKT1;1 was initially proposed to function in the recirculation of Na+ from the root to the shoot ( via loading of Na+ into the phloem in the shoots ) , Sunarpi et al later proposed that AtHKT1;1 functions primarily in the removal of Na+ from the xylem sap and eventually to the phloem through symplastic diffusion [44 , 45] . However , a later study showed that AtHKT1;1 was primarily involved in the retrieval of Na+ from the xylem in root tissue , and suggested that the function of AtHKT1;1 in shoot tissue may be dependent on the experimental conditions ( discussed in [3] ) [58] . For the case of HKT1;1 in rice , further studies ( outside the scope of this manuscript ) are required to provide the exact mechanism for the regulation of root Na+ content and/or shoot Na+ exclusion . Given the phenotypes exhibited by HKT1;1RNAi lines , as well as the proposed function described by Wang et al . , the absence of an association of HKT1;1 with shoot Na+ or Na+:K+ is surprising [50] . If HKT1;1 regulates retrieval of Na+ from the parenchyma or xylem in shoot tissues , one would expect that the high root Na+ allele would also have a large impact on shoot Na+ content . However , the concentration of Na+ in shoot tissue is likely more dependent on the amount of Na+ loaded into the xylem , and thus mechanisms which limit the delivery of Na+ to xylem stream would likely be more effective mechanism for shoot Na+ exclusion [3] . Without an effective mechanism to limit Na+ entry into the xylem stream in the root , very high expression of HKT1;1 , or a highly active variant of HKT1;1 would likely be necessary to reduce shoot Na+ content . While the indica ( high root Na+ content ) variant of HKT1;1 displayed higher transport activity compared to japonica variant ( low root Na content ) , it is likely that these biophysical differences are not sufficient to have an impact on shoot Na+ content . Other members of the HKT family have been identified that are expressed in the vascular tissue of the root , and primarily function to remove Na+ from the xylem to limit the delivery of Na+ to the shoot . In rice , this function is largely achieved through the action of HKT1;5 [25 , 59] . In contrast to HKT1;1 , HKT1;5 is mostly expressed in the root and therefore is essentially involved in xylem sap desalinization [25] . In the current study , the SalTol QTL that harbors SKC1/HKT1;5 explained only a small portion of phenotypic variation for shoot Na+ and shoot Na+:K+ ( ~6%; SNP-1 . 11472400 ) . Several studies have identified alleles within SKC1/HKT1;5 that are associated with Na+ exclusion and salt tolerance , but it is unclear whether the effects of these alleles are as strong as those reported by Gregorio et al . and Bonilla et al . [18 , 20 , 21 , 23 , 25 , 60] . Given the small effect of this QTL in the current study , as well as the large number of QTL identified for shoot Na+ and Na+:K+ , it is likely that natural variation for shoot Na+ and Na+:K+ involves additional genetic components in addition to SKC1/HKT1;5 .
This study included 383 of the 421 original RDP1 accessions , as well as seven check varieties [28–30] . Accessions were obtained from the USDA-ARS Dale Bumpers Rice Research Center and purified through single seed descent before they were phenotyped . Thirty-eight accessions of RDP1 were not included because of lack of seed availability and/or poor seed quality . The set of accessions from RDP1 included 77 indica , 52 aus , 92 temperate japonica , 85 tropical japonica , 12 groupV/aromatic , and 56 highly admixed accessions ( nine accessions were unassigned ) , according to the classification by Famoso et al [29] . A total of 365 accessions from RDP1 were genotyped using 700 , 000 SNPs [32] . Filtering SNPs based on minor-allele frequency ( MAF > 0 . 05 ) left ~397 , 812 high quality SNPs ( depending on the trait analyzed ) [32] . Previous results indicated LD decays to 0 . 20 between 0 . 5–1 . 0 Mb , indicating the marker density provided by the SNP array has suitable power to detect linked causal variants of moderate to large effect QTL [32] . The experiment was conducted between July to Sep 2013 in a controlled green house at Lincoln , NE . Rice ( Oryza sativa ) seeds were dehusked manually and germinated in the dark for two days at 28°C in a growth cabinet ( Percival Scientific ) . Twelve hours before transplanting seeds were exposed to light ( 120 μmol m−2 s−1 ) . The green house conditions were as follows: photoperiod ( 16:8 day:night ) , temperature 25–28°C and humidity 50–80% . Seedlings were transplanted into the pots filled with Turface ( Profile Products , LLC ) and were grown in tap water for four days after transplanting . For the remainder of the experiment the plants were supplemented with half strength Yoshida solution ( pH 5 . 8 ) [61] . Salt treatment was applied as described previously by Walia et al . with minor modifications [62] . Briefly , NaCl was mixed with CaCl2 in a 6:1 molar ratio and was added after 10 d of seedling growth . The stress treatment was started at 2 . 5 dS·m-1 which increased gradually up to 9 . 5 dS·m-1 in 4 steps over a period of four days ( ~2 dS·m-1 or 20 mM NaCl per day ) to avoid any osmotic shock to the plants . The stress treatment was stabilized at 9 . 5 dS·m-1 for next two weeks . The nutrient solution pH and electrical conductivity ( EC ) were monitored and maintained twice daily . The pH of the nutrient solution was maintained at 5 . 8 using H2SO4 and KOH . Root and shoot samples were collected separately and rinsed 3 times in tap water and once in deionized water to remove excess NaCl at the completion of the experiment ( 14 days of 9 . 5 dS·m-1; 28 days after transplant ) . The samples were oven dried at 60°C for one week prior to measuring root and shoot biomass . Shoot and roots from two plants were taken for biomass measurement . For the large-scale screening of RDP1 dried shoot samples were ground and 200–300 mg of total material was digested with 0 . 1N Nitric acid ( Fisher Scientific ) at 70°C for 8 hrs . Root samples were weighed and digested without any grinding . Samples were diluted and cation ( Na+ and K+ ) concentrations in the plant extract were determined with appropriate standard by dual Flame photometry ( Cole Parmer , USA ) . Data was combined across periods and a linear model was fit to calculate adjusted means for individual accession using the PROC GLM procedure of the Statistical Analysis System ( SAS Institute , Inc . ) . The linear model included period ( i . e . , June-July or Aug-Sept ) , replication nested within period , tub nested within replication , accession , and accession-by-period interaction . For the purpose of estimating variance components , a second similar linear model was fit using PROC MIXED in SAS . This time , all effects were assumed to be random effects . Broad-sense heritability ( H2 ) on an entry-mean basis was calculated as H2=σG2/ ( σG2+σGP2/2+σe2/6 ) Where σG2 is the variance among accessions , σGP2 is the accession-by-period interaction variance , and σe2 is the error variance . In this context , the divisor 2 is equal to the number of periods and the divisor 6 is equal the number of replications per period ( three ) multiplied by the number of periods . Broad-sense heritability provides a sense of how much of the total variation observed is due to genetic variation among accession , and indicates the power of GWAS . Marker-trait associations were tested using the linear mixed model y = Xβ + Cγ + Zu + e where y is a vector of phenotype; β is a vector of fixed marker effects; γ is a vector of principal component ( PC ) effects fit in order to account for population structure; u is a vector of polygenic effects caused by relatedness; e is a vector of residuals; X is a marker incidence matrix relating β to y; C is an incidence matrix relating γ to y which consists of the first four principal components ( PCs ) resulting from a PC analysis; Z is the corresponding design matrix relating y to u . It is assumed u∼MVN ( 0 , Kσu2 ) and e∼MVN ( 0 , Iσe2 ) where K is a standardized kinship matrix estimated using an allele-sharing matrix calculated from the SNP data . The above model was implemented using the efficient mixed-model association ( EMMA ) algorithm of Kang et al [33] . The method published by Li and Ji was used to determine a comparison-wise error rate to control the experiment-wise error rate [63] . Briefly , the correlation matrix and eigenvalue decomposition among 397 , 812 SNPs were calculated to determine effective number of independent tests ( Meff ) . The test criteria was then adjusted using the Meff with the Sidak correction below αp=1– ( 1-αe ) 1/Meff , where αp is the comparison-wise error rate and αe is the experiment-wise error rate [64] . An αe = 0 . 05 was used in this study . Analysis of variance ( ANOVA ) was used to estimate proportion of phenotypic variance accounted for by significant SNPs after adjusting for population structure effects . A 200 kb window was used to define groups of significant SNPs tagging the same locus . Only the most significant SNP within a 200 kb window was used to tag that locus . The percent variation explained by each significant SNPs was determined by comparing the linear models , y = Xβ + Cγ + e , and y = Cγ + e , where β is the SNP effect; γ is a vector of PCs effects to account for population structure; X is a vector of SNP genotypes; C is an incidence matrix relating γ to y which consists of the first four principal components ( PCs ) . Therefore , the effect of each SNP is reported after accounting for the effects of population structure . Genetic correlations between traits were estimated with and without correcting for population structure and family relatedness . The rationale behind correcting genetic correlations for population structure is to measure the correlation independent of long-range LD between loci caused by population structure [65] . To accomplish this , a multivariate mixed model was fit as described by Wisser et al . including all traits as response variables; fixed experimental design effects ( replication and tub nested within replication ) ; fixed population structure effects modeled using the four PCs as above; random polygenic effects modeled using the kinship matrix as in the GWAS model described above; and random residuals assumed to independent and identically distributed [65] . Restricted maximum likelihood implemented in ASReml-R v . 3 . 0 was used to estimate genetic and residual variances , and genetic and residual covariances among traits [66] . Estimates of genetic variances and covariances were used to calculate genetic correlations among traits . For estimation of genetic correlations uncorrected for population structure , the same methods were used except population structure and polygenic effects were not included in the mixed linear model . Haplotype blocks were constructed using the four gamete method ( 4gamete ) implemented in the software Haploview [67] . The method creates block boundaries where there is evidence of recombination between adjacent SNPs based on the presence of all four gametic types . We used a cut-off of 2% , meaning that if addition of a SNP to a block resulted in recombinant alleles at a frequency exceeding 2% , the SNP was not included in the block . To examine the frequency of high and low root Na+ forms of HKT1;1 in a set of 3 , 023 cultivated rice and 446 Oryza rufipogon accessions , a set of 206 SNPs was extracted from a ~37 kb ( 30 , 700 , 524–30 , 737 , 580 ) region on chromosome 4 . Sequence data for the cultivated rice was obtained from ~9 million genome-wide SNPs generated by the 3000 Rice Genomes Project ( 3K RGP ) [37] . The 206 SNPs for Oryza rufipogon was obtained from riceHap3 ( www . ncgr . ac . cn/ricehap3/ ) [38] . Since SNPs were mapped to different genome builds ( IRGSP4 . 0 to IRGSP1 . 0 for 3kg and RiceHap3 , respectively ) , the coordinates were converted by aligning a 37 kb region from IRGSP4 . 0 to IRGSP1 . 0 using BLAT [68] . Haplotype block analysis was performed using the 4Gamete rule , with a cutoff of 1% in Haploview [67] . The frequency of each haplotype within in each subpopulation was determined in R . A haplotype network for this 37 kb region was built with PopArt [69] . Nucleotide diversity ( π ) was determined at each position for indica ( ind1A , ind1B , ind2 , ind3 and indx ) , japonica ( temp , trop 1 , trop 2 , and japx ) and wild rice using the "site-pi" function in VCFtools [70] . For gene expression analysis , plants were grown in a controlled environment growth chamber . Temperatures were maintained at 28°C and 25°C in day and night respectively , relative humidity was maintained at 60% in both day and night . Lighting was maintained at 800 μmoles·m−2·s−1 using high pressure sodium lights ( Phillips ) . Seeds preparation and salt treatment were performed as described above . Eight day ( four days after transplant ) old rice seedlings were subjected to 6 dS·m-1 for a period of 24h . Salinity stress was increased to 6 dS·m-1 gradually in two 3 dS·m-1 intervals over a period of 24h . After 24h of 6 dS·m-1 , aerial parts of the seedlings were excised from the roots and frozen immediately in liquid nitrogen . The samples were ground with Tissuelyser II ( Invitrogen ) and total RNA was isolated with RNAeasy isolation kit ( Qiagen ) according to manufacturer’s instructions . On-column DNAse treatment was performed to remove genomic DNA contamination ( Qiagen ) . Sequencing was performed using Illumina HiSeq 2500 . Sixteen cDNA libraries were combined in each lane . After being examined using the package FastQC , short reads , obtained from Illumina 101-bp single-end RNA sequencing , were screened and trimmed using Trimmomatic to ensure each read has average quality score larger than 30 and longer than 15 bp [71 , 72] . The trimmed short reads were mapped against to the rice genome ( Oryza sativa MSU Release 6 . 0 ) using TopHat ( v . 2 . 0 . 10 ) , allowing up to two base mismatches per read . Reads mapped to multiple locations were discarded [73] . Numbers of reads in genes were counted by the HTSeq-count tool using gene annotations for the same version of rice genome and the “union” resolution mode was used [74] . For a given genotype , all mapped RNA-seq short reads were sorted and indexed by Samtools ( Version: 0 . 1 . 18 ) [75] . Single nucleotide polymorphisms ( SNPs ) and small insertions/deletions ( Indels ) were identified based on differences between short reads from the given genotype and the reference genome sequence with mapping quality larger than 25 , read depth more than 30 , but less than 500 . Variations in regions of interest in the rice genome were selected with their coordinates and gene annotations . First strand cDNA synthesis for real-time quantitative PCR ( qRT-PCR ) was performed using iScript Reverse Transcription Supermix ( Bio-Rad Laboratories , Inc . , Hercules , CA , USA ) using 2 μg of total RNA . For the qPCR reaction , 3 μL of the diluted cDNA ( 1:20 ) was used in the 15 μL reaction mixture . In the qPCR reaction volume , 7 . 5 μL of LightCycler 480 SYBR Green I Mastermix was used ( Roche Diagnostics , Indianapolis , IN , USA ) . The qRT-PCR was carried out using Roche LightCycle 480 II with the following parameter settings ( Roche Diagnostics , Indianapolis , IN , USA ) : 95 oC pre-incubation for 5 min , amplification was done for 40 cycles at 95 oC for 20 sec and 60 oC for 15 sec and extension at 72 oC for 15 sec; the melting curve was set-up for 95 oC , 65 oC , 97 oC; cooling was set-up at 40 oC for 30 sec . We used two independent tissue samples , with tissue from two to three plants pooled for each sample . LOC_Os04g02820 was used as an internal reference gene , which displayed stable expression in all samples analyzed . Relative expression was determined using the delta-delta Ct method [76] . Primer sequences are provided as S9 Table . For HKT1;1 , a 112 bp region was amplified from genomic DNA of the japonica rice variety ‘Kitaake’ , while a 95 bp region was amplified for HKT1;4 . The fragment from HKT1;4 was ligated into the pENTR-D-TOPO vector , while for HKT1;1 the fragment was inserted into pDONR221 using the BP reaction following the manufacturer’s instructions ( Invitrogen ) . Finally , each fragment was introduced into the pANDA RNAi expression vector [77 , 78] . Transformation of ‘Kitaake’ calli was performed according to the methods outlined by Cheng et al . using the EHA-105 strain of Agrobactrium [79] . Calli and plants were selected on ½ strength MS media supplemented with 50 μg/ml hygromycin . The expression of HKT1;1 and HKT1;4 in shoot and flag leaf tissue of T1 plants , respectively , was determined using realtime PCR using the same conditions as described above . Primer sequences are provided in S9 Table . To generate native overexpression lines for the two isoforms of HKT1;1 , a ~4 . 3 kb fragment was amplified from ‘Nipponbare’ and ‘Zhenshan 2’ . The fragments were cloned into pDONR221 vector via the BP reaction , and were subsequently cloned into a pMDC99 backbone with a NOS terminator [80] . Agrobacterium-mediated transformation and selection of transformants was performed as described above . T1 plants with a single insertion were selected on ½ strength MS media supplemented with 50 μg/ml hygromycin and used for phenotyping . Phenotyping of transgenic plants was performed in a controlled environment growth chamber . Three independent RNAi lines ( T2 ) for HKT1;1 and HKT1;4 was screened for salinity tolerance , while two independent native overexpression lines ( T1 generation ) were evaluated for each isoform of HKT1;1 ( HKT1;1Ni and HKT1;1Zh ) . Temperatures were maintained at 28°C and 25°C in day and night respectively , relative humidity was maintained at 60% in day and night . Lighting was maintained at 800 μM using high pressure sodium lights ( Phillips ) . Seeds were surface sterilized in a 40% bleach solution for 20 min , rinsed in sterile water and were germinated on ½ MS media supplemented with 50 ug/ml of hygromycin . WT seeds received the same treatment , but were grown on ½ MS . The seeds were germinated for 24h in complete darkness then were transferred to a growth cabinet ( Percival Scientific ) and grown for four days at 28°C in 16/8h light ( 120 μmol m−2 s−1 ) . Seedlings were transplanted into the pots filled with Turface ( Profile Products , LLC ) and were grown in tap water for four days after transplanting . For the remainder of the experiment the plants were supplemented with half strength Yoshida solution ( pH 5 . 8 ) [61] . Eight days after transplanting a gradual salt stress was applied in three 3 dS·m-1 intervals over a period of 24h . The final 9 dS·m-1 salt level was maintained for two weeks . Sample collection was performed as described above . To generate constructs for assessing Na+ transport activities in Xenopus laevis oocytes , HKT1;1 was amplified from cDNA from ‘Nipponbare’ and ‘Zhenshan 2’ , which are representative accessions for the high and low root Na+ groups at RNC4 , respectively , and ligated into the pGEM-Xho vector [81] . The pGEM-Xho contains the T7 promoter and 5′- and 3′-untranslated regions of the Xenopus β-globin gene , which enhances expression in Xenopus oocytes . For N-terminal GFP::HKT1;1 fusion constructs , HKT1;1 was amplified from cDNA from ‘Nipponbare’ and ‘Zhenshan 2’ and cloned into pGWB6 using the Gateway LR reaction . GFP::HKT1;1 was then amplified from each construct using primers with SpeI and SalI restriction sites , and introduced into the pGEM-Xho vector [82] . Capped and polyadenylated RNA were obtained from linearized vector by in vitro transcription , using the mMESSAGE mMACHINE T7 kit ( Ambion , USA ) . Oocytes isolated as previously described were injected with 50 ng of HKT1;1-Ni or HKT1;1-Zh cRNA ( equivalent amount of transporter cRNA in GFP-tagged form ) in 50 nL of RNase-free water , or with 50 nL of RNase-free water ( for control oocytes ) , and then kept for 24 to 48 h at 19°C in ND96 medium ( 96 mM NaCl , 2 mM KCl , 1 . 8 mM CaCl2 , 1 mM MgCl2 , 2 . 5 mM sodium pyruvate , and 5 mM HEPES/NaOH , pH 7 . 4 ) supplemented with 0 . 5 mg·L–1 of gentamicin , until experiments [81] . Whole oocyte currents and membrane potential were recorded using the two-electrode voltage-clamp technique with a GeneClamp 500B amplifier ( Axon Instruments , USA ) 1 to 2 days after cRNA injection . Voltage-pulse protocols , data acquisition and analysis were performed using pClamp9 software ( Axon Instruments ) . Correction was made for voltage drop through the series resistance of the bath and the reference electrode using two external electrodes connected to a bath probe ( VG-2A x100 Virtual-ground bath clamp; Axon Instruments ) . Electrodes were filled with 3 M KCl . The oocytes were continuously perfused during the voltage-clamp experiment with bath solutions containing varying concentrations of monovalent cations ( as glutamate or chloride salts ) in a background of 6 mM MgCl2 , 1 . 8 mM CaCl2 , and 10 mM MES-1 , 3-bis[tris ( hydroxymethyl ) methylamino]propane , pH 5 . 5 . The chloride concentration was constant in each set of solutions . D-Mannitol was added when necessary to adjust the osmolarity , which was set to 220–240 mosM in each set of solutions . Voltage-clamp protocol consisted in successive steps of membrane voltage application from -165 to +15 mV in +15 mV increments during 0 . 5 s , each step beginning with 0 . 15 s and ending with 0 . 3 s at the resting potential of the oocyte membrane in the tested bath solution . Mean currents recorded in water-injected control oocytes from the same batch and in the same ionic conditions as HKT-expressing ones were subtracted from those recorded in HKT-expressing oocytes in order to extract HKT-mediated currents from total oocyte currents . HKT1;1-Ni and -Zh current–voltage ( I–V ) relationships were constructed with transporter extracted currents . The activation potential of HKT currents was estimated as the lowest voltage at which the current in HKT-expressing oocytes reached twice that in control oocytes . Confocal observations were made on dark poles of oocytes of similar sizes on a Leica SP8 microscope , using a 20x/0 . 7dry objective . GFP was excited with a 488 nm laser , and spectral acquisitions of emitted fluorescent light were performed between 495 and 645 nm using a bandwidth of 5 nm , to assert GFP specificity . For each oocyte , mean fluorescence intensity at the membrane was determined from at least 2 optical sections , analyzing 3 ROIs per section using ImageJ ( https://imagej . nih . gov/ij/ ) software . | Despite intensive research , few genes have been identified that underlie natural variation for salinity responses in rice . In this study , we used a rice diversity panel for genome wide association mapping to identify HKT1;1 as a factor regulating Na+ distribution . Within the rice diversity panel we observed higher Na+ levels in root tissue in the indica subpopulation compared to japonica accessions . Three non-synonymous variants were identified within HKT1;1 that were associated with altered Na+ accumulation in root tissue , and displayed contrasting frequencies between indica and japonica subspecies . The introduction of HKT1;1 from an indica accession that contained the three non-synonymous variants into a japonica background resulted in a phenotype similar to that exhibited by the indica subpopulation . This work suggests that these allelic variants are likely responsible for the higher root Na+ observed in indica accessions . This study has identified a genetic resource for modifying Na+ content rice , and provides evidence that HKT1;1 underlies the divergence between indica and japonica subspecies in root Na+ content . | [
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... | 2017 | Allelic variants of OsHKT1;1 underlie the divergence between indica and japonica subspecies of rice (Oryza sativa) for root sodium content |
Structural genetic changes , especially copy number variants ( CNVs ) , represent a major source of genetic variation contributing to human disease . Tetralogy of Fallot ( TOF ) is the most common form of cyanotic congenital heart disease , but to date little is known about the role of CNVs in the etiology of TOF . Using high-resolution genome-wide microarrays and stringent calling methods , we investigated rare CNVs in a prospectively recruited cohort of 433 unrelated adults with TOF and/or pulmonary atresia at a single centre . We excluded those with recognized syndromes , including 22q11 . 2 deletion syndrome . We identified candidate genes for TOF based on converging evidence between rare CNVs that overlapped the same gene in unrelated individuals and from pathway analyses comparing rare CNVs in TOF cases to those in epidemiologic controls . Even after excluding the 53 ( 10 . 7% ) subjects with 22q11 . 2 deletions , we found that adults with TOF had a greater burden of large rare genic CNVs compared to controls ( 8 . 82% vs . 4 . 33% , p = 0 . 0117 ) . Six loci showed evidence for recurrence in TOF or related congenital heart disease , including typical 1q21 . 1 duplications in four ( 1 . 18% ) of 340 Caucasian probands . The rare CNVs implicated novel candidate genes of interest for TOF , including PLXNA2 , a gene involved in semaphorin signaling . Independent pathway analyses highlighted developmental processes as potential contributors to the pathogenesis of TOF . These results indicate that individually rare CNVs are collectively significant contributors to the genetic burden of TOF . Further , the data provide new evidence for dosage sensitive genes in PLXNA2-semaphorin signaling and related developmental processes in human cardiovascular development , consistent with previous animal models .
Tetralogy of Fallot ( TOF ) is the most common form of cyanotic congenital cardiac disease in humans . With surgical advances and increased longevity , attention has shifted from immediate outcomes to understanding causation . However , for most patients with TOF , the genetic basis for the disease remains unknown . Recently , there has been a focus on unbalanced structural genomic changes , or copy number variants ( CNVs ) , and disease [1] . Copy number variation contributes to the genetic heterogeneity of many complex human diseases [2] , [3] . Investigation of CNVs that overlap genes has led to the discovery of novel etiologies and disease pathways , especially for developmental disorders [1] , [4]–[6] . Our current understanding of the role of CNVs in the etiology of TOF , however , is limited . Early reports of CNVs in subjects with various types of congenital cardiac conditions , using low resolution methods , suggested that CNVs may be important [7]–[11] but there is just one report of genome-wide CNVs in 111 TOF patients using a high resolution microarray [12] . We used a high resolution genome-wide microarray and proven methods to: a ) investigate the burden of rare CNVs in TOF compared to controls , b ) identify putative candidate genes associated with rare and recurrent CNVs and c ) assess , using a pathway analysis , whether the exonic CNVs found in TOF could identify functional gene sets relevant to cardiac development .
We first compared the CNV burden of large ( >500 kb ) rare CNVs in the TOF cases and the OPGP controls . Consistent with our hypothesis , a significantly greater proportion of cases harbored large rare CNVs compared to controls ( OR 1 . 89 , 95% CI 1 . 06–3 . 35 , p = 0 . 0278 ) ( Table 1 ) . This was most notable for differences in large gains that overlapped exons ( OR 2 . 54 , 95% CI 1 . 17–5 . 50 , p = 0 . 0148 ) . However , if the 49 individuals with TOF and 22q11 . 2 deletions of European ancestry had been included , the odds ratio for large rare exonic losses would also have been significantly higher compared to controls ( OR 10 . 87 , 95% CI 4 . 80–24 . 08 , p<0 . 0001 ) . In contrast , the overall quantitative burden of rare CNVs of any size was similar between the TOF group and OPGP controls; most TOF and OPGP control subjects had one or more rare CNVs ( Table 1 ) . When CNV burden for individuals was defined as having two or more rare CNVs , there was no significant difference between cases and controls ( data not shown ) . For those subjects with TOF , large rare CNVs were enriched in the syndromic subgroup however this difference reached statistical significance only for subjects with large rare exonic losses ( OR 9 . 53 , 95% CI 2 . 89–31 . 41 , p = 0 . 0004 ) ( Table 1 ) . These results would have been even more significant if individuals with 22q11 . 2 deletions had been included ( data not shown ) . When individuals with one or more rare exonic loss CNVs of any size were considered , results were still significant but with a smaller OR ( OR 2 . 69 , 95% CI 1 . 35–4 . 60 , p = 0 . 0013 ) . A further TOF subgroup analysis comparing those with and without pulmonary atresia showed no significant enrichment of individuals with rare exonic loss CNVs in those with pulmonary atresia [48% ( 16 of 33 ) vs . 37% ( 115 of 307 ) , p = 0 . 2162 ) ] . Table 2 shows the 47 large ( >500 kb ) rare CNVs found in 43 of the 433 adults with TOF in the discovery sample . Most ( 39/47 ) were very rare , i . e . , not found in any of 2 , 773 controls ( 2 , 357 population controls or 416 OPGP controls ) and all but three overlapped genes . Several of these loci showed evidence for recurrence in TOF . The most compelling were 1q21 . 1 duplications ( Figure 2 in Supporting Information S1 ) ( OMIM #612475 ) identified in four ( 1 . 18% ) of 340 subjects of European ancestry . None met our syndromic criteria , however detailed examination of the phenotype revealed macrocephaly in two and tall stature in another of these subjects . There were two other subjects in the non-syndromic subgroup with genomic disorders at loci previously associated with congenital cardiac disease: one proband with a previously undetected 22q11 . 21 duplication ( OMIM #608363 ) [14] and another with a typical 16p11 . 2 duplication ( OMIM #611913 ) [15] . Amongst other large rare CNVs of note , one proband with syndromic features had a novel tandem duplication-deletion in the 18q22 . 3-q23 region that was transmitted to her daughter . Both had TOF , learning difficulties , short stature , obesity , and thyroid disease . This complex CNV overlapped the region involved in the 18q22 deletion syndrome , e . g . , the 16 Mb deletion of a subject excluded from our TOF cohort ( Figure 3 in Supporting Information S1 ) . There are three candidate genes in the distal end of the 3 . 5 Mb 18q23 deletion that may have an impact on cardiac development and/or are implicated by a relevant family of genes [16] , [17] ( Table 2 , Figure 3 in Supporting Information S1 ) : NFATC1 , PARD6G and SALL3 [18] . Another proband with syndromic features had a 1q41 deletion that may overlap the region of a translocation reported in a patient with TOF [19] and possibly the 1q41 deletion region ( OMIM #612530 ) associated with holoprosencephaly 10 . The first section of Table 3 shows the smaller ( <500 kb ) very rare CNVs in the TOF sample that implicate specific candidate genes of interest at loci associated with TOF , including: GJA5 in the 1q21 . 1 duplication region ( Figure 2 in Supporting Information S1 ) [20] , CDH19 at 18q22 . 1 ( Figure 3 in Supporting Information S1 ) , NBEA at 13q13 . 3 [21] and ANGPT2 at 8p23 . 1 [22] . Other candidate genes highlighted through overlap with results from other studies include: CECR5 in the cat eye syndrome region [23] , RAF1 involved in Noonan syndrome [12] and PPM1K [12] . Table 3 also shows novel very rare ( not found in 2 , 773 controls ) CNVs overlapping genes with evidence for cardiovascular involvement . Two unrelated probands had 1q32 . 2 loss CNVs overlapping the PLXNA2 gene ( Figure 1 ) , which were confirmed by qPCR and sequencing across the junction breakpoints . Plexins play an important role in cardiac development , including cardiac neural crest cell migration and outflow tract morphogenesis [24] . We therefore resequenced PLXNA2 exons and splice sites in a subset ( n = 192 ) of the TOF cases of European ancestry . This yielded nine missense variants but no additional nonsense or frame-shift mutations that would lead to haploinsufficiency of the gene ( Table 6 in Supporting Information S1 ) . No point mutations were detected in the two individuals with PLXNA2 deletions and in silico inspection of the intronic CNV revealed no conclusive evidence of regulatory region disruption . Two other loss CNVs involved adjacent semaphorin genes at 7q21 . 11 with previous evidence for structural cardiac phenotypes ( Table 3 ) . One overlapped three exons of the SEMA3D gene coding for semaphorin 3D and the other overlapped the first intron of the SEMA3E gene , previously associated with CHARGE syndrome [25] . We also identified a group of four subjects with novel small rare CNVs containing genes associated with ciliary dysmotility: DNAH11 ( n = 2 ) , BBS9 ( n = 1 ) and SNX8 ( n = 1 ) . Primary ciliary dyskinesis has several genetic causes , including mutations in DNAH11 , a gene coding for a dynein heavy chain component of the axoneme , the inner cytoskeletal core of cilia ( OMIM #6033 ) . Similarly , BBS9 is one of 14 genes known to be responsible for Bardet-Biedl syndrome , a multisystem disorder [26] . Loss of ciliary function results in a multisystem disease and loss of function during embryogenesis can lead to congenital cardiac lesions , typically abnormalities of cardiac situs ( heterotaxy ) , and less commonly TOF [27] . FGF10 was another plausible candidate gene for human congenital cardiac disease [28]–[30] implicated by a very rare exonic loss CNV . FGF10 codes for fibroblast growth factor 10 , a protein with dosage sensitive expression in several aspects of early murine cardiovascular development [28] , [29] . Notably , the loss CNV involved the entire gene , thus would encompass an evolutionarily conserved cis-regulatory module in intron 1 of the FGF10 gene recently reported to be functional during human cardiac development [30] . The proband with this CNV did not meet criteria for lacrimoauriculodentodigital ( LADD ) syndrome ( OMIM #149730 ) or autosomal dominant aplasia of lacrimal and salivary glands ( ALSG; OMIM #180920 ) , conditions associated with point mutations in FGF10 coding regions that may have different expression from that of an intronic point mutation [30] or a structural variant alone . In pathway analyses testing of case and control subjects with rare CNVs that overlapped 6 or fewer genes , only exonic losses led to significant results for the gene-set test ( permutation FDR< = 27 . 5% , nominal p-value< = 0 . 05 ) ( Table 7 in Supporting Information S1 ) , in line with previous findings for autism [5] . Nineteen gene-sets passed the significance thresholds ( Table 8 in Supporting Information S1 ) and were selected for visualization ( Figure 2 ) . The gene-sets identified belonged to five overlapping functional clusters , representing both those expected and more novel ( Figure 2 ) : vasculature development ( p = 0 . 0351 ) , chromosome organization ( p = 0 . 0224 ) , cell motility ( p = 0 . 0224 ) , chemotaxis ( p = 0 . 0440 ) and neuron projection and development ( p = 0 . 0440 ) . We also selected the three top-scoring previously reported TOF disease genes ( GATA4 , NKX2-5 and TBX5 ) and identified as potential disease candidates their high-confidence functional neighbors affected by a rare exonic CNV in two or more cases and none in controls ( Table 10 and Table 11 in Supporting Information S1 ) . PLXNA2-semaphorins was the only gene-set found exclusively in our systematic CNV review . Integrating the results of pathway analysis and systematic CNV review , we identified potentially important convergences ( Figure 2 ) . GJA5 was found in all disease gene neighborhoods . ANGPT2 and FGF10 were found in vasculature development , cell motility , chemotaxis and in association with at least one of the disease genes . PLXNA2 was found in cell motility , chemotaxis and neuron projection and development . In contrast , HDAC9 was a novel gene identified by gene-set association ( vasculature development and other clusters ) and disease gene neighbor analysis ( NKX2-5 ) , but not in our systematic CNV review . Figure 4 in Supporting Information S1 presents results of a manual review of further lines of evidence to reconstruct putative regulatory relations between the candidate genes in a potential disease pathway .
This is the first study of genome-wide CNVs in TOF to use a well-characterized cohort of adult patients , stringent molecular methods , and multiple converging analyses . We have identified novel candidate genes for TOF , in addition to providing replication of previous findings , including some from a smaller genome-wide study of CNVs in TOF [12] . There are , however , limitations to this novel study . The conservative laboratory and CNV analytic methods used , including the restricted focus on rare CNVs at the <0 . 1% level , may have resulted in missing some rare variants of interest . However , the fact that we used the same approach and adjudication control set to determine rarity meant that our a priori decision to minimize false positives , at the expense of such false negatives , would be expected to affect both cases and controls equally . Although our results overlapped certain previously described CNVs , further replication studies will be important to help define the significance and relative prevalence of the novel rare CNVs identified in this study . Large , multicentre studies may be useful , provided that comparable phenotyping and stringent quality control methods , as highlighted in this study , are maintained [2] . Meta-analyses could clarify if the lack of evidence for two or more rare CNVs per subject , as previously found for 22q11 . 2 deletions [61] , is due to insufficient power . Family studies are also needed to delineate inherited or de novo status and segregation patterns of CNVs . These data will be essential to determine the true penetrance and variable expression of individual CNVs . Examining CNVs in patients with other forms of conotruncal defects or other forms of congenital heart disease may also be informative , and could reveal a genetically-related spectrum of clinically-distinct cardiac maldevelopment as is increasingly appreciated for , e . g . , neuropsychiatric disorders [2] . Other study designs , e . g . , using whole genome sequencing , will be needed to fully delineate the genetic architecture of TOF , including detection of relevant sequence-based mutations , such as those in non-coding regulatory regions that may be important for cardiac development [30] . Pathway analyses were restricted to subjects with rare CNVs overlapping 6 or fewer genes , and insufficient numbers precluded separate analyses involving only large rare CNVs . However , pathway results were similar when subjects with multigenic CNVs overlapping >6 genes were included ( data not shown ) . Lastly , proving causality of specific genetic variants is beyond the scope of this study and more evidence , including replication of association in independent cohorts , will be needed to corroborate our putative candidate genes for tetralogy of Fallot . Fortunately , the functional significance of several key candidate genes implicated by our CNV results has already been validated in model organisms such as mice and zebrafish . In addition to well known 22q11 . 2 deletions , other structural genomic changes appear to be important contributors to the genetic heterogeneity of TOF . In particular , these include 1q21 . 1 duplications and other rare copy number changes that disrupt genes involved in cell migration and vasculature development pathways including PLXNA2-semaphorin signaling and perhaps ciliary motility . Further studies will help to improve our understanding of the complex etiology and pathogenesis of TOF and of congenital heart disease in general .
The study was approved by institutional research ethics boards at the University Health Network and the Centre for Addiction and Mental Health . We prospectively recruited 495 unrelated adults ( ≥18 years ) with TOF including a subset with TOF-pulmonary atresia or pulmonary atresia and ventricular septal defect ( collectively termed “TOF” in this study ) , without autosomal trisomies , from a single clinic ( Toronto Congenital Cardiac Centre for Adults ) . Patients with pulmonary atresia in the setting of more complex cardiac lesions , such as single ventricle lesions or transposition complexes , were not included . We excluded 62 subjects with documented syndromes , including 53 with 22q11 . 2 deletion syndrome associated with 1 . 5 to 3 . 0 Mb 22q11 . 2 deletions and genome-wide CNV data reported elsewhere [61] . The remaining 433 subjects formed a CNV discovery sample for this study . TOF diagnosis was confirmed using echocardiogram and/or cardiac catheterization together with other imaging and surgical data reviewed using lifetime medical records [62] . All subjects underwent direct clinical screening for potential syndromic features [63]; available medical records were also reviewed . Subjects were stratified into syndromic and non-syndromic subgroups using criteria previously validated for identifying 22q11 . 2 deletion syndrome in adults [63] . Individuals with at least two of three features ( history of learning difficulties , global dysmorphic facial features , hypernasal voice ) were placed in the syndromic subgroup [63] . All phenotyping was done blind to genotype . Further details regarding cardiac and extracardiac phenotypes are provided elsewhere [33] . We were underpowered to perform subgroup analyses involving individuals with specific congenital cardiac outcomes ( e . g . , heart failure ) . To optimize our analyses , we used an independent Canadian control sample from the Ontario Population Genomics Platform ( OPGP ) genetic epidemiological project that comprised adults of European ancestry [ ( 208 ( 50 . 0% ) male; mean age 44 . 96 ( SD 12 . 05 ) years] . To maximize quality control and minimize artefactual/laboratory-related findings [64] , all OPGP control samples were handled and experiments performed by the same laboratory using identical array methods and protocols , including CNV analyses and rarity assignation using separate large control cohorts , as for the TOF cases ( see below ) . High quality genomic DNA was genotyped using the high resolution Affymetrix Genome-Wide Human SNP Array 6 . 0 . CNV analysis and adjudication for all TOF case and OPGP control samples were performed at The Centre for Applied Genomics ( Toronto , Canada ) . Arrays meeting Affymetrix-recommended quality control guidelines of contrast QC>0 . 4 were used for further analysis as outlined below and in Figure 1 in Supporting Information S1 . To accurately estimate ancestry , in addition to self-reported ethnicities genotypes of the TOF cases from 1 , 120 genome-wide unlinked SNPs were clustered by the program STRUCTURE [65] together with those from 270 HapMap samples , which were used as references of known ancestry during clustering . Ancestries were assigned with a threshold of coefficient of ancestry >0 . 9 . Of the 433 TOF subjects , there were 340 of European , 61 of Admixed , 27 of East Asian and 5 of African ancestry . Genome-wide CNVs were determined using a multiple-algorithm approach to maximize sensitivity and specificity of CNV calling , as described previously [13] . Briefly , for each subject we defined “stringent” CNV calls as those detected by at least two of three different CNV calling algorithms: Birdsuite [66] , iPattern [67] and Affymetrix Genotyping Console , and spanning 10 kb in length and five or more consecutive array probes . In this dataset , the mean number of calls per sample was 51 , 50 and 32 for Birdsuite , iPattern and Genotyping Console , respectively . Overlapping calls at the sample level from Birdsuite and iPattern were merged with the outside probe boundaries . Singleton calls from iPattern or Birdsuite were also included in the stringent CNV set if they overlapped with a Genotyping Console call from the same sample . On average , 59% of CNVs in a sample were stringent . All subsequent analyses focused on the stringent CNVs , which in our experience have very high positive validation rates by independent methods such as quantitative PCR [5] , [6] , [13] . Merging CNV calls on a sample level across different algorithms has the additional advantage of correcting for the tendency of individual algorithms to segment single CNV events into multiple calls . Each stringently defined CNV identified in the TOF case and OPGP control samples was then adjudicated for rarity by comparison to those CNVs identified in two large population-based control cohorts comprising 2 , 357 individuals of European ancestry from Ontario and Germany , which had already been assessed using an identical microarray platform and CNV analysis strategy ( i . e . , as above ) [13] . We adopted a conservative definition of rare CNVs , retaining only those CNVs present in <0 . 1% of these 2 , 357 population controls . Further details of the comprehensive adjudication methods , including assessment of segmental duplications and Database of Genomic Variants ( http://projects . tcag . ca/variation/ ) CNVs , may be found elsewhere [5] , [13] . CNVs>6 . 5 Mb in size , likely to be detectable by karyotype and/or potentially indicating artefactual results , were excluded . To ensure consistency of data , for major analyses we used only autosomal CNVs>10 kb in size in individuals of European ancestry ( Figure 1 in Supporting Information S1 ) . Large CNVs were defined as those >500 kb in size . We prioritized smaller CNVs ( <500 kb ) meeting the following criteria for more detailed examination: a ) very rare ( i . e . , not present in any control sample using a 50% reciprocal overlap criterion ) [13] and b ) recurrent in unrelated TOF subjects , including those reported in the literature , and/or c ) overlapping ‘interesting’ gene ( s ) possibly involved in TOF . When available , immediate relatives were studied using the same methods as for the proband to determine if a CNV was de novo or inherited . Confirmatory studies of possible TOF-associated CNVs used Stratagene SYBR Green based quantitative-PCR ( qPCR ) . Each qPCR assay was performed in triplicate , for both the target region and for a control region at the FOXP2 locus on chromosome 7 . Where available , molecular cytogenetic or microarray results from clinical laboratories also confirmed CNVs . For candidate gene discovery in TOF we prioritized further sequencing characterization to a single gene selected based on our CNV results and previous animal model studies to be the most likely to be involved in cardiac development . We performed mutation screening of PLXNA2 coding sequence ( spanning 5 , 682 nucleotides ) using standard PCR-based Sanger sequencing . The PLXNA2 gene contains 31 coding exons ( 67 to 1 , 268 bp ) that were fully sequenced with 32 amplicons . The program Primer 3 ( http://frodo . wi . mit . edu/primer3/ ) was used to design primers . The amplified products were sequenced with the Big Dye Terminator kit using the ABI 3730XL capillary sequencer ( Applied Biosystems ) and analyzed for sequence variants using Sequencher ( Gene Codes , Ann Arbor , MI , USA ) . Putative sequence variants of interest were confirmed by sequencing in the reverse direction . SIFT [68] and POLYPHEN [69] were used for in-silico prediction of the effect of missense variants on protein function . Statistical analysis was performed using SAS software ( version 9 . 3 , SAS Institute Inc . , Cary , NC , USA ) . The main analyses compared rare CNVs in the 340 TOF cases of European ancestry with those in the 416 OPGP controls and within-group comparisons of syndromic versus non-syndromic TOF subjects . Chi-square or Fisher's exact tests were used to compare categorical variables and Student's t tests for continuous variables , as appropriate . All tests were two-side , with statistical significance defined as p<0 . 05 . For pathway analyses , we first assessed if pre-defined gene-sets ( corresponding to biological functions and pathways ) displayed a higher rare CNV load in TOF cases than in OPGP controls . Gene-sets were derived from Gene Ontology annotations ( downloaded from NCBI in April 2011 and up-propagated according to ontological relations ) , pathway databases ( KEGG , Reactome , BioCarta , NCI; March 2011 ) and protein domains ( PFAM; March 2011 ) . Only gene-sets with a number of member genes between 25 and 750 were tested: 2 , 456 total , with 1 , 939 from GO , 414 from pathways and 103 from PFAM domains . Gene-sets with fewer than 25 genes decrease the statistical power of the analysis , whereas those with more than 750 genes tend to have a very broad biological scope ( e . g . GO “regulation of biopolymer catabolism” ) and hinder the visualization of results . Subjects with rare CNVs overlapping more than 6 genes were not considered for the gene-set analysis , as these may have a more promiscuous set of gene functions perturbed by the rare variant . For exonic losses this led to the exclusion of 14 TOF cases and 11 OPGP controls . For each gene-set , we built a contingency matrix with subjects of European ancestry as sampling units . Subjects were categorized as ( a ) TOF cases or OPGP controls and ( b ) having at least one gene-set gene harboring a rare CNV or not . On the basis of this contingency table , a one-tailed Fisher's Exact Test was used to test higher prevalence of rare CNVs in TOF probands versus OPGP controls . This test can be regarded as an extension of a single-gene or single-variant association test; however , testing association for groups of genes , unlike single genes or single variants , provides sufficient power to detect significant association even when considering only rare variants [70] . To map CNVs to genes we used a stringent method , and restricted to CNVs overlapping exons . We tested all types of variants as well as losses-only and gains-only; only losses produced significant results ( see analysis method below ) , in line with our previous findings for autism [5] . The Fisher's Exact Test nominal p-value was corrected for multiple tests using a case/control class permutation procedure to estimate an empirical false discovery rate . We favored a permutation strategy over classical Benjamini-Hochberg false discovery rate owing to the highly complex dependency structure among gene-sets and overly conservative nature of this test [5] . Case and control labels were permuted 2 , 000 times , and for each permutation gene-sets were tested following exactly the same procedure . Real nominal p-values were ranked from lowest ( most significant ) to highest ( least significant ) and , for each real p-value , the empirical false discovery rate was computed as the average number of gene-sets with equal or smaller p-value over permutations . Therefore , the empirical false discovery rate can be interpreted as an estimate of the fraction of gene-sets that would be significant under the null hypothesis of no association at the chosen nominal significance level . We selected 27 . 5% as the empirical false discovery rate significance threshold for final results; we additionally required the nominal p-value to be <0 . 05 . Previously known TOF disease genes ( Table 9 in Supporting Information S1 ) were scored for association following a similar strategy , but using functional neighbors instead of functional gene-sets . For each known disease gene , we scored TOF case and OPGP control subjects of European ancestry . The score was defined as the highest functional weight between ( a ) the known disease gene being tested and ( b ) the CNV-harboring genes in the subject being scored . The functional weight was obtained from STRING , a publicly available resource that predicts the probability of two genes participating in the same pathways based on physical interaction , pathway membership , co-expression and PubMed co-citation . For each TOF disease gene , we tested if functional neighborhood scores were higher in TOF cases compared to OPGP controls by logistic regression analysis . All exonic CNVs ( gains and losses ) were used; unlike the gene-set association test , restricting to losses did not improve significance . We finally selected the three top-scoring known disease genes ( GATA4 , NKX2-5 , TBX5 ) . For visualization , we integrated results from gene-set association , disease gene neighborhood analysis and systematic CNV review as a gene-set overlap network using the Cytoscape plugin Enrichment Map [5] , [71] . Gene-sets significant after the gene-set association test were restricted to genes with higher prevalence in TOF cases than in OPGP controls [5] , whereas functional neighborhood gene-sets included the known TOF disease gene as well as its neighbors that had high interaction confidence according to STRING ( score>700 , equivalent to interaction probability >70% ) and harbored CNVs in 2 or more TOF case subjects but no OPGP control ( Table 10 in Supporting Information S1 ) . The combined jaccard-overlap index was used to generate the gene-set network , setting a threshold of 0 . 2 . Clusters of overlapping gene-sets were manually identified and colored . | Congenital heart disease affects nearly 1% of all live births . Tetralogy of Fallot ( TOF ) is the most common form of cyanotic congenital heart disease . This condition is associated with hemizygous deletions of chromosome 22q11 . 2 and chromosomal trisomies , but little else is known about the genetic heterogeneity of this complex disease . We used high-resolution microarrays and stringent methods to study structural ( copy number ) variants in a systematically phenotyped cohort of unrelated adults with TOF . We found that individually rare genic copy number variants ( CNVs ) were collectively significant contributors to the genetic burden in TOF . Among CNVs that implicated candidate genes of interest were loss CNVs overlapping the PLXNA2 gene that codes for plexin A2 . This is the first study to show a role for this semaphorin receptor in human congenital heart disease , consistent with a Plxna2 mouse knockout phenotype . Pathway analyses comparing rare exonic loss CNVs in the TOF sample to controls implicated other novel gene sets suggest new pathogenetic mechanisms . | [
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"cardiovascular"
] | 2012 | Rare Copy Number Variations in Adults with Tetralogy of Fallot Implicate Novel Risk Gene Pathways |
By binding to the adaptor protein SKP1 and serving as substrate receptors for the SKP1 Cullin , F-box E3 ubiquitin ligase complex , F-box proteins regulate critical cellular processes including cell cycle progression and membrane trafficking . While F-box proteins are conserved throughout eukaryotes and are well studied in yeast , plants , and animals , studies in parasitic protozoa are lagging . We have identified eighteen putative F-box proteins in the Toxoplasma genome of which four have predicted homologs in Plasmodium . Two of the conserved F-box proteins were demonstrated to be important for Toxoplasma fitness and here we focus on an F-box protein , named TgFBXO1 , because it is the most highly expressed by replicative tachyzoites and was also identified in an interactome screen as a Toxoplasma SKP1 binding protein . TgFBXO1 interacts with Toxoplasma SKP1 confirming it as a bona fide F-box protein . In interphase parasites , TgFBXO1 is a component of the Inner Membrane Complex ( IMC ) , which is an organelle that underlies the plasma membrane . Early during replication , TgFBXO1 localizes to the developing daughter cell scaffold , which is the site where the daughter cell IMC and microtubules form and extend from . TgFBXO1 localization to the daughter cell scaffold required centrosome duplication but before kinetochore separation was completed . Daughter cell scaffold localization required TgFBXO1 N-myristoylation and was dependent on the small molecular weight GTPase , TgRab11b . Finally , we demonstrate that TgFBXO1 is required for parasite growth due to its function as a daughter cell scaffold effector . TgFBXO1 is the first F-box protein to be studied in apicomplexan parasites and represents the first protein demonstrated to be important for daughter cell scaffold function .
Toxoplasma gondii is an intracellular apicomplexan parasite responsible for one of the most common parasitic infections in humans and animals [1 , 2] . Infections are initiated by digesting either bradyzoite-containing tissue cysts or sporozoite-laden oocysts that are disrupted in the stomach [3] . Released parasites then infect the small intestine and convert into tachyzoites , which triggers the recruitment of inflammatory cells , which are in turn infected and used to disseminate throughout the host where they convert into long-lived tissue cysts [4 , 5] . Occasionally , cysts reactivate and the released parasites will revert to tachyzoites , which replicate by a unique process termed endodyogeny where the two daughter parasites develop within the mother [6] . Since parasite replication underlies onset of disease in toxoplasmosis patients , it is critical to study the mechanisms regulating endodyogeny . Formation of the inner membrane complex ( IMC ) is a critical step in endodyogeny . The IMC is a unique organelle comprised of an intermediate-filament like cytoskeletal network and membranous sacs whose outer leaflet anchors the actin-myosin gliding machinery [7–9] whereas the cytoplasmic leaflet is associated with subpellicular microtubules that extend from the apical end of the parasite for most of its length [10 , 11] . During daughter cell development , the IMC emerges from a structure named the daughter cell scaffold ( DCS ) that is located apically to the parasite’s outer core of its bipartite centrosome [12 , 13] . The DCS serves as the docking site for Rab11b-regulated vesicles containing the material needed to form the emerging IMC [14] . Despite its central role in endodyogeny , it is largely unknown what proteins localize to the DCS nor is it understood how the DCS regulates IMC assembly . F-box proteins ( FBPs ) are a family of proteins defined by the presence of a F-box domain and can be divided into three classes , FBXW , FBXL and FBXO [15] according to the presence or absence of canonical substrate recognition domains [15 , 16] . F-box proteins are best known as subunits of the SKP1/Cullin/F-box containing E3 ubiquitin ligase ( SCF-E3 ) complex [17] in which the F-box domain binds SKP1 to recruit itself and/or other substrates for polyubiquitination [17 , 18] . Although well studied in fungi , plants , and metazoans , the function of F-box proteins in apicomplexan parasites remains unexplored . Previously , we reported that an O2-regulated Toxoplasma prolyl hydroxylase modifies a proline residue in TgSKP1 , which can then be modified by a series of glycosyltransferases [19–21] . TgSKP1 prolyl hydroxylation/glycosylation is predicted to alter F-box protein binding leading to changes in substrate recognition [22] . Here , we identify 18 putative F-box proteins in the Toxoplasma genome that , relative to other organisms , represents a significantly streamlined repertoire of F-box proteins [23 , 24] . We focused on Toxoplasma F-box protein 1 ( TgFBXO1 ) and found that it localizes to the DCS early during endodyogeny and is required for DCS function . Thus , TgFBXO1 represents the first protein identified to be an executor of DCS function .
F-box proteins contain an F-box domain of ~50 amino acids that are organized into a triple α-helical bundle that mediates binding to SKP1 [25] . We employed a series of reciprocal BLASTp and hidden Markov searches seeded with 43 F-box sequences from known F-box proteins including 7 from crystal structures ( S1A Fig ) and 37 from two well studied yeast species ( S1B Fig ) , to assemble a list of 18 predicted Toxoplasma gondii F-box proteins ( S1 Table; S1C Fig ) . Five are of the FBXW and FBXL classes , while the others possess C-terminal domains that are poorly if at all are related to F-box proteins outside of protists . All but TgFBXO10 have clear homologs in Neospora caninum but only four had clear Plasmodium homologs , suggesting rapid evolutionary specialization . In a parallel study , we sought to identify candidate F-box proteins by investigating the TgSKP1 interactome . To maximize coverage , TgSKP1 was captured i ) from lysates of extracellular parasites ( 80% extracellular ) prepared at two different detergent concentrations using bead-bound affinity purified polyclonal antiserum ( UOK75 ) generated against TgSKP1 [19] , or ii ) by using bead-bound anti-FLAG M2 monoclonal antibody to recover TgSKP1 from detergent lysates of extracellular or intracellular parasites of a strain named TgSKP1SF in which the TgSKP1 locus was modified by the addition of a C-terminal Strep/Flag ( SF ) tag . To differentiate specific from adventitious interactors , non-immune rabbit IgG was used as a control for the UOK75 immunoprecipitation , and the parental RHΔhxgprtΔku80 strain was used as a control for mAb M2 immunoprecipitation . The immunoprecipitates were subjected to nLC-MS/MS analysis and proteins were assigned from peptide identifications at 1% FDR using Sequest , and quantified based on Abundance in Proteome Discoverer 2 . 2 . After filtering to remove non-specific interactions and major organellar proteins , and relaxing the protein-level FDR for predicted FBPs from the bioinformatics search ( S1 Fig ) , twenty-one proteins were identified as candidate SKP1-interacting proteins . Of these , five were present in both the TgSKP1SF and UOK75 immunoprecipitations ( Fig 1A , S2 Fig and S2 Table ) . These included the expected SCF complex proteins TgSKP1 , CUL-1 and RBX1 , a Skp1 glycosyltransferase ( GAT1 ) , and TgFBXO1 ( TgGT1_310930 ) . Three additional FBPs predicted from the bioinformatics approach were detected in the TgSKP1SF immunoprecipitates , but two were assigned only with medium confidence . We focused efforts on TgFBXO1 because it is conserved in Plasmodium , is important for Toxoplasma fitness according to a genome-wide CRISPR screen [26] , and is the most highly expressed Toxoplasma F-box protein throughout the tachyzoite cell cycle https://toxodb . org/toxo/app/record/gene/TGME49_310930 . To confirm that TgFBXO1 interacts with TgSKP1 , we cloned a C-terminal 3XHA epitope tag onto TgFBXO1 ( TgFBXO1HA ) via homologous recombination [27] . Western blotting lysates from TgFBXO1HA expressing parasites but not the parental RHΔhxgprtΔKu80 strain with anti-HA antibodies revealed a single immunoreactive band at ~100 kDa , which is the approximate expected molecular weight of TgFBXO1HA ( Fig 1B; Input 21% O2 ) . Next , TgSKP1 was detected by Western blotting TgFBXO1HA immunoprecipitates with anti-TgSKP1 antisera ( Fig 1B; IP 21% O2 ) . Taken together , the FBP and interactome discovery pipelines led to the identification of TgFBXO1 as an evolutionarily conserved F-box protein that stably associates with TgSKP1 , and is potentially a major component of the SCF-E3 complex in Toxoplasma . Previously , we reported that 154Pro of TgSKP1 is hydroxylated and glycosylated and that these post-translational modifications are predicted to change the conformation of the F-box protein-binding region of TgSKP1 as they do in the social amoeba Dictyostelium discoideum when a conserved proline residue in DdSKP1 is similarly modified [22] . An O2-regulated prolyl hydroxylase modifies 154Pro and under low O2 conditions can be observed by Western blotting as a decrease in the apparent molecular weight of TgSKP1 [19 , 20 , 28] ( Fig 1B; TgSKP1 Input Fractions ) . To test whether TgSKP1 and TgFBXO1 interactions were O2-dependent , TgSKP1 levels were compared in TgFBXO1HA immunoprecipitates from parasites grown at either 21% or 0 . 5% O2 . The data revealed modestly increased levels of TgSKP1/TgFBXO1 interactions at 0 . 5% O2 ( Fig 1B; compare 21% O2 to 0 . 5% O2 ) . Because TgFBXO1 is important for parasite fitness [26] , we used a conditional knockdown system where the TgFBXO1 native promoter was replaced with an anhydrotetracycline ( ATC ) -repressible promoter and an amino-terminal 3XHA epitope tag was cloned in frame to generate HA ( ATC ) TgFBXO1 ( Fig 2A ) [29] . HA ( ATC ) TgFBXO1 protein was undetectable after 24 h treatment with 1 μg/ml ATC ( Fig 2B ) . Next , HA ( ATC ) TgFBXO1 growth was assessed by plaque assay and found that relative to the parental strain growing with or without ATC that a ~60% decrease in numbers of plaques were present when HA ( ATC ) TgFBXO1 expression was decreased ( Fig 2C and S3 Fig ) . Moreover , the size of the HA ( ATC ) TgFBXO1 plaques that did form were ~70% smaller than plaques formed by HA ( ATC ) TgFBXO1 expressing parasites ( Fig 2D and S2 Fig ) . Although TgFBXO1/TgSKP1 interactions were enhanced at 0 . 5% O2 , this growth phenotype was not enhanced at low O2 levels . Therefore the rest of this study’s experiments were performed under normoxic ( 21% O2 ) conditions . Taken together , these data indicate that TgFBXO1 is important for optimal Toxoplasma growth . Although detectable by Western blotting , HA ( ATC ) TgFBXO1 could not be visualized by immunofluorescence staining suggesting inaccessibility of the N-terminal tag in fixed parasites . We therefore assessed TgFBXO1 localization using the C-terminal-tagged TgFBXO1HA strain and found that in interphase parasites TgFBXO1 localized to the apical end of the parasite and to the periphery where it appeared to colocalize with the IMC protein IMC3 , which is a cytoskeletal IMC protein ( Fig 3A; G1 ) [30] . We next examined TgFBXO1 localization during endodyogeny and found that early during endodyogeny TgFBXO1 localized to a perinuclear location before IMC3 associated with daughter IMCs ( Fig 3A; S-phase ) . Later during M-phase ( note lobe-shaped nuclei that are migrating into nascent daughter cells ) , TgFBXO1 remained primarily at the apical end of the growing daughter parasites while IMC3 extended posteriorly ( Fig 3A; M-phase ) . ISP1 is another IMC protein that is inserted into the membranous alveoli via fatty acylation . Before IMC3 associates with the daughter IMC , ISP1 forms an apical cap structure in the daughter parasites that remains throughout endodyogeny and into interphase [31] . Comparing ISP1 and TgFBXO1 localization revealed that TgFBXO1 attained its perinuclear localization before ISP1 did ( Fig 3B ) . As parasites progressed through endodyogeny , the ISP1 cap was found near TgFBXO1 although TgFBXO1 was distinct and apical to ISP1 suggesting that the two proteins were in distinct structures . In interphase parasites , apical TgFBXO1 staining did not colocalize with RNG1 , which is a component of the parasite’s microtubule organizing center , which is apical to the ISP1 cap ( S4 Fig ) [32] , indicating that these two proteins also reside in distinct structures . We next used biochemical extraction to assess how TgFBXO1 associates with the IMC [33] . Parasites were mechanically lysed under hypo-osmotic conditions and cytoskeletal fractions were collected by low speed centrifugation . The pellets were resuspended in isotonic buffer and IMC embedded proteins were differentially extracted with either high salt ( to disrupt electrostatic interactions ) , Triton X-100 ( to extract membrane-associated proteins ) or SDS ( to extract cytoskeletal proteins ) . TgFBXO1 was extracted with both Triton and SDS indicating that it is most likely associated with the IMC as a membrane-associated protein in a manner similar to ISP1 ( Fig 3C ) . However , we noted that in contrast to ISP1 that was exclusively associated with the IMC that a significant proportion of TgFBXO1 was soluble . The first step in endodyogeny is duplication of the parasite’s novel bipartite centrosome , followed by kinetochore duplication , assembly of subpellicular microtubules , formation of the IMC , and finally daughter cell budding and emergence [6 , 12 , 34] . To more definitively determine when during endodyogeny the TgFBXO1 apical structure forms , TgFBXO1HA parasites were fixed and stained with anti-TgCentrin-1 antibodies to detect the outer centrosome core . We were able to detect parasites where TgFBXO1 remained associated with the mother IMC but containing duplicated outer centrosome cores ( Fig 4A; S ) indicating that centrosome duplication precedes TgFBXO1 recruitment to the forming daughter parasite . As endodyogeny progressed , TgFBXO1 were positioned apically to the duplicated outer centrosome cores ( Fig 4A; EM ) and as mitosis progressed ( denoted by segregating nuclei ) , TgCentrin-1 staining was associated with medial segments of TgFBXO1 structures ( Fig 4A; LM ) . Toxoplasma genome duplication and daughter cell development are coordinated by the parasite MAP kinase , TgMAPK-L1 , and in the absence of TgMAPK-L1 signaling centrosome duplication continues although IMC development cannot [29] . We therefore tested whether the apical TgFBXO1 structures formed independently of centrosome duplication by treating TgFBXO1HA parasites with either DMSO ( vehicle control ) or the TgMAPK-L1 inhibitor , SB505124 [35] . In contrast to control parasites , TgFBXO1HA staining remained peripheral in SB505124-treated parasites containing ≥2 outer centrosome cores and numbers of apical TgFBXO1 structures in daughter parasites were significantly reduced ( Fig 4B & 4C ) . Although it is unclear how TgMAPKL1 signaling is linked to TgFBXO1 apical structure formation , these data indicate that formation of this structure is dependent on the parasite signaling that genome duplication is completed . The Toxoplasma kinetochore duplicates and separates after centrosome duplication but before ISP and IMC proteins form the daughter cell bud [29 , 36] . We therefore compared timing of TgFBXO1 apical structure formation and kinetochore duplication/migration by staining TgFBXO1HA cells with antibodies against the kinetochore protein TgNuf2 [37] . We identified parasites with distinct TgFBXO1 apical structures that contained duplicated kinetochores that had yet to complete separation suggesting that formation of the TgFBXO1 apical structures is not dependent on kinetochore positioning ( note lobed appearance of TgNuf2 structures ) ( Fig 4D; S ) . Subpellicular microtubules , which provide positional and structural cues for daughter cell IMC development and organization [11] , form after centrosome/kinetochore duplication and separation [38 , 39] . To assess the relationship between the TgFBXO1 apical structures and daughter cell microtubules , TgFBXO1HA cells were stained with antibodies against acetylated tubulin , which denote stable microtubule filaments [40] . Parasites were identified that contained TgFBXO1 apical staining but before acetylated tubulin could be detected in the daughter cells ( Fig 5A ) . As cell cycle progressed , acetylated tubulin colocalized with apical TgFBXO1 although F-box protein staining was consistently more apical . To test whether TgFBXO1 apical localization was dependent on microtubule assembly , TgFBXO1HA parasites were treated with either DMSO or oryzalin ( 2 . 5 μM ) , which blocks polymerization of parasite but not host microtubules [39] . Oryzalin treatment , which caused the expected defects in microtubule assembly and cytokinesis [41] , led to an accumulation of apical TgFBXO1 staining with most parasites possessing 8 structures ( Fig 5B & 5C ) , indicating that TgFBXO1 apical localization was independent of microtubules . Collectively , the localization data indicate that TgFBXO1 is recruited to a structure that lies apically to the outer centrosome core as well as the emerging IMC . The DCS , which serves as a nucleation site for the daughter IMC and microtubules to emerge from , has such a localization [34 , 42] . The small molecular weight GTPase TgRab11b localizes early during endodyogeny to the DCS and regulates IMC protein trafficking to the nascent IMC [14] . To compare TgRab11b and TgFBXO1HA localization , TgFBXO1HA parasites were transfected with a Rab11b expression construct which contains a N-terminal myc-tagged ddFKBP domain to control protein levels by adding a synthetic ligand , Shield1 [14] . Parasites grown in the presence of low Shield1 concentrations ( 0 . 1 μM ) express low levels of TgRab11b with no apparent effect on growth [14] . Under these conditions , examination of >50 parasites in early S-phase revealed thatTgFBXO1 and TgRab11b co-localized indicating that TgFBXO1 is a DCS component ( Fig 6A; S ) . On the other hand , the two proteins displayed limited colocalization in interphase parasites ( Fig 6A; G1 ) . To test whether DCS targeting of TgFBXO1 is TgRab11b dependent , TgRab11b-transfected TgFBXO1HA parasites were grown with 1 μM Shield1 , which leads to TgRab11b dysfunction due to its overexpression [14] . Under these conditions , overexpressed TgRab11b was mislocalized from the DCS and Golgi to the periphery ( Fig 6A 1 bottom panels ) , which is consistent with [14] . TgFBXO1 was also mislocalized but unlike TgRab11b appeared as cytoplasmic punctae . The appearance of TgFBXO1 cytoplasmic punctae in TgRab11b overexpressing parasites suggests that TgFBXO1 traffics to the DCS via a membrane trafficking pathway . In addition , TgFBXO1 detergent extractability from the IMC ( Fig 3C ) suggested that it is membrane-associated . The amino acid sequence of TgFBXO1 predicts neither a transmembrane domain nor a signal sequence but does have a consensus N-myristoylation site at 2Gly and this site is conserved amongst the coccidian TgFBXO1 homologs ( S5 and S6 Figs ) . To test whether this potential N-myristoylation site is important for DCS and IMC targeting , RHΔhxgprtΔKu80 parasites were transfected to express either wild-type TgFBXO1 ( ptubTgFBXO1HA ) or a mutant where 2Gly is mutated to an alanine ( ptubTgFBXO1HAG2A ) . In both interphase and mitotic parasites , ptubTgFBXO1HA was properly localized to the periphery ( arrow ) and DCS ( arrow heads ) , respectively ( Fig 6B ) . In contrast , ptubTgFBXO1HAG2A localization appeared to be largely cytoplasmic and was not localized to either the IMC or DCS . To test the importance of TgFBXO1 in DCS function , HA ( ATC ) TgFBXO1 ( or parental TATiΔKu80 ) parasites were grown in the absence or presence of ATC , fixed 1–5 days later and TgCentrin-1/ISP1 were detected by immunofluorescence staining . In TgFBXO1-depleted daughter parasites , ISP1 was localized near TgCentrin-1 but it was disorganized and the typical apical caps observed in untreated parasites were lacking ( Fig 7A and S7A Fig ) . Development of this phenotype was time dependent and by 5 days post-infection , was evident in ~76% parasites . Similarly , the ISP1 cap normally found at the mother cell’s apical end was absent in the TgFBXO1-depleted parasites . Next , we assessed IMC3 localization and observed a time-dependent increase in IMC3-containing daughter cell buds that were malformed relative to TgCentrin-1 . This phenotype was displayed as either: i ) IMC3-labelled daughter buds associated with nuclei but centrin-1 staining was absent ( Fig 7B ( arrows ) and S7B Fig ) . ii ) Dividing parasites with only a single IMC ( Fig 7B ( triangles ) and S7B Fig ) . iii ) Nuclei devoid of both TgCentrin1 and IMC3 ( Fig 7B ( asterisks ) and S7B Fig ) . We quantified numbers of HA ( ATC ) TgFBXO1-depleted parasites with IMC3 defects and found that , like ISP1 , they progressively increased to ~55% of the parasites counted . Finally , we also noted interphase parasites containing IMC3 whirls ( Fig 7B ( X ) and S7B Fig; ) , which was reminiscent of the effect of TgRab11b overexpression on IMC3 localization [14] . Besides IMC development , the DCS also promotes assembly of the daughter cell subpellicular microtubules . To examine whether microtubule assembly is affected in TgFBXO1-depleted parasites , HA ( ATC ) TgFBXO1 parasites were grown in the absence or presence of ATC and then newly assembled microtubules were imaged using anti-acetylated tubulin antibodies . In contrast to parasites grown in the absence of ATC , microtubules in HA ( ATC ) TgFBXO1 parasites with ATC were often missing or not properly organized by five days after adding ATC ( Fig 7C and S7C Fig ) . However , these cytoskeletal defects took longer to develop than either the ISP1 or IMC3 phenotypes suggesting that TgFBXO1 likely regulates IMC development and that the microtubule phenotypes are a secondary consequence . Having established TgFBXO1 as a DCS effector protein , we next used super resolution microscopy to gain insight into the organization and structure of this complex . First , we sought to define the spatial organization between the DCS and the developing IMC by staining dividing TgFBXO1HA parasites with anti-IMC3 and anti-HA antibodies . At an early stage during endodyogeny ( after IMC3 begins to accumulate at the daughter cell bud but before it is cleared from the mother cell IMC ) , TgFBXO1 was found to accumulate as a distinct cap-like structure that was positioned apically from IMC3 structure ( Fig 8A; S1 Video ) . However , we also noted that TgFBXO1 extended posteriorly from the cap . In addition , TgFBXO1 was not homogenously distributed throughout the DCS ( Fig 8A ) . TgFBXO1HA expressing parasites were similarly stained to detect TgFBXO1 and ISP1 . In contrast to IMC3 whose localization was distinct from TgFBXO1 , ISP1 was more proximal to TgFBXO1 although TgFBXO1 was positioned more apical than ISP1 ( Fig 8B and S2 Video ) . As noted earlier , TgRab11b is another DCS-localized protein . Thus , we assessed TgRab11b and TgFBXO1 localization in dividing parasites using super resolution microscopy . Remarkably , TgFBXO1 and TgRab11b localization did not overlap in the DCS but instead appeared to localize to distinct regions and in some cases appeared to be interdigitated although we cannot exclude the possibility that antigen accessibility limits detection of either protein ( Fig 8C and S3 Video ) . However , we discount this possibility since examination of > 50 parasites revealed only interdigitation but no colocalization between TgFBXO1 and TgRab11b . Taken together , these data indicate the DCS is likely composed of at least different microdomains and that TgFBXO1 and TgRab11b are distinct markers for each .
Comprising a ubiquitous eukaryotic protein family , F-box proteins play important roles in diverse biological functions [15 , 16] . Although extensively studied in metazoans , plants , and fungi , little is known about their function ( s ) in protists and in particular apicomplexans . Here , we identified 18 putative Toxoplasma F-box proteins , which approximates the number found in two well studied yeast systems but is considerably less than the number in Dictyostelium , humans and other organisms [23 , 24] . TgFBXO1 was reported to be important for parasite fitness [26]; a finding we corroborate using a conditional expression system . We also demonstrated by reciprocal co-immunoprecipitation assays that TgSKP1 and TgFBXO1 interact with each other supporting TgFBXO1 designation as a F-box protein . We further found that TgFBXO1 is a DCS component; a conclusion supported by multiple lines of data . First , it localizes to a structure positioned apically to the daughter cell’s outer centrosome core and developing IMC . Second , daughter cell TgFBXO1 apical structures form before ISP1 and IMC3 recruitment to developing daughter parasites . Third , TgFBXO1 becomes localized to the DCS after centrosome duplication but before the kinetochore completely separates . Finally , TgFBXO1 colocalized with TgRab11b at the DCS , albeit in distinct microdomains , and TgFBXO1 recruitment to the DCS was TgRab11b-dependent . Sequence homology-based studies suggest that proteins related to TgFBXO1 consist of at least 3 domains ( S5 Fig ) . The C-terminal domain and features of the middle predicted F-box domain are conserved in proteins throughout the alveolate clade of protists ( S5 Fig ) , which includes ciliates and dinoflagellates as well as apicomplexans . The protein acquired a 330-amino acid N-terminal domain only in Toxoplasma and closely related cyst-forming coccidia ( no trace of this sequence exists in other extant proteins ) . Thus , a core function of the TgFBXO1 clade may have emerged early in evolution to serve a coccidia specific function , such as organization of the membranous villi that lie underneath the plasma membrane , which is the IMC in apicomplexans [43–45] . Decreased TgFBXO1 expression had distinct effects on two types of IMC proteins . The IMC alveoli meshwork embedded protein IMC3 formed daughter cell IMCs that were not properly aligned with the outer centrosome core . In contrast , ISP1 , which is component of a distinct IMC subcompartment , was unable to form its apical cap structure during endodyogeny and this phenotype was maintained in interphase parasites . It should be noted that ISP1 is not an essential gene thus indicating that it is likely that the observed growth defects are not due to ISP1 [31] but rather ISP1 is serving as a marker for the ISP compartment . While these data are consistent with an emerging model in which IMC components are recruited to the daughter cell through distinct mechanisms [31] , they also reveal that the DCS is required for both to occur . It is also possible that only ISP1 or IMC3 require TgFBXO1 and that defects observed with the other protein is a downstream effect . Discrimination between these two possibilities awaits characterization of TgFBXO1-interacting proteins . IMC protein trafficking to the DCS and emerging bud is a poorly understood process . The small molecular weight GTPase , TgRab11b , is required for this process although it remains unclear where it executes its function and what are its effectors [14] . TgRab11b and TgFBXO1 both localize to the DCS but their effects on IMC proteins differed–defects in IMC formation vs misaligned daughter buds , respectively . These differences could be due to different approaches to achieving target protein loss of function ( overexpression of an inactive TgRab11b mutant and TgFBXO1 knockdown ) or could represent distinct roles for TgRab11b and TgFBXO1 in IMC biogenesis and organization . But given that TgFBXO1 requires TgRab11b for its DCS localization , we favor a model where TgRab11b functions to recruit diverse types of cargo and membrane vesicles to the DCS and that TgFBXO1 is important for executing DCS function in the development of the new IMC . This model is consistent with our finding that the DCS is composed of distinct microdomains and that each likely has distinct functions . Fatty acylation is emerging as a critical regulator of targeting of IMC and IMC-related proteins to an emerging apicomplexan daughter cells although different proteins utilize different combinations of fatty acid moieties [31 , 46–49] . Here , we report that N-myristoylation is required for TgFBXO1 targeting to the DCS . Since N-myristoylation is thought to mediate protein-membrane interactions and is a co-translational process [50 , 51] , it was surprising that ~50% of total TgFBXO1 protein was soluble . Explanations for this could include a TgFBXO1-interacting protein that acts to sequester the N-terminal myristoylated glycine or that N-myristoylation of TgFBXO1 occurs post-translationally . While our future work will address these possible mechanisms , these data suggest that TgFBXO1 fatty acylation is a dynamic process that regulates the protein’s trafficking and targeting . Using a conditional expression system , TgFBXO1 was found to be required for optimal parasite growth most likely due to its function in regulating IMC development and organization . TgFBXO1 expression using this system leads to undetectable levels of the tagged protein within 24 h after adding ATC . Yet , the IMC defects were only minimally evident at this time point but rather accumulated with time over the 120 h time course that we performed . One reason for such a delay is that low but undetectable levels of TgFBXO1 are sufficient to sustain replication , a finding previously reported for Toxoplasma actin [52] . Alternatively , a second F-box protein can compensate for the loss of TgFBXO1 albeit not as efficiently . We are currently unable to differentiate whether TgFBXO1 acts in a SCF-E3 ubiquitin ligase dependent or independent manner [15 , 53 , 54] . It is possible that TgFBXO1 functions in IMC protein ubiquitylation to regulate their turnover and/or targeting during endodyogeny . Such a scenario is supported by the finding that many ubiquitylated Toxoplasma proteins are cell cycle regulated including ISP1 and a large number of other IMC proteins [55] . It is also possible that TgFBXO1 is itself an SCF-E3 substrate and its ubiquitylation is required during endodyogeny for either trafficking of TgFBXO1 from the IMC to the DCS or for its turnover from the mature IMC early during endodyogeny . It is noteworthy that a parasite deubiquitinase , TgOTUD3 [15 , 53 , 54 , 56 , 57] was recently reported to coordinate mitosis and cytokinesis by properly matching the correct numbers of centrosomes with numbers of daughter buds . However , we do not believe that TgFBXO1 functions in this manner because in contrast to TgOTUD3 , loss of TgFBXO1 leads to unmatched numbers of outer centrosome cores and daughter buds without an increase in parasite ploidy . Alternatively , TgFBXO1 may function in a SCF-E3 ubiquitin ligase independent manner [58–60] . Such a mechanism could occur by the TgSKP1/TgFBXO1 heterodimer , which according to precedent is expected to have high affinity [61–63] , being incorporated into a supramolecular complex where TgSKP1 is shielded from interacting with cullin-1 to form a functional SCF complex . Recent cryo-EM studies of the yeast kinetochore provide an example of how the Skp1/Ctf13 ( a yeast F-box protein ) complex contributes to kinetochore structure in a way where Skp1 is inaccessible to Cul-1 interaction [59 , 60] . Certainly , it is conceivable that SCF-E3 dependent or independent mechanisms need not be mutually exclusive , because association with the SCF-E3 could be a mechanism to turn over excess TgFBXO1 that is not associated with the DCS or IMC . In summary , this work represents the first investigation of F-box proteins in apicomplexan parasites and reveals the first DCS-localized protein that is required for DCS function . Identification of TgFBXO1-interacting proteins will allow us to determine how TgFBXO1 executes this function . In addition , it remains to be determined whether TgFBXO1/TgSKP1 interactions are regulated by O2-dependent hydroxylation and glycosylation of TgSKP1 and whether these impact DCS function or another process .
Previously , 4 putative F-box-like motifs were identified in the Toxoplasma genome based on a hidden Markov algorithm [64] . Further BLASTp [65] and hidden Markov approaches using F-box-like motifs originating from known F-box domains from 7 crystal structures ( S1A Fig ) and the entire collection of budding and fission yeast F-box proteins ( S1B Fig ) identified 14 additional putative F-box proteins in Toxoplasma ( S1C Fig ) . Databases searched included EupathD ( https://eupathdb . org/eupathdb/ ) ( Toxoplasma , Neospora caninum , Plasmodium spp , and Eimeria spp . ) , Saccharomyces Genome Database ( https://www . yeastgenome . org ) , and PomBase ( https://www . pombase . org ) ( Schizosaccharomyces ) . Each candidate was used to re-search the genome until a comprehensive candidate list was created , which was trimmed of sequences that exceeded the range of variation at individual positions of known F-box sequences ( see S1A and S1B Fig ) with greatest emphasis given to the pattern of hydrophobic residues . Other domain identities were searched using Conserved Domains and Protein Classification search ( http://www . ncbi . nlm . nih . gov/Structure/cdd/cdd . shtml ) based on database CDDV3 . 05–42589 PSSMs with E<0 . 1 [66] . Searches were also performed at Uniprot ( www . uniprot . org/ ) . To identify TgFBXO1 homologues , the NCBI non-redundant database ( November 2018 ) was searched using the standard BLASTp algorithm at e<100 , initially with N- , F- , and C-domains ( S2 Fig ) of TgFBXO1 as query sequences . Except for the F-domain , only sequences from alveolates were retrieved . Repeat searches with non-apicomplexan alveolates failed to identify additional sequences outside of alveolates . Top-scoring sequences representing the diversity of known alveolate genomes were initially aligned in COBALT and manually curated for optimal alignment of hydrophobic residues and minimal indels except adjacent to Gly and Pro residues . All parasite strains , including parental RHΔhxgprt , TgSKP1SF [20] , TATiΔku80 [67] and RHΔhxgprtΔku80 ) strains [68] , were maintained in human foreskin fibroblasts ( ATCC; Manassas , VA ) in Dulbecco’s Modification of Eagle’s Medium ( DMEM ) ( VWR; Radnor , PA ) supplemented with 10% fetal bovine serum ( VWR ) , 2 mM L-glutamine ( VWR ) , and 100 IU/ml penicillin– 100 μg/ml streptomycin ( VWR ) . Parasites were released from host cells by passage through a 27-gauge needle [69] . All parasite strains and host cell lines were routinely tested for Mycoplasma contamination with the MycoAlert Mycoplasma Detection Kit ( Lonza , Basel , Switzerland ) and found to be negative . The HA ( ATC ) TgFBXO1 conditional expression mutant containing an N-terminal 3x-HA tag was generated by amplifying the 5’ end of TgFBXO1 using the following primers ( regions of homology to TgFBXO1 are underlined ) 5`-GCCAGATCTATGGGCAACACGGAATCC-3`and 5`GCCGCGGCCGCCGATTGATTTTCATGAACCAGTGTG 3` , digested with BglII/NotI and ligated to replace the CEP250 cassette in ptetO7sag4-HA-CEP250-DHFR-TS [29] . The resulting construct was linearized , transfected into the TATiΔku80 strain [29 , 67] , and clones isolated by limiting dilution using pyrimethamine resistance . TgFBXO1HA , containing a C-terminal 3X-HA tag , was generating by PCR amplifying the 3’ region of TgFBXO1 using primers 5’-TACTTCCAATCCAATTTAGCTGAAGCAAGTCCCGAAAGAT 3`AND 5’-TCCTCCACTTCCAATTTTAGCCGCATTGGCTCCGCCCT 3`and incorporated into plasmid pLIC-HA3X-HXGPRT via ligation independent cloning [68] . The construct was linearized and transfected into RHΔhxgprtΔku80 strain by electroporation and clones isolated by limiting dilution using mycophenolic acid/xanthine resistance . ptubTgFBXO1HA was generated by synthesizing TgFBXO1 coding sequence ( GenScript , Piscataway , NJ ) and ligating it into p5RT70DDmycRab11b-HXGPRT [14] to replace the DDmycRab11b cassette . Mutation of glycine at position 2 to alanine was accomplished using the Quick-Change Mutagenesis kit ( Agilent , Santa Clara , CA ) to generate ptubTgFBXO1HAG2A . Anti-TgSKP1 ( UOK75 ) was affinity purified using TgSKP1 , as described before for Dictyostelium Skp1A ( S3 Table and [28] ) , bound to protein A/G magnetic agarose beads ( Pierce , 78609 ) at 4 mg IgG protein/ml , and stably cross-linked with dimethyl pimelimidate as described [70] . Non-immune rabbit IgG ( Jackson ImmunoResearch; West Grove , PA ) was coupled in parallel in identical fashion . SDS-PAGE analysis indicated that >90% of the bound IgG was covalently linked . TgSKP1SF was captured using anti-FLAG M2 agarose magnetic beads ( Sigma ) . We used the minimal volume of beads required to maximally capture TgSKP1 ( ~60% ) or TgSKP1SF ( ~80% ) . RHΔhxgprtΔku80 or TgSKP1SF ( RHΔhxgprtΔku80;CAT+ ) tachyzoites were harvested from infected HFF monolayers by scraping and passage through a 27-gauge needle , centrifuged at 2000 × g for 8’ at room temperature , resuspended in phosphate-buffered saline , counted , pelleted , and frozen at -80°C . For TgSKP1SF and control untagged strain co-IPs with mAb M2 , lysates were prepared from both intracellular ( IC ) , and extracellular parasites ( EC ) . For the anti-TgSKP1 co-IPs with UOK75 and control non-immune IgG , RHΔhxgprtΔku80 parasites ( 60% intracellular ) were analyzed at two detergent concentrations . Frozen parasite pellets ( 2–6 × 108 ) were resuspended on ice in IP buffer ( 50 mM HEPES-NaOH , pH 7 . 4 with 0 . 2 , 0 . 5 or 1% Nonidet P-40 ) , varied ( 100 or 150 mM ) NaCl concentration , and protease inhibitors ( 1 mM PMSF , 10 μg/ml aprotinin , 10 μg/ml leupeptin ) . Lysates were centrifuged at 21 , 000 × g for 20 min at 4°C , and supernatants ( 1 . 2 ×108 cell equivalents ) wer incubated with 10 μl of antibody-conjugated beads under slow rotation for 1 h at 4°C . Beads were captured in a DynaMag-2 magnet ( Life Technologies ) the unbound fraction removed according to the manufacturer’s directions . Beads were then washed 3× with corresponding IP buffer , 3× with 10 mM Tris-HCl ( pH 7 . 4 ) , 50 mM NaCl , and once with 50 mM NaCl in water . Bound proteins were eluted twice with 60 μl 133 mM triethylamine ( TEA , Sequencing Grade , Pierce , 25108 ) by incubating for 15’ at 25°C and then immediate neutralization with 40 μl of 0 . 2 M acetic acid . The eluted fractions were pooled , dried under vacuum and reconstituted in 8 M urea in 10 mM Tris-HCl ( pH 7 . 4 ) . The reconstituted eluates were reduced in 10 mM DTT for 40 min at 25°C and alkylated in 50 mM 2-chloroacetamide for 30’ at 25°C . Samples were then diluted to 2 M urea with 10 mM Tris-HCl ( pH 7 . 4 ) and digested with 10 μg/ml Trypsin Gold ( Mass Spectrometry Grade , Promega; Madison , WI ) overnight at 25°C . Trypsin activity was quenched in 1% trifluoroacetic acid ( TFA , Pierce ) on ice for 15’ and centrifuged at 1 , 800 × g for 15 min at 4°C to remove precipitate . Peptides were enriched by adsorption to C18 pipette tips ( Bond Elut OMIX C18 , Agilent; Sunnyvale , CA ) and eluted in 100 μl 50% ( v/v ) acetonitrile ( ACN , Optima LC/MS Grade , Thermo Fisher; Waltham , MA ) , 0 . 1% ( v/v ) formic acid ( FA , LC-MS Grade , Pierce ) , followed by 100 μl 75% ACN , 0 . 1% formic acid . Eluted material was vacuum dried and reconstituted in 40 μl 5% ACN , 0 . 05% TFA . 4 to 8 μl of the reconstituted peptides were loaded onto an Acclaim PepMap C18 trap column ( 300 μm , 100 Å ) in 2% ACN , 0 . 05% TFA at 5 μl/min , eluted onto an Acclaim PepMap C18 column ( 75 μm × 150 mm , 2 μm , 100 Å ) , and eluted with a linear gradient consisting of 4–90% solvent B ( solvent A: 0 . 1% FA; solvent B: 90% ACN , 0 . 08% FA ) over 180 min at a flow rate of 300 nl/min on an Ultimate 3000 RSLCnano UHPLC system , and eluted into the ion source of an Orbitrap QE+ mass spectrometer ( Thermo Fisher ) . The spray voltage was set to 1 . 9 kV and the temperature of the heated capillary was 280°C . Full MS scans were acquired from m/z 350 to 2000 at 70k resolution , and MS2 scans following higher energy collision-induced dissociation ( HCD , 30 ) were collected for the Top10 most intense ions , with a 30” dynamic exclusion . Intervening blank runs ensured absence of carryover between replicates . The acquired raw spectra were analyzed using Sequest HT ( Proteome Discoverer 2 . 2 , Thermo Fisher ) with a full MS peptide tolerance of 10 ppm and MS2 peptide fragment tolerance of 0 . 02 Da , and filtered to generate a 1% target decoy peptide-spectrum match ( PSM ) false discovery rate for protein assignments , which were filtered at 1% FDR or relaxed at the protein level to 5% FDR ( as indicated ) . Variable modifications were Met oxidation , Q and N deamidation , and N-terminal acetylation; static modification was Cys carbamidomethylation . One hundred parasites were added to each well of a 6 well plate containing confluent HFFs in the presence or absence of 1μg/ml ATC . After 5 days , monolayers were methanol-fixed , stained with 0 . 1% crystal violet , and counted as described [71] . Briefly , plaques imaged using an Olympus SZ61 stereomicroscope equipped with a video camera . Plaque areas were measured using ImageJ software ( https://imagej . nih . gov/ij/ ) . TgFBXO1HA tachyzoites grown at 21% or 0 . 5% O2 in a Baker Hypoxia Chamber ( Sanford , ME ) were harvested by syringe lysis and washed in ice-cold PBS . Parasites ( 1x108 ) were resuspended in 50 mM Tris-HCl pH 7 . 4 with 1% Triton X-100 , 100 mM NaCl , 1 mM NaF , 0 . 5 mM EDTA , 0 . 2 mM Na3VO4 , 1X protease inhibitor cocktail ( Thermo Fisher Scientific ) , incubated on ice for 30 min and then subjected to sonication . Lysates were clarified by centrifugation at 16 , 000 xg , incubated with mouse α-anti-HA clone 12CA5 conjugated with protein G beads ( Sigma-Aldrich ) for 16 hours at 4°C . Immune complexes were separated with SDS-PAGE and then Western blotted with antibodies against rat anti-HA clone 3F10 ( Roche ) or rabbit anti-TgSKP1 UOK75 [19] . IMC fractionation was done essentially as described in [33] . Briefly , freshly harvested tachyzoites were resuspended in hypotonic lysis buffer ( 10 mM Tris HCl pH 7 . 8 , 5 mM NaCl ) and lysed by repeated freeze thawing followed by centrifugation at 1 , 000 xg to generate fractions S1 ( supernatant ) and P1 ( IMC ) . S1 was collected and centrifuged at 100 , 000 xg and fractions S2 ( cytosol ) and P2 ( non-IMC membrane ) were collected . Fraction P1 was resuspended in isotonic buffer ( 10 mM Tris HCl pH 7 . 8 , 150 mM NaCl ) . SDS or Triton X-100 in isotonic buffer were added to P1 to final concentration of 1% and then centrifuged at 16 , 000 xg . Alternatively , P1 fraction in the presence of 500 mM NaCl were centrifuged at 100 , 000 xg for 1 h to isolate proteins that interact with the IMC via electrostatic interactions . All manipulations were performed at 4°C and all buffers were supplemented with 1X Protease Inhibitor cocktail . Toxoplasma-infected HFFs grown on coverslips were fixed either with 4% w/v paraformaldehyde in phosphate buffered saline ( PBS ) for 20 min at room temperature or by ice-cold methanol for 15 min . Cells were permeabilized with 0 . 1% Triton X-100 in PBS for 10 min , blocked in 5% w/v bovine serum albumin ( BSA ) in PBS for 60 min , incubated overnight with primary antibodies ( S2 Table ) at 4°C . Coverslips were washed with PBS and then incubated for 60 ‘with Alexa Fluor 488- or Alexa Fluor 594-conjugated secondary antibodies ( 1∶2000 , Thermo Fisher Scientific ) . DNA was stained either by incubation with 1 μg/ml DAPI ( Thermo Fisher Scientific ) for 5 minutes followed by mounting in ProLong Glass Antifade Mountant ( Thermo Fisher Scientific ) or with DAPI-containing VECTASHIELD mounting medium ( Vector Labs; Burlingame , CA ) . Images were acquired using a 100X Plan Apo oil immersion 1 . 46 numerical aperture lens on a motorized Zeiss Axioimager M2 microscope equipped with an Orca ER charge-coupled-device ( CCD ) camera ( Hamamatsu , Bridgewater , NJ ) . Images were collected as a 0 . 2-μm z-increment serial image stacks , processed using Volocity ( version 6 . 1 , Acquisition Module ( Improvision Inc . , Lexington , MA ) ) . Images were deconvolved by a constrained iterative algorithm , pseudocolored , and merged using the Volocity Restoration Module . For super resolution microscopy , 20 images were acquired as 0 . 15 μM z-increment serial images using Leica Hyvolution Acquisition Software ( Leica; Buffalo Grove , IL ) with a Leica TCS SP8 confocal microscope equipped with a 100x/1 . 47 TIRF oil immersion objective lens and both white light laser ( 470nm-670nm ) and 405 nm diode laser . Image datasets were then deconvolved using Huygens deconvolution software ( Hilversum , Netherlands ) and 3D volumes generated Leica visualization software . Images from same experiments were processed using identical settings . Unless otherwise noted , data were quantified from at least 50 randomly selected images for each condition from three independently performed experiments . When appropriate , one-way ANOVA with Tukey’s post hoc test or Student’s t test was performed with GraphPad Prism ( GraphPad , La Jolla , CA ) . | Toxoplasma gondii is a protozoan parasite that can cause devastating and life-threatening disease in immunocompromised patients and in fetuses . Its replication is important to study because parasite growth is responsible for the pathology that develops in toxoplasmosis patients . The parasite replicates by a unique process named endodyogeny in which two daughter parasites develop within the mother cell . Early during this process the parasite creates a structure called the Daughter Cell Scaffold whose function is to mark the site from which daughter parasites will emerge . Here , we report the identification of one of the first proteins recruited to the Daughter Cell Scaffold and its importance in executing its function . | [
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"ana... | 2019 | Toxoplasma F-box protein 1 is required for daughter cell scaffold function during parasite replication |
Multivariate decoding methods , such as multivoxel pattern analysis ( MVPA ) , are highly effective at extracting information from brain imaging data . Yet , the precise nature of the information that MVPA draws upon remains controversial . Most current theories emphasize the enhanced sensitivity imparted by aggregating across voxels that have mixed and weak selectivity . However , beyond the selectivity of individual voxels , neural variability is correlated across voxels , and such noise correlations may contribute importantly to accurate decoding . Indeed , a recent computational theory proposed that noise correlations enhance multivariate decoding from heterogeneous neural populations . Here we extend this theory from the scale of neurons to functional magnetic resonance imaging ( fMRI ) and show that noise correlations between heterogeneous populations of voxels ( i . e . , voxels selective for different stimulus variables ) contribute to the success of MVPA . Specifically , decoding performance is enhanced when voxels with high vs . low noise correlations ( measured during rest or in the background of the task ) are selected during classifier training . Conversely , voxels that are strongly selective for one class in a GLM or that receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against . Furthermore , we use simulations to show that this is a general property of fMRI data and that selectivity and noise correlations can have distinguishable influences on decoding . Taken together , our findings demonstrate that if there is signal in the data , the resulting above-chance classification accuracy is modulated by the magnitude of noise correlations .
The development of fMRI has made it possible to observe the human brain noninvasively as it responds to stimuli or engages in cognitive tasks . For example , participants might be presented with a series of stimuli drawn from two or more categories ( e . g . , faces and scenes ) , while the blood oxygenation level-dependent ( BOLD ) contrast is measured over time from tens of thousands of volumetric pixels ( voxels ) . Different events in the experiment can then be linked to changes in BOLD activity , permitting inferences about the neural basis of cognition ( in the example above , about category-selective object perception ) . However , this is a challenging endeavor because both the physiological processes underlying BOLD activity and the measurement of BOLD activity with fMRI are noisy , and because the resulting datasets can be large and statistically complex [1 , 2] . Traditionally , fMRI analyses have focused on the information contained in the timecourse of individual voxels or regions . Such methods are “univariate” because they seek to relate experimental events to single dimensions of BOLD variability , such as the activity averaged across voxels in a region of interest ( ROI ) . Univariate methods have long been the dominant approach when using brain-imaging data to draw inferences about the neural basis of different aspects of cognition [3] , including: object perception [4] , episodic memory [5] , and cognitive control [6 , 7] . However , given that cognitive processes are often realized in highly distributed [8] and dynamic [2] ways in the brain , and given that fMRI data have considerable spatial resolution and thus natively live in a high-dimensional space [9] , performance achievable with univariate methods may be inherently limited . A different class of analyses , multivariate pattern analysis ( MVPA ) , was developed to examine such complex neural representations , treating patterns of BOLD activity across voxels and their link to experimental events as a classification problem [1 , 10] . MVPA involves training a simple statistical model , in a supervised fashion , to extract regularities in patterns of BOLD activity obtained from different experimental conditions . The trained model is then used to classify or decode the condition under which previously unanalyzed test data were obtained . MVPA has led to a wide range of discoveries about the human brain that often go beyond those achievable by applying univariate methods to the same data , including about: perception [11 , 12] , attention [13–15] , memory [16–19] , language processing [20 , 21] and decision-making [22 , 23] . Although MVPA has been successful across a range of applications , why it is successful has been harder to pin down [10 , 24 , 25] . One early and still prominent proposal is that MVPA is sensitive to local biases in the manner in which sub-voxel information is represented across populations of voxels [8 , 11 , 12] . For example , orientation information in the primary visual cortex is represented in sub-millimeter columns [26 , 27] and thus would be obscured at the level of voxels , which typically span a couple of millimeters . However , because the distribution of orientation columns across voxels is irregular , any given voxel may have a random over-representation of , and thus a weak bias toward , a particular orientation . Prior studies have argued that by aggregating such weak biases across a population of voxels , the orientation of a stimulus can be reliably decoded using MVPA [11] . Another possibility is that MVPA allows for the identification of information represented at a larger scale that spans multiple , spatially disparate voxels . For instance , it is possible to decode stimulus orientation based on the systematic way in which areas of retinotopic visual cortex over-represent the orientation perpendicular to the radius from the fovea [28] . Regardless of the scale of neural representations , the assumption underlying this prior work is that considering patterns of activity across voxels rather than averaging over them ( as in univariate ROI analyses , for example ) provides additional or different sensitivity . These theories view neural representations as points in a high-dimensional activity space , with each voxel in the pattern representing a potentially informative dimension . Although two stimulus categories may be hard to distinguish along any one dimension in this space , jointly considering many voxels allows for better inference by exploiting more dimensions of information . This interpretation of MVPA downplays an important factor known to influence the representation of information in populations of neurons—that neural variability is correlated in vivo [29–33] . Both experimental [29 , 30 , 32] and computational [34–36] studies have shown that correlations in neural variability have a significant impact on the information content of neural populations; see [37 , 38] for reviews . More relevant for present purposes , accurate decoding depends on taking such noise correlations into account [39 , 40] . Given that noise correlations are important for neural decoding , they may also influence decoding of fMRI data . Indeed , noise correlations amongst voxels are widespread in fMRI , both during rest [41 , 42] and in the background of tasks [43 , 44] , driven in part by anatomical connections [45] . Yet , prevailing interpretations of why multivariate decoding is effective have not sufficiently acknowledged the relevance of noise correlations to the decoding of information from populations of voxels . This is not to say that the classification algorithms themselves disregard correlations among voxels . Indeed , in most cases these algorithms are sensitive to the presence of correlations [46] , and decoding performance is influenced by them . Our argument is instead that prevailing interpretations of why MVPA is effective generally center on the benefits of aggregating the information conveyed by patterns of mean activity across voxels , and overlook the influence of correlations . Even when theories have explicitly considered the influence of correlations , they have generally considered signal correlations: moment-to-moment correlations in the representation of task-dependent stimulus information across multiple voxels in the population ( i . e . , overlap in the representation of the underlying signal across multiple voxels ) . For instance , if two voxels contain the same signal across training patterns , classification algorithms such as support vector machines ( SVM ) and regularized logistic regression can assign one voxel a higher weight than the other [47] . Here we propose that noise correlations—which exist persistently before , during , and after experimental events—help explain the effectiveness of MVPA . In contrast to signal correlations , noise correlations reflect the extent to which noise in the activity of a voxel is correlated with noise in the activity of other voxels in the population . The theory that motivates this hypothesis is from a recent computational study [36] . This study showed that the impact of noise correlations on multivariate decoding depends on whether the correlations are between neurons from homogeneous vs . heterogeneous populations , with the latter being beneficial and the former being detrimental . When considering homogeneous populations—neurons that code for the same stimulus variable—decoding performance worsens as noise correlations increase . That is , when neurons in a population are selective for the same stimulus , lower noise correlations between them allow the decoder to exploit more dimensions of information . Indeed , experimental [29 , 30 , 32] and computational studies [48] have found a relation between lower noise correlations in homogeneous populations and increased information . Importantly , in contrast to homogeneous populations , decoding performance for heterogeneous populations of neurons that code for different stimulus variables can improve as noise correlations increase [36] . The intuition is that , given a constant amplitude of noise , the presence of noise correlations between neurons coding for different stimulus variables allows a multivariate decoder to recognize that the correlated ( or shared ) variance can be attributed to dimensions that are irrelevant for discriminating between the variables , and can thus be ignored . This reduces the dimensionality of the classification problem and , more importantly , the amount of overlap between the categorical distributions , thereby improving performance [49] . Indeed , a recent theoretical study [46] similarly argued that weight vectors in decoding models , such as MVPA , are influenced by both the signal and noise in brain imaging data , thereby suggesting a similar influence of heterogeneous noise correlations on classification performance . Here we extend this theory—developed [36] and supported [50–52] at the level of neurons—to populations of voxels in fMRI ( Fig 1 ) . Two challenges arise from this extension: First , it is impossible to know whether a given voxel contains a homogenous neuronal population and even whether multiple voxels with similar selectivity can be considered truly homogenous . Thus , we focus on the theoretical predictions associated with decoding from heterogeneous populations ( i . e . , that noise correlations among voxels selective for different stimuli will improve decoding of these stimuli ) . Second , the theory was developed to account for the influence of positive noise correlations . However , negative correlations can arise in fMRI ( e . g . , depending on preprocessing steps ) , so our analyses consider the influence of both positive and negative noise correlations . We find that MVPA decoding performance is influenced not only by the selectivity of individual voxels but also by noise correlations between heterogeneous populations of voxels . Across several analyses of an fMRI dataset , we demonstrate a positive relationship between the magnitude of noise correlations and decoding performance , and we show that as expected with such classifier algorithms [46 , 49] , MVPA exploits noise correlations by assigning higher weights to voxels with higher noise correlations . We also show that selectivity and noise correlations influence decoding in a complementary fashion—as long as there is signal in the data , performance is modulated by the magnitude of noise correlations . Indeed , voxels that were highly selective for one class also exhibited higher noise correlations with voxels selective for the other class . Finally , using a simple model of BOLD activity , we simulate different levels of selectivity and noise correlations in artificial data and show that the benefit of noise correlations for decoding is a ubiquitous property of fMRI data beyond the example dataset .
We used a subset of the data from an fMRI study on attentional control [53] . Seventeen participants were presented with blocks of face or scene stimuli interleaved with blank periods during two “localizer” runs . In addition , data were collected during two “rest” runs in which participants only fixated a central point . Using one of the localizer runs , we fit a general linear model ( GLM ) to the activity observed in ventral temporal cortex , and labeled each voxel as either face-selective or scene-selective based on whether that voxel had greater activation in response to the presentation of face vs . scene stimuli . Then , we used the rest runs to compute noise correlations , since there were no stimuli or tasks in these runs . We were specifically interested in heterogeneous noise correlations ( i . e . , noise correlations between voxels with different selectivity ) and thus calculated , for every voxel , the average correlation between its timecourse and the timecourse of all voxels selective for the opposite category . Finally , to examine how these noise correlations influenced decoding performance , we selected voxels from both face- and scene-selective populations with either high or low noise correlations , and used the other , separate localizer run to train and cross-validate a multi-way ( face/scene/blank ) classifier based on the patterns of activity from these voxels . If MVPA is sensitive to noise correlations , then classification accuracy should be better for patterns of activity from voxels that are strongly vs . weakly correlated with voxels selective for the opposite category . As a first pass , we focused on voxels with the highest vs . lowest 1% of noise correlations ( Fig 2A ) and found that classification was better for voxels with the highest noise correlations ( t16 = 7 . 24 , p < 0 . 0001 ) . This sorting was based on raw values ( high more positive , low more negative ) , but the same result was obtained when we analyzed positive correlations ( high more positive , low closer to zero; t16 = 4 . 12 , p < 0 . 001 ) and negative correlations ( high closer to zero , low more negative [54]; t16 = 3 . 19 , p < 0 . 01 ) . For a more continuous sense of this relationship , we divided voxels into percentiles of raw noise correlations ( Fig 2B ) . Classification accuracy improved monotonically as MVPA was applied to voxel sets with greater noise correlations ( slope vs . 0: t16 = 6 . 66 , p < 0 . 0001 ) . Taken together , these results demonstrate a clear influence of the magnitude of heterogeneous noise correlations on decoding performance . We chose an arbitrary , small bin size of voxels ( 1% ) in the analyses above . To examine how this parameter affected our findings , we repeated the analysis of raw values with larger bin sizes of high and low noise correlations: 6% , 12 . 5% , 25% , 37 . 5% and 50% ( Fig 3 ) . While overall decoding performance improved with increasing bin size , decoding was consistently better for patterns of activity from voxels with high vs . low noise correlations ( ps < 0 . 02 ) . A 2 ( noise correlation magnitude: high vs . low ) x 6 ( bin sizes ) repeated-measures ANOVA revealed that the difference was greater for smaller bin sizes: In addition to main effects of noise correlation magnitude ( F1 , 16 = 28 . 57 , p < 0 . 0001 ) and bin size ( F5 , 80 = 164 . 30 , p < 0 . 0001 ) , there was a reliable interaction between these variables ( F5 , 80 = 14 . 12 , p < 0 . 0001 ) . This interaction is also consistent with the monotonic relationship across percentiles reported above ( Fig 2B ) : As bin size increased , both the high and low sets included more voxels with intermediate magnitudes of noise correlation , thereby bringing performance closer to the mean across magnitudes . The analyses above use an L2-norm regularized logistic regression classifier for MVPA . Such regularization helps avoid over-fitting—which was a risk given that the number of samples in the training set was much smaller than the number of voxels whose weights were learned—by constraining the learning process . In the case of L2-norm regularization , the sum of squares of the voxel weights is penalized ( here , penalty parameter = 1 ) . Because all voxels contribute to this sum , this regularization induces interactions between voxels when determining weights . It could be possible that the influence of noise correlations on decoding performance reflects their effects on such interactions per se rather than the placement of the classifier boundary . To evaluate this possibility , we repeated the bin size analysis with regularization turned off . Classification accuracy decreased across the board ( presumably because of over-fitting ) , but we still found greater accuracy for high vs . low noise correlations ( S1 Fig ) . This suggests that the benefit of noise correlations was not an artifact of regularization . So far , we have calculated noise correlations from the rest runs and performed classification on the localizer runs . In using a different run to compute noise correlations , we tacitly assumed that they were stationary across rest and localizer runs . However , noise correlations may depend on the task condition or may be most closely tied to decoding when actually obtained from the data being decoded . To examine this possibility , we computed noise correlations between voxels during the localizer run used for crossvalidation . This is challenging because stimulus-evoked responses can induce signal correlations . Thus , we first regressed out these responses ( and global noise sources ) and examined BOLD correlations in the residuals . This “background connectivity” approach has been used successfully across a range of tasks to study noise correlations [44 , 55–57] . We again identified face- and scene-selective voxels from one localizer run , but then calculated heterogeneous noise correlations ( i . e . , in the residuals ) and classified the other localizer run . The pattern of results was nearly identical to that obtained when noise correlations were calculated from the separate rest runs , as seen by repeating the bin size analysis ( Fig 4A ) . Classification accuracy was again consistently better for high vs . low noise correlations ( ps < 0 . 01 ) , and there were main effects of noise correlation magnitude ( F1 , 16 = 18 . 28 , p < 0 . 001 ) and bin size ( F5 , 80 = 152 . 77 , p < 0 . 0001 ) , and an interaction ( F5 , 80 = 6 . 78 , p < 0 . 0001 ) . In fact , the heterogeneous noise correlation for a given voxel was fairly stable across rest and localizer runs ( Fig 4B ) . This was quantified with Spearman’s rank order correlation across voxels within participant ( mean rho = 0 . 21; t16 = 5 . 30 , p < 0 . 0001 ) . Given these results , and because the rest dataset was fully separate , we returned to using the rest runs for calculating noise correlations in the remaining analyses . We next compared the classification accuracy obtained by selecting voxels with high or low noise correlations in the rest runs across the six bin sizes to classification accuracy obtained for sets of voxels of equal size chosen randomly ( irrespective of noise correlation ) . If MVPA automatically exploits noise correlations in a given population of voxels , as long there are enough voxels in the population with high noise correlations , MVPA should assign high weights to these voxels and achieve similar performance to a classifier trained only on voxels with high correlations . For the smallest bin size of 1% , the high noise correlation set produced better decoding performance than the random set ( t16 = 2 . 38 , p = 0 . 03 ) , consistent with the notion that there were not enough voxels with high noise correlations in the random set ( Fig 5 ) . However , starting at the 6% bin size , decoding performance was indistinguishable between high noise correlation and random sets ( ps > 0 . 09 ) . Critically , highlighting the efficiency of MVPA at exploiting noise correlations , the random sets exceeded the low noise correlation sets at all bin sizes ( ps < 0 . 001 ) . Taken together , these results suggest that a small number of voxels with high correlations dominate MVPA decoding performance even when considering large sets of voxels . We assumed in the previous analysis that MVPA as typically applied ( i . e . , without explicitly considering noise correlations during feature selection ) performed as well as MVPA over voxels with high noise correlation because it automatically assigned these voxels higher weights . Here we test this directly by carrying out MVPA over all ventral temporal voxels without feature selection and examining the relationship between assigned classifier weights and average heterogeneous noise correlations . That is , if a voxel was determined to be face-selective in one localizer run , how correlated was ( a ) its average noise correlation with scene voxels in the rest runs , with ( b ) its weight assigned for the face category in a classifier trained on the second localizer run ? We first summarize this relationship using a median-split analysis on the noise correlations ( Fig 6 ) , which revealed that voxels with higher noise correlations were assigned higher weights ( t16 = 3 . 96 , p = 0 . 001 ) . Another way to look at this relationship is to calculate the Spearman rank order correlation between noise correlation and classifier weight across voxels . This correlation was reliable across participants ( mean rho = 0 . 045; t16 = 3 . 58 , p = 0 . 002 ) . The analyses above demonstrate that MVPA decoding performance is enhanced when voxels with high vs . low noise correlations ( measured during rest or in the background of the task ) are selected during classifier training , and that voxels which receive high classification weights in MVPA tend to exhibit high noise correlations with voxels selective for the other class being discriminated against . However , in addition to the magnitude of noise correlations , decoding performance is also influenced by the selectivity of individual voxels ( i . e . , how differently a voxel responds to the conditions being classified ) . In this section , we examine the relative influence of selectivity on MVPA decoding performance . We first consider the extent to which selectivity and noise correlations interact . For instance , when we divided voxels in our dataset into percentiles of raw noise correlations , we observed a monotonic improvement in MVPA decoding performance with an increase in the magnitude of noise correlations ( Fig 2B ) . How does selectivity vary across these sets of voxels ? To answer this question , we took the absolute value of the selectivity scores that had been used to identify face and scene voxels in one localizer run ( i . e . , for determining which voxels should count as having opposite selectivity when calculating heterogeneous noise correlations ) . As a reminder , these scores reflect the face vs . scene contrast from the GLM , specifically the z-scored difference of the parameter estimates modeling the average evoked response from face and scene blocks , respectively . Average selectivity increased monotonically ( Fig 7 ) as we moved from voxels with low noise correlations to voxels with high noise correlations ( slope vs . 0: t16 = 5 . 03 , p < 0 . 001 ) , and the Spearman rank order correlation between noise correlation and selectivity across voxels was reliable ( mean rho = 0 . 076; t16 = 4 . 17 , p < 0 . 001 ) . In other words , voxels with higher selectivity for one of the two categories also had higher noise correlations with voxels selective for the other category . Given the link between selectivity and noise correlations across voxels in our empirical dataset , we next sought to examine their cumulative influence on decoding . We selected voxels with the top vs . bottom 12% of noise correlations , and within each set selected voxels with high vs . low selectivity based on a median split of voxel selectivity from the GLM . We then examined MVPA classification accuracy for the patterns of activity from voxels in each of the resulting four bins with 6% of voxels ( Fig 8 ) . Of particular note in this analysis is the comparison between low noise correlation/low selectivity and high noise correlation/low selectivity , which had comparable levels of selectivity ( 1st and 3rd columns of Fig 8A ) but dramatically different classification accuracy ( same columns of Fig 8C ) . This suggests that as long as there is a minimum amount of signal conveyed by selectivity , which allows for above-chance classification , noise correlations can be sufficient to increase decoding performance ( same columns of Fig 8B ) . This claim is further reinforced by the comparison of low noise correlation/high selectivity to high noise correlation/low selectivity . Although there was a dramatic difference in signal conveyed via selectivity ( 2nd and 3rd columns of Fig 8A ) , classification accuracy did not differ and was in fact numerically in the opposite direction ( same columns of Fig 8C ) , suggesting that the selectivity difference was offset by the reverse difference in noise correlations ( same columns of Fig 8B ) . Taken together , these results support the notion that when selectivity differences are present , noise correlations can influence classification accuracy . So far , we have used an existing fMRI dataset to demonstrate that MVPA is highly attuned to noise correlations between voxels , and that decoding performance may be sensitive to the information carried both by the selectivity of individual voxels and the noise correlations between them . We next sought to expand upon these findings in two ways: First , as described above , selectivity and noise correlations were inherently confounded in the empirical dataset . How might we better examine the cumulative contributions of noise correlations and selectivity to decoding performance ? Second , all of the findings reported above were based on one fMRI dataset with particular characteristics . To what extent do our conclusions apply to other datasets and reflect a general principle about the computational underpinnings of MVPA ? To address these issues , we developed a simple model of selective coding in the presence of noise correlations , wherein we could independently vary voxel selectivity and heterogeneous noise correlations . By performing MVPA over artificial BOLD activity generated from this model , we could then simulate the influence of different parameters . The model included a set of voxels roughly matched in number to the 1% bin size in our earlier analyses . By construction , half of the voxels responded preferentially to face stimuli and the other half to scene stimuli . The mean responses , variances , and correlations of all voxels in the model were drawn from the range observed in our empirical dataset , ensuring that the simulated voxels produced physiologically realistic activity . Following Azeredo da Silveira & Berry ( 2014 ) , we used a Gaussian approximation in each of 100 model “participants” to sample data for time points from face and scene blocks ( matched to the number of face and scene TRs in the empirical dataset ) . To ensure that the resulting timecourses were temporally autocorrelated like real BOLD activity [58] , we convolved them with a canonical hemodynamic response function ( HRF ) . Finally , we performed cross-validated MVPA over the artificial patterns of activity obtained from the simulated voxels . We first sought to examine the influence of heterogeneous noise correlations on decoding performance . Noise correlations across pairs of voxels varied according to whether the voxels were drawn from the pool of face-selective voxels , the pool of scene-selective voxels , or one from each of the pools . We performed 20 simulations manipulating the magnitude of across-pool noise correlations linearly between 0 and 0 . 22 ( i . e . , the range of positive noise correlations in the empirical dataset ) , while holding all other parameters constant . There was a monotonic increase in classification accuracy as the magnitude of heterogeneous noise correlations increased ( blue curve in Fig 9A ) . This is precisely the pattern predicted by the computational theory on which our study was based [36] , and is similar to the pattern of results observed in our empirical dataset . Notably , by allowing noise correlations to vary while the selectivity of the voxels in the two pools was held constant , these results show that noise correlations are sufficient to influence above-chance decoding performance . To examine the influence of voxel selectivity , we repeated the analysis above but further manipulated the strength of face and scene selectivity in the mean responses of voxels from the two pools , over a fixed range of noise correlations . As expected , when voxel selectivity decreased across three levels , overall decoding performance also decreased ( Fig 9A ) . However , at all levels , we observed the same monotonically increasing relationship between classification accuracy and the magnitude of noise correlations . Notably , selectivity affected classification accuracy even with near-zero noise correlations , but the effect of selectivity was stronger in the regime of stronger noise correlations . We next sought to examine the extent to which these results depend on the specific parameters used in our simulations . For instance , in the simulations described thus far , the variance of all voxels was matched to the median variance observed in our empirical dataset . Given that overall noise in the system , correlated or otherwise , is ultimately a function of the variability in the activity of individual voxels , we examined the extent to which our results depended on the magnitude of voxelwise variance . Repeating our analysis across three levels of variance , spanning the range observed in our empirical dataset , we found a similar influence of noise correlations on classification accuracy ( Fig 9B ) . Specifically , as voxel variance increased , thereby increasing noise in the system , overall decoding performance went down; however , at every level of variance , we observed the same relationship between classification accuracy and the magnitude of noise correlations . Another modeling choice we made was to sample activity within the face- and scene-selective voxel pools based on homogeneous mean , variance , and correlation values matched to population averages from our empirical dataset . We next examined the influence of introducing heterogeneity in the response properties of the simulated population of voxels . We generalized our model to include greater voxelwise diversity by randomly varying the population covariance matrix according to a Gaussian distribution with SD equal to 10% of the original value . We similarly varied the mean responses of individual voxels ( while maintaining selectivity ) in each population according to a Gaussian distribution with SD matched to the mean within-population SD from our empirical dataset . We obtained the same pattern of results from MVPA , with classification accuracy increasing monotonically as the magnitude of noise correlations increased ( Fig 9C ) . Indeed , greater population diversity led to a steeper increase in classification accuracy , consistent with the notion that heterogeneity can be beneficial , especially at higher levels of noise correlation .
MVPA has proven useful for decoding information from brain imaging data [1 , 10] , with insights often extending what has been learned from univariate methods . Although the effectiveness of MVPA has been widely acknowledged , which aspects of neural representation MVPA taps into are still debated [2 , 10 , 24 , 25 , 28] . Prior theories argued that MVPA benefits from aggregating signals across voxels—either local biases in the mapping of micro-scale representations onto voxels [11 , 25] or more global , macro-scale representations that span multiple voxels [28] . In both cases , the argument was that MVPA exploits the distribution of weak or uncertain feature-selective signals to identify regularities that discriminate experimental conditions . Our findings show that this interpretation is incomplete: Instead of thinking of each voxel as making a distinct contribution to the information represented collectively by the population of voxels , MVPA is also highly attuned to noise correlations between voxels . This reflects the mechanics of classification algorithms [49] and builds on neurophysiological studies showing both that noise correlations impact the information content of neural populations [36–38] and that accurate decoding of this information requires taking these noise correlations into account [39 , 40] . Specifically , our study was inspired by a recent computational theory [36] , which proposed that multivariate decoding is enhanced for heterogeneous neural populations with high noise correlations . Extending this proposal to the problem of multivariate decoding with fMRI data , we show that noise correlations between heterogeneous populations of voxels influence MVPA . The same result was obtained across numerous analyses , with the magnitude of noise correlations positively related to classification accuracy . Indeed , MVPA tends to assign greater weights to voxels with high noise correlations . Furthermore , by constructing a simple model that produces artificial BOLD data , we were able to simulate the complementary effects of noise correlations and selectivity on decoding and show that our results generalize across parameter settings . Why do noise correlations influence multivariate decoding ? Most common forms of MVPA work by finding some kind of discrimination boundary or hyperplane in a high-dimensional activity space ( Fig 1 ) . Due to variability in BOLD activity , each class to be discriminated is represented by a multivariate distribution in this space , and classification errors result from overlap in these distributions . The intuition is that sensitivity to the noise correlations between voxels coding for different classes allows MVPA to ignore components of variance shared between classes ( and thus unhelpful for discriminating between them ) by down-weighting dimensions on which this variance loads . This reduces the effective dimensionality of the classification problem , lessening over-fitting given the same amount of training data , and minimizing the overlap between multivariate distributions , thereby improving discrimination between classes . A recent study [46] similarly considered the influence of noise in BOLD activity on multivariate decoding methods . Specifically , it examined the strategies that are typically used to draw inferences from brain imaging data , and sought to distinguish between “forward models” ( e . g . , GLMs ) evaluating the manner in which experimental variables are encoded in the brain , and “backward models” ( e . g . , MVPA ) seeking to read out experimental variables from brain data . The authors showed that the presence of noise makes the weights from backward models uninterpretable , because these weights are necessarily functions of both signal and noise in the data . In other words , the weight assigned to a given “channel” ( or voxel in the case of fMRI ) need not only reflect how well it represents the signal of interest—it may be assigned a high weight if the structure of noise in this channel also contributes to the classifier’s effectiveness . Our study complements and builds upon this and other prior work . Inspired by a computational theory in the literature [36] , and in line with [46] , we argue that MVPA is effective precisely because it is sensitive to both the signal and noise ( i . e . , selectivity and noise correlations ) in patterns of activity across populations of voxels , and the weights assigned to voxels are functions of both of these variables . Our findings go beyond these prior theoretical proposals by providing empirical evidence from a real fMRI dataset and by simulating fMRI data with a range of characteristics . We demonstrate that if a voxel has high noise correlations with voxels selective for the other class , then considering this voxel’s activity allows the classifier to find a better decision boundary ( by providing an excellent marker for the noise ) , thus resulting in the classifier assigning this voxel a higher weight . Furthermore , our analysis scheme highlights a previously overlooked empirical result: The representation of information in human ventral temporal cortex seems to be dominated by a small subset of voxels that are both highly selective for one of the task-relevant categories , and also exhibit high noise correlations with voxels selective for the other category . Thus , at least with the dataset considered here , sensitivity to both selectivity and noise correlations makes MVPA particularly effective at extracting the relevant information . Finally , using a model to simulate different levels of signal and noise in the data , we show that the benefit of noise correlations for decoding is a broadly applicable property of fMRI data , and illustrate how various network parameters influence this finding . Taken together , although other studies have proposed similar ideas from a theoretical perspective , to our knowledge no prior study has validated them at this level of detail , using both empirical and simulated data , and shown how they play out in practice in the context of a widely used multivariate decoding strategy . In this study , we focused on the classification of face and scene information from ventral temporal cortex as a canonical example of the kind of problem for which MVPA has proven effective . Moreover , this dataset was well suited to an initial exploration of the influence of noise correlations because it contained multiple rest and task runs for each participant allowing for independent definitions of selectivity and noise correlations . We expect that our conclusions will apply to multivariate decoding with brain imaging data more generally . Indeed , the findings from our model—where we observed a similar pattern of results with artificial datasets generated from simulated populations of voxels with a range of physiologically realistic selectivity profiles and noise correlation structures—provide an initial validation of the general applicability of our conclusions . Nevertheless , it will be important for future studies to apply the approach outlined here to other datasets and brain regions . Another caveat relates to our inability to draw conclusions at the level of neurons from fMRI data . Each voxel in fMRI likely reflects the activity in thousands of neurons , with the exact sampling of neural responses by voxels inaccessible to analysis . Furthermore , the BOLD signal obtained from each voxel reflects a change in blood oxygenation across a broader swath of brain tissue than the neural activity that precipitated this influx of metabolic resources , blurring the link between BOLD contrast and local neural activity . As such , we cannot directly link noise correlations in voxels to noise correlations in neurons , nor draw definitive inferences at the neuronal level from our fMRI results . Nevertheless , at a different level of analysis , our findings support the computational theory that noise correlations can be helpful for extracting information from the brain . As such , although often overlooked , noise correlations should be considered when interpreting the basis and meaning of MVPA .
Nineteen naïve adults with normal or corrected-to-normal vision participated for monetary compensation . Two participants were excluded because of excessive head motion . The Princeton University Institutional Review Board approved the study protocol and all participants provided informed consent . Each participant completed two face/scene “localizer” runs , each of which consisted of an alternating on-off block design , with 18-s blocks of stimulation interleaved with 18-s blocks of “blank” passive fixation . Stimulation blocks contained 12 1-s presentations of either face or scene images ( the order of face and scene blocks was counter-balanced across participants ) , each separated by a 500-ms inter-stimulus interval . Face images consisted of 24 photographs from the NimStim dataset ( http://www . macbrain . org/resources . htm , neutral expressions ) and scene images consisted of 24 photographs of single houses collected from the internet and stock photograph discs [59] . Images were presented in grayscale , cropped using a circular mask , and subtended 6° of visual angle in radius . In one run , face and scene stimuli were presented in the left visual field , and in the other run , face and scene stimuli were presented in the right visual field . Each run began with a 9-s fixation period and included a total of 12 blocks of stimulation ( 6 face , 6 scene ) , which lasted 7m 21s . During blank periods , participants were presented only with a central , white point to fixate ( radius = 0 . 2° ) . Data from two “rest” runs were also collected for each participant , during a second session . Each rest run had the same duration as the localizer runs , but with only the central fixation point . Participants were instructed to passively view the fixation point without performing any overt task . fMRI data were acquired with a 3T scanner ( Siemens Skyra ) using a 16-channel head coil . Functional images for both the localizer and rest runs were acquired with a T2* gradient-echo echo-planar imaging sequence ( repetition time [TR] = 1 . 5 s; echo time [TE] = 28 ms; flip angle [FA] = 64°; matrix = 64 x 64; resolution = 3 x 3 x 3 . 5 mm ) , with 27 interleaved axial slices aligned to the anterior/posterior-commissure line . TRs during the localizer were time-locked with the presentation of photos . In addition , a high-resolution T1 MPRAGE anatomical scan was acquired for spatial registration . To improve registration , an additional T1 FLASH anatomical scan was acquired at the end of each session , co-planar to the functional scans . To correct for B0-field inhomogeneity , phase and magnitude field maps were collected at the end of all sessions , co-planar to the functional scans and with the same resolution . fMRI data were analyzed using FSL ( http://fsl . fmrib . ox . ac . uk/fsl/ ) and Matlab ( MathWorks ) . All functional images were skull-stripped to improve registration , and registered to the anatomical images , and the MNI standard brain . The volumes from the initial 9-s fixation period were removed and the remaining volumes were corrected for slice-acquisition time and head motion , high-pass filtered ( 100-s period cutoff ) and spatially smoothed ( 5 mm FWHM ) . Despite potentially blurring the spatial activity patterns used for classification , we applied spatial smoothing for two reasons: First , we wanted our preprocessing and analysis steps to replicate as closely as possible a standard fMRI study in order to quantify how noise correlations influence decoding performance in a situation frequently encountered in cognitive neuroscience . Second , there is debate about the benefits/costs of spatial smoothing for MVPA ( benefit being reduced noise , cost being dampened patterns ) , but the evidence suggests that smoothing with the amount we used is likely beneficial to performance , especially for categorical distinctions [60] . Nevertheless , it will be important in the future to further investigate the impact of smoothing on noise correlations ( and how this impacts their utility for feature selection ) . Data from the localizer and rest runs were masked to include the temporal occipital fusiform cortex and the parahippocampal gyrus ( posterior division ) , defined anatomically from the Harvard-Oxford cortical atlas in standard MNI space . These regions were chosen because of their general preference for face and scene stimuli , respectively . This mask produced a median of 5875 voxels , which varied less than 2% across participants because of small changes in head position . To identify voxels as face- or scene-selective , we fit a GLM to the BOLD activity observed across the masked ventral temporal voxels during one of the localizer runs ( counter-balanced across participants ) . The GLM contained two main regressors , one for face blocks and the other for scene blocks , as well as six nuisance covariates ( one for each motion direction ) . For each main regressor , a boxcar function lasting the duration of each block was placed at the block onset time , and it was then convolved with a double-gamma hemodynamic response function . The resulting voxelwise parameter estimates for these regressors reflect the average evoked response in each condition . Auto-correlation in the timeseries was corrected with FILM pre-whitening . We labeled voxels as face-selective if the z-scored parameter estimate for the face regressor was greater than the z-scored parameter estimate for the scene regressor , and scene-selective if the opposite was true . We then used the timeseries of BOLD activity for these voxels from the two rest runs to compute their average heterogeneous noise correlations . For each voxel , we calculated the Pearson correlation over time of that voxel with all voxels with the opposite label ( e . g . , for a face-selective voxel , its correlations with all scene-selective voxels were averaged ) . Correlations were computed separately for each rest run and averaged across the two runs . Since there were no stimuli or tasks during the rest runs , resulting connectivity can be interpreted as stimulus- or task-independent covariation of variability , i . e . noise correlations . In addition to computing noise correlations from rest runs , we also computed noise correlations from the localizer run used for crossvalidation ( counter-balanced across participants ) . We followed a background connectivity approach [2 , 44] . After preprocessing , the BOLD activity in the localizer run was scrubbed of nuisance and stimulus-evoked variance using two GLMs . The first ( nuisance ) model contained regressors for the global mean activity , six motion correction parameters obtained from preprocessing , and the activity from four seeds in white matter and from four seeds in the ventricles . Residuals from the nuisance model served as input to the second ( evoked ) model . As described earlier , each localizer run consisted of 6 identically structured blocks per category . To precisely capture the averaged evoked response for each category , we created 48 finite impulse response ( FIR ) regressors—one for each volume of a full 72-s cycle of two blocks ( face-blank-scene-blank ) . Each regressor had a constant height of 1 at one specific volume of every block , and height of 0 elsewhere . That is , one regressor modeled the average evoked response in the first volume of all face blocks , another the second volume , and so forth . We used an FIR model because it avoids a priori assumptions about the shape and timing of the hemodynamic response . Correlations computed over the residuals from the evoked model , just as described above for the rest runs , allowed us to assess heterogeneous noise correlations orthogonal to global noise sources and stimulus-evoked responses . For classification analyses , we used the Princeton Multi-Voxel Pattern Analysis Toolbox ( www . pni . princeton . edu/mvpa ) . Specifically , we used subject-specific logistic regression classifiers penalized using L2-norm regularization ( penalty = 1; preliminary analyses showed negligible influence of this parameter on the qualitative pattern of our results ) . We performed three-way ( face/scene/blank ) classification by learning weights for three logistic regression models during the training phase ( discriminating TRs as face vs . not , scene vs . not , and blank vs . not , respectively ) and then generating guesses during the test phase by labeling each TR according to the model with maximal output evidence . We verified in preliminary analyses that including the blank blocks and performing multi-way classification ( as opposed to binary face vs . scene classification ) did not affect the pattern of results . To quantify classification accuracy , we averaged the results of 6-fold cross-validation . The classifier in each fold was trained on 5/6th of the data and tested on the left-out 1/6th of the data . Because only one localizer run was used for this cross-validation ( the other was used to independently define selectivity ) , these divisions of the data into training and test sets occurred in the same fMRI run . Data from the same run can have dependencies , both locally when activity in the previous block spills over into the current block , and globally as a result of non-task factors like head motion or arousal . Despite this , our within-run approach was unbiased . With respect to local dependencies , all conditions being classified were present in each run and alternated between each other , and thus any spill-over ( into a period with a different label ) would hurt performance . With respect to global dependencies , because again the full design existed within each run ( and training/test sets ) , any general factors would apply to all conditions and not systematically support classification between conditions . Chance classification accuracy was calculated empirically by randomly permuting the category labels across TRs in the localizer run before performing MVPA ( block-level scrambling produced identical results ) . This process was repeated 10 , 000 times for each participant , and the average classifier accuracy across permutations and participants provided the baseline level of performance that would be expected due to chance . We developed a simple model of face/scene selectivity in BOLD data from human ventral temporal cortex to examine the separate influence of noise correlations and selectivity on MVPA . We simulated a set of 30 face-selective voxels and 30 scene-selective voxels . Mean activity in each of the face-selective voxels took on a larger value in response to a face stimulus ( MFF ) than in response to a scene stimulus ( MFS ) , and vice versa for scene-selective voxels ( MSF and MSS ) . Based on the empirical dataset , these parameters were set at baseline to ( in arbitrary units ) : MFF = 708 , MFS = 705 , MSS = 740 and MSF = 735 . Pairwise correlations in the activity of voxels took on different values within the face-selective pool of voxels ( cFF ) , within the scene-selective pool of voxels ( cSS ) , and across the two pools ( cFS , cSF ) . These parameters were set at baseline to: cFF = 0 . 2 , cSS = 0 . 2 , and cFS = cSF = 0 . The activity of voxels in both pools had the same effective variance ( σ2 ) , set at baseline to σ = 12 . For each of 100 simulated participants , we independently sampled voxel data from these distributions for 72 face and 72 scene timepoints . The resulting voxel timecourses were convolved with a canonical hemodynamic response function . Model parameters were modulated to examine the influence of selectivity and noise correlations on classification accuracy . These parameters are listed below for each of the simulations , grouped by the subpanel of the figure containing the results: ( 1 ) Fig 9A: cFS and cSF were linearly varied between 0 and 0 . 22; “high selectivity” , MFF , MFS , MSS and MSF were set to the baseline values; “med selectivity” , MFF = 708 , MFS = 706 , MSS = 740 and MSF = 736; “low selectivity” , MFF = 707 , MFS = 706 , MSS = 739 and MSF = 736 . All other parameters were set to baseline values . ( 2 ) Fig 9B: cFS and cSF were linearly varied between 0 and 0 . 22; “low variance” , σ = 9; “med variance” , σ was set to the baseline value; “high variance” , σ = 15 . All other parameters were set to baseline values . ( 3 ) Fig 9C: For each model participant and voxel , we randomly drew from a Gaussian distribution with vanishing mean and standard deviation of 100 , and added this value to the baseline mean response of the voxel; we also randomly varied the population covariance matrix according to a Gaussian distribution with vanishing mean and standard deviation equal to 10% of the baseline value of the corresponding matrix element . | A central challenge in cognitive neuroscience is decoding mental representations from patterns of brain activity . With functional magnetic resonance imaging ( fMRI ) , multivariate decoding methods like multivoxel pattern analysis ( MVPA ) have produced numerous discoveries about the brain . However , what information these methods draw upon remains the subject of debate . Typically , each voxel is thought to contribute information through its selectivity ( i . e . , how differently it responds to the classes being decoded ) , with improved sensitivity reflecting the aggregation of selectivity across voxels . We show that this interpretation downplays an important factor: MVPA is also highly attuned to noise correlations between voxels with opposite selectivity . Across several analyses of an fMRI dataset , we demonstrate a positive relationship between the magnitude of noise correlations and multivariate decoding performance . Indeed , voxels more selective for one class , or heavily weighted in MVPA , tend to be more strongly correlated with voxels selective for the opposite class . Furthermore , using a model to simulate different levels of selectivity and noise correlations , we find that the benefit of noise correlations for decoding is a general property of fMRI data . These findings help elucidate the computational underpinnings of multivariate decoding in cognitive neuroscience and provide insight into the nature of neural representations . | [
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"and... | 2017 | Noise correlations in the human brain and their impact on pattern classification |
High rates of error-prone replication result in the rapid accumulation of genetic diversity of RNA viruses . Recent studies suggest that mutation rates are selected for optimal viral fitness and that modest variations in replicase fidelity may be associated with viral attenuation . Arthropod-borne viruses ( arboviruses ) are unique in their requirement for host cycling and may necessitate substantial genetic and phenotypic plasticity . In order to more thoroughly investigate the correlates , mechanisms and consequences of arbovirus fidelity , we selected fidelity variants of West Nile virus ( WNV; Flaviviridae , Flavivirus ) utilizing selection in the presence of a mutagen . We identified two mutations in the WNV RNA-dependent RNA polymerase associated with increased fidelity , V793I and G806R , and a single mutation in the WNV methyltransferase , T248I , associated with decreased fidelity . Both deep-sequencing and in vitro biochemical assays confirmed strain-specific differences in both fidelity and mutational bias . WNV fidelity variants demonstrated host-specific alterations to replicative fitness in vitro , with modest attenuation in mosquito but not vertebrate cell culture . Experimental infections of colonized and field populations of Cx . quinquefaciatus demonstrated that WNV fidelity alterations are associated with a significantly impaired capacity to establish viable infections in mosquitoes . Taken together , these studies ( i ) demonstrate the importance of allosteric interactions in regulating mutation rates , ( ii ) establish that mutational spectra can be both sequence and strain-dependent , and ( iii ) display the profound phenotypic consequences associated with altered replication complex function of flaviviruses .
Lack of proofreading mechanisms and high replication rates among most RNA viruses make them inherently error-prone , yet there is also variation in mutation rates among both species and strains of RNA viruses , making fidelity itself a trait with a genetic basis subject to some fine-tuning by selection [1–3] . The generally accepted belief is that genetic diversity provides a benefit for RNA viruses for which success depends on the capacity to effectively proliferate in a range of internal environments and evade host immunity [4–6] . Such plasticity could be particularly beneficial for arthropod-borne viruses ( arboviruses ) , which require successful infection , replication and transmission by taxonomically divergent vertebrate and invertebrate hosts . On the other hand , some would argue that mutation rate is simply coupled to replication rate and that the low fidelity of RNA viruses is not a requirement , but rather a consequence of selection for maximum replicative fitness [7] . There is indeed a clear relationship between replication fidelity and replication rate [8] , but there is also evidence that the two can be uncoupled . For example , the high-fidelity variant of poliovirus ( PV ) , G64S , was shown to have replicative kinetics equivalent to wildtype virus in vitro [9–11] . Pushing mutation rate beyond maximum replicative fitness creates a scenario in which genetic information is lost and selection can no longer outpace the accumulation of deleterious mutations , termed lethal mutagenesis [12 , 13] . Selection for mutational robustness could buffer somewhat against the negative impacts of increased mutational load , yet there is clearly a limit to this , as demonstrated by the effectiveness of ribavirin and other mutagenic antivirals against a range of RNA viruses [14 , 15] . In addition , previous studies demonstrate that mutator variants of chikungunya virus ( CHIKV ) , coxsackievirus ( CV ) , SARS-coronavirus and PV are highly attenuated [16–19] . Conversely , high-fidelity variants of PV , CV , foot and mouth disease virus and CHIKV have also been shown to be attenuated in various hosts [10 , 20–25] , suggesting that there is likely a delicate balance between the need for accuracy and diversity among RNA viruses . With the exception of important studies with CHIKV , studies directly evaluating the phenotypic impact of mutation rates of arboviruses are lacking . Given the species-specific differences in selective pressure and virus-host interactions , there is clearly a need to individually characterize these relationships for other medically important arboviruses [26] . In addition , direct evidence linking specific arbovirus mutations to biochemical alterations affecting fidelity have not been presented , and therefore the mechanism of altered fidelity , including the role of specific structural changes in the RNA-dependent RNA polymerase ( RdRp ) and allosteric interactions with other proteins in the replication complex , are not well defined . Lastly , there is intriguing evidence that not just the effect of altered mutation rate , but fidelity itself could be host or even cell-specific [27] , which could be particularly relevant for arboviruses . West Nile virus ( WNV; Flaviviridae , Flavivirus ) is the most geographically widespread arbovirus in the world and there remains no effective therapeutics or prophylactics against WNV disease in humans . Although WNV is one of the most well characterized arboviruses in terms of evolution and host-virus interactions , there are gaps in our understanding of host-specific selective pressures and genetic correlates of viral fitness and pathogenesis . While there is evidence of superior WNV fitness in mosquitoes with highly homogeneous strains [28] , and the accumulation of diversity in mosquitoes could simply be a product of relaxed purifying selection as a result of mutational robustness [29] , there is also evidence for a correlation between WNV fitness and intrahost diversity in mosquito cell culture and Culex mosquitoes [30–32] . Increased diversity has also been associated with decreased WNV virulence in mice [31] , suggesting that altering the capacity to accumulate mutations could have host-specific phenotypic consequences . In order to gain insight into the phenotypic correlates and mechanism of WNV mutation restriction and expansion , we utilized experimental evolution in the presence of the antiviral ribavirin to identify mutations in the WNV replication complex important in regulating fidelity and characterized WNV mutants possessing these changes . Our results provide new insight into the specificity of genome replication and fidelity , the importance of allosteric changes in the regulation of mutation , and the host-specific consequences of alterations to fidelity .
A WNV infectious clone ( WNV-IC ) , generated from WNV strain 3356 ( NY99; AF404756 ) as previously described [33] was serially passaged in HeLa cells ( ATCC ) in the presence of the antiviral nucleoside analog ribavirin ( Sigma ) in duplicate ( Lineage I & II ) . Both lineages were passaged in the presence of 50 , 100 and 250uM ribavirin , and the virus with the highest infectious titer at 5 days post-infection ( pi ) was used to initiate the subsequent passage at all concentrations of ribavirin . Ribavirin-treated HeLa cell monolayers were also infected with fresh stock of WNV-IC at each passage as a naive control for comparative antiviral resistance of serially passaged virus . A multiplicity of infection ( MOI ) of 0 . 1 was used to initiate all passages and resistance assays . In addition to ribavirin , susceptibility to 50uM 5-fluorouracil ( Sigma ) was also determined for select WNV strains . HeLa cells were grown in EMEM supplemented with 100ug/ml penicillin streptomycin and 2% fetal bovine serum ( FBS ) . For all cells treated with antiviral compounds growth media was removed and monolayers in 6-well cell culture plates were overlaid with 1ml media containing the antiviral compound and incubated for 1h at 37°C . Media was then removed and replaced with 100ul of virus diluted in media supplemented with antiviral compound and incubated for 1 hour at 37°C . After incubation , 3mls of media supplemented with desired concentration of antiviral was added to each well . Supernatants were harvested at day 5 pi and titrated by plaque assay on African Green Monkey kidney ( Vero ) cells ( ATCC ) according to standard protocol [34] . In order to isolate clonal strains with decreased antiviral susceptibility , 20 individual plaques were harvested from Vero monolayers following the completion of passage 6 , re-suspended in 100ul of EMEM , inoculated onto fresh Vero monolayers and grown in liquid culture for 96h . Susceptibility of mutagens was reported as log10 reduction and titer and compared using t-tests following confirmation of normality ( GraphPad Prism , Version 5 . 0 ) . Full-genome consensus sequencing was performed in order to determine changes accrued with passage and to verify sequences of mutated viruses . RNA was extracted from cell culture supernatant and subjected to reverse transcription ( RT ) and polymerase chain reactions ( PCR ) using the SuperScript III one-step RT-PCR kit ( Life technologies ) with 5–10 overlapping fragments ( sequences available upon request ) . RT-PCR products were concentrated using Zymo-5 DNA spin columns ( Zymo Research ) . Sequencing was carried out using the same RT-PCR primer sets and all sequencing reactions were completed at the Wadsworth Center Applied Genomics Technology Core ( WCAGTC ) on an ABI 3100 or 3700 automated sequencer ( Applied Biosystems ) . WNV amplicons of nucleotides 1311–3248 ( envelope/ns1 genes; [29] ) were created for deep-sequencing using the same methodology with WNV RNA isolated following 72 h growth on Aedes albopictus cells ( C6/36 , ATCC ) . C6/36 cells were used in order to maximize viral titer for sequencing and were grown in MEM supplemented with 10% FBS , 2 mM L-glutamine , 1 . 5 g/L sodium bicarbonate , 0 . 1mM non-essential amino acids , 100 U/ml of penicillin , and 100 ug/ml of streptomycin and maintained at 28°C in 5% CO2 . Deep-sequencing was performed at the WCAGTC on the Ion Torrent Personal Genome Machine ( IT-PGM ) using a 316 semiconductor chip . Sequences were compiled and edited using the SeqMan module of the DNAStar software package ( DNAStar ) and a minimum of two-fold redundancy throughout each clone or consensus fragment was required for sequence data to be considered complete . Ion Torrent generated sequence data was analyzed by the Wadsworth Center Bioinformatics Core facility using CLC Genomics Workbench ( CLC bio ) software . Quality trimming of sequence reads , reference mapping and SNP ( single-nucleotide polymorphism ) detection was done in CLC Genomics Workbench v5 . 0 . 1 . Quality trimming and reference mapping were done with default parameters . Reference mapping was completed using the WNV-IC sequence ( AF404756 ) . SNP detection was performed with default parameters except minimum coverage was set to 20 , minimum variant frequency was set to 1 . 0% and ploidy was set to 1 . WNV mutants including C8423T ( T248I ) , G10057A ( V793I ) , and G10096A ( G806R ) were generated by site-directed mutagenesis ( SDM ) of the WNV-IC using mutagenic primer sets along with the QuikChange XLII SDM kit ( Stratagene ) as per the manufacturer’s protocol . Mutant WNV-IC DNA was then amplified in E . coli and plasmid harvested by Highspeed Midiprep ( Qiagen ) . Full-genome sequencing of NS5 mutant WNV-IC plasmids indicated no other mutations were present except those engineered . Transcription of mutant and control WNV-IC plasmids was carried out by linearization with Xba1 and transcription using the MEGAscript kit ( Ambion ) supplemented with Anti-reverse cap analog ( Ambion ) and assembled as per manufacturer’s protocol . Transcription reactions were incubated at 37°C for 4h . Resulting RNA was purified with the MEGAclear kit ( Ambion ) and quantified on a Nanodrop 2000 ( Thermo Fisher Scientific ) . RNA was stored in 10μg aliquots at -80°C . Wild-type WNV-IC RNA and mutant RNA were electroporated into 0 . 8 x 107 C6/36 cells in PBS using a GenePulser ( BioRad ) . Transfected cells were seeded into T75 flasks and supernatants were collected from day 3 to 7 post-transfection , aliquoted and stored at -80°C . WNV infectious particles were quantified by plaque titration on Vero cells . The WNV NS5 gene was cloned into the pET26Ub-CHIS bacterial expression plasmid [35] . This system allows for the production of ubiquitin fusion proteins containing a carboxy-terminal hexahistidine tag that are then co and/or post-translationally processed by the ubiquitin protease , co-expressed from a second plasmid , pUBPS . Briefly , the WNV NS5 coding region was amplified using the WNV NY99 strain ( AF404756 ) as template , oligonucleotides 1 and 2 ( Table 1 ) and Deep Vent DNA polymerase ( NEB ) . The PCR product of WNV NS5 was gel purified and cloned into the pET26Ub-CHIS plasmid using SacII and BamHI sites and by using In-Fusion ligation independent cloning . The final construct ( pET26Ub-WNV NS5-CHIS ) was confirmed by sequencing at the Pennsylvania State University’s Nucleic Acid Facility . Expression plasmids for the WNV NS5 derivatives ( T248I and V793I , G806R ) were constructed using the same strategy . E . coli Rosetta ( DE3 ) pUBPS cells were transformed with the pET26Ub-WNV-NS5-CHIS plasmid for protein expression . Rosetta ( DE3 ) pUBPS cells containing the pET26Ub-WNV-NS5-CHIS plasmid were grown in 100 mL of media ( NZCYM ) supplemented with kanamycin ( 25 μg/mL ) , chloramphenicol ( 20 μg/mL ) and spectinomycin ( S50 ) at 37°C until an OD600 of 1 . 0 was reached . This culture was then used to inoculate 1L of K75 , C60 , S150-supplemented ZYP-5052 auto-induction media studier [36] , to an OD600 = 0 . 1 . The cells were grown at 37°C to an OD600 of 0 . 8 to 1 . 0 , cooled to 15°C and then grown for 36–40 h . Typically , after 36–40 h at 15°C the OD600 reached ~7 . 0–10 . 0 . Cells were harvested by centrifugation ( 6000 x g , 10 min ) and the cell pellet was washed once in 200 mL of TE ( 10 mM Tris , 1 mM EDTA ) , centrifuged again , and the cell paste weighed . Typically , yields were 20 g of cell paste per liter of culture . The cells were then frozen and stored at -80°C until used . Frozen cell pellets were thawed on ice and suspended in lysis buffer ( 100 mM potassium phosphate , pH 8 . 0 , 500 mM NaCl , 5 mM 2-mercaptoethanol , 20% glycerol , 1 . 4 μg/mL leupeptin , 1 . 0 μg/mL pepstatin A and one Roche EDTA-free protease tablet per 10 g cell pellet ) , with 5 mL of lysis buffer per gram of cells . The cell suspension was lysed by passing through a French press ( SLM-AMINCO ) at 15 , 000 psi . After lysis , phenylmethylsulfonylfluoride ( PMSF ) and NP-40 were added to a final concentration of 1 mM and 0 . 1% ( v/v ) , respectively . While stirring the lysate , polyethylenimine ( PEI ) was slowly added to a final concentration of 0 . 25% ( v/v ) . The lysate was stirred for an additional 30 min at 4°C after the last addition of PEI , and then centrifuged at 75 , 000 x g for 30 min at 4°C . The PEI supernatant was decanted to a fresh beaker , and while stirring , pulverized ammonium sulfate was slowly added to 60% ( w/v ) saturation . This supernatant was stirred for 30 min after the last addition of ammonium sulfate , and centrifuged at 75 , 000 x g for 30 min at 4°C . The supernatant was decanted , and the pellet was suspended in buffer A ( 100 mM potassium phosphate , pH 8 . 0 , 500 mM NaCl , 5 mM 2-mercaptoethanol , 20% glycerol , 1 . 4 μg/mL leupeptin , 1 . 0 μg/mL pepstatin A , 5 mM imidazole ) . The resuspended ammonium sulfate pellet was loaded onto a Ni-NTA column ( Qiagen ) at a flow rate of 1 mL/min ( approximately 1 mL bed volume/100 mg total protein ) equilibrated with buffer A . After loading , the column was washed with fifty column volumes of buffer A and five column volumes of buffer A containing 50 mM imidazole . Protein was eluted from the Ni-NTA column with buffer A containing 500 mM imidazole . Fractions were collected and assayed for purity by SDS-PAGE . Fractions with the highest purity were pooled and dialyzed against buffer B ( 50 mM HEPES pH 7 . 5 , 500 mM NaCl , 5 mM 2-mercaptoethanol and 20% glycerol; MWCO of 24 , 000 Da ) . The dialyzed protein was then passed thru a Hi-Load 16/600 Superdex 200 prep grade gel filtration column ( GE Healthcare ) equilibrated with buffer B at 1 ml/min using an AktaPrime system . Fractions ( 3 mL ) were collected , assayed for purity by SDS-PAGE , pooled and then concentrated to 150 μM ( ~15 mg/mL ) using a Vivaspin concentrator ( 30 , 000 MWCO ) . The protein concentration was determined by measuring the absorbance at 280 nm by using a Nanodrop spectrophotometer and using a calculated molar extinction coefficient of 221 , 730 M-1 cm-1 . Purified , concentrated protein was aliquoted and frozen at -80°C until use . Typical WNV NS5 yields were 1 mg/5 g of E . coli cells , which can be produced from 0 . 25 L of culture . To assemble WNV NS5 elongation competent complexes , 1 or 5 μM WNV NS5 was mixed with 10 μM pGGC RNA primer , 1 μM RNA template ( either 5’-AAACUGAGAAGGAGAAAGCC-3’ or 5’-AAAUCGAGAAGGAGAAAGCC-3’ ) , 20 μM CTP , 20 μM UTP and 0 . 1 μCi/μL [γ-32P]-UTP for 30 min in 50 mM HEPES pH 7 . 5 , 5 mM MgCl2 and 10 mM 2-mercaptoethanol . For single nucleotide incorporation assays , the NS5 elongation competent complex was mixed with 25 μM heparin , 50 mM NaCl and 100 μM NTP substrate ( either ATP or GTP ) in 50 mM HEPES pH 7 . 5 , 5 mM MgCl2 and 10 mM 2-mercaptoethanol at 30°C . After mixing , reactions were quenched at various times by the addition of 50 mM EDTA . Products were resolved from substrates by denaturing PAGE . An equal volume of loading buffer , 5 μL , ( 70% formamide , 0 . 025% bromophenol blue and 0 . 025% xylene cyanol ) was added to 5 μL of quenched reaction mixtures and heated to 70°C for 2–5 min prior to loading 5 μL on a denaturing 20% polyacrylamide gel containing 1X TBE ( 89 mM Tris base , 89 mM boric acid , 2 mM EDTA ) and 7 M Urea . Electrophoresis was performed in 1X TBE at 90 W . Gels were visualized by using a PhosphorImager ( GE ) and quantified by using ImageQuant software ( GE ) . Data were fit by nonlinear regression using the program KaleidaGraph ( Synergy Software ) . All experiments shown are representative , single experiments that have been performed after at least three individual trials to define the concentration or time range shown . In all cases , values for parameters measured during individual trials were within the limits of the error reported for the final experiments . Kinetic data were fit by nonlinear regression using the program KaleidaGraph ( Synergy Software , Reading , PA ) . Observed rate constants ( kobs ) for nucleotide incorporation were obtained by fitting product-versus-time data to an equation defining a single exponential ( Eq 1 ) , where A is the amplitude , kobs is the observed rate constant and C is the endpoint . Confluent monolayers of baby hamster kidney cells ( BHK; ATCC ) and Culex tarsalis mosquito cells ( CxT; kindly provided by A . Brault , CDC Fort Collins ) were infected with virus , in triplicate , using 6-well plates , at a MOI of 0 . 01 pfu/cell ( multi-step ) , 10 . 0 pfu/cell ( BHK one-step ) , or 8 . 0 pfu/cell ( CxT one-step ) . BHK cells were grown in minimal essential medium ( MEM , Gibco ) supplemented with 10% fetal bovine serum , 2 mM L-glutamine , 1 . 5g/L sodium bicarbonate , 100 U/ml of penicillin , and 100 ug/ml of streptomycin and maintained at 37°C in 5% CO2 . CxT cells were grown in Schneider’s media ( GIBCO ) supplemented with 10% FBS , 2 mM L-glutamine , 1 . 5 g/L sodium bicarbonate and maintained at 28°C in 5% CO2 . After a one hour absorption period at 37°C ( BHK ) or 28°C ( CxT ) , the inoculum was removed , cells were gently washed , overlaid with 2 ml of maintenance media and incubated at appropriate temperatures . Samples consisting of 50ul supernatant were harvested at 24 , 48 , 72 , 96 , and 120 ( CxT only ) hpi for multi-step growth kinetics and 3 , 6 , 9 , 12 , 24 and 30 ( CxT only ) hpi for one-step kinetics , diluted 1:10 in media containing 20% FBS , and stored at -80°C . Titrations were performed in duplicate , by plaque assay on Vero cells and mean titers for each time point were calculated . WNV RNA genomes were also quantified following extraction with QIAamp viral RNA spin columns ( Qiagen ) using a TaqMan quantitative real-time RT-PCR assay ( Applied Biosystems ) with a primer/probe designed for WNV E gene amplification [37] . Growth kinetics were compared using repeated measured ANOVA and Tukey’s post hoc tests and infectivity was compared by t-test following confirmation of normality ( GraphPad Prism , Version 5 . 0 ) . Mosquito susceptibility was determined as previously described [38] in both highly colonized Cx . quinquefasciatus originally obtained from Benzon Research Inc . and F4 Cx . quinquefasciatus collected as egg rafts from Orange County , CA ( kindly provided by Robert Cummings , Orange County California Vector Control District ) . Briefly , individual WNV strains were diluted to equivalent titers ( ~8 . 0 log10 pfu/ml ) , mixed 1:5 , virus: defibrinated sheep blood ( Colorado Serum ) + 2 . 5% sucrose , and offered to ~500 female mosquitoes using a Hemotek membrane feeding system ( Discovery Workshops ) . Following 1 h , mosquitoes were anesthetized using CO2 and fully engorged mosquitoes were saved and housed at 27°C for subsequent testing . Twenty-five to 50 mosquitoes per strain were saved at -80°C in 1 ml mosquito diluent [MD; 20% heat-inactivated fetal bovine serum ( FBS ) in Dulbecco’s phosphate-buffered saline ( PBS ) plus 50 μg/ml penicillin/streptomycin , 50 μg/ml gentamicin , and 2 . 5 μg/ml Fungizone] at days 5 , 7 , 10 , 14 and 21 days post-feeding . Samples were thawed and homogenized for 30 seconds at 24hz in a Mixer Mill MM301 ( Retsch ) . Debris was then pelleted by centrifugation at 6000 rcf for 5 minutes and the supernatant screened by plaque assay on Vero cells to determine infection status .
Passaging in the presence of the antiviral ribavirin was used to select for WNV variants with decreased susceptibility and putative alterations to polymerase fidelity . Ribavirin susceptibility , as measured by reduction in viral titer following treatment , was monitored throughout the passage series and again assessed following the completion of passage 6 ( Fig 1A ) . Significantly lower reductions in viral titer relative to WNV-IC controls were measured in both lineage I and II following passage 5 and 6 ( t-test , df = 5 , p<0 . 05 ) , such that lineage I viral titer decreased just 1 . 4-fold following ribavirin treatment after 6 passages , as compared to a greater than 25-fold titer reduction measured for WNV-IC . In order to select for clonal strains with decreased mutagen susceptibility , ribavirin sensitivity was assessed for individual biological clones isolated from the lineage I passed virus and strains with the highest levels of resistance ( WNV pp3 , pp9; Fig 1B ) were chosen for further characterization . In order to identify shared WNV amino acid ( aa ) substitutions associated with mutagen resistance , full-genome sequencing of clonal strains WNV pp3 and WNV pp9 was completed . A total of 15 ( pp3 ) and 18 ( pp9 ) nt substitutions were identified , resulting in 9 and 8 aa substitutions , respectively ( Table 2 ) . Of these , 10 nt and 7 aa substitutions were shared . Given the assumption that substitutions outside of the replication complex were more likely to be associated with adaptation to Hela cell culture or drift , shared aa substitution in the WNV RdRp and methyltransferase ( Mtase ) genes exclusively were chosen for further characterization . These included C8423T , resulting in a threonine to isoleucine change at position 248 of the Mtase , as well as G10057A and G10096A , resulting in valine to isoleucine and glycine to arginine changes at positions 793 and 806 of the RdRp , respectively ( Table 2 ) . Mapping of these residues on the known flavivirus RdRp and Mtase structures demonstrates that T248I is located at the C-terminal loop ( aa 245–267 ) , which is expected to interact with the RdRp domain , and both V793I and G806R exist in locations outside of the RdRp active site , although are within the priming loop ( Fig 2; [39–42] ) . Both T248 and G806 are conserved among lineage I WNV strains , yet not across lineages or species , while V793 is shared among flaviviruses . No naturally circulating strains were found to possess the identified mutations at these locations . To confirm that these amino acid substitutions independently conferred decreased ribavirin susceptibility , and to assess if this corresponded to generalized mutagen resistance , mutations were engineered independently and in combination into the WNV-IC and susceptibility to both ribavirin and 5-fluorouracil was assessed with WNV mutants . Full-genome sequencing following mutagenesis confirmed the exclusive presence of desired mutations . WNV mutants created included WNV T248I , V793I , G806R , double mutant V793I/G806R , and triple mutant ( T248I/V793I/G806R ) . Mutagen resistance assays demonstrated significantly decreased reduction in titer ( susceptibility ) relative to untreated controls for all mutant strains as compared to WNV-IC following treatment with both ribavirin and 5-fluorouracil ( t-test , df = 6 p<0 . 05; Fig 3 ) . The highest level of mutagen resistance was measured with the RdRp double mutant , WNV V793I/G806R , for which 2 . 2 and 4 . 6-fold mean titer reduction were measured following ribavirin and 5-fluorouracil treatments , respectively , as compared to 55 and 37-fold mean titer reductions measured with WNV-IC ( Fig 3 ) . In order to test the hypothesis that mutagen resistance corresponded to alterations in mutation rate , deep-sequencing was used to quantify accumulation of unique SNPs following a single passage on mosquito cell culture ( Fig 4A ) . Levels of WNV RNA for both WNV-IC and mutant strains were statistically similar for all samples chosen for sequencing ( ~9 . 0 log10 WNV copies/ml ) , suggesting differences in replication were not likely to account for differences in the number of mutations accumulated . Assays were completed in duplicate for each WNV strain and sequencing results identified fewer SNPs in all mutant strains relative to WNV-IC , with the exception of the methyltransferase mutant , WNV T248I , for which the number of unique SNPs identified was approximately 2 . 5 fold higher than WNV-IC . The RdRp double mutant , WNV V793I/G806R , showed the fewest number of unique SNPs; approximately 2 . 5 fold lower than WNV-IC ( Fig 4B ) . Mutations were distributed throughout the sequenced regions for all WNV strains , yet mutant swarm composition varied significantly among strains ( Fig 4C ) . Specifically , transition to transversion ratios were ~2:1 for WNV-IC and WNV T248I , yet <1 for WNV V793I/G806R . Decreased mutation of WNV V793I/G806R relative to WNV-IC resulted primarily from lack of U to C and G to A mutations , which accounted for 11/28 mutations for WNV-IC and 0/12 mutations for WNV V793I/G806R ( chi-squared , p = 0 . 011 ) . Increased mutation of WNV T248I , on the other hand , resulted primarily from A misincorporations , which accounted for 33/62 mutations for WNV T248I , and just 7/28 mutations of WNV-IC ( chi-squared , p = 0 . 045 ) . Approximately the same number of U to A mutations were identified for WNV V793I/G806R as WNV T248I ( 4 vs 3 ) , yet no G to A mutations were identified for WNV V793I/G806R , in stark contrast to the 20 identified for WNV T248I ( Fig 4C ) . These results demonstrate strain-specific differences in the misincorporation of different nucleotides and , more specifically , in the propensity for particular mispairs , suggesting mutation frequencies may be dependent on both replication complex and template sequences and/or context . Purified IC , V793I/G806R , and T248I WNV NS5 proteins were used to quantify and compare the kinetics of nucleotide misincorporation of NS5 elongation—competent complexes ( Fig 5 ) . Complexes were assembled using a 5’-phosphorylated trinucleotide primer ( pGGC ) , single stranded RNA template , UTP and CTP ( Fig 5A ) . The template was designed such that two nucleotides led to production of a 15-mer RNA . Labeling of the elongation complex was achieved by using α-32P-labeled nucleotide . Once assembled , the complex was stable and capable of rapid incorporation of the next correct nucleotide substrate ( elongation ) to produce a 16-mer RNA product . The elongation competent complex was then used to interrogate the kinetics of nucleotide misincorporation . The initial substrate , designed to measure G:U mispairs was used to quantify GMP misincorporation for each NS5 protein ( Fig 5B and 5C ) . Comparing the percentage of RNA product produced over time , it was demonstrated that WNV-IC , V793I/G806R and T248I NS5 proteins displayed similar in vitro kinetics for GMP misincorporation ( Fig 5C ) . These results were consistent with a lack of biological differences in fidelity among NS5 variants , but were also in agreement with deep-sequencing data , for which the number of A to G substitutions were similar among WNV-IC , WNV V793I/G806R , and WNV T248I ( Fig 4C ) . In order to evaluate fidelity differences implied by deep-sequencing data , the template for biochemical assays was redesigned to quantify A:C mispairs , equivalent to G to A substitutions . The number and proportion of G to A substitutions identified following growth in mosquito cells differed among strains , with means of 6 , 0 and 20 identified in WNV-IC , WNV V793I/G806R , and WNV T248I , respectively ( Fig 4C ) . Biochemical assays were consistent with these results , clearly demonstrating an increasing rate of A misincorporation for WNV T248I relative to WNV-IC and a decreasing rate of A misincorporation for WNV V793I/G806R ( Fig 5C ) . Taken together , these results demonstrate sequence-specific fidelity differences among WNV mutant strains . Comparison of one-step and multi-step growth kinetics of WNV mutants to WNV-IC in vertebrate ( BHK ) and invertebrate ( CxT ) cell lines demonstrates host-specific effects of replication complex mutations ( Fig 6 ) . No differences in overall kinetics ( repeated measures ANOVA , F = 0 . 14 , df = 6 , p = 0 . 98 ) or viral titers at individual time points ( t-test , p>0 . 05 ) were measured when comparing WNV mutants to WNV-IC on vertebrate cell culture , while significant differences in viral kinetics were measured in mosquito cell culture at both MOIs for all replication complex mutants relative to WNV-IC ( repeated measures ANOVA , F = 12 . 83 , df = 6 , p<0 . 0001 ) . Specifically , consistently lower viral titers were measured for all RdRp and Mtase mutants in mosquito cells relative to WNV-IC ( Tukey’s multiple comparison test , p<0 . 05 ) , and kinetics were similar among mutants with the exception of WNV V793I , which despite having significantly lower titers than WNV-IC had modestly higher titers relative to other mutants ( Fig 6 ) . In addition , WNV RNA was quantified with qRT-PCR following one-step growth and particle/pfu ratios were quantified and compared among WNV-IC , WNV V793I/G806R and WNV T248I in order to assess the relationship between fidelity and infectivity . Specific infectivity was elevated in mosquito cells as compared to vertebrate cells for all WNV strains ( Fig 7 ) . Trends measured with infectivity are consistent with identified fidelity differences in both cell lines , with the highest infectivity measured with the RdRp double mutant WNV V793I/G806R and the lowest infectivity measured with the Mtase mutant WNV T248I , yet differences were only significant relative to WNV-IC for WNV T248I in BHK cells ( t-test , df = 4 , p = 0 . 0028; Fig 7 ) . These differences do not therefore account for differences in growth kinetics identified among WNV strains in CxT cells . In order to determine if modest attenuation in mosquito cell culture and fidelity differences corresponded to differences in mosquito competence in vivo , infectivity of WNV-IC was compared to infectivity of WNV T248I and WNV V793I/G806R in colonized Cx . quinquefasciatus mosquitoes following exposure to infectious bloodmeals . Input titers were comparable to a natural dose and similar among WNV strains and experimental replicates ( Table 3; [43] ) . Despite the fact that levels of infection were somewhat lower than have been measured with other wildtype WNV strains ( 35 . 8% ) , stark and highly significant differences were measured when compared to WNV mutants ( chi-squared , p<0 . 001 for all mutants relative to WNV-IC ) . Specifically , over the course of 3 experimental replicates and multiple time points , a total of just 2 of 210 ( V793I/G806R ) and 3 of 203 ( T248I ) mosquitoes acquired measurable infections . To confirm that this was generalizable phenotype which was relevant in natural populations , infectivity experiments were repeated in Cx . quinquefasciatus recently acquired from the field . Although modestly lower input titers were used , infectivity was slightly higher for WNV-IC relative to colonized mosquitoes ( 39 . 2% ) and a general lack of infectivity was confirmed for replication complex mutants ( Table 3 ) . Given the inefficient infectivity of mutant strains , dissemination and transmission were not evaluated in this study . Although viral load was not determined for individual mosquitoes , it is notable that the 8 total mosquitoes identified as positive following exposure to WNV mutants all showed relatively low levels ( less than 20 plaques ) with undiluted plaque screens .
As has been successfully accomplished with other systems [21 , 22 , 24 , 25 , 44] , we exploited selection in the presence of a mutagen to identify mutations altering WNV replicase fidelity and utilized WNV fidelity mutants to interrogate the consequences and mechanisms of altered mutation rates . Although ribavirin is not considered an efficacious antiviral for the treatment of active WNV infections , it has been shown to cause both error-prone replication and WNV attenuation in vitro , particularly in Hela cell culture [45] . The relative decrease in mutation frequency measured for the WNV high fidelity variant V793I/G806R ( ~2 . 5 fold ) is similar or modestly higher than has been shown with other systems [21 , 22 , 24 , 25 , 44] . With the exception of coronaviruses , which employ a proofreading exoribonuclease system unique among RNA viruses [46] , these data together demonstrate that either the lack of biochemical capacity or the extent of phenotypic consequences by-in-large prevent highly significant alterations to RdRp fidelity . Despite this , data presented here and in previous studies clearly demonstrate that subtle alterations to mutation rates can have profound phenotypic effects on RNA viruses . Although fully characterizing the mechanism by which these RdRp residues alter fidelity would require further biochemical and biophysical investigations , mapping of T248 , V793 and G805 residues on the crystal structures of NS5 [41 , 42] provides some indication of possible mechanisms ( see Fig 2 ) . Despite the lack of full-length NS5 structure from WNV , the relative orientation of the Mtase domain with respect to the polymerase domain can be defined using the recent crystal structure of the full length dengue virus ( DENV ) NS5 [47] as a guide . The two NS5 structures are highly similar with an RMSD of 1 . 18 and 0 . 65 Å for the polymerase and Mtase domains , respectively . The residues V793 and G805 are located in the priming loop ( aa 789–812 ) , which is a long loop that links two α-helices in the thumb subdomain and protrudes to reach the active site . Conformational dynamics of the priming loop is believed to be necessary to form a stable initiation complex [42] . A model of the initiation complex is shown in Fig 2B; in this model a 4-mer ssRNA substrate , taken from the complex structure of the related ɸ6-RdRp ( PDB 1HI0 ) [48] , and rNTP modeled at the priming site ( P-site ) and catalytic site ( C-site ) based on the HCV RdRp complex structure ( PDB 1GX5 ) [40] can be accommodated in the WNV NS5 RdRp active site with minimal steric clashes with the protein atoms . To form a stable initiation complex , the active-site residue Trp-800 would alter its sidechain conformation to be able to stack against the priming nucleotide . The priming loop maintains numerous interactions with residues from the thumb , fingers and palm subdomains and substitutions similar to V793I and G806R could potentially disrupt these interactions , impacting the dynamics of the priming loop and subsequently affecting the initiation process of the RdRp . It is not difficult to conceive that V793I and G806R may restrict the dynamics of the priming loop , leading to a higher fidelity mutant in a scenario similar to what has recently been shown for the G64S high-fidelity mutant of PV RdRp [49 , 50] . In these studies of PV RdRp , remote site mutations altered the polymerase fidelity by causing changes to the dynamics of conserved structural elements and motifs including residues at the active site . Although the interactions of the flavivirus RdRp and Mtase have now been well-documented [51 , 52] the finding that modifications to allosteric interactions resulting from mutation of a single residue of the Mtase can significantly alter replication fidelity is novel . The T248I mutation is located at the C-terminal loop ( aa 245–267 ) of the MTase domain; which is expected to be at the interface between the two domains of the WNV NS5 ( Fig 2C ) , similar to what is observed in the homologous DENV NS5 structure . The loop harboring T248 is predicted to interact with the region of the fingers in the polymerase domain ( aa 350–365 ) . It is very likely that amino acid substitution of T248 by an isoleucine could affect the interactions between the two domains and the inter-domain dynamics , eventually affecting the polymerase active site and altering fidelity . Findings with WNV are therefore consistent with previous data demonstrating that RdRp fidelity is determined by a complex network of interactions and checkpoints by which remote site mutations may alter the dynamics of conserved structural elements and motifs including residues at the active site [9 , 49 , 53 , 54] . Although selection for ribavirin resistance did , as predicted , result in the isolation of high fidelity WNV variants , the fact that a mutator variant also displayed resistance could be explained by antiviral mechanisms independent of lethal mutagenesis for WNV in this system . Similar results were attained with FMDV , for which a low fidelity RdRp was found to have a decreased capacity for ribavirin incorporation [55] . In addition , previous studies with another flavivirus , yellow fever virus , demonstrate that the antiviral actions of ribavirin are conferred primarily by the depletion of intracellular GTP pools [56] . Additional antiviral mechanisms of ribavirin have also been proposed , including inhibition of virus transcription [57] and inhibition of both guanyltranferase and Mtase activity [58 , 59] . The flavivirus Mtase is required for RNA capping [60] , a process partially enabled by GTP binding [61] and competitively inhibited by ribavirin with DENV NS5 [58] . Although T248 is not within the nucleotide binding site it is possible that this mutation could perturb these interactions and subsequently interfere with antiviral susceptibility in this manner . On the other hand , given that WNV T248I also displays resistance to 5-fluorouracil , it is possible that the strain-dependent mutational biases could result in unique evolutionary trajectories and , subsequently , strain-specific differences in mutational robustness and susceptibility to lethal mutagenesis . This sequence-dependent nature of the fidelity alterations also demonstrates that broad assumptions about fidelity and mutagen susceptibility likely discount the specificity of interactions of individual nucleotides and/or base analogs with the replication complex . Although others have demonstrated that modifications to fidelity are attainable , the possibility that unique strains may possess unique mutational biases has novel functional and evolutionary implications . Specifically , if mutational landscapes are strain-specific , so too are fitness landscapes of viral swarms and therefore evolutionary pressures acting on them . Such biases could be exploited by evolution as a means of increasing the probability of producing favorable mutant swarms following genetic bottlenecks or could have the opposite effect of constraining deleterious strains by not permitting adequate exploration of sequence space to escape unfit landscapes . Consistent with previous studies with CHIKV [24] in vitro kinetics were generally similar for the high fidelity WNV V793I/G806R relative to WNV-IC , with modest attenuation measured in mosquito but not vertebrate cell culture . Although fitness differences were only measurable with direct competition of CHIKV and not individual growth assays , the decreases in mutation rate measured for WNV V793I/G806R were also more substantial than those measured for CHIKV , likely due to combining two RdRp mutations which appear to have an additive effect on fidelity . These host-specific effects are consistent with previous studies demonstrating increased swarm diversity in the mosquito for both WNV and its close relative St . Louis encephalitis virus [31 , 62] , but further suggest that the invertebrate environment is not simply a more robust environment which tolerates diversity , but one in which diversity itself likely provides a fitness benefit [30] . It is possible that this fitness benefit results from an inherent need to escape RNAi or other innate invertebrate immune responses [63] , or that enhancements in fitness could result from cooperative interactions among distinct genotypes and viral proteins [64] . Despite this , previous passage studies in Cx . pipiens suggest that this need for diversity may be overcome by individual variants with highly superior fitness [28] and results presented here demonstrating attenuation of the low fidelity mutant WNV T248I suggest , not surprisingly , that there is a limit to the benefit of diversity . The association of mutator phenotypes with either similar or attenuated viral growth kinetics is consistent with what has been observed in other systems [17–19] , and gives further credence to the idea that replication and mutation rate are not necessarily inextricably bound phenotypes . Given that vertebrate environments have been found to be more restrictive both in vitro and in vivo [32 , 65] , it is somewhat surprising that a virus with a mutator phenotype would not also be attenuated in vertebrate cell culture , yet even if WNV T248I is more mutationally robust than WNV-IC , attenuation may be observed if this strain were repeatedly passaged , therefore accumulating diversity and , presumably , deleterious mutants [17 , 18] . Consistent with this is the fact that the Mtase mutant was also found to be less infectious in vertebrate cell culture . In addition , competition assays with increased sensitivity for detecting more subtle fitness differences [24 , 66] or in vivo models that more accurately represent natural infections could reveal important phenotypic differences in vertebrate systems [67] . Although in the current studies results confirm that inherent biochemical differences account for differences in mutation rate independent of cell type , it is also feasible that fidelity itself could be host-dependent , as a recent study with vesicular stomatitis virus demonstrates slower mutation rates in insect cells as compared to mammalian cells [27] . Although few have investigated this concept [68] , it is not necessarily surprising that the biophysical and biochemical properties of the replication complex might differ significantly in environments with variable temperature , pH , and nucleotide availability . Future studies exploiting new sequencing technologies to evaluate mutation rates in a range of systems will help to clarify these differences [17 , 69 , 70] . Although the modest attenuation in mosquito cell culture may be explained by the modest alterations to fidelity , it is much more surprising that an approximately 2 . 5 fold alteration to mutation rate could almost entirely eliminate the capacity for infection and/or sustainable WNV replication in mosquitoes . Although studies with CHIKV also demonstrate that fidelity variants are associated with decreased infectivity in mosquitoes , differences measured for WNV here are much more profound . These results suggest either that WNV replication in gut epithelial cells is uniquely sensitive to alterations in fidelity or that alternative mechanisms of attenuation related to host interaction with the flavivirus NS5 exist . Regardless , these variants provide powerful tools to elucidate the determinants of flavivirus mosquito competence and novel targets for viral attenuation . | West Nile virus ( WNV ) is the most geographically widespread arthropod-borne virus ( arbovirus ) in the world . Like most arboviruses , WNV is a RNA virus which is highly mutable and exists in nature as genetically diverse mutant swarms . Although many recent studies have investigated the relationship between virus mutation rate and viral fitness , this had not previously been determined for WNV or other flaviviruses . We identified WNV mutations associated with variation in mutation rate using cell culture passage in the presence of a mutagen and engineered these mutations into an infectious WNV clone in order to investigate the causes and consequences of altered fidelity . Our results demonstrate that interactions among proteins which comprise the WNV replication complex can significantly alter both the extent and types of mutations that occur . In addition , we show that both increasing and decreasing WNV fidelity has host-specific effects on replication in cell culture and is associated with nearly complete ablation of WNV infection in mosquito vectors . These results have significant implications for our understanding of arbovirus evolution , replication complex function and arboviral fitness in mosquitoes , and identify important targets to study the determinants and mechanisms of vector competence and arbovirus fidelity . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Sequence-Specific Fidelity Alterations Associated with West Nile Virus Attenuation in Mosquitoes |
The sterol sensor SCAP is a key regulator of SREBP-2 , the major transcription factor controlling cholesterol synthesis . Recently , we showed that there is a global down-regulation of cholesterol synthetic genes , as well as SREBP-2 , in the brains of diabetic mice , leading to a reduction of cholesterol synthesis . We now show that in mouse models of type 1 and type 2 diabetes , this is , in part , the result of a decrease of SCAP . Homozygous disruption of the Scap gene in the brains of mice causes perinatal lethality associated with microcephaly and gliosis . Mice with haploinsufficiency of Scap in the brain show a 60% reduction of SCAP protein and ∼30% reduction in brain cholesterol synthesis , similar to what is observed in diabetic mice . This results in impaired synaptic transmission , as measured by decreased paired pulse facilitation and long-term potentiation , and is associated with behavioral and cognitive changes . Thus , reduction of SCAP and the consequent suppression of cholesterol synthesis in the brain may play an important role in the increased rates of cognitive decline and Alzheimer disease observed in diabetic states .
The brain is the most cholesterol rich organ in the body , containing more than 20% of the sterol pool and almost all of the cholesterol is produced in situ [1] . Multiple in vitro studies have indicated that cholesterol in the brain is important for synapse biogenesis and vesicle formation [2] , [3] . Cholesterol synthesis is a highly regulated process controlled by the master transcriptional regulator SREBP-2 . SREBP-2 is transcribed and translated into an inactive precursor that is sequestered in the endoplasmic reticulum ( ER ) . However , when sterol levels are low , the sterol sensor SCAP is able to chaperone SREBP-2 to the Golgi apparatus where it is cleaved to release a transcriptionally active form that can enter the nucleus [4] . Conversely , in times of sterol abundance , SCAP is bound by sterols and remains sequestered in the ER along with the unprocessed SREBP-2 [4] . Diabetes mellitus is a multifactorial disease due to deficient insulin secretion and/or action , resulting in hyperglycemia , alterations in lipid metabolism , and a variety of complications in tissues throughout the body . These complications extend to the central nervous system ( CNS ) , including cognitive dysfunction and behavioral changes , and are observed both in type 1 and type 2 diabetes [5] . Studies have shown altered information processing , psychomotor efficiency , attention , and mental flexibility in type 1 diabetes , whereas type 2 diabetes more often affects memory , psychomotor speed , and executive function [6] . In addition , changes have been observed on imaging of the hippocampi of both type 1 and type 2 diabetics [7] , [8] . We recently demonstrated that in mouse models of diabetes there is a broad reduction in the expression of genes in the cholesterol biosynthetic pathway throughout the brain , resulting in decreased brain cholesterol synthesis [9] . This is accompanied by decreased expression of SREBP-2 , which is more pronounced at the protein than the mRNA level [9] . Simultaneous reduction in cholesterol content and cholesterol biosynthesis suggests that , in the brain , diabetes causes a defect at the level of the sterol-sensing molecules , which constitute a negative-feedback system on SREBP-2 processing and maintenance of cellular cholesterol content . Here we show that the sterol sensing protein SCAP , which plays a key role in the post-transcriptional regulation of SREBP-2 , is decreased in the brains of diabetic mice . Brain-specific heterozygous SCAP knockout mice have reduced levels of cholesterol genes and exhibit a 30% reduction in brain cholesterol synthesis , mimicking what is observed in diabetes . This results in defects at the electrophysiological and cognitive level . Thus , diabetes results in a reduction of SCAP expression , which contributes to a reduction of brain cholesterol synthesis . Reduction in SCAP leads to an impairment in neuronal transmission and cognitive dysfunction that may contribute to the neurological complications observed in diabetic states .
Recently we demonstrated that diabetes produces a global suppression of the enzymes of cholesterol biosynthesis and their master transcriptional regulator SREBP-2 in the brain; this results in reduced cholesterol biosynthesis and altered ability of neurons to form synapses and synaptic vesicles [9] . Since reduction of cholesterol and its precursors would normally drive a compensatory increase in SREBP-2 and cholesterol biosynthesis , we speculated that sterol-sensing might be impaired in the brains of diabetic mice . SCAP , a binding partner and a chaperone protein of SREBP-2 , plays an essential role in sterol feedback regulation , serving as the major sterol-sensing molecule [4] . Western blotting of extracts of brains from streptozotocin ( STZ ) -treated mice ( a model of type 1 diabetes ) revealed an ∼50% decrease in SCAP protein in the mouse cerebral cortex ( Figure 1A ) . This reduction of SCAP protein in the STZ diabetic mouse was not limited to the cortex and was observed throughout the brain , with a 33% decrease in the hypothalamus ( Figure 1B ) and a 31% decrease in the thalamus ( unpublished data ) . There is also a significant , but smaller , reduction of SCAP protein in the cerebral cortices of db/db mice ( a model of type 2 diabetes ) , consistent with a modest down-regulation of genes of the cholesterol synthesis pathway in the brains of these animals ( Figure 1C ) . The decrease in Scap mRNA in STZ diabetic mice was completely rescued by three injections of insulin into the intracerebroventricular ( ICV ) space over the course of 30 h ( Figure 1D ) . ICV insulin also resulted in a partial rescue of SCAP protein during this short treatment period ( Figure 1E ) . This occurred with no change in blood glucose levels [9] . By contrast , there were no changes in Scap message after treatment of leptin deficient ob/ob mice with leptin ( unpublished data ) . To determine the consequences of reduced SCAP content in the brain in the absence of diabetes , we created mice with homozygous and heterozygous loss of SCAP specifically in the brain by crossing mice carrying a modified Scap gene with LoxP sites surrounding exon 1 [10] with mice expressing Cre-recombinase under control of the rat nestin promoter and enhancer [11] . This results in inactivation of the Scap gene in both neuronal and glial cells beginning on day e11 . 5 [11] . Homozygous Scap-ablated embryos ( nestin-Cre+/−:Scaplox/lox , designated as N-Scap [−/−] ) were obtained at normal Mendelian frequency; however , all of the homozygous N-Scap ( −/− ) mice died immediately after birth on postnatal day 0 ( P0 ) . Analysis of brains of these mice revealed an almost complete ( >95% ) absence of Scap mRNA and SCAP protein ( Figure 2A and 2B ) . The N-Scap ( −/− ) neonates exhibited almost normal gross morphology , but had flat skulls and uninflated lungs ( Figure 2C ) . Likewise , when mice were delivered by Cesarean section on embryonic day 18 . 5 ( E18 . 5 ) , all of the control ( nestin-Cre−/−:Scaplox/wt or nestin-Cre−/−:Scaplox/lox ) and N-Scap ( +/− ) ( nestin-Cre+/−:Scaplox/wt ) littermates initiated breathing , whereas N-Scap ( −/− ) failed to do so . The flat skull in N-Scap ( −/− ) was due to microcephaly; brains from N-Scap ( −/− ) mice exhibited an overall reduction in size of 43% by weight compared with those from the control littermates ( 50±10 mg versus 87 . 5±15 mg in controls , p = 0 . 003 ) ( Figure 2D ) . There were no gross morphologic changes in the brain other than the decrease in size . Gene expression analysis of the brains of N-Scap ( −/− ) mice revealed a pattern consistent with a loss of SCAP activity . Thus , SREBP-2 ( Srebf2 ) and its downstream genes ( Hmgcr , Sqle , Fdps , Idi1 , and Ldlr ) were all markedly down-regulated by 70%–90% ( Figure 2E ) . Expression of the SREBP-1c ( Srebf1c ) gene was also reduced by 88% , whereas expression of its downstream genes ( Fasn and Acaca ) were only decreased by half ( Figure 2E ) . The reduction in cholesterol synthetic enzymes was accompanied by a 50% reduction in total cholesterol content per gram of brain tissue ( 2 . 3±0 . 6 versus 4 . 7±1 . 2 µg of cholesterol/mg of brain in controls , p<0 . 01 ) ( Figure 2F ) . In contrast , total brain triglyceride content was increased in N-Scap ( −/− ) mice compared to controls ( 19 . 9±0 . 4 versus 15 . 9±1 . 7 µmol of triglycerides/mg of brain in controls , p<0 . 05 ) ( Figure 2G ) . Measurement of triglycerides in adult N-Scap ( +/− ) brains showed no difference when compared to controls ( 26 . 8±3 . 3 versus 27 . 4±1 . 3 µmol of triglycerides/mg of brain in controls ) ( Figure 2G ) . Homozygous deletion of the Scap gene in the brain also caused marked changes in brain histology and cell type distribution . Expression of glial fibrillary acidic protein ( Gfap ) , an astrocyte marker , was up-regulated by more than 3-fold in the whole brain of N-Scap ( −/− ) mice , whereas the microglia marker F4/80 ( Emr1 ) was reduced by 59% and the neuronal marker MAP-2 ( Matp2 ) was unchanged ( Figure S1 ) . The increase in Gfap mRNA correlated with a significant increase in GFAP protein in the N-Scap ( −/− ) brain ( Figure 2H ) , which was confirmed by immunohistochemistry showing a robust increase of GFAP- positive fibers in N-Scap ( −/− ) brains resulting in a histological picture resembling gliosis ( Figure 2I ) . To better mimic the 50% reduction in SCAP in the brains of STZ-diabetic mice we characterized mice with haploinsufficiency of the Scap gene in the brain . qPCR analysis of Scap mRNA in extracts of brain regions dissected from N-Scap ( +/− ) mice revealed an approximately 40% reduction of Scap gene expression in all parts of the brain examined , including cerebral cortex and hypothalamus ( Figure 3A ) . By Western blotting of extracts of the cortex , expression of SCAP protein was reduced slightly more ( 60% , when normalized with β-tubulin ) in N-Scap ( +/− ) mice ( Figure 3B ) . Previous studies have shown that knockout of the Scap gene in the liver causes a reduction in the amounts of both the precursor and the nuclear forms of SREBP-2 and SREBP-1 [10] . We found a similar significant reduction of both SREBP-2 and SREBP-1 precursors in the cytoplasmic extract of cerebral cortices of N-Scap ( +/− ) mice ( Figure 3C ) , as well as a reduction in the mature nuclear forms of these transcription factors ( Figure 3D ) . This resulted in a decrease in the expression of genes downstream of SREBP-2 ( Hmgcr , Sqle , Idi1 , and Ldlr ) by 10%–27% in the cerebral cortex and hypothalamus , as well as some decrease in Srebf2 mRNA ( Figure 3E ) . The reduction of squalene epoxidase at the protein level was also confirmed by Western blotting ( Figure 3C ) . The decrease in SREBP-1 did not result in a decrease in expression of the downstream protein fatty acid synthase but did cause a 20% reduction in acetyl-CoA carboxylase in the N-Scap ( +/− ) mice . There were large decreases in these proteins in the N-Scap ( −/− ) mice ( Figure S2 ) . The reduction in cholesterol synthesis was not accompanied by a similar decrease in cholesterol catabolism . Levels of Cyp46a1 mRNA were the same in the cerebral cortices and hypothalami of control and N-Scap ( +/− ) mice , and whole brains of control and N-Scap ( −/− ) mice ( Figure S3 ) . To determine if the down-regulation of cholesterologenic genes could affect in vivo cholesterol synthesis , we assessed cholesterol synthesis in the brains of N-Scap ( +/− ) mice in vivo using tritiated water . This revealed a 29% reduction in cholesterol synthesis in the brains ( p<0 . 05 ) of N-Scap ( +/− ) mice ( Figure 3F ) , closely paralleling the change observed in STZ-diabetic mice [9] . Synapse marker proteins , including syntaxin-1A ( STX1A ) and post-synapse density 95 ( PSD95 ) were also decreased in the N-Scap ( +/− ) mouse cerebral cortices ( Figure 3G ) ; similar to what has been observed in STZ-diabetic mice [12] . This occurred with no change in glucose levels ( after a 4-h fast 182±6 . 7 versus 175±8 mg/dl in controls ) or fed insulin levels ( 0 . 32±0 . 02 versus 0 . 33±0 . 04 ng/ml in controls ) . Thus , the heterozygous N-Scap ( +/− ) mice mimic the reduction of SCAP protein , the impaired cholesterol biosynthesis , and the decrease in synapse markers observed in the brains of diabetic mice , but without the concomitant changes in glucose or insulin levels . To determine whether changes in cholesterol metabolism induced by the decrease of brain SCAP protein might alter synaptic transmission , we performed intracellular and extracellular electrophysiological recordings from neurons in the area CA1 region of the hippocampus , an area of the brain with demonstrated abnormalities on imaging in humans with type 1 and type 2 diabetes [7] , [8] . Intracellular recording of miniature excitatory postsynaptic current ( mEPSC ) events were monitored by continuous voltage-clamp to assess differences in frequency and amplitude of presynaptic events at CA1 neurons . The basal frequency of mEPSCs of area CA1 neurons was reduced by more than 60% in N-Scap ( +/− ) mice ( 0 . 5±0 . 1 Hz; n = 15 cells; p<0 . 01 ) compared to control mice ( 1 . 4±0 . 3 Hz; n = 15 cells ) ( Figure 4A and 4B ) , but no differences in mEPSC amplitudes were observed between the two genotypes ( control: 8 . 9±0 . 2 pA; N-Scap ( +/− ) : 8 . 7±0 . 1 pA; n = 1500 events ) ( Figure 4C ) . These results strongly implicate an effect of SCAP reduction on presynaptic neurotransmitter release , with no differences at the postsynaptic membrane . To further assess the effect of reduced SCAP levels on synaptic transmission , we tested the synaptic efficacy of the Schaeffer collateral pathway between control and N-Scap ( +/− ) mice via paired-pulse facilitation and long-term potentiation ( LTP ) . Paired-pulse facilitation was elicited with interpulse intervals of 50 , 100 , 150 , and 200 ms , and then calculated as the paired-pulse ratio of field excitatory postsynaptic potential ( fEPSP ) amplitude elicited by the second to first test stimuli . The paired-pulse ratio was greatest when elicited with an interpulse interval of 50 ms and was significantly reduced in N-Scap ( +/− ) hippocampal slices ( control: 1 . 73±0 . 08; n = 17; N-Scap ( +/− ) : 1 . 45±0 . 04; n = 12; p<0 . 01 ) ( Figure 4D and 4E ) , suggesting significant alterations in synapse plasticity in N-Scap ( +/− ) hippocampi . Further studies will be required to define the mechanism of this defect at the synaptic level . We also tested the efficacy of neurotransmitter release following high frequency stimulation to determine if a robust and inducible LTP can be generated at CA1 synapses of N-Scap ( +/− ) hippocampal slices . High frequency stimulation ( 4×100 Hz ) reliably induced a robust and reproducible LTP in hippocampal slices from both control and N-Scap ( +/− ) mice . However , the change in fEPSP slope was markedly reduced in N-Scap ( +/− ) slices ( Figure 4F and 4G ) . The mean fEPSP slopes measured 10 , 30 , 60 , and 120 min after LTP induction in control slices were 255±1 , 189±4 , 196±1 , 188±1 , and 183±1 , respectively ( n = 10 ) ( Figure 4H ) . All corresponding slope values were significantly lower in slices from N-Scap ( +/− ) mice ( 198±3 , 147±1 , 139±2 , 127±2 , and 125±2 , respectively; n = 8; p<0 . 001 ) ( Figure 4H ) . Taken together , these findings indicate a significant impairment of synaptic transmission at CA1 neurons of the hippocampus in the brains of SCAP heterozygous knockout mice . Memory is impaired in patients with type 2 diabetes and to a lesser extent in patients with type 1 diabetes [13] , [14] . To assess memory in our mouse model we used the novel object recognition test , which takes advantage of the natural bias of preference for novelty in rodents [15] . As expected , after a training session , control mice spent significantly more time exploring the novel object as compared to the training object ( 16 . 4±3 s versus 9 . 4±2 s , p<0 . 01 ) , resulting in a 63% preference for the novel object ( Figure 5A and 5B ) . By contrast , N-Scap ( +/− ) mice failed to demonstrate any significant preference for the novel object over the training object ( 13 . 8±2 . 5 s versus 12 . 9±2 . 1 s ) , spending almost identical amounts of time with each object ( Figure 5A and 5B ) . Diabetic patients have an increased prevalence of anxiety disorders [16] , [17] . We used two different tests to assess anxiety in the N-Scap ( +/− ) mice . We performed the elevated open-platform test , which is used to assess psychological stress [18] . While controls spent 33 . 9±7 . 7 s in a frozen posture on the open platform , the N-Scap ( +/− ) mice exhibited a much shorter duration of freezing behavior ( 14 . 3±2 . 7 s , p<0 . 05 ) induced by this stress ( Figure 5C ) . The novelty-suppressed feeding test assesses the effects of conflicting motivations of the drive to eat food after fasting and the fear of venturing into a novel arena of white paper on which the food pellets have been placed [19] . N-Scap ( +/− ) mice showed a markedly reduced latency to enter the arena compared to control mice , ( 17 . 1±6 . 4 versus 61 . 5±19 s , p<0 . 05 ) i . e . , were more adventuresome or reckless in their behavior than controls ( Figure 5D ) . Once they entered the novel area , however , there was no difference in the latency to start eating the food pellets ( 151 . 2±34 . 1 versus 150 . 7±18 . 8 s ) ( Figure 5D ) , demonstrating that hunger was not the driver of the reduced latency . Interestingly , in unstressed conditions , circulating levels of the stress-related hormones corticosterone , epinephrine , and norepinephrine did not differ between control and N-Scap ( +/− ) mice . However , when the mice were isolated in individual transport containers for an hour , there was a robust elevation of stress hormone levels in N-Scap ( +/− ) mice compared with control mice with elevations in corticosterone ( 1 . 8-fold ) and norepinephrine ( 2 . 2-fold ) and a trend towards increased epinephrine ( 15-fold ) ( Figure 5E ) . The surprising increase in stress hormones in these mice despite an apparent decrease in anxiety behaviors may indicate an inappropriate reaction to stress rather than a true anxiolytic effect . To further explore the possible relationship between behavior and the abnormal recordings found in the hippocampi of these mice we performed two behavioral tests which specifically use hippocampal function: contextual fear conditioning and the Morris water maze . In the contextual fear conditioning experiment N-Scap ( +/− ) mice showed impaired acquisition of the fear conditioning in response to a foot shock , as demonstrated by a decrease in freezing ( Figure 5F ) . In addition , on the second day there was reduced freezing during the first minute in the context and the first and second minute in the altered context ( Figure 5G ) , with later time points maintaining the same trend but losing statistical significance . This testing suggests impairments in hippocampal functioning , although we cannot rule out the possibility that there is some difference in pain thresholds between the mouse genotypes . We also performed a Morris water maze . We were unable to see significant differences between the two genotypes in this test though the N-Scap ( +/− ) mice showed a trend towards spending more time in the incorrect quadrants during the probe trials ( Figure S4 ) . Similar discordance between abnormal recordings in the hippocampus and abnormalities in the water maze have been previously observed in other models [20] . Finally , we evaluated feeding behavior and energy expenditure as measures of hypothalamic function in these mice . The N-Scap ( +/− ) mice consumed more food than the control mice , especially during the dark phase ( Figure 6A and 6B ) . This phenotype is consistent with mice following SREBP-2 gene silencing in the hypothalamus shown in our previous study [9] . This correlates with mildly reduced expression of the anorexigenic neuropeptide CART ( Cartpt ) , and increased expression of the orexigenic neuropeptide Agrp in the hypothalamus of the N-Scap ( +/− ) mice ( Figure 6C ) . Energy expenditure , as represented by oxygen consumption in a metabolic cage , was about 20% higher in the N-Scap ( +/− ) mice than in control mice ( Figure 6D ) . Consistent with this , locomotor activity of N-Scap ( +/− ) mice during this period indicated a significant , parallel increase ( Figure 6E and 6F ) . The increase in energy expenditure compensated for the increase in food intake , resulting in similar body composition ( Table S1 ) . The respiratory exchange ratio ( RER ) was also high in the N-Scap ( +/− ) mice during the first 6 h of the dark cycle ( Figure 6G ) , suggesting increased utilization of carbohydrate as a fuel .
In our previous study we showed that cholesterol synthesis is significantly impaired in multiple mouse models of diabetes due to a down-regulation of most of the genes in the cholesterol biosynthesis pathway [9] . In this study we show that this is due , at least in part , to a significant reduction of the sterol sensor protein SCAP in the brains of diabetic mice . Here we have used genetically modified mice with a defect in cholesterol synthesis to determine the consequences of decreased brain cholesterol , in the absence of the hyperglycemia and impaired insulin signaling found in diabetic mice . Homozygous deletion of Scap in the brain causes microcephaly , gliosis , and early postnatal lethality . Mice with a brain-specific Scap heterozygous knockout , on the other hand , closely mimic the decrease in sterol synthesis observed in the diabetic brain , and this leads to significant attenuation of synaptic transmission and cognitive dysfunction , even in the absence of any systemic metabolic derangement . Because Scap is knocked out during the prenatal period we cannot rule out a developmental contribution to the changes observed . Cholesterol comprises a significant component of the neuronal membrane and is thus critical for proper neuronal transmission . Reduction in cholesterol synthesis decreases the formation of synaptic contacts between neurons [9] . We show here that the reduction of cholesterol synthesis in the brains of N-Scap ( +/− ) mice produces adverse effects on neuronal transmission by decreasing the efficacy of synaptic transmission . Diabetic rodents exhibit impaired performance in the novel object recognition test and contextual fear conditioning test—functional measures of memory in rodents [21] , [22]—and this is mimicked in the N-Scap ( +/− ) mouse . Impairment of LTP expression in the CA1 region of the hippocampus in diabetic animals has also been reported [23]–[25] and is also seen in the N-Scap ( +/− ) mouse . The impairment in LTP provides an electrophysiologic correlate for the impaired memory observed in the novel object recognition test and the contextual fear conditioning test . Coupled with our previous studies showing altered brain cholesterol metabolism in diabetes [9] , the current results provide the first evidence indicating that failure in regulation of cholesterol homeostasis in the brain may contribute to the cognitive impairment seen in diabetes ( see model in Figure 7 ) . While the N-Scap heterozygous mouse model system does not allow for the precise dissection of the relative contributions of SREBP-2 processing and its effects on cholesterol synthesis versus SREBP-1 processing and its potential effects on fatty acid and triglyceride synthesis , it seems likely that SREBP-2 and cholesterol are more important . Although the knockout of SCAP produces changes to SREBP-1 processing , we find no defect in free fatty acid synthesis in the brains of N-Scap ( +/− ) mice ( unpublished data ) , nor is there a decrease in triglyceride content in the brain . This is in contrast to a conditional knockout of SCAP in the liver , which causes decreases in serum free fatty acids and triglycerides of 46% and 53% , respectively , but only a 24% decrease in serum cholesterol [10] . This may be explained by the fact that while cholesterol is unable to cross the blood-brain barrier , fatty acids and glycerol , the building blocks of triglycerides , are able to pass . While fatty acid and glycerol transport across the blood-brain barrier may not fully compensate for all of the defects created by the decrease of SREBP-1 in the brain , it seems to significantly mitigate the effects given the normal to increased triglyceride levels observed in this manuscript . Nonetheless , the SREBP-1 pathway may have a greater impact in diabetes as there is a 15% decrease in fatty acid synthesis in brains of STZ diabetic mice ( unpublished data ) . Further , there is also the possibility that components of the SREBP-2 pathway beyond what are explored here , such as isoprenoid production , may be contributing to the observed phenotype . Exactly how lowering cellular cholesterol in the brain might affect brain function is unclear and likely multifactorial . Cholesterol depletion has been shown to retard or prevent clathrin-mediated endocytosis , including internalization of acetylcholine receptors [26] and block vesicle biogenesis in neurosecretory cells , consistent with a role for cholesterol in regulating membrane fluidity and the changes in curvature necessary for full vesicle formation [2] , [27] . Moreover , cholesterol binds the synaptic vesicle protein synaptophysin and modulates interaction with the essential vesicular SNARE protein , synaptobrevin , to regulate exocytosis [28] . Knockdown of SREBP-2 in primary neuronal cultures produces decreases in expression of the synaptic vesicle marker VAMP2 [9] . Interestingly , deletion of low-density lipoprotein receptor-related protein 1 ( LRP1 ) in forebrain neurons in mice leads to a decrease in brain cholesterol levels and memory loss with selective reduction of glutamate receptor subunits , which is partially rescued by restoring neuronal cholesterol [29] . These reports are consistent with the phenotypes of attenuated synapse transmission and cognitive impairment with decreased synaptosomal markers in N-Scap ( +/− ) mice . Activation of glial cells , as demonstrated by increased GFAP staining , is a common feature of many types of neural insults including trauma , toxins , neurodegenerative triggers , or infection [30] . Multiple studies have reported increased GFAP expression or astrocyte content in cerebral cortices and hippocampi of diabetic animal models [21] , [31] , [32]; however , the mechanisms producing these changes have not been elucidated . The brains of N-Scap ( −/− ) mice show increased GFAP expression and gliosis . Whether this is a compensatory or reactive response induced by cholesterol deficiency in the brain remains to be determined . Several neurodegenerative diseases have been associated with alterations in cholesterol metabolism . Reduction of cholesterol synthesis in astrocytes is thought to contribute to neurodegeneration in multiple models of Huntington disease [33] . Alzheimer disease has been associated with both diabetes and cholesterol metabolism [34]–[37] . The ε4 allele of ApoE , a cholesterol transport protein , is the only known risk factor , other than aging , for late onset Alzheimer disease [35] . On the other hand , studies examining the relationship of serum cholesterol levels in the elderly to dementia have differing conclusions , and several clinical trials in humans using the statin class of lipid lowering drugs have yielded conflicting results ( reviewed in [36] ) . Part of this may reflect the broad range of human physiology . For example , in our previous study we showed a 3-fold range of expression of Srebf2 and Hmgcr and a 2-fold range in synaptosomal cholesterol content in brains of 16 elderly humans with and without diabetes and dementia [9] . Based on our studies and others [36] , one might predict that too much cholesterol , as well as too little , could have detrimental effects on neuronal function . However , because cholesterol in the brain is controlled independently of serum cholesterol , we do not currently have a clinically useful tool for assessing cholesterol levels in the human brain . Interestingly , a recent study used intranasal delivery of insulin to the brain as a therapy for human dementia patients with some improvement seen in memory and self care tasks [38] . The source of this benefit is unknown , but one of the responses to this therapy may be an increase in cholesterol synthesis in the brain . Taken together with our previous work [9] we propose a model for how diabetes may affect cholesterol in the brain , leading to changes in behavior and brain function ( Figure 7 ) . In this model , diabetes mellitus reduces expression of SCAP in the brain , and this reduction of SCAP causes a defect in SREBP-2 processing , leading to a reduction in active SREBP-2 . This leads to a down-regulation of genes involved in brain cholesterol synthesis , which leads to impaired synaptic transmission and abnormal cognitive function . These findings provide a novel view on the role of cholesterol regulation in the brain in diabetes , and open the possibility of therapeutic strategies for reversing the effects of diabetes on the brain and nervous system .
All animal studies followed the US National Institutes of Health guidelines and were approved by the Institutional Animal Care and Use Committees at the Joslin Diabetes Center and Beth Israel Deaconess Medical Center . Scap-floxed mice [10] , nestin-Cre mice [11] , C57BL/6 mice , and db/db mice ( C57Bl/Ks background ) were purchased from the Jackson Laboratory . Scap-floxed mice with the nestin-Cre transgene were maintained on a C57BL/6×129Sv mixed genetic background; therefore for all studies littermates were used for analysis . For STZ-induced diabetes experiments , 7-wk-old C57BL/6 mice were treated with a single intraperitoneal injection ( 200 µg/g body weight ) of STZ ( Sigma ) . Some of the STZ diabetic mice were treated ICV with insulin . For these experiments 7-wk-old C57Bl/6 mice were placed in a stereotactic device under anesthesia , and a 26-gauge guide cannula ( Plastics One Inc ) was inserted into the right lateral cerebral ventricle ( 1 . 0 mm posterior , 1 . 0 mm lateral , and 2 . 0 mm ventral to the bregma ) . A dummy stylet was inserted into each cannula until used . After 1 wk of recovery , the mice received a single IP injection of STZ to induce diabetes . Twelve days later , the mice received three ICV injections of insulin ( 3 mU in 2 µl ) or the same volume of PBS ( 9am , 7pm , and 9am the following day ) through an internal cannula using a Hamilton microsyringe . Four hours after the last injection the hypothalami were collected . All mice were maintained on a 12-h light/12-h dark cycle and fed a standard mouse chow diet ( LabDiet Mouse Diet 9 F , PMI Nutrition International ) . All mice used for experiments were male . For analysis of gene and protein expression , the brain was quickly removed under anesthesia with 2 . 5% Avertin ( 15 µl/g body weight , IP ) , placed on ice , and dissected into the hypothalamus and cerebral cortex using a mouse brain matrix ( ASI Instruments Inc . ) . RNA from murine brain tissue was isolated using an RNeasy kit ( Qiagen ) . As a template , 1 µg of total RNA was reverse-transcribed in 50 µl using a High Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) according to the manufacturer's instructions . Three microliters of diluted ( 1∶4 ) reverse transcription reaction was amplified with specific primers ( 300 nM each ) in a 25 µl PCR reaction with a SYBR Green PCR Master Mix ( Applied Biosystems ) . Analysis of gene expression was done in the ABI PRISM 7000 sequence detector with initial denaturation at 95°C for 10 min followed by 40 PCR cycles , each cycle consisting of 95°C for 15 s and 60°C for 1 min , and SYBR green fluorescence emissions were monitored after each cycle . For each gene , mRNA expression was calculated relative to Tbp expression as an internal control . Brains were immersed in 4% paraformaldehyde ( PFA ) overnight at 4°C then embedded in paraffin , and 8 µm coronal sections were collected . Sections were deparaffinized and blocked with a Mouse Ig Blocking Reagent ( M . O . M . Immunodetection kit , Vector Laboratories ) containing avidin for 1 h , washed with PBS , treated with M . O . M diluent for 5 min , and incubated with a mouse monoclonal antibody recognizing GFAP ( 1∶500 , Millipore ) and biotin solution for 30 min at room temperature . After washing with PBS , the sections were incubated with a biotinylated anti-mouse IgG secondary antibody for 20 min . The samples were washed with PBS , treated with a streptavidin/peroxidase complex reagent , washed with PBS again , and stained with a VIP Substrate kit ( Vector Laboratories ) . Hematoxylin was used for counter-staining . Nuclear and cytoplasmic extracts of brain tissue were prepared per the manufacturer's directions ( NE-PER kit , Pierce ) . Whole tissue extracts were prepared using RIPA buffer containing 1% SDS and protease inhibitor cocktail ( Sigma ) . Protein concentrations were measured using a BCA assay ( Pierce ) . Immunoblotting was performed with antibodies against SCAP ( Santa Cruz ) , β-tubulin , CREB , fatty acid synthase , acetyl-CoA carboxylase ( Cell Signaling Technology ) , SQLE ( ProteinTech ) , SREBP-1/SREBP-2 ( gifts from Jay Horton and Guosheng Liang ) , STX1A and PSD95 ( Abcam ) , and SYP and MBP ( Millipore ) . Brain tissue was homogenized in 50 mM NaCl . Lipid fraction was then extracted through multiple washes with a 2∶1 chloroform∶methanol solution . Samples were dried down with 10% triton-X 100/acetone . Cholesterol content was assayed by enzymatic assay ( Wako Chemicals ) . Triglycerides were measured by colorimetric assay ( Abnova ) . Each anesthetized animal was injected intraperitoneally with 50 mCi of [3H]water in 0 . 2 ml of PBS . One hour after injection ( which is a time long enough to reflect endogenous rates of synthesis and short enough to avoid significant inter-organ redistribution [39] ) , blood was collected by retro-orbital puncture , and the [3H]water specific activity in the plasma was measured . The brain was removed , and the whole cerebrum ( 250–290 mg ) was saponified with 2 . 5 ml of 2 . 5 M KOH ( 75°C , 2 h ) . Sterol-containing lipids were extracted with 10 ml hexane and 5 ml 80% ethanol . Cholesterol was isolated by thin layer chromatography ( hexane∶diethyl ether∶glacial acetic acid = 80∶20∶1 ) , and the incorporated tracer was measured by a scintillation counter . The synthesis rates were calculated as nmol of [3H]water incorporated into cholesterol per gram of tissue per hour . Transverse hippocampal slices ( 400 µm ) were prepared from the brains of 4–6-wk-old male control or N-Scap ( +/− ) littermates that were submerged in an ice-cold high sucrose solution containing ( in mM ) : 250 sucrose , 2 . 5 KCl , 1 . 24 NaH2PO4 , 10 MgCl2 , 10 glucose , 26 NaHCO3 , 0 . 5 CaCl2 that was aerated with 95% O2/5% CO2 . Slices were then maintained at 30°C in artificial cerebrospinal fluid ( ACSF ) containing ( in mM ) : 124 NaCl , 2 . 5 KCl , 1 . 24 NaH2PO4 , 1 . 3 MgCl2 , 10 glucose , 26 NaHCO3 , 2 . 5 CaCl2 , and allowed to recover for at least 1 h before being transferred to the recording chamber for the start of experiments . Voltage-clamp whole-cell recordings of mEPSC events at area CA1 neurons were recorded at a holding potential of −65 mV in the presence of 500 nM tetrodotoxin ( TTX ) using a glass microelectrode ( 9–10 MΩ ) filled with an internal pipette solution containing ( in mM ) : 137 K-gluconate , 2 KCl , 5 HEPES , 5 MgATP , 0 . 3 NaGTP , 10 creatine ( 290 mOsm/l [pH 7 . 4] ) . fEPSPs were elicited by stimulating the Schaeffer collaterals with a concentric bipolar electrode ( FHC ) and recorded with an ACSF-filled glass microelectrode ( 1–2 MΩ ) positioned in the stratum radiatum of area CA1 . Baseline test stimuli were applied once per min at a test stimulation intensity ( 0 . 1 ms pulse width ) adjusted to evoke fEPSP amplitudes that were 40% of maximal size . Paired-pulse stimulation was elicited by two consecutive test stimuli; the interpulse interval was varied from 50–200 ms , at 50 ms increments . LTP was induced by four trains of 100 Hz ( 1 s train duration ) elicited 5 min apart . fEPSPs were not monitored between each high frequency train but were monitored with the test stimuli for up to 120 min after the induction of LTP . All recordings were acquired with a Multiclamp 700B amplifier ( Molecular Devices ) via a Digidata 1440A ( Molecular Devices ) digitizer interface then recorded with pCLAMP 10 . 2 software . Offline analysis of mEPSCs and fEPSPs were performed with MiniAnalysis ( Synaptosoft ) and Clampfit 10 . 2 ( Molecular Devices ) , respectively . Statistics and graphs were produced using Prism 5 ( GraphPad Software ) . Statistical significance for mean comparisons was determined by the unpaired Student's t test at p<0 . 05 . Data were represented as mean ± SEM , where appropriate , and n refers to the number of hippocampal slices , unless indicated otherwise . The novel object recognition test was performed as described previously [40] with slight modifications . Mice were maintained in group housing over the course of the experiment . Briefly , the mice were habituated for 1 h to a plastic cage box on the first day . The floor was covered with 1 cm of wood bedding . On the second day , a familiarization trial was performed for 5 min , allowing each mouse to explore the two identical objects ( objects A and B ) in the same box . The two objects were placed along the long axis of the box . Mice were filmed while exploring objects A and B and this was quantified to ensure that the mice did not show preference for the object on one side of the cage over the other . There was not a significant difference between exploration of object A and object B . Then , the mouse was removed from the trial box and placed in its home cage for 1 h . After each exposure , the objects were wiped with 70% ethanol to eliminate odor clues . One hour after the familiarization , each mouse was placed in the box with one of the old objects ( object A ) and a new one ( object C ) . The position of object C was the same as object B in the familiarization trial , and the time to explore them was again 5 min . A mouse was considered to be engaging in exploratory behavior if the animal touched the object with its forepaw or nose or sniffed at the object within a distance of 1 cm . Activity was monitored and calculated from a timed video of the experimental field . In a separate cohort of mice both the old object ( object A ) and the new object ( object C ) were presented to mice for 30 s to determine if one object was more interesting to the mice than the other . There was no difference in exploratory behavior between the two objects when presented simultaneously . The novelty-suppressed feeding test was performed as described previously [19] with slight modifications . Twenty-four hours before the test , food was removed from the cages . At the time of testing , a pair of food pellets ( regular chow ) were placed on a white paper disk positioned in the center of a trial box without wood bedding . An animal was placed in the corner . The latency to enter the arena and then to chew the pellets were recorded within a 5 min period . Activity was monitored and calculated from a timed video of the experimental field . The elevated open platform test was performed as described previously [18] with slight modifications . In brief , a transparent glass cylinder ( 12 cm diameter , 21 cm high ) was placed upside-down and each mouse was positioned at the top ( open platform ) . Freezing behavior was defined as no movement , excluding respiratory movement , while in a crouching posture . The duration of freezing was the total amount of time that the animal showed freezing . If the mouse slipped off the platform , it was immediately placed back on the platform and the experiment was continued . The mouse behavior on the elevated open platform was video recorded for 5 min . During the acquisition phase mice were randomly dropped at one of four points; N , S , E , or W . A hidden platform was located in the southwest ( SW ) quadrant . Mice were given 1 min to find the hidden platform and latency was recorded . Mice were led to the hidden platform if they did not reach it within 1 min . Each mouse went through eight trials per day . Latency times leveled off on day 4 . On day 5 the probe trial was conducted . The hidden platform was removed and the mouse was dropped off at the N drop point . Time spent in each quadrant was recorded over 1 min . Data was analyzed using TopScan software from Cleversys , Inc . On day one each mouse was placed in a novel box for 2 min and freezing activity was recorded ( baseline ) . At the end of the 2 min the mouse received a 0 . 5 mA shock for 2 s . Freezing time was again measured over 2 min ( post shock 1 ) . At the end of the 2 min a second 0 . 5 mA shock was delivered over 2 s and freezing was recorded for 1 min . The mouse was then returned to its home cage . On day 2 each mouse was placed back in the cage from day 1 and freezing was recorded for 3 min ( context ) . The mice were then placed in an unfamiliar box and freezing was again measured for 3 min ( altered context ) . Data was analyzed using TopScan software from Cleversys , Inc . Data are expressed as the mean ± SEM . Statistical significance was calculated using an unpaired Student's t-test for comparison between two groups , and by an analysis of variance ( ANOVA ) for multigroup comparison . Fisher's PLSD was used for the Morris water maze and contextual fear conditioning . | Diabetes is associated with an increased risk of Alzheimer disease , depression , and cognitive decline , but the causal link underlying these associations is unclear . We previously showed that in diabetic mice there is a reduction in brain synthesis of cholesterol , which is required for normal formation of synapses between neurons . Here we show that this deficit is caused , in part , by a reduction in the levels of SCAP , a protein known to help regulate cholesterol synthesis by promoting the relocalization , cleavage , and liberation of the key transcription factor SREBP2 . These changes in cholesterol biosynthesis are rescued by treatment of the diabetic mice with insulin . When the level of SCAP in the brains of non-diabetic mice is lowered by genetic manipulation , there is a decrease in cholesterol synthesis in the brain , and this results in impaired signaling between neurons , memory deficits , and abnormal responses to stress . These findings indicate that the reduction in SCAP associated with diabetes can contribute to changes in cognitive function in this disease . | [
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] | 2013 | Reduction of the Cholesterol Sensor SCAP in the Brains of Mice Causes Impaired Synaptic Transmission and Altered Cognitive Function |
DNA interstrand crosslinks ( ICLs ) are toxic lesions that block the progression of replication and transcription . CtIP is a conserved DNA repair protein that facilitates DNA end resection in the double-strand break ( DSB ) repair pathway . Here we show that CtIP plays a critical role during initiation of ICL processing in replicating human cells that is distinct from its role in DSB repair . CtIP depletion sensitizes human cells to ICL inducing agents and significantly impairs the accumulation of DNA damage response proteins RPA , ATR , FANCD2 , γH2AX , and phosphorylated ATM at sites of laser generated ICLs . In contrast , the appearance of γH2AX and phosphorylated ATM at sites of laser generated double strand breaks ( DSBs ) is CtIP-independent . We present a model in which CtIP functions early in ICL repair in a BRCA1– and FANCM–dependent manner prior to generation of DSB repair intermediates .
Cellular DNA can be chemically modified and damaged when exposed to environmental agents , metabolic byproducts , or chemotherapeutic agents . The most toxic of these lesions is the interstrand crosslink ( ICL ) , a covalent bridge formed between complementary strands of DNA . If not repaired , ICLs prevent DNA strand separation resulting in a block to replication and transcription . ICL generating agents are commonly used in the treatment of cancer . Sensitivity to crosslinking agents is a defining characteristic of Fanconi Anemia ( FA ) , a rare hereditary syndrome characterized by an increased risk in cancer development and hematopoetic abnormalities frequently resulting in bone marrow failure [1] . Elucidation of the cellular pathways that repair ICLs is highly relevant to understanding carcinogenesis , development of novel therapies to treat FA patients , and to the development of better targeted chemotherapeutic drugs . Sensitivity assays suggest that eukaryotic cells have evolved multiple complex systems to repair ICLs that involve the intersection of several different repair pathways ( reviewed in [2] , [3] ) . However , the specific mechanism by which ICLs are detected and repair is initiatedremains unknown . A major ICL repair pathway in higher eukaryotes functions during S-phase and is thought to be replication dependent [4]–[7] . ICLs can also be repaired in a replication independent manner [8]–[10] . Current models of replication mediated ICL repair , suggest that repair is initiated when a fork stalls due to encountering an ICL [6] , [11] . FANCM/FAAP24 then binds to the ICL stalled fork [12]–[17] . Next , single stranded DNA ( ssDNA ) is generated and bound by RPA [11] , [15] , [18] and the DNA damage response kinase ATR/ATRIP localizes to the damaged chromatin through binding to RPA [19] . Localization of ATR/ATRIP to damaged DNA is essential for activation of the S-phase checkpoint and ICL repair [20] , [21] . The ability of ICLs to activate the checkpoint is dependent on the FA core complex ( FANCA/B/C/E/F/G/M ) [10] , but not FANCI-FAND2 [18] . The generation of ssDNA at stalled replication forks is thought to be critical for ATR activation . However , the factors required to generate ssDNA under circumstances in which the ICL poses a structural barrier to helicase uncoupling from the DNA polymerase at the replication fork are not known [22] . It has been shown ssDNA arises at an ICL stalled fork in Xenopus extracts due to resection of the lagging strand [11] . In addition this ssDNA is competent for Rad51 loading prior to generation of a DSB ICL repair intermediate [23] . The FANCI-FANCD2 complex is phosphorylated by activated ATR in response to ICL stalled replication forks [20] , [24] , [25] . This phosphorylation facilitates FANCI-FANCD2 monoubiquitination by the FA core complex [24] , [25] . Monoubiquitination is essential for localization of the FANCI-FANCD2 complex to damaged chromatin where it directs downstream repair steps [18] , [26]–[28] . The FANCI-FANCD2 complex is required for the initial ICL incision step in replication competent Xenopus extracts [18] . Several candidate nucleases have been identified that may function to excise the ICL . These nucleases include XPF/ERCC1 , MUS81/EME1 , their regulator SLX4 ( also known as the Fanconi Anemia gene FANCP ) [29]–[37] , SNM1A [37] , [38] and SNM1B [39] . Whether they act redundantly or in concert is not clear , however they are all characterized by showing higher sensitivity to crosslinking agents compared to other forms of damage . MUS81/EME1 and XPF/ERCC1 have been proposed to function both early [40] , [41]and late in the ICL repair process [29]–[32] , [42] , [43] . The nuclease FAN1 also confers resistance to ICL inducing agents and has been shown to interact physically with the monoubiquitinated form of the FANCD2 nuclease [44]–[46] . Although a large number of nucleases are implicated in ICL repair , it remains unclear at which specific step these proteins exert their functions . Following ICL incision , translesion synthesis by an error prone polymerase repairs the strand opposite the incised ICL [11] , [47]–[50] . Excision of the ICL and polymerase extension up to the excised region results in the generation of double stranded ends resembling a double strand break ( DSB ) . The DSB is repaired by homologous recombination as evidenced by the extreme sensitivity of homologous recombination impaired cell lines to ICL inducing agents [51]–[53] . We hypothesized that CtIP might play an important and unique role in replication dependent ICL repair for the following reasons . First , studies of the CtIP homologs in A . thaliana ( AtGR1 ) and S . cerevisiae ( SAE2 ) have revealed a conserved role for CtIP in ICL repair due to their ability to confer resistance to the ICL inducing agent mitomycin C ( MMC ) [54] , [55] . Second , CtIP expression is enhanced in S through G2 phase [56] , [57] when ICL repair primarily occurs . Third , CtIP plays a conserved role in coordinating the recognition and resection of double strand breaks with the MRE11-RAD50-NBS1 ( MRN ) complex [58]–[63] and may act to facilitate the generation of ssDNA ends following ICL incision . To investigate the mechanism of ICL repair in live mammalian cells we developed a system that takes advantage of the chemical and physical characteristics of the cross-linking agent , 8-methoxy psoralen ( 8-MOP ) . 8-MOP intercalates into DNA and forms DNA ICLs upon exposure to long wavelength UVA light ( 365 nm ) [64] . In this system we used laser directed 2-photon activation of 8-MOP via infrared light ( 730 nm ) to generate ICLs in individual human S-phase nuclei . This approach restricts damage to precisely defined subnuclear volumes allowing one to monitor the spatiotemporal dynamics of DNA repair factors after ICL induction . This system was used to determine what role CtIP plays in ICL repair . We show that CtIP plays an important and unexpectedly early role in ICL repair in S phase human cells . In addition to the known role of CtIP and MRE11 in DSB end resection [59] , [60] , [62] , the data presented here demonstrates that CtIP plays an additional role in ICL repair prior to generation of DSB repair intermediates . The data show that CtIP acts prior to RPA loading and localization of ATR and FANCD2 to ICL containing DNA . CtIP is required for the accumulation of known DSB repair factors phospho-histone H2AX ( γH2AX ) , and ATM autophosphorylated on serine-1981 ( ATM-pS1981 ) at ICLs , but not at DSBs . The data support a model in which CtIP is recruited to ICLs in a FANCM dependent manner and is required to initiate ICL processing of stalled forks prior to ICL incision .
To determine if CtIP plays a role in ICL repair , we assayed CtIP depleted human cells for sensitivity to the ICL inducing agents MMC or 8-MOP plus whole cell UVA irradiation ( 365 nm ) . The products of activated 8-MOP on DNA are predominantly ICLs , but monoadducts are also formed [64] , [65] . The psoralen angelicin , which is structurally related to 8-MOPs , forms monoadducts upon UVA exposure and was used to control for cellular sensitivity to DNA monoadducts [66] . CtIP was depleted from Human Embryonic Kidney Cells ( HEK293 ) by transient transfection of two previously described independent small interfering RNAs ( siRNAs ) [59] . Luciferase siRNA was used as a nontargeting control . CtIP RNA levels were monitored by RT-qPCR and were found to be reduced on average by 70% . Sensitivity to ICL and monoadduct inducing compounds was determined by assessing the fraction of surviving cells after 8 days . As shown in Figure 1A , CtIP depleted cells were sensitive to the ICL inducing agents 8-MOP+UVA and to MMC . CtIP depleted cells were not sensitive to angelicin plus UVA . CtIP depleted cells were only slightly sensitive to IR induced DSBs ( Figure S1 ) . This sensitivity profile is similar to that reported for A . thaliana AtGR1/AtCom1 mutated cells [55] , and suggests that CtIP plays an important and conserved role in ICL repair . To define the role of CtIP in ICL repair , we established a system that enables the examination of the spatial and temporal recruitment and retention of repair proteins at region specific ICLs . An analogous approach has been used to generate study ICL repair using a UV laser plus light activated psoralen [9] , [67] . Our approach uses a near infrared femtosecond laser to produce 730 nm light which activates 8-methoxypsoralen ( 8-MOP ) by two-photon activation ( effective wavelength of 365 nm at the focal point ) . This enabled us to define precisely a subnuclear volume in which DNA damage was created while avoiding damage outside the focal points [68] . This method allowed specific activation of 8-MOP with doses of light that caused no detectable damage in psoralen free control cells ( Figure 1B ) . To define the optimal laser power , we first identified the lowest laser power that gave a robust γH2AX signal in cells treated with 8-MOP , and no observable γH2AX by laser alone ( Figure 1B ) . Cells were subject to thymidine block and release to enrich for replicating cells [69] . One hour after thymidine release the indicated drug was added , and cells were microirradiated with 730 nm light using a femtosecond laser . Following microirradiation , cells were maintained in culture for 2 hours allowing time for replication forks to encounter the damage prior to fixation ( Figure S2 ) . Angelicin was used as a control for the amount of γH2AX at the laser tracks that resulted from the generation and/or processing of monoadducts . To verify that the γH2AX along laser tracks was replication dependent a control experiment was performed in which cells were held in thymidine during drug treatment and laser microirradiation . γH2AX staining signal along microirradiated tracks was quantified in individual cells using Image J ( See Materials and Methods ) . The proportion of cells with positive γH2AX staining along microirradiated tracks are summarized in Figure 1C . The number of cells scored positive for γH2AX signal along laser tracks was highly increased in S-phase cells microirradiated in the presence of 8-MOP ( 83% ) relative to microirradiated drug free cells ( 0% ) ( n = 29 and 25 cells respectively ) . Blocking replication in 8-MOP microirradiated cells reduced the number of γH2AX positive cells almost to background levels ( 4 . 5% ) ( n = 21 ) . Angelicin treated microirradiated cells did not yield bright γH2AX signal ( 0% ) ( n = 25 ) ( Figure 1B , 1C ) , however we do not exclude the possibility that adduct formation by angelicin may be less efficient than that of 8-MOP . Thus we conclude that the γH2AX signal detected in 8-MOP microirradiated cells is primarily due to the replication dependent processing of ICLs . Previously published reports have shown that CtIP plays a role in G1/S progression in mouse fibroblasts ( MEFs , NIH 3T3 ) [57] , [70] . We wanted to examine whether CtIP depletion affected cell cycle and S-phase progression in HeLa which could have an effect on the efficiency of ICL repair . Cell cycle analysis was performed on CtIP depleted and control HeLa cells . No significant difference in cell cycle distribution was observed between control cells and 2 independent CtIP siRNAs in 3 independent experiments ( Figure 2A , 2B ) . Figure 2C shows that both CtIP siRNAs effectively depleted CtIP in the samples analyzed for cell cycle distribution . Quantification of the nucleotide analog bromodeoxyuridine ( BrdU ) incorporation in individual siRNA transfected cells was determined to compare the number of S phase cells and the relative rates of replication associated nucleotide incorporation under the same conditions used in laser experiments . ICL detection and initiation of repair in S phase occurs primarily when a replication fork encounters and stalls at an ICL . Therefore it was important to verify that fork progression , as determined by BrdU incorporation , is not affected in CtIP depleted cells relative to control cells . As judged by quantification of pixel intensities in individual cells there was no significant difference in replication rate between control and CtIP depleted cells Figure 2D . In addition , under the conditions used for laser irradiation experiments >95% of the cells were in S phase . The lack of an effect on cell cycle distribution or BrdU incorporation in CtIP depleted HeLa cells suggests that the decrease in γH2AX generation at ICLs in CtIP depleted cells is due to a defect in ICL processing as opposed to a cell cycle progression defect . To determine when CtIP acts in the ICL repair process , known markers of the DNA damage response and repair process were monitored in CtIP depleted HeLa cells . The first marker examined was γH2AX . To compare the function of CtIP in ICL repair to its known role at directly generated DSBs [59] , [62] , parallel experiments were conducted in which DSBS were generated directly by microirradiation with 532 nm laser light ( 64 ) . Laser doses for 730 nm and 532 nm light were predetermined to use the minimal doses that give readily detectable γH2AX signals 2 hour post microirradiation . ( Figure S2 ) . γH2AX phosphorylation was found to be strongly reduced in CtIP depleted S-phase HeLa cells along ICL containing microirradiated tracks ( Figure 3A , top panel ) . In contrast , CtIP depletion had no detectable effect on H2AX phosphorylation at DSBs induced directly by laser irradiation ( Figure 3A , bottom panel ) . The percentage of cells containing bright γH2AX signal , as determined by quantification of pixel intensity in individual cells along ICL containing laser tracks , was reduced significantly , over 4 fold , in cells depleted with 2 independent siRNAS targeting CtIP ( 13% and 19% respectively ) relative to control cells ( 76% ) , ( n = 48 , 29 and 66 cells respectively; P-values = 0 . 003 ( CtIP siRNA_1 ) and 0 . 009 ( CtIP siRNA_2 ) ( Figure 3B ) . In contrast , the percent cells with bright γH2AX along DSB containing laser tracks was not reduced in CtIP depleted cells ( CtIP siRNA_1 82% , CtIP siRNA_2 94% ) relative to control cells ( 85% ) , ( n = 19 , 19 and 26 cells respectively; P-value > = 0 . 5 ) . The differential effects of CtIP depletion on H2AX phosphorylation at ICLs and DSBs suggests that CtIP plays a role in ICL repair that can be distinguished from its role in DSB repair . To examine the function of CtIP relative to MUS81 and XPF , two nucleases that have been suggested to act early in ICL repair [40] , [71] , the effects of MUS81 and XPF depletion on γH2AX accumulation at ICLs was examined . Depletion of either XPF or MUS81 did not have a measurable effect on γH2AX accumulation along ICL containing laser tracks ( Figure S3 ) . This indicates that Mus81 and XPF function downstream of γH2AX generation in ICL repair . ATM becomes activated in an MRN dependent manner and phosphorylates H2AX at DSBs early in DSB repair [72]–[74] , while ATR contributes to ATM activation and H2AX phosphorylation as a result of replication stress [75]–[77] . ATM has also been found to function in the FA pathway [24] . To test if CtIP is required for ATM phosphorylation at ICLs , and thus functions upstream of DSB generation , we examined the effects of CtIP depletion on phosphorylation at serine-1981 , a characterized autophosphorylation site . [72] . Consistent with the premise that DSBs are formed as an intermediate during ICL repair , ATM-pS1981 signal was readily detectable in control cells treated with 8-MOP and irradiated to form ICLs . In contrast , ATM-pS1981 signal along ICL containing laser tracks was reduced significantly in CtIP depleted S-phase HeLa cells ( 38% ) relative to control cells ( 69% ) , ( n = 32 and 27 cells respectively; P-value = 0 . 05 ) ( Figure 4A , upper panel ) . By contrast , CtIP depletion did not significantly reduce the number of cells with bright ATM-pS1981 along DSBs ( 86% ) relative to control cells ( 79% ) ( Figure 4A , lower panel ) , ( n = 33 and 29 cells respectively; P-value > = 0 . 1 ) ( Figure 4B ) . Biochemical analysis has demonstrated that the FANCI-FANCD2 complex is required for the incision of interstrand crosslinks in replication competent Xenopus extracts [18] . We therefore examined whether FANCD2 was properly localized to laser activated ICLs in CtIP depleted cells . FANCD2 accumulation was found to be reduced along ICL containing microirradiated tracks in CtIP depleted cells ( Figure 5A ) . The number of cells containing bright FANCD2 signal was reduced significantly by 2 . 7 fold in CtIP depleted cells ( 33% ) relative to the control cells ( 88% ) ( n = 24 and 26 respectively; P-value = 0 . 026 ) ( Figure 5B ) . These results demonstrate that CtIP acts upstream of FANCD2 localization to chromatin and point to a role for CtIP in ICL repair prior to ICL incision and unhooking . ATR phosphorylates the FANCI-FANCD2 complex which is required for its monoubiquitination and localization to ICLs [20] . RPA coated ssDNA is required for ATR activation [19] and CtIP/MRN is required for resection at double strand breaks [59] , [62] . We hypothesized that CtIP might facilitate resection of DNA ends present at an ICL stalled replication fork . Therefore we examined RPA2 accumulation at ICL containing microirradiated tracks in CtIP depleted cells compared to control cells . RPA2 accumulation at laser tracks was found to be strongly reduced in CtIP depleted S-phase HeLa ( Figure 6A ) . The percent cells containing bright RPA2 tracks was reduced 2 . 7 fold in CtIP depleted cells ( 27% ) relative to control cells ( 73% ) ( n = 29 and 41 cells respectively , P value = 0 . 005 ) ( Figure 6B ) . CtIP is targeted for phosphorylation at Thr847 by cyclin dependent kinase ( CDK ) [63] . Phosphorylation at this site is required to promote resection at DSBs , but is not required for CtIP recruitment to DSBs [63] , [78] . We tested whether a Thr-847 to Ala CtIP mutation would also affect resection at ICLs . Endogenous CtIP was depleted by siRNA and replaced by either wildtype GFP-CtIP or GFP-CtIPT847A in U2OS cells . HeLa were found to be sensitive to increased levels of CtIP , therefore we used U2OS cells for experiments involving ectopic expression of CtIP . U2OS cells were shown to react similarly to CtIP depletion compared to HeLa cells ( Figure S4 ) . S-phase cells were irradiated to form ICLs and stained for RPA2 . GFP-CtIPT847A accumulated at ICL containing laser tracks comparable to wildtype levels , however RPA2 accumulation was impaired relative to cells expressing wildtype GFP-CtIP ( Figure 6C ) . The percent cells containing bright RPA2 tracks was reduced 7 . 9 fold in cells expressing the CtIP phosphorylation mutant ( 8% ) relative to control cells ( 63% ) ( n = 20 and 20 cells respectively , P value = 0 . 03 ) ( Figure 6D ) . We next examined whether reduced RPA accumulation at ICLs in CtIP depleted cells was associated with reduced ATR recruitment . The percent cells containing bright ATR signal along ICL containing laser tracks was reduced over 7 fold in CtIP depleted cells ( 8% ) relative to control cells ( 58% ) ( n = 24 and 34 cells respectively; P value = 0 . 003 ) ( Figure 6E , 6F ) . The requirement of CtIP for both RPA and ATR localization to ICL containing chromatin suggests that CtIP acts early in ICL repair prior to ssDNA generation and ICL incision . BRCA1 ubiquitinates CtIP and is required for its localization to DSBs [79] , [80] . In order to further characterize the requirements for CtIP recruitment to ICLs we examined whether BRCA1 was required for GFP-CtIP accumulation at ICLs . Cells were either BRCA1 depleted using two previously described independent siRNAs [81] , [82] or treated with control siRNA . Endogenous CtIP was depleted by siRNA and CtIP expression reconstituted with silencing resistant GFP-CtIP . GFP-CtIP accumulation at ICL containing laser tracks was examined in S phase siRNA treated cells . CtIP accumulation at ICLs was found to be dependent on the presence of BRCA1 ( Figure 7 ) . In 3 independent experiments all cells ( >25 ) treated with BRCA1 siRNA that lacked visible BRCA1 expression as verified by immunofluorescence staining and imaging , did not contain visible GFP-CtIP along the micro-irradiated region . In contrast , all control siRNA treated cells ( >25 ) that had robust BRCA1 expression levels also contained GFP-CtIP at the ICL containing laser tracks . These results demonstrate that BRCA1 is required for CtIP recruitment to ICLs . FANCM has been shown to be required for RPA loading at ICLs as well as for activating the S-phase checkpoint [16] , [83] . Data presented in Figure 6 indicates that CtIP is required for both RPA and ATR accumulation at laser activated ICLs in replicating cells . In order to determine when CtIP acts relative to FANCM we examined whether FANCM is required for CtIP localization to ICLs . U2OS cells stably expressing GFP-CtIP in which endogenous CtIP is depleted were treated with two independent FANCM siRNA s or control siRNA . Depletion was confirmed by immunoblot ( Figure 8A ) . Cells were microirradiated to form ICLs and CtIP accumulation was examined . The percent of cells containing GFP-CtIP along ICL containing laser tracks was significantly reduced in FANCM depleted cells ( 22% FANCM siRNA_1 , 0% FANCM siRNA_2 ) relative to control cells ( 67% ) ( n = 26 , 11 and 30 cells respectively; P value = 0 . 0002 ) ( Figure 8B , 8C ) . Next we examined whether FANCM was required for GFP-CtIP recruitment to DSBs . Cells treated with FANCM siRNA_2 or control siRNA were irradiated with 532 nm light to form DSBs and GFP-CtIP localization was monitored . In contrast to what was observed at ICLs , FANCM depletion did not have a significant effect on the percent of cells containing GFP-CtIP at DSB containing laser tracks ( 75% FANCM siRNA_2 ) relative to control cells ( 76% ) ( n = 20 and 17 cells respectively ) ( Figure 8D , 8E ) . This data suggests that FANCM acts upstream of CtIP and is required for its localization to ICL but not DSB containing chromatin .
The cellular response to ICLs involves the intersection of the DNA repair and replication checkpoint pathways . The FANCM/FAAP24 complex binds to , remodels and stabilizes stalled replication forks at an early step in ICL repair [13] , [67] . FANCM/FAAP24 is also required for ATR mediated checkpoint activation in response to ICL stalled replication forks [15] , [83] . In addition , FANCM deficient cells have decreased levels of FANCD2 monoubiquitination and chromatin bound FANCD2 [17] , [84] , [85] . We have shown that FANCM is required for CtIP localization to ICLs in replicating cells . CtIP in turn is required for proper accumulation of RPA , ATR and FANCD2 at ICLs . This places CtIP within the initiating steps of ICL repair , downstream of FANCM and upstream of RPA ( Figure 8F ) . We propose the following model . First , FANCM/FAAP24 remodels ICL stalled replication forks to enable CtIP access . BRCA1 interacts with and ubiquitinates CtIP enabling it to bind to the damaged chromatin . Next , CtIP , presumably in concert with MRN , supports initiation of resection of the lagging strands of the previously active replication fork [59] , [62] , [63] . This resection activity is dependent on CDK phosphorylation site T847 on CtIP . ssDNA-RPA provides a platform for ATR-ATRIP binding . Once localized to the stalled fork activated ATR promotes S phase checkpoint activation and phosphorylates the FANCI-FANCD2 complex . The phosphorylated FANCI-FANCD2 complex is then monoubiquitinated , localized to damaged chromatin to facilitate ICL incision and generation of a DSB repair intermediate . This proposed order of events is consistent with that described for Xenopus extracts in which replication dependent ICL repair initiates at an ICL stalled fork , followed by generation of ssDNA , activation of ATR as measured by Chk1 phosphorylation , and excision of the crosslink in a FANCI-FANCD2 dependent manner [11] , [18] . Cell cycle distribution and rate of BrdU incorporation are unaffected in CtIP depleted HeLa cells compared to control cells . This suggests that ICL processing defects observed in CtIP depleted cells are due to a deficiency in initiation of repair as opposed to an inhibition of cell cycle progression . The difference observed in cell cycle distribution of our CtIP depleted cells compared to those observed in mouse fibroblasts [70] are likely due to a difference in cell type as well as to a difference in the timing and method used to deplete CtIP . A function for CtIP prior to ICL incision , and upstream of a repair intermediate that contains double stranded ends , is supported by the observation that CtIP depleted cells show a striking reduction in the accumulation of the DSB markers ATM-pS1981 and γH2AX at laser generated ICL tracks . In contrast , the accumulation of ATM-pS1981 and γH2AX at DSBs produced by direct laser irradiation are not affected by CtIP depletion . The effect of CtIP depletion on γH2AX at DSBs is in agreement with previously published observations in CtIP depleted U2OS cells [59] . This indicates that initiation of DSB repair , is not grossly affected by CtIP depletion . These results do not preclude an additional downstream role for CtIP where it may act together with MRN in a manner analogous to its function at directly generated DSBs to facilitate the processing of the double stranded DNA ends produced following ICL incision . In summary , our data supports a model in which CtIP functions early in replication associated ICL repair downstream of FANCM and is required for the accumulation of RPA , ATR , FANCD2 , ATM-pS1981 , and γH2AX at ICLs . Thus , CtIP plays a critical role in initiating the DNA damage response at ICL stalled replication forks , prior to the generation of a double stranded DNA repair intermediate . It will be of interest to see how CtIP and MRN are regulated at ICL stalled forks and whether this response is different from that observed at other stalled forks in which helicase uncoupling contributes to checkpoint activation .
Robolase III ( RLIII ) is a multi-modality laser ablation system that is based on a femtosecond pulsed Ti:Sapphire laser ( Mai Tai , Spectraphysics , Newport Corp . , Mountain view , CA ) and a motorized inverted microscope ( Axiovert 200 M , Zeiss ) that utilizes a custom Labview-based software package developed specifically for the Robolase series of microscope systems [86] . In this study , the laser wavelength was set to 730 nm . The effective wavelength at the focal point is 365 nm at which psoralen is activated by a 2-photon mechanism [87] . The pulse duration was estimated at 200 fs and the repetition rate was 76 MHz . The laser was focused by a Zeiss 63×/1 . 4 plan-apochromat PH3 oil objective with measured transmission of 67% at 730 nm using the double-objective method to determine transmission [88] . The power used in this study was 2 mW before the objective which yielded a peak irradiance of 2 . 8×1010 W/cm2 at the focal point . For each cell , a 10 µm by 636 nm region of interest ( ROI ) was chosen inside the nucleus using real-time phase contrast imaging . An image of the designated ROI was recorded as reference . Laser exposure over each 10 µm long ROI was performed four times over each ROI with a total energy of 2 . 4 mJ . H2AX phosphorylation was used as a marker to determine the minimum power that provided a signal in the presence of 8-MOP . Double strand breaks were generated with the Robolase II ( RLII ) system that uses a frequency doubled 532 nm12 ps pulsed Nd:YVO4 laser [62] , [86] . The ROI for each cell was 10 µm by 464 nm . The laser power used in this study was 5 mW in the focal spot for an irradiance of 2 . 3×109 W/cm2 and the total energy deposited along the laser track inside each cell was 3 . 2 mJ . For experiments in which DSBs were generated cells were laser microirradiated on the same dish as cells treated to form ICLs and fixed at 2 hours 10 minutes post laser . Asynchronous or rapidly proliferating HeLa and HEK293 cells were maintained in Dulbecco's modified Eagle's medium ( Invitrogen , Carlsbad , CA ) supplemented with 10% bovine calf serum L-glutamine , and sodium pyruvate ) at 37°C and 5% CO2 . Small interfering double stranded RNAs ( siRNAs ) were introduced into HeLa cells by transfecting cells in a 6 well dish with 50 pmol siRNA , and 5 µL Dharmafect 1 ( Dharmacon , Lafayette , CO ) per reaction . The following synthetic siRNAs used were obtained from Dharmacon , siLuciferase ( control ) and CtIP ( siCtIP_1 and siCtIP_2 ) [59] , FANCM_1 ( GCAAAGUAGCCUAAAGAAAUU ) , FANCM_2 [83] , siMUS81 ( D-016043 ) and siRNA pool containing 4 siRNAs targeting XPF ( siERCC4 D-019946-01 . ) The following siRNAs were obtained from Integrated DNA Technologies , BRCA1_1 ( AATGCCAAAGTAGCTAATGTAUU ) [82] and BRCA1_3 ( AAGGAACCUGUCUCC ACA AAG UU ) [81] . HEK293 cells were transfected with the indicated siRNA using Dharmafect 1 , sixteen hours later the cells were washed with sterile PBS and fresh media was added . After an eight hour recovery the cells were trypsinized and seeded at 104 per well in a six well plate . After 12 hours 10 µg/ml 8-MOP or angelicin were added to the cells . After 30 minutes incubation the cells were then exposed to the indicated amount of UVA radiation at approximately 360 nm using a Stratalinker ( Stratagene , La Jolla , CA ) . Alternatively , cells were treated with indicated amount of MMC for 2 hours . Post treatment the cells were washed with sterile PBS , fresh media added and replaced every three days . On day 8 the cells were trypsinized and viable cells were counted using a CASY cell counter ( Scharfe systems ) . A retroviral expression vector containing EGFP-CtIP ( pBabepuro ) was infected into U2OS cells for 48 hrs , followed by puromycin selection for 2–3 days to generate stable cell lines . For transient expression endogenous CtIP was first depleted by transfecting CtIP siRNA_1 into cells . Cells were transfected 24 hours later with silencing resistant GFP-CtIP using Effectene [59] ( Qiagen ) . Experiments were performed 24 hours post GFP-CtIP transfection . GFP-CtIPT847A was generated using Quikchange Site Directed Mutagenesis Kit ( Agilent Technologies ) . Total RNA was isolated from control or CtIP siRNA transfected cells 48 hours post transfection using the RNeasy Kit ( Qiagen ) . Target RNA was amplified using the Bio-rad iScript One Step RT-PCR kit with SYBR green kit ( Bio-rad , Hercules , CA ) on a Bio-rad real time quantitative PCR machine . The One step kit was used according to manufacturer's directions with the exception of 20 µL reactions , 2 ng total RNA per reaction , and 2 µL of each primer ( 5 uM stock ) . The following primers were used , housekeeping gene PBGD was amplified as a control ( PBGDF- TCC AAG CGG AGC CAT GTC TG , PBGDR- AGAATC TTG TCC CCT GTG GTG GA ) CtIP ( CTIPF- AAG AGG AGG AAT TGT CTA CTG C , CTIPR- AGA ATC TTG TCC CCT GTG GTG GA ) . Reactions were run on Chromo-4 qPCR I system ( MJ Research , Waltham , MA ) . CtIP RNA depletion was verified to be between 60 and 70% by RT-QPCR for CtIP siRNA 1 and CtIP siRNA 2 in all experiments relative to control depleted cells . HeLa cells were transfected with siRNA as described above and seeded onto coverslips in 12-well plates . Thirty-six hours later , 2 mM thymidine was added to cultures for 16 hours . Cells were released from the thymidine block by washing 2 times with fresh media containing 100 uM bromodeoxyuridine ( BrdU ) . Cells were incubated in BrdU containing media for 20 minutes to label S phase nuclei . . Cultures were fixed with 3 . 7% formaldehyde , stained with mouse monoclonal anti-BrdU antibody coupled to FITC ( eBioscience , San Diego , CA ) , and counterstained with DAPI . ImageJ software was used to determine the total number of nuclei , BrdU-positive nuclei , and relative levels of fluorescence in individual cells . siRNA and control cells were prepared for flow cytometry analysis 48 hours post transfection . Cells were treated with trypsin/EDTA and washed twice in PBS before fixing them in 70% ethanol overnight at −20°C . Cells were then washed twice in PBS and incubated in staining solution ( 20 µg/ml propidium iodide , 20 µg/ml RNase A , 0 . 1% Triton X-100 in PBS ) for 30 min at 37°C . Data were acquired on a BD FACS Calibur flow cytometer . Percentages of G1 , S , and G2 phase cells were determined from cell cycle profiles by using the Watson pragmatic algorithm of the cell cycle platform within FlowJo software ( Tree Star Inc . , Ashland , OR ) with the “remove doublets” and “remove debris” options enabled . P values were determined using an unpaired two-tailed t test . Cells for laser microirradiation were cultured on gridded glass bottom dishes ( Mattek , Ashland , MA ) for 16 hours in 2 mM thymidine . Cells were washed twice with warm media to remove thymidine one hour prior to microirradiation and fresh media was added . Twenty minutes prior to laser microirradiation drugs were added to the cells; 10 µg/ml 8-MOP ( MP Biomedicals , Solon , Ohio ) , or an equivalent molar amount of angelicin ( 8 . 6 µg/ml Sigma-Aldrich , St . Louis , MO ) . Cells were allowed to recover for 2 hours post micoirradiation prior to fixation in all experiments except where noted . Soluble proteins were extracted in cytoskeletal buffer on ice for 5 minutes ( 10 mM Pipes pH6 . 8 , 100 mM NaCl , 300 mM sucrose , 3 mM MgCl2 , 1 mM EDTA and 0 . 5% TritonX-100 ) [89] , followed by fixation in 3 . 7% formaldehyde phosphate buffered saline ( PBS ) at room temperature for 10 minutes , and permeabilized with 0 . 2% Triton-X . Cells were then stained with primary antibody in 3% BSA/PBS . The following primary antibodies were used in immunofluorescent staining: 1∶100 rabbit anti-RAD51 1∶100 ( Abcam , Cambridge , MA ) , 1∶1000 mouse anti-γH2AX ( Upstate Biotechnology , Waltham , MA ) , 1∶250 Rabbit anti-NBS1 NB100-143 ( Novus Biologicals , Littleton , CO ) , 1∶200 Mouse anti ATM-pS1981 clone 10H11 . E12 ( Chemicon , Billerica , MA ) , 1∶200 Rabbit anti-FANCD2 NB100-182 ( Novus Biologicals , Littleton , CO ) , 1∶50 Mouse Anti-RPA2 ( Calbiochem , Gibbstown , NJ ) , 1∶100 Mouse anti-BrdU ( ebioscience , San Diego , CA ) , 1∶500 Rabbit anti-RAD51 H92 sc-8349 ( Santa Cruz Biotechnology , Santa Cruz , CA ) , 1∶1000 Mouse anti-MUS81 ab14387 ( Abcam ) , 1∶500 XPF Ab17798 ( Abcam ) , 1∶500 Rabbit anti-BRCA1 PA1-14072 ( Thermo Fisher ) 1∶1000 Rabbit anti-Ku86 SC9034 ( Santa Cruz Biotechnology , Santa Cruz , CA ) . The following secondary antibodies were used at a 1∶5000 dilution in 3%BSA/PBS 1∶5000 Alexa Fluor 488 goat anti-mouse IgG ( Molecular Probes , Eugene , OR ) and 1∶5000 Alexa Fluor 594 goat anti-rabbit IgG ( Molecular Probes , Eugene , OR ) . Nuclei were visualized by staining with 1 µg/ml Dapi ( Invitrogen ) . Glass coverslips were mounted with Vectashield ( Vectorlabs , Burlingham , CA ) . Samples were visualized and images acquired using a 63× objective on a Leica DM IRE2 microscope equipped with a Hamamatsu C4742-95 digital charge-coupled-device camera . Images of cells were analyzed using Image J software ( NIH , Bethesda , MD ) . The fluorescence along the microirradiated track was quantified in each individual cell as follows . Images were thresholded and converted into binary images . The same threshold was applied to all images from a single experiment . The number of pixels above threshold in 3 oval ROIs ( 150×50 pixels ) were acquired per cell: one measurement for the area containing the laser track , and two measurements to determine the level of background signal in the nuclear region outside the laser track . The average number of background pixels was subtracted from the number of pixels measured along the microirradiated track for each individual cell . This resulted in a corrected pixel number for the intensity of a fluorescent signal along a laser track . For each positive control the average pixel number along the laser track was determined similarly . The average value from the positive control group per experiment was used to define the cut-off for experimental signals designated “bright” ( corrected pixel number >50% of average positive control ) or “low” ( corrected pixel number <50% of average positive control ) . Each cell in the control and experimental siRNA group was then assigned a low signal or bright signal accordingly . The number of cells that fell into the “bright” and “low” categories were determined for each experiment . Cells which did not contain pan-nuclear γH2AX or RPA2 foci were considered to be outside of S-phase and were excluded from analysis . For analysis of GFP-CtIP expressing cells , cells were scored as positive if there was any detectable GFP-CtIP along laser track . Statistical analysis was performed using Microsoft Excel . Differences between control and experimental groups were considered statistically significant when the P-value , determined by a two-tailed unpaired Student's t-test , was ≤0 . 05 . HeLa cells were lysed ( 10 mM Hepes pH 7 . 9 , 10 mM KCl , 1 . 5 mM MgCl2 , 0 . 34 M sucrose , 10% glycerol , 1 mM DTT , 0 . 1% triton , and protease inhibitors ) . Nuclear fraction was harvested by centrifugation at 1 , 399×g for 5 minutes . Pellet was resuspended in loading buffer , boiled for 5 mintues and put through 25 gauge syringe . Proteins were resolved by SDS-PAGE and transferred to nitrocellulose . Immunoblots were probed with indicated primary antibody , anti-FANCM ( 1∶1000 ) ab35620 ( Abcam ) and anti-Ku86 ( 1∶1000 ) ( Santa Cruz ) . R . Baer provided a mouse monoclonal antibody to CtIP [90] . Membranes were washed , incubated with HRP-linked secondary antibodies ( Invitrogen ) , and detected by chemiluminescence ( Pierce , Fisher Scientific ) . For Mus81 and XPF blots cells were lysed by boiling in Laemmli buffer ( 4% SDS , 10% glycerol , 62 . 5 mM Tris pH6 . 8 , 10% β-mercaptoethanol ) , and sonicated on ice . Immunoblots were probed with indicated primary antibody , anti-MUS81 ( 1∶1000 ) ( Abcam ) and anti-XPF ( 1∶1000 ) ( Abcam ) , and anti-Ku86 ( 1∶1000 ) ( Santa Cruz ) . | One of the most lethal forms of DNA damage is the interstrand crosslink ( ICL ) . An ICL is a chemical bridge between two nucleotides on complementary strands of DNA . An unrepaired ICL is toxic because it poses an unsurpassable block to DNA replication and transcription . Certain forms of cancer treatment exploit the toxicity of ICL generating agents to target rapidly dividing cells . Sensitivity to crosslinking agents is a defining characteristic of Fanconi Anemia ( FA ) , a hereditary syndrome characterized by an increased risk in cancer development and hematopoietic abnormalities frequently resulting in bone marrow failure . The mechanism underlying ICL repair is important to human health; however , the sequence of molecular events governing ICL repair is poorly understood . Here we describe how the repair protein CtIP functions to initiate ICL repair in replicating cells in a manner distinct from its previously described role in other forms of DNA repair . | [
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] | 2012 | CtIP Is Required to Initiate Replication-Dependent Interstrand Crosslink Repair |
Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study . However , even in the model organism Escherichia coli , established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts . Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors ( TFs ) , ArcA and Fnr of E . coli , sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic , catabolic , and energy generation pathways . We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events . We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions . Based on the data , we propose a feedforward with feedback trim regulatory scheme , given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr . We further corroborated this regulatory scheme by showing a 0 . 71 r2 ( p<1e-6 ) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions . Finally , we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network .
Regulation of metabolism in response to shifting availability of electron acceptors is a fundamental process in all of biology and is a critical subject for the understanding of pathogenesis , cancer metabolism , and industrial biotechnology . However , even in the model organism Escherichia coli , the regulatory network for this fundamental metabolic function has not been fully elucidated . It has long been known that facultative anaerobes will hierarchically utilize external electron acceptors relative to the free energy change provided by each [1] , [2] . Oxygen exists at the top of the hierarchy , electron acceptors like NO3 in the middle , and lactate or acetate or other fermentation products are at the bottom [3]–[5] . Many detailed studies have determined that the transcription factors ( TFs ) ArcA and Fnr are the key players in managing this hierarchy through the activation or repression of the electron transport chain ( ETC ) machinery specific to an available electron acceptor [6]–[11] . It is also largely understood how ArcA senses redox via the flow of reducing equivalents through the ETC , and how Fnr directly senses levels of dissolved O2 [1] , [12] , [13] and glutathione [14] , [15] . However , it is not clear how these two TFs work together and more importantly why they regulate hundreds of gene products that lie outside of the ETC and energy metabolism [3] , [5] ? Even though many biochemical details of redox regulation have been elucidated [6] , [8] , [16] , systems level principles for the global regulatory response throughout the anaerobic shift remain elusive . An important missing piece is a clear framework , or design principle , that elucidates how hundreds of transcriptionally regulated gene products are coordinately regulated to produce the necessary quantitative shifts in metabolic flux states . On the purely metabolic side , certain design principles have emerged through the analysis of stoichiometric models that identified growth and energy generation as the two principal dimensions of metabolic network function [17]–[19] . It was further shown that linear combinations of these two dimensions could account for observed flux patterns throughout nutrient limitations and the anaerobic shift [18] , [20] . A question now becomes , what are the corresponding global TFs and how do they coordinately regulate all the gene products which enable the metabolic flux map to shift from one optimal state to another ? Here we show how the global TFs ArcA and Fnr coordinately regulate the primary metabolic dimensions of growth and energy generation . We integrated polyomic data sets and used genome-scale metabolic models to enable a mechanistic understanding of hundreds of simultaneous and individual regulatory events . This analysis subsequently provides a link between global regulatory circuits and global optimality in microbial metabolism .
We first identified individual TF regulatory events at the genome-scale . Side-by-side measurements of RNA transcript abundance and TF binding were carried out to determine the structure and causality in E . coli's transcriptional regulatory network ( TRN ) . ChIP-chip assays for ArcA and Fnr were performed under both fermentative and nitrate respiratory conditions ( Figure 1A ) . Gene expression measurements were then used to determine causality of activation or repression for each ArcA or Fnr binding site under these same two conditions ( as detailed in the later heatmap figure legend , Figure S1 ) . We found 102 , and 86 ( and 143 and 132 ) binding regions and 58 and 54 ( and 95 and 55 ) causal regulatory events for ArcA and Fnr under fermentation ( and nitrate respiration ) conditions , respectively ( Figure 1A , Tables S1 , S2 , S3 , S4 ) . We then compiled the set of genomic sequences underlying these binding regions for each of the TFs and used the MEME program [21] to recover previously identified binding motifs [22] , [23] ( Figure 1B , Tables S5 , S6 ) . We confirmed 180 of 216 ( 83% ) previously known regulatory events [24] and discovered 132 new binding regions relative to RegulonDB ( Figure 1A ) , representing an increase of 74% over current knowledge of the regulatory functions of these two TFs . We further performed a detailed comparison of our results to recently published works [16] , [25] to determine a 78% overlap in ArcA binding sites and a 50% overlap in Fnr binding sites under fermentative conditions ( Figures S5 , S6 , S7 ) . In addition , we report 88 novel binding sites for ArcA and 52 novel binding sites for Fnr under nitrate respiratory conditions highlighting plasticity of the network throughout shifting external electron acceptors . We then integrated transcription start sites ( TSS ) [26] with TF binding regions to identify promoter architectures [27] . The location of TF binding motifs within experimentally determined binding regions were used to prepare histograms of the frequency of TF binding relative to the TSS ( Figure 1B ) . This analysis showed that ArcA spans the TSS or −35 box region and represses transcription while Fnr spans the −41 . 5 or alpha carboxy terminal domain and activates transcription [27] . While each of these regulatory strategies have been shown previously , here can we show that each strategy is ubiquitous at the genome-scale . Novel cases of divergent transcriptional regulation were found in this data . The integration of binding regions with gene expression data revealed 42 regions where two divergent transcriptional units ( TUs ) were simultaneously regulated by a single binding event . Divergent transcriptional regulation has been observed previously [28] and is known to be mediated by transcription factors in certain cases . However , systematic regulation by global TFs has only been observed in limited cases [29] . We observe a total of 19 inverse , 16 dual activation , and 13 dual repression events for a total of 48 events spread across the 42 regions as some recur under different experimental conditions . Two examples ( Figure 1C ) highlight this ‘hard coupling’ of the transcriptional regulation of seemingly unrelated but contextually dependent pathways . The acs-nrfABCDE system represents a lowest common denominator coupling between acetyl-coA synthetase ( acs ) acetate scavenging to acetyl-coA and usage of acetyl-coA via the TCA cycle and nrfABCDE nitrite reductase . Similarly the aroP-pdhR system couples the transport of aromatic amino acids to the regulation of pyruvate that acts as their principal precursor molecule . The link between the acs and nrfABCD systems has been inferred/suggested in previous work which attempted to understand how E . coli could survive on acetate as a sole carbon source under anaerobic conditions [30] . In particular , E . coli cannot utilize acetate under fully anaerobic conditions because acetate must be scavenged into acetyl-coA via acs and then utilized by the TCA cycle . Anaerobically the TCA cycle cannot be used unless there is an electron acceptor in the ETC to enable oxidative phosphorylation . Thus , some usage of the TCA cycle via an alternative electron acceptor such as nitrite or nitrate is necessary for E . coli to utilize acetate and acetyl-coA anaerobically . This metabolic feature is physiologically crucial in the gut environment that is rich in fatty acids that cannot be used if E . coli does not utilize alternative electron acceptors like nitrite . Hence , the direct coupling of acs and nrfABCD through bidirectional transcriptional regulation is consistent with the necessity of a flux through the nrfABCD system in order for the acetyl-coA formed by acs to be utilized . The transcriptional coupling acts as bidirectional gate controlled by ArcA and the redox state of the cell to coordinate this evolutionarily crucial metabolic capability . Similarly the aroP-pdhR system couples the transport of aromatic amino acids to the regulation of pyruvate that acts as their principal precursor molecule through the action of Fnr . To understand the network level connection between the aromatic amino acid transporter ( aroP ) and the pyruvate dehydrogenase repressor TF ( pdhR ) one can examine Figure 2 , which shows the connection between catabolic biomass precursors and biosynthetic pathways . Tyrosine and tryptophan are both made directly from PEP that is rapidly dephosphorylated into pyruvate . The corresponding activation of aroP and repression of pdhR is consistent with an increased need for amino acid transport when the precursors for biosynthesis ( PEP ) are critical to maintain cellular energy levels . This characteristic is supported by a dampening of the switch upon the transition to nitrate respiration , resulting in decreased transporter expression when less pyruvate is needed for fermentation and can thus be shuttled to amino acid biosynthesis . In general , pdhR acts as a classic repressor that “pops off” of its binding site in the presence of pyruvate and hence allows expression of pyruvate dehydrogenase and other oxidative enzymes . Anaerobically pyruvate dehydrogenase ( aceEF-lpd ) is repressed regardless of pdhR by ArcA and Fnr and given that there is also a higher concentration of pyruvate it would presumably not be active . Thus , while this switch is highlighted anaerobically in that full repression of pdhR is concomitant with aroP activation its physiological significance is more prevalent under nitrate or even fully aerobic conditions in which it can function to directly couple and balance the catabolic and anabolic demands around pyruvate which acts as a critical second messenger in the aerobic-anaerobic shift [6] . It is very insightful to view such a switch as it is ramped fully up under anaerobic conditions and then turned down under nitrate respiration to maintain a physiologically crucial metabolic balance . Previous work has identified biomass production and energy production as the two principal dimensions characterizing the overall function of metabolic networks [17]–[19] . This duality in function is conceptually equivalent to considering heterotrophic metabolism as the standard combustion equation ( Figure 2 ) in which an electron donor ( glucose ) is broken apart with an electron acceptor ( oxygen , nitrate , etc . ) to form biomass , energy , waste and heat . Here we use the terms catabolism to describe oxidation of the electron donor , anabolism to describe biomass formation , and chemiosmosis to describe energy generation . The genes in each of these categories were determined by a manual curation of the E . coli metabolic model [31] and associated literature sources [4] , [26] . Catabolic genes correspond to nutrient transporters , recycling machinery , and central catabolic machinery . Anabolic genes correspond to biosynthetic and macromolecular synthesis pathways . Chemiosmotic genes correspond to the electron transport chain ( ETC ) , fermentation pathways , and ion pumps ( Figure 3 ) . From the data sets described above , the regulation of these three classes of genes by ArcA and Fnr can be analyzed using their metabolic functions as context . ArcA and Fnr directly regulate a total of 127 catabolic genes including 49 transporter genes , 38 recycling or secondary catabolic enzymes , 33 central metabolic genes , and 7 associated TFs ( Figures 2 , 3 ) . In particular , recovery of all of the classic targets of ArcA and Fnr is complemented by the simultaneous discovery of transporter genes and recycling enzymes like peptidases and proteases ( Figure 3 ) . It can also be recognized that there existed many classically unknown glycolytic targets along with generally unrecognized activation of the glucose transporter ptsG . Activation of ptsG by Fnr is consistent with the fact that cells nearly double their uptake of carbon during fermentative growth compared with aerobic growth . In anabolism , ArcA and Fnr directly regulate 54 genes including 34 metabolite synthesis genes , 14 macromolecular synthesis genes , and 6 TFs . Broad trends of nucleotide biosynthesis activation and amino acid biosynthetic activation of nucleotide precursors is consistent with redox related demands . However , perhaps the most important of these findings is the regulation of both transhydrogenases ( sthA , pntAB ) in E . coli . Previous work has shown that a large portion of the NADPH used for biosynthetic reactions comes from the membrane bound transhydrogenase PntAB [32] and that the soluble SthA is used for re-oxidation of NADPH under aerobic growth with excess glucose . Our data shows that ArcA activates pntAB and represses sthA in a redox-dependent fashion consistent with an increased need for NADPH under nitrate respiration relative to fermentation ( Figure 3 ) . This regulatory shuttling of reduction equivalents thus plays a critical role in maintaining the balance between growth and energy generation by increasing growth only once when energy demands are satisfied . In the chemiosmotic category we observe regulation of 120 genes including 83 genes of the ETC , 6 for fermentation , 21 for ion pumps , 2 for motility , and 8 TFs . Nearly all of the regulation can be shown to coincide with redox related demands including regulation of ion pumps which coincides with an increased need to maintain a positive electrical gradient across the inner membrane to make up for the diminished proton gradient . We also observed strong regulation of the flhDC , gadW , and appY transcription factors . The flhDC system is a master regulator for the motility and flagellum apparatus of the cell that feeds off the chemiosmotic gradient in search of nutrients . appY and gadW are key regulators of cytochromes and acidic tolerance , respectively . After including regulation through appY we can conclude that ArcA and Fnr exhibit control either directly or indirectly over 15 out of the 16 known dehydrogenase and oxidoreductase reactions in E . coli [4] ( Figures 2 , 3 ) . Enumerating regulatory events is informative , but how do they all together form a coherent regulatory logic that produces meaningful physiological states ? Network analysis of these regulatory interactions reveals a qualitative feedforward and feedback flow-based model of the primary metabolic dimensions ( Figure 4A ) . The model input is the total set of catabolites ( glucose or electron donor ) available to the cell that are oxidized based on the availability of an electron acceptor into a ratio of reduced to oxidized components . These components ( primarily NADH/NAD and NADPH/NADP ) are then used by the anabolic machinery to generate biomass , or by the chemiosmotic machinery to generate energy as outputs . The ratio of reduced-to-oxidized components is sensed by ArcA and Fnr [1] , and they can feedback and feedforward regulate the catabolic , anabolic , and chemiosmotic processes in a coordinated fashion to maintain the ratio . Consistent with this schema , it has been shown that TFs are ideal flux sensors [33] . Analyzing the regulatory events within the context of the qualitative flow-based model reveals a feedforward with feedback-trim architecture of the overall regulatory logic . Counting the number of genes that are activated or repressed ( Figure 3 ) provides a measure of the extent of feedforward or feedback regulation exerted ( Figure 4B ) . Under fermentation ArcA represses 70 catabolic genes and Fnr activates 75 chemiosmotic genes . Under nitrate respiration ArcA represses 73 catabolic genes and Fnr activates 61 chemiosmotic output genes . A similar trend is observed for regulation of the anabolic circuitry in which Fnr activates 14 and 11 genes under fermentation and nitrate respiration . This circuitry is consistent with fast sensing of oxygen by Fnr and slow but continuous sensing of redox flow through the ETC by ArcA [34] . The regulatory architecture revealed by this qualitative model is comprehensive and novel , but primarily topological . To more quantitatively assess the functions of the observed transcriptional regulatory architecture on the metabolic network that it regulates we sampled all allowable network flux states of a highly curated genome-scale metabolic model of E . coli metabolism [31] under both fermentative and nitrate respiratory conditions . This sampling of allowable flux states of the metabolic network was then integrated with the experimentally determined regulatory architecture to discern the amount of total flux ( sum of flux loads across all reactions ) regulated by ArcA and Fnr under each of the conditions studied . This calculation revealed that 60% and 57% ( and 88% and 80% ) of all metabolic flux is directly ( and indirectly ) controlled by ArcA and Fnr under fermentative and nitrate respiratory conditions respectively ( Tables S7 , S8 ) . We further show that 69% and 62% of the catabolic fluxes producing each of the redox molecules and biomass precursors along with 71% and 69% of the downstream anabolic and chemiosmotic fluxes are directly regulated under fermentative and nitrate respiratory conditions respectively ( Figure S3 , Table S9 , S10 ) . From a gene level we find that 246 genes are differentially expressed ( fdr< . 05 , fold change >2 ) between fermentative and nitrate respiratory conditions and that 236/246 or ∼96% of the genes are directly ( 73 ) or indirectly ( 163 ) regulated by ArcA or Fnr ( Table S12 ) . Taken together , these measurements quantify the global metabolic regulation of flux by ArcA and Fnr and provide further evidence towards the proposed feedforward with feedback-trim regulatory architecture . To provide more validation for the feedforward with feedback-trim architecture at the genome-scale we first assessed the set of 91 reactions that significantly differed ( flux cutoff of 0 . 25 mmol/gDW-1 -h-1 ) between fermentation and nitrate respiration; gDW is denotes grams dry weight . We were then able to show that 89 of the 91 reactions were regulated directly ( 40 reactions ) or indirectly ( 49 reactions ) by ArcA or by Fnr ( Table S11 ) . We then calculated the change in flux for each of these 89 reactions between the two conditions along with the change in regulatory strength for the genes encoding these 89 reactions across the same conditions ( Table S11 ) . We plotted the change in flux versus the change in regulation ( Figure 5A ) and calculated an r2 correlation value of 0 . 71 ( p<1e-6 ) for the directly regulated genes . This correlation provides quantitative evidence for the logic of the regulatory circuit in the transition from fermentation to nitrate respiration . The linear positive slope shows not only that the reactions responsible for the redox shift are regulated , but also that these reactions are quantitatively regulated to help minimize the redox ratio in concert with the quantitative model predictions . Most of the ArcA regulated reactions are de-repressed , as indicated by the lightening shade of blue under nitrate respiration ( Figure 5B ) . Most of the Fnr regulated reactions are de-activated as highlighted by the lightening shade of yellow under nitrate respiration ( Figure 5B ) . The broad repression of crucial catabolic genes by ArcA and activation of chemiosmotic genes by Fnr is also shown through analysis of C-13 MFA data generated between wild type and Δfnr or ΔarcA strains ( Figure S8 ) . This trend of redox ratio minimization was so strong that the only outliers resulted in identification of new biology in the form of transport-coupled redox balancing for allosterically regulated amino acid biosynthetic reactions ( Figure S4 , Text S1 ) . We then sought to show that this quantitative regulatory model was truly redox dependent and not just fermentative/nitrate respiration specific . We thus took C-13 measured flux data [35] for E . coli grown aerobically in batch under either fully respiratory galactose conditions or partially fermentative glucose conditions . Even though both conditions are aerobic , we hypothesized that a similar shift in the redox ratio as observed between fully fermentative and nitrate respiration would occur given the comparison between a partially fermentative and fully respiratory condition . We made the same plot ( Figure 5C ) as in Figure 5a and even used regulatory strengths taken from the fermentative/nitrate shift . Only 16 flux measurements could be mapped of which only 9 showed any difference between glucose and galactose conditions . Of those 9 fluxes we were able to see a clear correlation for 7 and an overall weak but significant r2 correlation value of . 26 ( p = . 079 ) . This plot again shows genes regulated by ArcA being de-repressed and genes regulated by Fnr being de-activated upon the shift to more oxidative conditions ( Figure 5D ) . An expansion of the top-level of the flow-based model contextualizes the function of the hundreds of individual gene products and provides a window into the structure of the full metabolic-regulatory network ( Figure 6A ) . Each different type of catabolite ( Figure 3 , Figure 4A , Figure 6A ) is maintained via production fluxes ( transport or recycling ) and consumption fluxes ( secondary catabolism or central catabolism ) . The catabolism specific production set consists of genes for amino acid , carbohydrate , lipid , and nucleic acid transport and recycling . The same expansion can be performed for anabolism and chemiosmosis . For anabolism , the total biomass is a result of the sum of the rate of metabolite biosynthesis plus the rate of macromolecular synthesis [36] minus the rate of dilution and recycling . For chemiosmosis , the total gradient is a sum of protons pumped across the inner membrane via the ETC , proton equivalents pumped across the inner membrane via fermentation , and ions translocated across the inner membrane minus the usage of the gradient for ATP production , nutrient transport , and motility [37] . This expansion also accounts for the classically observed hierarchy [38] of the TRN via sensing of lower level metabolites and subsequent regulatory control of the TFs themselves or of the production or consumption pathways for sensed metabolites ( Figure 6B ) . A full tracing of the TRN to explain the effects of the global TF deletion is consistent with 69% of observed differential expression ( Figure S2 ) .
This work presents a systems level and genome-scale mechanism for the coordinate action of global transcription factors throughout an electron acceptor shift . Our mechanism accounts for the previously unexplained genes regulated by ArcA and Fnr , it predicts changes in flux patterns , and perhaps most importantly shows that the classically observed hierarchy of transcriptional regulation mirrors the hierarchy of dimensions in the metabolic network . By basing our work off of the extensive body of detailed biological literature and the more recent work of principal dimensionality in metabolic networks we are able to present a systematic and remarkably consistent genome-scale mechanism . At the local level , we first greatly expanded the number of cases of promoter architectures [39] . This validates and highlights the importance of understanding initiation mechanisms , as they may be extendable to a systems level in future development of computational models . We were then able to make the novel discovery that 42 regions across the genome contained divergently transcribed TUs controlled by a single global TF binding region . We recognize that due to ChIP-chip resolution it is possible ( and even likely ) that multiple binding sites exist under the larger ChIP peak , however the local proximity still affords the same hard-coupling within the regulon . This hard coupling suggests switch like mechanisms in which sets of seemingly unrelated genes are jointly regulated to obey non-obvious systems level constraints . We identify two such cases of this in the acs-nrfABCDE operon and the aroP-pdhR operon . To understand systems level mechanisms of transcriptional regulation we turned to previous work that showed the principal dimensions of a metabolic space were biomass and energy generation . We hypothesized that global regulators must play a role in regulating globally decisive metabolic dimensionality . This hypothesis is supported by broad regulation across all of these main categories and the abilities of ArcA and Fnr to sense the molecules that govern the branch point between the two dimensions . Although we were able to make an unbiased characterization of the genes in each of the categories using the iJO1366 model we were still unsatisfied with such a coarse grained approach and sought to understand the composition of each of the categories . This led us to a hierarchical expansion and classification of pathways around key metabolic intermediates . Going on in this fashion led us to realize that the global transcriptional regulatory hierarchy plays out not only on the level of TF-TF regulation , but perhaps more importantly at the level of global TFs regulating the production or consumption fluxes of lower level metabolites which are correspondingly sensed by other intermediate regulators . In essence , the regulatory network is shaped by the underlying metabolite pools and vice versa . After determining the broad circuitry of the metabolic-regulatory network we mapped our data onto it and discovered that a strong feedforward with feedback trim architecture dominates at the genome scale . This occurs via ArcA's strong repression of input catabolic circuits coupled with Fnr's strong activation of downstream chemiosmotic and anabolic circuitry . This circuit is corroborated by Fnr's ability to sense oxygen [13] which will diffuse quickly whereas ArcA will more continuously sense the flow of reducing equivalents through the ETC by sensing of the ratio of reduced to oxidized quinones [12] . This pattern of a fast component feeding forward for downstream “planning” coupled with a slower but continuous feedback sensor is a common pattern in basic process control schemes [40] . If coupled with other common process control patterns such as hierarchical and PID control one can envision a process control based model for the entire joint metabolic-regulatory network . This work presents a formal integration and reconstruction of over 50 years of research on E . coli metabolism and its transcriptional regulation . The result is a detailed and coherent hierarchical view of the regulation of the principal dimensions of metabolism through a critical environmental shift . We find that the mathematical notions of optimality in metabolic functions are in line with our observations of global regulation . TRNs are not just TF-gene networks but rather TF-gene-enzyme-reaction flux networks , that are tightly integrated as levels or ratios of metabolites can drive TF activity [41] , [42] . The full elucidation of an electron acceptor response in the important model organism , E . coli , may have implications for similar metabolic responses in other organisms . For cancer , recent focus has shifted towards an understanding of the metabolic drivers and Warburg effect , where the hypoxia inducible factor ( HIF ) [43] senses the redox ratio and feedforward or feedback regulates genes producing or consuming reduction potential . Taken together , we are able to show how the two principal dimensions of metabolism are controlled in a shifting environment by global TFs through the use of polyomic data sets and genome-scale metabolic models . This study is likely to be useful as a guide for similar studies in other organisms where the same tools for experimentation and analysis are available .
All strains used in this study were E . coli K-12 MG1655 and its derivatives . The E . coli strains harboring Fnr-8myc and ArcA-8myc were generated as described previously [44] . The deletion mutants ( Δfnr and ΔarcA ) were constructed by a λ red and FLP-mediated site-specific recombination method . Glycerol stocks of E . coli strains were inoculated into M9 minimal medium containing 0 . 2% ( w/v ) carbon source ( glucose ) and 0 . 1% ( w/v ) nitrogen source ( NH4Cl ) , and cultured overnight at 37°C with constant agitation . The cultures were diluted 1∶100 into fresh minimal medium and then cultured at 37°C to an appropriate cell density with constant agitation . For the anaerobic cultures , the minimal medium were flushed with nitrogen and then continuously monitored using a polarographic-dissolved oxygen probe ( Cole-Parmer Instruments ) to ensure anaerobicity . For nitrate respiration 20 mmol potassium nitrate was added . To identify Fnr and ArcA binding regions in vivo , we used the ChIP-chip approach as described previously [44] , [45] . Briefly , cells at appropriate cells density were cross-linked by 1% formaldehyde at ∼20°C for 25 min . Following the quenching of the unused formaldehyde with a final concentration of 125 mM glycine at ∼20°C for 5 min , the cross-linked cells were harvested and washed three times with 50 ml of ice-cold Trisbuffered saline . The washed cells were resuspended in 0 . 5 ml lysis buffer composed of 50 mM Tris-HCl ( pH 7 . 5 ) , 100 mM NaCl , 1 mM EDTA , 1 µg/ml RNaseA , protease inhibitor cocktail ( Sigma ) and 1 kU Ready-Lyse lysozyme Epicentre ) . The cells were incubated at 37°C for 30 min and then treated with 0 . 5 ml of 2 Å∼IP buffer with the protease inhibitor cocktail . The lysate was then sonicated four times for 20 s each in an ice bath to fragment the chromatin complexes using a Misonix sonicator 3000 ( output level , 2 . 5 ) . The range of the DNA size resulting from the sonication procedure was 300–1 , 000 base pairs ( bp ) . The specific antibodies that specifically recognizes myc tag ( 9E10 , Santa Cruz Biotech ) were used to immunoprecipitate each chromatin complex , respectively . For the control ( mock-IP ) , 2 µg of normal mouse IgG ( Upstate ) was added into the cell extract . The remaining ChIP-chip procedures were performed as described previously [44] , [45] . The high-density oligonucleotide tiling arrays used to perform ChIP-chip analysis consisted of 371 , 034 oligonucleotide probes spaced 25 bp apart ( 25 bp overlap between two probes ) across the E . coli genome ( Roche NimbleGen ) . After hybridization and washing steps , the arrays were scanned on an Axon GenePix 4000B scanner and features were extracted as a pair format by using NimbleScan 2 . 4 software ( RocheNimbleGen ) . To monitor the enrichment of promoter regions , 1 µL immunoprecipitated DNA was used to carry out gene-specific qPCR . The quantitative real-time PCR of each sample was performed in triplicate using iCycler ( Bio-Rad Laboratories ) and SYBR green mix ( Qiagen ) . The real-time qPCR conditions were as follows: 25 µL SYBR mix ( Qiagen ) , 1 µL of each primer ( 10 pM ) , 1 µL of immunoprecipitated or mock-immunoprecipitated 3DNA and 22 µL of ddH2O . All real-time qPCR reactions were done in triplicates . The samples were cycled to 94°C for 15 s , 52°C for 30 s and 72°C for 30 s ( total 40 cycles ) on a LightCycler ( Bio-Rad ) . The threshold cycle values were calculated automatically by the iCycler iQ optical system software ( Bio-Rad Laboratories ) . Any primer sequences used were described previously [44] . Samples for transcriptome analysis were taken from exponentially growing cells . From the cells treated by RNAprotect Bacteria Reagent ( Qiagen ) , total RNA samples were isolated using RNeasy columns ( Qiagen ) in accordance with manufacturer's instruction . Total RNA yields were measured using a spectrophotometer ( A260 ) , and quality was checked by visualization on agarose gels and by measuring the sample A260/A280 ratio ( >1 . 8 ) . Affymetrix GeneChip E . coli Genome 2 . 0 arrays were used for genome-scale transcriptional analyses . cDNA synthesis , fragmentation , end-terminus biotin labeling , and array hybridization were performed as recommended by Affymetrix standard protocol . Raw CEL files were analyzed using robust multi-array average for normalization and calculation of probe intensities . The processed probe signals derived from each microarray were averaged for both the wild type and deletion mutant strains . The ArcA and Fnr binding motif analysis was completed using the MEME and FIMO tools from the MEME software suite [21] . We first determined the proper binding motif and then scanned the full genome for its presence . The elicitation of the motif was done using the MEME program on the set of sequences defined by the ArcA and Fnr binding regions respectively . Using default settings the previously determined ArcA and Fnr motifs were recovered and then tailored to the correct size by setting the width parameter to 18-bp and 16-bp respectively . We then used these motifs and the PSPM ( position specific probability matrix ) generated for each by MEME to rescan the entire genome with the FIMO program . We integrated transcription start sites ( TSS ) with our TF binding regions to identify promoter architectures genome wide [27] , [47] . We first determined the location of motif binding sites within experimentally determined binding regions . We then calculated the distance between motif center position and previously determined TSS locations [26] . Finally , we prepared a histogram of the number of motifs that occur at varying distances relative to the TSS ( Figure 1B ) and included the gene expression data to determine the regulatory outcome of each binding event . The results showed that ArcA spans the TSS or −35 box region and represses transcription while Fnr spans the −41 . 5 or alpha carboxy terminal domain [47] and activates transcription . The histograms also reveal the previously reported trend [48] of motif frequency oscillation at a roughly 10 . 5 bp interval consistent with helical phasing of the DNA strand . To perform sampling we first generated pFBA [49] constrained models of the iJO1366 [31] metabolic model corresponding to fermentative and nitrate respiratory conditions . Fermentative conditions were simulated by setting the lower bound of the oxygen exchange reaction ( EX_o2 ) to zero . Nitrate respiratory conditions were simulated by setting the lower bound for nitrate uptake ( EX_no3 ) to −20 mmol gDW−1 h−1 ( mirroring experimental addition of 20 mmol KNO3 ) along with the lower bound of EX_o2 set to zero . pFBA constrained models were generated by first using the convertToIrreversible ( ) function of the COBRA toolbox [50] followed by a standard FBA for growth rate . This growth rate was then imposed as a constraint in a subsequent optimization that found the minimum sum of flux able to achieve that growth rate . Finally , using the gpSampler ( ) [50] method we sampled each of the pFBA constrained models . All sampling runs were for a full 24 hours to ensure a mixing fraction below . 55 . After sampling was performed we took the average across the 7046 sampling points ( 2n where n = 3 , 523 reactions in the metabolic model ) . Sampling results were then interfaced with the regulatory network and metabolic model via the COBRApy project ( http://opencobra . sourceforge . net/openCOBRA ) , iPython notebook [51] , and in-house databases . | All heterotrophic organisms must balance the deployment of consumed carbon compounds between growth and the generation of energy . These two competing objectives have been shown , both computationally and experimentally , to exist as the principal dimensions of the function of metabolic networks . Each of these dimensions can also be thought of as the familiar metabolic functions of catabolism , anabolism , and generation of energy . Here we detail how two global transcription factors ( TFs ) , ArcA and Fnr of Escherichia coli that sense redox ratios , act on a genome-wide basis to coordinately regulate these global metabolic functions through transcriptional control of enzyme and transporter levels in changing environments . A model results from the study that shows how global transcription factors regulate global dimensions of metabolism and form a regulatory hierarchy that reflects the structural hierarchy of the metabolic network . | [
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"met... | 2014 | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
The calcium-gated potassium channel SLO-1 in Caenorhabditis elegans was recently identified as key component for action of emodepside , a new anthelmintic drug with broad spectrum activity . In this study we identified orthologues of slo-1 in Ancylostoma caninum , Cooperia oncophora , and Haemonchus contortus , all important parasitic nematodes in veterinary medicine . Furthermore , functional analyses of these slo-1 orthologues were performed using heterologous expression in C . elegans . We expressed A . caninum and C . oncophora slo-1 in the emodepside-resistant genetic background of the slo-1 loss-of-function mutant NM1968 slo-1 ( js379 ) . Transformants expressing A . caninum slo-1 from C . elegans slo-1 promoter were highly susceptible ( compared to the fully emodepside-resistant slo-1 ( js379 ) ) and showed no significant difference in their emodepside susceptibility compared to wild-type C . elegans ( p = 0 . 831 ) . Therefore , the SLO-1 channels of A . caninum and C . elegans appear to be completely functionally interchangeable in terms of emodepside sensitivity . Furthermore , we tested the ability of the 5′ flanking regions of A . caninum and C . oncophora slo-1 to drive expression of SLO-1 in C . elegans and confirmed functionality of the putative promoters in this heterologous system . For all transgenic lines tested , expression of either native C . elegans slo-1 or the parasite-derived orthologue rescued emodepside sensitivity in slo-1 ( js379 ) and the locomotor phenotype of increased reversal frequency confirming the reconstitution of SLO-1 function in the locomotor circuits . A potent mammalian SLO-1 channel inhibitor , penitrem A , showed emodepside antagonising effects in A . caninum and C . elegans . The study combined the investigation of new anthelmintic targets from parasitic nematodes and experimental use of the respective target genes in C . elegans , therefore closing the gap between research approaches using model nematodes and those using target organisms . Considering the still scarcely advanced techniques for genetic engineering of parasitic nematodes , the presented method provides an excellent opportunity for examining the pharmacofunction of anthelmintic targets derived from parasitic nematodes .
Infections with parasitic nematodes heavily affect the well-being , health , and productivity of humans and animals worldwide [1] . Since the 1960s several broad spectrum anthelmintic compounds have been available . During decades of frequent and sometimes inappropriate use of these anthelmintics , resistance to currently available drugs has developed and is an increasing problem in parasitic nematodes , especially in livestock [2] . In human medicine , where mass anthelmintic treatment programmes were employed during recent years in countries with endemic gastro-intestinal nematode infections , there is also growing concern regarding anthelmintic resistance , and several reports of treatment failure were published during recent years [3]-[6] . In livestock non-chemical worm control procedures such as pasture management , feeding , and breeding are being tested , but they are cost- and labour-intensive and often not practical [7] . In parasites of companion animals , resistance is less common . Nevertheless , populations of the canine hookworm Ancylostoma caninum were recently reported to show high degrees of resistance to pyrantel [8] . Therefore , the need for anthelmintic compounds with new modes of action is urgent . Recently , three groups of anthelmintic compounds employing new mechanisms of action have been introduced . The oxindole alkaloid paraherquamide was described first in 1981 [9] . Paraherquamide and its derivative 2-deoxoparaherquamide ( derquantel ) are anthelmintically active by blocking acetylcholine receptors and therefore inhibiting neurotransmission [10] , [11] . Derquantel has been launched in combination with abamectin as a drench for sheep in New Zealand in 2010 . The combination showed high efficacies against field infections with strongyles in sheep [12] . The second group , comprising the amino-acetonitrile derivatives ( AAD ) , was recently reported to act mainly through the nicotinic acetylcholine receptor ACR-23 . This receptor is not present in mammals and is not involved in the action of levamisole , ivermectin , benomyl , dimethyl-4-phenylpiperazinium , and aldicarb . The derivative AAD 1470 was shown to have good efficacy against different species of gastrointestinal nematodes [13] . The first available AAD on the market was AAD 1566 ( monepantel ) , which has been launched as a sheep drench . The third group are the cyclooctadepsipeptides . The parent compound of this class is PF1022A , which was discovered as a fermentation product of the fungus Mycelia sterilia [14] . The semi-synthetic derivative emodepside has a broad spectrum of anthelmintic activity [15] , indicating that the mechanism of action might be conserved throughout nematode clades . Emodepside and PF1022A were also shown to be effective against anthelmintic-resistant populations of the sheep nematode Haemonchus contortus and the cattle nematode Cooperia oncophora [16] . Commercially , emodepside was first available as a spot-on preparation in combination with praziquantel for cats . Recently , emodepside has been launched as a tablet for dogs , also in combination with praziquantel . In Caenorhabditis elegans , emodepside potently inhibits locomotion , egg-laying , and pharyngeal pumping [17] . Previous studies identified nematode latrophilin ( LAT-1 ) as a target for emodepside [18] , [19] , but LAT-1 is not required for the inhibitory effects of emodepside on locomotion [19] , [20] . Indeed , a mutagenesis screen revealed the large conductance calcium-gated potassium channel SLO-1 as a key component for the mechanisms of action of emodepside [20] . SLO-1 channels are regulated by voltage and by intracellular concentration of calcium ions [21]–[24] . They were first identified in experiments with the slowpoke mutant of Drosophila melanogaster , which exhibits abnormal locomotory behaviour and decreased flight ability [22] , [24] . In C . elegans , SLO-1 was previously shown to control excitatory neurotransmitter release . It is expressed in the nerve ring and in the body wall muscle [21] . The slo-1 loss-of-function mutants show a characteristic locomotor phenotype consisting of an increase in locomotor reversal frequency [20] , [21] . The mutagenesis screen for emodepside-resistant C . elegans mentioned above revealed nine independent lines that were able to move and to reproduce on agar plates with an emodepside concentration as high as 1 µM , a concentration that immobilises wild-type C . elegans . All nine lines fell into a single complementation group that mapped closely to the slo-1 locus on chromosome V . Four of them were sequenced and showed mutations in the slo-1 locus predicted to lead to a reduced or abolished function of the channel . In locomotion assays , the slo-1 mutants had different degrees of resistance to emodepside . Reduction-of-function mutants showed reduced susceptibility to emodepside whilst loss-of-function mutants were not at all inhibited after exposure to emodepside [20] . The putative slo-1 null allele reference strain NM1968 slo-1 ( js379 ) V [21] was also highly resistant to emodepside . The expression of slo-1 in slo-1 ( js379 ) animals from the pan-neuronal promoter snb-1 [21] , [25] and the muscle cell-specific promoter myo-3 [21] , [26] , either in combination or separately , restored emodepside susceptibility to different degrees [20] . In this study , we identified slo-1 orthologues in H . contortus , A . caninum and C . oncophora . The slo-1 coding sequences of A . caninum and C . oncophora were subsequently expressed in the emodepside-resistant C . elegans strain slo-1 ( js379 ) to investigate their ability to rescue emodepside susceptibility of slo-1 loss-of-function mutants . Furthermore , we compared the ability of different C . elegans promoters as well as the slo-1 5′ flanking regions of A . caninum and C . oncophora to drive expression of slo-1 in slo-1 loss-of-function mutants and examined the locomotor phenotype as well as the degree of emodepside susceptibility in the transformants . Finally , we showed that penitrem A , an inhibitor of mammalian SLO-channels [27] , is able to antagonise the paralysing effect of emodepside on infective A . caninum larvae as well as on the locomotion of young C . elegans adults in a dose-dependent manner .
The animals used for the maintenance of the parasitic nematode strains were helminth-free prior to infection . All animals used in this study were handled in strict accordance with good animal practice as defined by the relevant national and local animal welfare bodies , and all animal work was approved by the appropriate committee . Calves were infected with approx . 30 , 000 C . oncophora third-stage larvae , and sheep were infected with 6 , 000-8 , 000 infective larvae of H . contortus . After 21 to 30 days , the animals were necropsied , and the small intestine or the abomasum , respectively , was removed . The worms were either washed off or picked directly from the mucosa . Dogs were infected with 400-500 infective A . caninum larvae . After reaching patency , the dogs were treated with 4 mg/kg arecoline . The subsequently deposited faeces were collected and sieved through a 100 µm mesh sieve . The adult A . caninum were picked directly from the sieve . The recovered parasites were sorted according to sex , washed in 0 . 9% NaCl solution and subsequently in DEPC-treated water . The worms were frozen at -80°C in sterile GIT buffer ( 4 M guanidine; 0 . 1 M Tris , pH 7 . 5; 1% β-mercapto-ethanol ) . All experiments with animals were performed in strict accordance to the German law for animal welfare ( Tierschutzgesetz ) and with the approval of the respective local authority , the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit ( LAVES ) under the reference numbers 01A38 , 01A48 and 06A395 . All efforts were made to avoid and minimize suffering of the animals . Total RNA was isolated using Trizol reagent ( Invitrogen , Karlsruhe , Germany ) , according to the manufacturer's recommendations . For cDNA synthesis and Rapid Amplification of cDNA Ends ( RACE ) , the BD SMART RACE cDNA Amplification Kit ( Clontech , St-Germain-en-Laye , France ) was used following the manual . For isolation of genomic DNA , a standard phenol-chloroform method was used [28] . The GenomeWalker Universal Kit ( Clontech ) was used to amplify the putative slo-1 promoter regions of A . caninum and C . oncophora . Primers to amplify the putative C . elegans slo-1 promoter region were designed based on the sequence of YAC clone Y51A2D ( GenBank Acc . No . AL021497 ) . The first primers for fragments of the slo-1 coding sequence of H . contortus were designed based on EST ( Expressed Sequence Tag ) sequences revealed by the H . contortus EST Basic Local Alignment Search Tool ( BLAST ) of the Wellcome Trust Sanger Institute server , using the C . elegans slo-1 sequence ( GenBank Acc . No . NM_001029089 , accordant with slo-1 splice variant b ) as template . The same primers were used to amplify a partial slo-1 coding sequence of C . oncophora . Primers for A . caninum slo-1 were designed based on a partial coding sequence detected in the whole genome shotgun library AIAAGSS 001 using the BLAST application of the Nematode Net [29] . Sequences of primers are given as supporting data , Table S1 . PCR products were cloned into the pCR4-TOPO vector , using the TOPO TA Cloning Kit ( Invitrogen ) or into the pCR-Blunt vector , using the Zero Blunt PCR Cloning Kit ( Invitrogen ) and transformed into TOP10 Escherichia coli cells ( Invitrogen ) . Vectors containing full-length slo-1 coding sequences were transformed into JM109 E . coli cells ( Stratagene , La Jolla , CA , USA ) . Plasmid DNA preparation was performed using the NucleoSpin Plasmid Kit or the NucleoBond AX 100 Kit ( Macherey and Nagel , Düren , Germany ) . To introduce the required restriction sites , PCR was performed using primers carrying the restriction sites ( refer to supporting data , Table S1 ) with a plasmid , containing the respective full-length sequence , or with cDNA as template . The PCR products were cloned as described above and subcloned into the respective expression vector using T4 DNA ligase ( Invitrogen ) . The basis of the expression plasmids was pBK3 . 1 [20] , [21] ( kindly provided by Lawrence Salkoff , Washington University School of Medicine , St . Louis ) , carrying the C . elegans slo-1 coding sequence downstream of the C . elegans snb-1 promoter , leading to neuron specific expression [21] , [25] . The expression plasmids were propagated in XL10-Gold Ultracompetent Cells ( Stratagene ) . The coding sequences of A . caninum and C . oncophora slo-1 , respectively , were cloned between the XbaI and BamHI restriction sites within the pBK3 . 1 , thus replacing the C . elegans slo-1 coding sequence . To test the functionality of the slo-1 coding sequences to be analysed in as natural an expression pattern as possible , constructs were built carrying the slo-1 coding sequences downstream of the C . elegans slo-1 promoter . To achieve a construct carrying the C . elegans slo-1 promoter and the C . elegans slo-1 coding sequence , a ligation was set up with three DNA fragments , since the coding sequence of C . elegans slo-1 contained an additional HindIII restriction site: 1 ) the vector backbone of pBK3 . 1 digested with BamHI/HindIII , 2 ) the promoter sequence ( HindIII/partial XbaI digest ) , and 3 ) the coding sequence of pBK3 . 1 digested with XbaI/BamHI . The plasmids carrying the parasite slo-1 coding sequences downstream of the C . elegans slo-1 promoter were derived by modifying pBK3 . 1 constructs which already carried the slo-1 coding sequence of the parasitic nematodes . The snb-1 promoter was excised and replaced by the C . elegans slo-1 promoter sequence using the HindIII and XbaI restriction sites flanking the promoter region . For this purpose , the plasmid carrying the C . elegans slo-1 promoter sequence had to be digested completely with HindIII , but only partially with XbaI , since the promoter sequence had an additional XbaI restriction site . The plasmid carrying the C . oncophora slo-1 coding sequence downstream of the C . elegans slo-1 promoter was not used for functional analysis but as a starting point to construct a plasmid with the C . oncophora slo-1 coding sequence downstream of the C . oncophora slo-1 promoter region ( see below ) . To test the functionality of the parasite promoter sequences , the parasite promoters were used to drive expression of the respective parasite slo-1 in C . elegans . For this purpose , the putative promoters were inserted between the HindIII and XbaI restriction sites in the modified pBK3 . 1 as described above , replacing the C . elegans slo-1 promoter . Due to additional HindIII and XbaI restriction sites in the C . oncophora slo-1 promoter sequence , the plasmid construction was done by blunt end ligation . All plasmids used for expression experiments in C . elegans were sequenced by custom sequencing ( SeqLab Laboratories Goettingen , Germany ) , ensuring that the coding sequences and the ligation sites were intact . For an overview of constructs used for the transformation experiments refer to Table S2 ( supporting data ) . Sequences were analysed using the Sci Ed Central Align Plus 5 software , version 5 . 04 ( Scientific and Educational Software; Cary , NC , USA ) , and the NCBI BLAST [30] . The predicted SLO-1 amino acid sequences and selected sequences of potassium channels of other species revealed by the BLAST search were aligned using the ClustalX2 [31] software package with default settings except that the alignment parameters were changed to BLOSUM . ClustalX2 calculates scores as percentages of the number of identities in the best alignment divided by the number of residues compared , excluding gap positions . The alignment constructed was manually edited and , after elimination of all positions containing gaps , a phylogenetic tree was built using bootstrap analysis ( 1000 replicates ) and the Neighbour Joining method by the Mega4 software package [32] using the default Poisson correction model for multiple substitutions at the same site and assuming homogenous substitution rates for all sites . The analysis of the putative promoter regions was performed using the Genome2Promoter and MatInspector software packages ( Genomatix , Munich , Germany ) . The putative slo-1 promoters of the three nematode species were compared by alignments using the BLAST bl2seq ( filter inactivated for low complexity regions ) [30] . The C . elegans strains were grown on nematode growth medium ( NGM ) agar plates containing 50 µl of E . coli ( OP50 ) overnight culture as a food source at 20°C or room temperature . Strains employed were Bristol N2 and NM1968 slo-1 ( js379 ) V [21] . The latter contains a mutation within the transmembrane region of the SLO-1 channel which leads to the early termination of the protein and is therefore predicted to encode a non-functional ion-channel . Thus , slo-1 ( js379 ) animals show a slo-1 null phenotype due to a translational knock-out . Emodepside was prepared as five different stock solutions ( 2 mM to 200 nM ) in ethanol . 0 . 5 ml of stock solution was added to 100 ml NGM agar after autoclaving and at a temperature of 42°C . Accordingly , control plates were prepared containing 0 . 5 ml ethanol per 100 ml NGM agar , leading to a final concentration of 85 mM ethanol . This ethanol concentration does not significantly impair C . elegans locomotion [33] , [34] . All plates were seeded with 50 µl E . coli OP50 . In some of the experiments , agar plates also contained 1 µM penitrem A ( Enzo Life Sciences , Lörrach , Germany ) in 28 mM DMSO ( final concentration ) or only the DMSO vehicle as control . For the body bend counts , experiments were performed in the absence of E . coli , i . e . on plain un-seeded NGM plates . Hermaphrodite C . elegans were transformed by microinjection of plasmids into the gonads . Transformation with the differentially modified pBK3 . 1 plasmids ( 30 ng/µl ) was accomplished by co-injecting the pPD118 . 33 ( Addgene plasmid: 1596; 50 ng/µl ) GFP-expressing marker . Successful transformation was determined by identification of the selection marker . For the behavioural and pharmacological analysis only worms carrying the selection marker were used as they were predicted to express the transgene of interest as well . To confirm the transcription of the introduced slo-1 coding sequences in transgenic worms , RT-PCR was performed . Total RNA was isolated from a bulk of worms using the TriFast method ( PeqLab ) , and contaminating DNA was removed by a DNase I treatment . 1 µg of total RNA was used for cDNA synthesis ( RevertAid First Strand cDNA Synthesis Kit , Fermentas , St . Leon-Rot , Germany ) , and a -RT control ( lacking the Reverse Transcriptase ) was performed for each sample . PCR was performed using 1 µl of template in a 25 µl setup ( High Fidelity PCR Enzyme Mix , Fermentas , St . Leon-Rot , Germany ) . Each cDNA was analyzed with all test primer pairs . For primer sequences refer to Table S3 ( supporting data ) . The C . elegans slo-1 knockout strain NM1968 slo-1 ( js379 ) V shows an abnormal behaviour of locomotion in terms of increased reversals , i . e . to stop and reverse direction [21] . To analyse the impact of the heterologously expressed SLO-1 on this behaviour , the number of reversals was counted for all lines . Therefore , a total of 10 L4 stage larvae of each line were selected and placed on an OP50 seeded NGM agar plate . After 24 hours the young adult worms were transferred separately away from the bacterial lawn for one minute to allow removal of bacteria adherent to the worm . Then the worm was put on an un-seeded NGM-agar plate , and , after one minute of acclimatisation , the reversals were counted for 3 min . Numbers of body bends per minute and of reversals in different C . elegans lines were compared using One-Way-ANOVA and individual lines were then compared with Tukey's post hoc test implemented in GraphPad Prism . A p-value <0 . 05 was considered as statistically significant . For locomotion assays L4 stage larvae of stable lines ( at least F2 generation ) were used . For each strain ( transformed and control strains ) ten worms were analysed for each concentration of emodepside ( 1 nM , 100 nM , 1 µM , and 10 µM , and in case of expression from the parasite promoters also 100 µM ) and the ethanol control , respectively . The assays were repeated using two independent stable lines , so that in total 20 worms for each construct and concentration were analysed . The experiments were not repeated for the worms expressing the A . caninum slo-1 from the C . elegans slo-1 and snb-1 promoters due to the lack of sufficient numbers of transformants . The setup for the locomotion assay was as follows: L4 stage larvae of N2 , slo-1 ( js379 ) and the transformed slo-1 ( js379 ) lines were transferred to NGM plates containing E . coli OP50 and either different concentrations of emodepside ( 10 µM to 1 nM ) or ethanol vehicle . Worms were maintained on emodepside or control plates for 24 hours at 20°C and locomotion was examined afterwards . For that purpose , worms were transferred for one minute to plain un-seeded NGM plates to remove bacteria . Subsequently , the worms were transferred to a fresh un-seeded NGM plate and , after one minute , body bends were counted for each worm for another minute . A single body bend is defined as one full sinusoidal movement of the worm . For analysis of a transformant line at a certain concentration of emodepside , N2 and slo-1 ( js379 ) worms were tested on the same day as parallel controls . For statistical comparisons , four-parameter logistic concentration-response-curves with variable slope were fitted using GraphPad Prism 5 . 0 after plotting the log10 of the emodepside concentration vs . the relative body bend activity at that concentration ( percentage of maximum number of body bends in each data set ) . Bottom values were always constrained to greater than 0 . Top values , Hill slopes and EC50 were not constrained . Calculation of means and 90% confidence intervals and statistical tests for differences in 1 ) EC50 , 2 ) bottom or 3 ) all four parameters ( top , bottom , Hill slope , and EC50 ) were also done using GraphPad Prism . For slo-1 ( js379 ) , linear regression including testing for linearity and a significance test for a slope differing from 0 was performed with the same software . Statistical significance was assumed for p<0 . 05 . Infective larvae of A . caninum ( non-exsheathed ) were incubated for 24 h at room temperature in 1×PBS buffer containing either penitrem A or the vehicle dimethylsulfoxide ( DMSO ) in combination with different concentrations of emodepside or the respective vehicle ethanol . Penitrem A ( 500 µM stock solution in DMSO ) was used in a final concentration of 1 µM penitrem A , resulting in a final DMSO concentration of 28 mM ( 0 . 2% ) . Emodepside ( 1 mM stock solution in ethanol ) was used in final concentrations of 1 µM , 5 µM , and 10 µM , respectively . The final ethanol concentration was 170 mM ( 1% ) in these experiments . The concentration of the vehicles was adjusted to the same final concentration in all setups by adding DMSO and/or ethanol . Furthermore , one control was performed without vehicles to estimate the impact of the vehicles . After 24 h , the larvae were used for a modified larval migration inhibition test ( LMIT ) , similar to that described by Demeler et al . [35] . Briefly , 1800 µl containing approximately 100 larvae was pipetted onto precision sieves ( mesh size 20 µm ) in a 24 well plate . The volume of 1800 µl was sufficient that the sieves were hanging in the liquid and motile larvae were able to penetrate the meshes . After further incubation for 24 h at room temperature , the sieves were removed and the bottom side was carefully rinsed with approximately 300 µl 1×PBS to gather any adherent larvae . Thus , this well contained the migrated larvae . Then , the sieves were turned upside down , and each sieve was rinsed by carefully pipetting 1000 µl 1×PBS through the sieve meshes and collecting the buffer in a so far empty well to recover the non-migrated larvae . For each setup , migrated and non-migrated larvae were counted individually , and the percentage of migrated larvae was calculated as follows: Each setup was performed in triplicate , and the whole experiment was performed three times in total . The results were compared to each other using a One-Way-ANOVA followed by a Tukey's post hoc test ( GraphPad Prism ) A p-value <0 . 05 was considered to be statistically significant . Nucleotide sequences: C . elegans YAC clone Y51A2D containing the putative slo-1 promoter region ( AL021497 ) ; C . elegans slo-1 splice variant b ( NM_001029089 ) ; partial coding sequence of A . caninum slo-1 ( CW974961 ) ; partial coding sequence of H . contortus slo-1 ( genome version 20060127: contigs >004261 , >0045106 , >001213 , and >057289 ) ; A . caninum slo-1 complete coding sequence ( EU828635 ) ; C . oncophora slo-1 complete coding sequence ( EF494185 ) ; H . contortus slo-1 complete coding sequence ( EF494184 ) ; Proteins sequences: C . elegans SLO-1a ( AAL28102 ) ; C . elegans SLO-1b ( AAL28103 ) ; C . elegans SLO-1c ( AAL28104 ) ; C . briggsae hypothetical protein CBG12923 ( XP_001675579 . 1 ) ; A . caninum SLO-1 ( EU 828635 ) ; C . oncophora SLO-1 ( EF494185 ) ; H . contortus SLO-1 ( EF494184 ) ; Ixodes scapularis putative calcium-activated potassium channel ( EEC10339 . 1 ) ; Cancer borealis calcium-activated potassium channel ( AAZ80093 . 4 ) ; Manduca sexta calcium-activated potassium channel alpha subunit ( AAT44358 . 1 ) ; Pediculus humanus corporis putative calcium-activated potassium channel alpha subunit ( EEB13088 . 1 ) ; Drosophila melanogaster slowpoke , isoform P ( NP_001014652 . 1 ) ; Tribolium castaneum predicted protein similar to slowpoke CG10693-PQ ( XP_968651 . 2 ) ; Aplysia californica high conductance calcium-activated potassium channel ( AAR27959 . 1 ) ; Xenopus laevis potassium large conductance calcium-activated channel , subfamily M , alpha member 1 ( NP_001079159 . 1 ) ; Danio rerio novel calcium activated potassium channel ( CAX13266 . 1 ) ; Trachemys scripta calcium-activated potassium channel ( AAC41281 . 1 ) ; Gallus gallus calcium-activated potassium channel alpha subunit ( AAC35370 . 1 ) ; Monodelphis domestica predicted protein similar to large conductance calcium-activated potassium channel subfamily M alpha member 1 ( XP_001367795 . 1 ) ; Mus musculus mSlo ( AAA39746 . 1 ) ; Homo sapiens potassium large conductance calcium-activated channel , subfamily M , alpha member 1 , isoform CRA_d ( EAW54600 . 1 ) ; Bos taurus BK potassium ion channel isoform C ( AAK54354 . 1 ) ; Canis familiaris calcium-activated K+ channel , subfamily M subunit alpha-1 ( Q28265 . 2 ) ; Strongylocentrotus . purpuratus predicted protein similar to calcium-activated potassium channel alpha subunit ( XP_783726 . 2 )
The search of the Wellcome Trust Sanger Institute H . contortus EST BLAST server using C . elegans slo-1 as template revealed four short fragments of 83 – 150 bp ( from the contigs 004261 ( two fragments ) and 0045106 and 001213 ) within the coding sequence and a 599 bp fragment containing the last twenty codons of the coding sequence , the stop codon , and part of the 3′ untranslated region ( UTR ) ( from contig 057289 ) . Based on these sequences , primers were designed to amplify the partial coding sequence of H . contortus slo-1 . The same primers were used to amplify the respective fragment of C . oncophora slo-1 . A partial coding sequence of A . caninum slo-1 was detected in a whole genome shotgun library fragment ( GenBank Acc . No . : CW974961 ) and primers were designed , according to that sequence . RACE-PCR completed the coding sequences and the 5′ and 3′ UTRs . The full-length coding sequences were 3309 bp ( EU828635; 1103 predicted amino acids ) for A . caninum slo-1 , 3333 bp ( EF494185; 1111 predicted amino acids ) for C . oncophora slo-1 , and 3315 bp ( EF494184; 1105 predicted amino acids ) for H . contortus . GC-contents of the coding sequences were 47 . 1 – 51 . 9% , molecular weight and isoelectric point of the proteins were predicted to be 125 . 02 - 125 . 88 kDa and 5 . 77-5 . 80 , respectively . None of the 5′ UTR sequences contained a spliced leader 1 ( SL1 ) sequence . Compared to the predicted sequences of A . caninum and H . contortus SLO-1 , C . oncophora SLO-1 had six additional NH2-terminal amino acids . The identities of the nucleotide sequences within the coding region were 80% between A . caninum and C . oncophora , 79% between A . caninum and H . contortus , and 85% between C . oncophora and H . contortus . Based on the predicted amino acid sequences , the identities were 95% between A . caninum and C . oncophora , 95% between A . caninum and H . contortus , and 98% between C . oncophora and H . contortus . The splice variants slo-1a , b , and c of the C . elegans slo-1 cDNA coding sequence were all 73% identical with A . caninum , C . oncophora , and H . contortus slo-1 , respectively . Based on predicted amino acid sequences , the identities were 87-88% between C . elegans SLO-1 ( splice variants SLO-1 a , b , and c ) and all three newly identified parasitic nematode SLO-1 sequences . A phylogenetic tree ( Figure 1 ) shows the relationship of selected SLO channels on the protein level from several animal genera and species . All known nematode SLO-1 orthologues group together: however , within this nematode SLO-1 group , the predicted SLO-1 proteins of the parasitic nematodes cluster in a group distinct from the non-parasitic nematodes C . elegans and Caenorhabditis briggsae . Analysing EST and genome databases for putative SLO-1 orthologues in other nematodes , fragments of coding sequences were identified for a range of species , including Brugia malayi , Trichinella spiralis , Strongyloides ratti , and Trichuris muris ( data not shown ) . As these sequences were either incomplete or of insufficient quality , they were not included in the phylogenetic analysis . The amplified putative promoter sequences covered approximately 3 kb upstream of the start codon ( A . caninum slo-1 promoter 2997 bp , C . oncophora slo-1 promoter 3421 bp , C . elegans slo-1 promoter 3084 bp ) . The 5′ UTR of A . caninum slo-1 included an intron , which was not present in C . oncophora slo-1 . The sequence analysis identified no known promoter elements or transcription factor binding sites in any of the slo-1 promoters employed . Just a few consensus sequences were detected , which might indicate RNA polymerase binding sites . No TATA or CAAT elements could be detected . Comparison of the putative slo-1 promoters of the three nematode species by alignments did not reveal any significant similarities . Comparing the sequences with the respective putative promoter regions of C . briggsae and Caenorhabditis remanei slo-1 ( 3000 bp upstream of the start codon ) also revealed no significant similarities ( data not shown ) . In cDNA samples of all analysed transgenic lines , transcripts of the respective expression construct were detected . The primer pairs targeting the expression constructs containing slo-1 coding sequences of the other species gave no amplicon in PCR . In cDNA samples of the slo-1 null mutant strain slo-1 ( js379 ) – representing the genetic background of the transgenic strains – and in the Bristol N2 wild-type strain , no transcript of any expression construct could be detected , confirming the authenticity of the PCR results for the transgenic lines . To ensure that the absence of specific PCR products was not due to insufficient RNA-isolation or cDNA-synthesis , a control primer pair was used and gave a PCR product in all analysed cDNA samples ( data not shown ) . In all transgenic strains expressing functional slo-1 from one of the expression constructs , the phenotype of increased reversals exhibited by the slo-1 null mutant strain slo-1 ( js379 ) was completely rescued as the rate of reversals was statistically not significantly different ( p = 0 . 87 in a one-way ANOVA ) from that observed in Bristol N2 wild-type worms ( Figure 2A ) but significantly ( p<0 . 001 ) lower than in mutant slo-1 ( js379 ) . It was previously shown that C . elegans slo-1 loss-of-function mutants are highly resistant to the inhibition of locomotion behaviour by emodepside [20] . In our study , we expressed slo-1 orthologues of the parasitic nematodes A . caninum and C . oncophora in the emodepside-resistant slo-1 ( js379 ) genetic background in order to rescue sensitivity to emodepside and to investigate involvement of these proteins in the mode of action of emodepside . Locomotion was determined by measuring body bends of the worms in the absence of food . By transformation of C . elegans slo-1 ( js379 ) , stable transgenic lines were obtained expressing 1 ) A . caninum slo-1 from the neuronal snb-1 promoter , 2 ) C . oncophora slo-1 from the snb-1 promoter , 3 ) A . caninum slo-1 from the C . elegans slo-1 promoter , 4 ) C . elegans slo-1 from the C . elegans slo-1 promoter 5 ) A . caninum slo-1 from the A . caninum slo-1 promoter , and 6 ) C . oncophora slo-1 from the C . oncophora slo-1 promoter ( an overview is given in supporting data , Table S2 ) . Transgenic lines were analysed for their susceptibility to emodepside . Their locomotion behaviour was compared to that of the wild-type strain Bristol N2 and to that of the loss-of-function mutant slo-1 ( js379 ) over a wide range of emodepside concentrations and concentration-response-curves were fitted to the data to allow statistical comparisons . Animals of all analysed lines showed a comparable basic locomotion , measured as body bends per minute , on the control plates without emodepside ( Figure 2B ) . Locomotion of the slo-1 ( js379 ) mutant strain was not at all affected by any of the emodepside concentrations tested ( Figure 3 ) as revealed by concentration-response-curves that are not significantly different from a straight line with slope 0 ( p = 0 . 91 ) . In contrast , locomotion of the Bristol N2 wild-type strain was concentration-dependently inhibited by emodepside . The EC50 for this effect varied between 127 . 3 nM and 144 . 2 nM ( Table 1 ) in this set of experiments . At the highest concentration used ( 10 µM ) , the Bristol N2 wild-type worms were nearly completely paralysed or dead . The transgenic worms expressing A . caninum ( Figure 3A ) or C . oncophora ( Figure 3B ) slo-1 from the snb-1 promoter showed significantly different concentration-response-curves ( p<0 . 0001 ) with increased susceptibility to emodepside compared to the slo-1 ( js379 ) mutant but were not as susceptible as Bristol N2 wild-type animals . Although the EC50 values were not altered , the lines expressing parasitic nematode slo-1 from the snb-1 promoter showed significantly increased bottom values ( refer to Table 1 ) indicating that even extremely high emodepside concentrations were not able to cause complete paralysis . At the highest concentration of 10 µM , worms of the transgenic lines were still able to show nearly half the body bend activity as the ethanol control , while the wild-type worms were almost completely immobilised . Expression of A . caninum slo-1 from the C . elegans slo-1 promoter ( Figure 3C ) showed a marked susceptibility to emodepside that was equivalent to N2 wild-type worms: worms expressing the parasite slo-1 from the C . elegans slo-1 promoter in slo-1 ( js379 ) animals fully restored susceptibility to emodepside as revealed by the absence of any significant differences in top and bottom values , Hill slope or EC50 ( Table 1 ) . A comparable effect was observed when the emodepside susceptibility of the slo-1 ( js379 ) mutant was rescued through the C . elegans slo-1 from the C . elegans slo-1 promoter ( Figure 3D and Table 1 ) . Transgenic worms expressing A . caninum or C . oncophora slo-1 from the respective A . caninum or C . oncophora slo-1 promoter showed increased susceptibility to emodepside compared to the slo-1 ( js379 ) mutant as well ( Figure 4 ) . However , the observed concentration-dependent effects were not as marked as seen for the transgenic worms expressing slo-1 from the C . elegans slo-1 promoter . The lines expressing A . caninum or C . oncophora slo-1 from the A . caninum or C . oncophora slo-1 promoter showed a 62- and 72-fold higher EC50 than the wild type worms . EC50 and 95% confidence intervals and significance levels for comparisons are given in Table 2 . In all experiments , the susceptibility appeared not only as a simple reduction of the number of body bends , but also as an altered pattern of movement , since the worms seemed to be stiffened in the forepart of their body . None of the transformed strains showed coiling as was observed previously at 1 µM emodepside after transformation of slo-1 ( js379 ) with pBK3 . 1 , the plasmid containing the C . elegans slo-1 coding sequence and the snb-1 promoter [20] . To conclude , a total functional rescue of the wild-type phenotype regarding the inhibitory effect of emodepside on locomotion was achieved with heterologous slo-1 genes expressed under the control of the C . elegans slo-1 promoter in C . elegans , as revealed by our statistical analysis showing no significant differences in the four parameters of the logistic concentration-response curve . These findings provide evidence that the slo-1 genes cloned from A . caninum and C . oncophora are functional , as well as structural , orthologues of C . elegans slo-1 . The vehicles DMSO and ethanol in the concentrations used here did not have any statistically significant effect on the migration of A . caninum larvae through 20 µm meshes . In the presence of emodepside , a concentration-dependent inhibition of migration was observed ( Figure 5A ) . The additional presence of 1 µM penitrem A clearly antagonized the effect of emodepside on migration . The difference in migration of larvae incubated with emodepside either with or without penitrem A was statistically highly significant with p-values of <0 . 001 for all emodepside concentrations tested . Body-bend assays with C . elegans worms produced highly similar results ( Figure 5B ) .
In the present study , we identified orthologues of the Ca2+-activated K+ ( BK ) channel C . elegans slo-1 in the parasitic nematodes H . contortus , C . oncophora , and A . caninum . Subsequently , we analysed the ability of A . caninum and C . oncophora slo-1 to functionally rescue emodepside susceptibility in slo-1 knockout mutant C . elegans . The examination of anthelmintic targets of parasitic nematodes is of great importance , since , in contrast to their orthologues in C . elegans , they are the direct targets for drugs used in veterinary and human medicine . Unfortunately , the parasitic stages of the nematodes , which mainly represent the target population for drugs , cannot be examined easily , and especially functional analysis of gene products in parasitic nematodes is usually not feasible . Up to now , parasitic nematodes cannot be maintained in in vitro cultures for their complete life cycle . Therefore , although it has been occasionally successful in some species such as filaria or Strongyloides spp . [36]-[38] , genetic engineering , i . e . expression or knockout of genes , in parasitic nematodes is still an unsolved problem [39] . RNAi experiments in parasitic nematodes had very variable outcomes , depending on the target gene , the delivery method , and the species tested [40]–[44] . This might be due to the fact that parasitic nematodes seem to lack orthologues for a transporter responsible for the systemic spread of RNAi in C . elegans , facilitating the accessibility of cells for RNAi in the latter [45] . Therefore , the use of C . elegans as a model and expression system is currently one of the most powerful tools for the functional analysis of genes of parasitic nematodes , especially if the genes have close orthologues in C . elegans [39] . One approach is the overexpression of a parasitic nematode gene in C . elegans with a wild-type genetic background for the respective gene . This approach can be used if the knockout mutant phenotype for the gene to be examined is lethal or not evident . Couthier et al . [46] expressed the H . contortus transcription factor elt-2 ectopically in C . elegans and found that this expression had similar effects as ectopic expression of the endogenous elt-2 . Another experimental setup is exemplified by the experiments described here for slo-1 , namely the rescue of the C . elegans loss-of-function mutant by expression of the homologous gene of a parasitic nematode . For that purpose , the mutant should have a clear phenotype and the effects of the rescue should be measurable . Similar experiments examining functionality of parasitic nematode genes in C . elegans have been performed previously . In the study of Kwa et al . [47] , ß-tubulin ( isotype 1 ) of H . contortus was expressed in benzimidazole-resistant mutants of C . elegans ( TU1054 ben-1 ( u462 ) ) . The benzimidazole-resistance of the ben-1 ( u462 ) C . elegans mutants is due to a mutation disrupting the ß-tubulin gene ben-1 [47] , [48] . The mutants showed a significantly higher EC50 with regard to the benzimidazole thiabendazole in a larval development inhibition assay compared to the wild-type Bristol N2 . In contrast to the resistant ben-1 mutants , H . contortus ß-tubulin expressing ben-1 ( u462 ) mutants showed a lower EC50 , though not as low as the wild-type larvae [47] . Thus , a total rescue of the wild-type phenotype regarding the effect of thiabendazole on egg-development was not achieved . The effect of expression of H . contortus ß-tubulin on susceptibility of adult ben-1 ( u462 ) worms to benzimidazoles has not been reported . Cook et al . [49] expressed the α-subunit of the glutamate-gated chloride channel ( GluClα ) of H . contortus in C . elegans GluClα mutants , which show a lower sensitivity to ivermectin and a decreased duration of forward movement . Here , a rescue of the wild-type phenotype in respect of the natural locomotion behaviour was observed . However , the effect of ivermectin was not described . Another study showed that expression of the transcription factor of the FOXO/FKH family of Strongyloides stercoralis in C . elegans daf-16 mutants was able to rescue the dauer-forming capability [50] . Very recently , the acetylcholinesterase of the plant-parasitic nematode Globodera pallida was expressed in C . elegans and was shown to functionally rescue the phenotype of the C . elegans double mutant ace-1;ace-2 [51] . In another recent study , Gillan et al . expressed the heat-shock protein 90 ( hsp-90 ) of H . contortus and Brugia pahangi in C . elegans . While expression of H . contortus hsp-90 in C . elegans daf-21 heat shock protein 90 mutants ( C . elegans daf-21 ( nr2081 ) ) partially rescued the phenotype of the mutant , the B . pahangi hsp-90 failed to do so , although the construct was transcribed and translated [52] . The great advantage of C . elegans as an expression system for parasite genes is that posttranslational modifications of recombinantly expressed proteins , which can be necessary for the biological function of the protein , are more conserved between nematodes than between nematodes and standard expression systems [53] . In our experiments , we did not use the recombinantly expressed protein , but the whole transgenic organism to measure the influence of the heterologously expressed proteins on susceptibility to emodepside . The expression of A . caninum slo-1 and C . oncophora slo-1 in the emodepside-resistant C . elegans slo-1 ( js379 ) mutant fully rescued the phenotype of worm locomotion: transgenic worms no longer showed increased reversal movement . These findings indicate a complete functional rescue and at least sufficient expression to restore SLO-1 dependent signalling to wild-type levels in the locomotor circuits . The subsequent pharmacological analysis showed that the transgenesis also rescued the phenotypic behaviour of the animals in terms of inhibited locomotion activity in the presence of emodepside . Animals expressing parasitic nematode slo-1 driven by the snb-1 promoter responded to emodepside in a manner qualitatively similar to wild-type animals , although the inhibition of locomotion was significantly weaker than that of the wild-type worms as determined by counting body bends . No complete paralysis was obtained even with an emodepside concentration that completely paralysed the wild-type animals . This phenotype might reflect the fact that expression of slo-1 was only reconstituted in one of its normal compartments , neuronal cells , whereas it was absent from another compartment , the muscle cells . The findings with parasite slo-1 under control of the snb-1 promoter are similar to previous experiments , in which C . elegans slo-1 ( js379 ) mutants were rescued by expression of endogenous slo-1 from the snb-1 promoter [20] . Interestingly , the coiled paralysis of the transgenic C . elegans upon exposure to emodepside observed in earlier experiments with the snb-1 promoter driven expression and also with the combination of snb-1 and myo-3 promoter driven expression of the endogenous slo-1 was not observed in our experiments . The coiling previously observed for slo-1 ( js379 ) animals expressing slo-1 from the snb-1 promoter in the presence of 1 µM emodepside was supposed to be due to overexpression or to ectopic expression in neurons usually not expressing slo-1 [20] . The most likely reason for the absence of this phenotype in the present study is the altered plasmid used for transformation . Although the linkage between the promoter and the slo-1 coding sequence was identical for the plasmids carrying the parasite slo-1 and the parental pBK3 . 1 plasmid used in the previous study , the downstream coding sequence may have influenced the level of expression . While the earlier study by Guest et al . [20] aimed to determine whether the mediation of the effects of emodepside is controlled via a neuronal or a muscular pathway , we were now interested in whether the parasitic nematode SLO-1 channels were also able to act as key components for emodepside action . Therefore , we chose to express the parasite slo-1 not only from the neuronal promoter snb-1 , which showed a stronger effect in that former study than the muscle-specific promoter myo-3 , but also from the putative endogenous C . elegans slo-1 promoter to achieve a pattern resembling the natural expression pattern , and from the putative parasite slo-1 promoters to test their ability to drive expression in C . elegans . The constructs were designed to be comparable to the pBK3 . 1 construct , which carries the snb-1 promoter sequence , 2987 bp in size . The transgenic animals expressing parasitic nematode slo-1 driven by the C . elegans slo-1 promoter were highly susceptible to emodepside , and since their susceptibility was statistically not different from the susceptibility of the wild-type worms , we considered this phenotype as a full rescue . For some drug targets , such as β-tubulin , a single nucleotide polymorphism can abolish their functionality as a drug target [54] . Therefore , the overall sequence identity between parasite and C . elegans SLO-1 orthologues of 87-88% per se did not ensure a conserved function with regard to emodepside . In the study of Gillan et al . the H . contortus hsp-90 sequence showed 88% identity with the C . elegans orthologue , but its expression rescued the mutant phenotype only partially [52] . The finding that expression of slo-1 from different nematode species restored the susceptibility to emodepside in the slo-1 ( js379 ) mutants emphasises that the mode of action is most likely conserved between these species . Generally , SLO-1 channels belong to a relatively conserved ion channel family [23] . This was also confirmed by our BLAST search results , which identified channels in very distantly related genera . The expression of parasite slo-1 under control of the putative slo-1 promoters from A . caninum and C . oncophora aimed to examine the capacity of the parasite-derived promoters to drive expression of the coding sequence of their natural gene within the heterologous background of C . elegans . The transformants showed only partial rescue of emodepside susceptibility . However , in contrast to the lines with snb-1 driven expression , the lines expressing slo-1 from the putative slo-1 promoters of A . caninum and C . oncophora , respectively , did not show increased bottom values . In these experiments the rescued lines clearly had a higher EC50 , suggesting that the expression pattern might have been qualitatively restored but that expression levels in general were too low . Since , as was shown in our experiments using the C . elegans slo-1 promoter , the coding sequences of parasite slo-1 appeared to be able to rescue the resistant phenotype completely , the reason for the incomplete rescue is most likely the promoter . The lack of TATA or CAAT elements which we observed for the slo-1 promoters from A . caninum , C . oncophora as well as from C . elegans is consistent with other studies on nematode promoters and strengthens the assumption that the absence of these elements is a characteristic feature of protein-coding genes of this phylum [26] , [55]-[59] . Transcriptional regulatory elements can be located at large distances from the start codon , within intron sequences , and also within the 3′ UTR . Furthermore , expression can be influenced by post-transcriptional regulation , e . g . by microRNAs [60] . Nevertheless , most common reporter gene constructs only use upstream intergenic sequence , and it is recommended to include as much of the upstream sequence as possible . Even so , all phenotypes obtained with such reporter constructs must be interpreted with caution as they may not necessarily reflect the endogenous gene expression pattern [61] . We conclude from the present experiments that the parasite slo-1 promoters drive expression in a functionally appropriate pattern , as the parasite slo-1 expressed from the respective parasite slo-1 promoter qualitatively restored emodepside susceptibility in resistant slo-1 ( js379 ) C . elegans . The fact that the emodepside susceptibility of the transformants was significantly lower than in transformants expressing parasite slo-1 from the C . elegans snb-1 or slo-1 promoter , respectively , in turn indicates that the expression pattern obtained with the parasite promoters is not equivalent to that obtained with the C . elegans promoters used in this study . Interestingly , the phenotype of slo-1 ( js379 ) C . elegans concerning increased reversals was completely rescued by the parasite slo-1 expressed from the parasite slo-1 promoters . The fact that the rescue regarding emodepside susceptibility was less complete again strengthens the assumption that the spatial pattern or some other characteristics of expression such as expression levels in certain cell types might not have been sufficient to completely fill in the function of the wild-type slo-1 expression . An approach to use the slo-1 promoters of C . elegans , A . caninum , and C . oncophora to express GFP for localisation studies in C . elegans was only partially successful . Within the offspring of the microinjected hermaphrodites only single worms were found exhibiting GFP-expression . Fluorescence was detected as punctate structures in the pharynx region of the transformed animals , indicating expression in pharyngeal neurons , furthermore in the neuron-rich anal region of the worms and in locations consistent with expression in the nerve cords ( data not shown ) . For the C . elegans slo-1 promoter reporter construct , GFP expression was observed in body wall muscle cells within the forepart of the body ( data not shown ) . However , due to the restricted number of observations these investigations thus far do not allow to draw final conclusions and therefore need to be further pursued . The hypothesis of the functional involvement of SLO-1 in the mechanism of action of emodepside in parasites was further supported by a series of experiments with emodepside and penitrem A . Penitrem A is a tremorgenic mycotoxin known to completely suppress bovine BK channel currents at a concentration of 10 nM [27] . It has also been used as a BK channel inhibitor in a study on muscle fibres of the liver fluke Fasciola hepatica [62] . The concentration in those experiments was 10 µM , but the authors do not report , whether they tested other concentrations . In our experiments , we used penitrem A in a concentration of 1 µM and showed its ability to antagonise the paralysing effect of up to 10 µM emodepside on A . caninum larvae and young C . elegans adults . While lower concentrations of penitrem A ( 10 nM and 100 nM , data not shown ) did not impair the effect of 10 µM emodepside , 1 µM penitrem A antagonised emodepside at all emodepside concentrations analyzed . The need for higher penitrem A concentrations than in experiments with cultured mammalian cells might be explained by a lower accessibility of the target in the intact nematode larvae , e . g . due to the cuticula – at least for the non-feeding A . caninum third-stage larvae . Currently there are no data available on whether penitrem A is indeed also a specific BK channel inhibitor in nematodes and on what penitrem A concentrations are needed for this inhibition . However , the present data show antagonistic effects of emodepside and penitrem A , indicating that both drugs target the same pathway requiring SLO-1 . To conclude , the examination of the actual role of SLO-1 in the signalling of emodepside is still under way . The prevailing view is that emodepside directly or indirectly activates SLO-1 [20] , [63] . In contrast to the effects of emodepside on pharyngeal pumping , the effects of emodepside on locomotion are not mediated by the previously described latrophilin-activating pathway [19] . The current model includes latrophilin and SLO-1 for the pharyngeal neurons and SLO-1 but not latrophilin for the body wall musculature [63] . The presented study aimed primarily to test the hypothesis that the mechanism of action of emodepside as far as currently known is conserved in nematodes . Our results are based on functional expression of A . caninum and C . oncophora slo-1 in C . elegans driven by different promoters and demonstrate the ability of the parasitic SLO-1 to act in the mode of action of emodepside . These results are further supported by the experiments with the BK channel inhibitor penitrem A antagonising emodepside . Therefore the current findings suggest that the mode of action is conserved across the three nematode species , providing an important example for functional analysis of the role of individual parasite genes as targets for anthelmintic drugs . Furthermore , these experiments emphasise the potency of C . elegans as an authentic functional model for expression of parasitic nematode genes – at least from clade V – and the subsequent physiological examination of drug/target interactions . Experiments of this type close the gap between research in model organisms and in parasitologically relevant target species . The results presented in this work open new perspectives on functional analysis of parasitic nematode genes in general and in particular allow further analysis of putative targets for emodepside and the elucidation of the mode of action in detail . Transgenic worms from the present study expressing C . elegans slo-1 driven by the C . elegans slo-1 promoter have already been used as a control in a parallel study regarding the expression of the human slo-1 orthologue kcnma1 in C . elegans ( Crisford et al . , submitted ) . Another possible application of the system is its use to analyse the impact of certain mutations on emodepside susceptibility , for instance single nucleotide polymorphisms ( SNP ) , identified in resistant populations and suspected to contribute to resistance development . In the long-term , these methods might also enhance development of new anthelmintically active agents . | In parasitic nematodes , experiments at the molecular level are currently not feasible , since in vitro culture and genetic engineering are still in their infancy . In the present study we chose the model organism Caenorhabditis elegans not only as a mere expression system for genes from parasitic nematodes , but used the transformants to examine the functionality of the expressed proteins for mediating anthelmintic effects in vivo . The results of our experiments confirmed that SLO-1 channels mediate the activity of the new anthelmintic drug emodepside and showed that the mode of action is conserved through several nematode species . The chosen method allowed us to examine the functionality of proteins from parasitic nematodes in a defined genetic background . Notably , expression of the parasitic nematode gene in anthelmintic-resistant C . elegans completely restored drug susceptibility . As C . elegans is highly tractable to molecular genetic and pharmacological approaches , the generation of lines expressing the parasite drug target will greatly facilitate structure-function analysis of the interaction between emodepside and ion channels with direct relevance to its anthelmintic properties . In a broader context , the demonstration of C . elegans as a heterologous expression system for functional analysis of parasite proteins further strengthens this as a model for anthelmintic studies . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"pharmacology/drug",
"development",
"infectious",
"diseases/helminth",
"infections",
"microbiology/parasitology",
"infectious",
"diseases/antimicrobials",
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] | 2011 | SLO-1-Channels of Parasitic Nematodes Reconstitute Locomotor Behaviour and Emodepside Sensitivity in Caenorhabditis elegans slo-1 Loss of Function Mutants |
Tick-borne diseases are increasing all over the word , including Turkey . The aim of this study was to determine the bacterial and protozoan vector-borne pathogens in ticks infesting humans in the Corum province of Turkey . From March to November 2014 a total of 322 ticks were collected from patients who attended the local hospitals with tick bites . Ticks were screened by real time-PCR and PCR , and obtained amplicons were sequenced . The dedected tick was belonging to the genus Hyalomma , Haemaphysalis , Rhipicephalus , Dermacentor and Ixodes . A total of 17 microorganism species were identified in ticks . The most prevalent Rickettsia spp . were: R . aeschlimannii ( 19 . 5% ) , R . slovaca ( 4 . 5% ) , R . raoultii ( 2 . 2% ) , R . hoogstraalii ( 1 . 9% ) , R . sibirica subsp . mongolitimonae ( 1 . 2% ) , R . monacensis ( 0 . 31% ) , and Rickettsia spp . ( 1 . 2% ) . In addition , the following pathogens were identified: Borrelia afzelii ( 0 . 31% ) , Anaplasma spp . ( 0 . 31% ) , Ehrlichia spp . ( 0 . 93% ) , Babesia microti ( 0 . 93% ) , Babesia ovis ( 0 . 31% ) , Babesia occultans ( 3 . 4% ) , Theileria spp . ( 1 . 6% ) , Hepatozoon felis ( 0 . 31% ) , Hepatozoon canis ( 0 . 31% ) , and Hemolivia mauritanica ( 2 . 1% ) . All samples were negative for Francisella tularensis , Coxiella burnetii , Bartonella spp . , Toxoplasma gondii and Leishmania spp . Ticks in Corum carry a large variety of human and zoonotic pathogens that were detected not only in known vectors , but showed a wider vector diversity . There is an increase in the prevalence of ticks infected with the spotted fever group and lymphangitis-associated rickettsiosis , while Ehrlichia spp . and Anaplasma spp . were reported for the first time from this region . B . microti was detected for the first time in Hyalomma marginatum infesting humans . The detection of B . occultans , B . ovis , Hepatozoon spp . , Theileria spp . and Hemolivia mauritanica indicate the importance of these ticks as vectors of pathogens of veterinary importance , therefore patients with a tick infestation should be followed for a variety of pathogens with medical importance .
Ticks are important vectors of a variety of diseases all over the world , including Turkey . They may transmit different kind of pathogens including bacteria , viruses , and protozoa affecting humans , domestic and wild animals [1 , 2] . Turkey is composed from a mosaic of habitats for ticks due to its diverse climate , vegetation , and large variety of wild and domestic animals [1 , 3] . Today , 48 tick species are known from this country , 31 of which have been found infesting humans [3] . Nineteen tick-borne diseases ( TBDs ) have been detected either in animals or humans in Turkey [1] . From 2002 to 2015 , a total of 9 , 787 human cases of Crimean Congo hemorrhagic fever ( CCHF ) have been reported , 469 of which resulted in death [4] . Lyme borreliosis were reported in Turkey [5] , while the sero-prevalence of Borrelia burgdorferi in humans was 4% [6] . Between 2005 and 2011 , 4 , 824 human cases with tularemia were reported to the Ministry of Health [7] . Anaplasmosis is known from farm animals [8] , while in humans , sero-positivity was 10 . 62% [9] . Ehrlichiosis and hepatozoonosis have been diagnosed in dogs [10 , 11] . The sero-prevalence for bartonellosis was 18 . 6% in cats [12] , 6% in human blood donors [13] , and 22 . 2% in cattle breeders and veterinarians [14] . Rickettsiosis was reported in Thrace and East Mediterranean regions of Turkey [15 , 16] , the most prevalent being the Mediterranean Spotted Fever ( MSF ) [17] . Q fever cases in humans were reported from the Black Sea region of Turkey [18] . Babesiosis in animals is highly prevalent in Turkey , but there are no reports about clinical cases in humans [1] . Toxoplasmosis is one of the more common parasitic zoonosis worldwide , and in Turkey the prevalence in humans was found to vary between 13 . 9% and 76 . 6% [19] . Between the years 1988–2010 , 50 , 381 cases of cutaneous leishmaniasis were reported to the Turkish Ministry of Health [20] . According to recent studies , ticks can be also possible vectors of toxoplasmosis and leishmaniasis [21 , 22] . The first CCHF cases in Turkey were observed in the province of Tokat which is a neighboring province of Corum; both cities are located in Kelkit Valley where the main vector , Hyalomma marginatum is prevalent [1 , 4] . Recently , 327 cases of CCHF and other TBDs such as rickettsial infections were reported from Corum [3 , 23–27] . The present study aims to investigate the human infested ticks species distribution; to determine their broad-ranges pathogens like Rickettsia spp . , Anaplasma spp . , Ehrlichia spp . , Coxiella burnetii , Borrelia burgdorferi sensu lato , Francisella tularensis , Bartonella spp . , Leishmania spp . , Toxoplasma gondii , Babesia spp . , Theileria spp . , Hepatozoon spp . , and Hemolivia mauritanica in Corum province of Turkey .
This study was carried out in the province of Corum ( 40° 33′ 00′′ N , 34° 57′ 14′′ E ) , which is located in Central Anatolia region of Turkey ( Fig 1 ) . It has a surface area of 12 , 820 km2 , a population of 527 , 220 people , 152 , 244 of which live in the country site and another 374 , 926 in urban centers . The mean altitude is 801 m , the mean annual precipitation 429 mm , and the mean temperature 10–11°C . Due to the influences of the Black Sea and continental climates , the summers are hot and dry , while the winters are cold and rainy . Wild animals such as deer , boar , bear , badger , fox , rabbit , wolf , marten , squirrel and beaver are abundant throughout the province ( Special Provincial Administration , Anonymous , 2009 ) , while in rural areas farm animals are bred . From March to November 2014 specimens were collected from patients who applied to the Emergency Service of the Hitit University Research and Training Hospital with a tick infestation . Ticks were morphologically identified under the stereomicroscope ( Leica MZ16 , Germany ) using standard taxonomic keys [28–30] . Individual ticks were mechanically homogenized by crushing with liquid nitrogen using disposable micro pestle and the DNA was extracted using the Tissue and Bacterial DNA Purification Kit ( EURx DNA , Gdansk , Poland ) according to the manufacturer’s protocols . All Polymerase Chain Reaction ( PCR ) amplifications were conducted with final volumes of 25 μl with 2 . 5 μl of template DNA , while negative and positive controls for each pathogen were used . With the exception of Francisella tularensis and protozoa , ticks were molecularly screened for pathogens by real-time-PCR using Evagreen master mix ( Biotium , State , USA ) , while suspected samples were subjected to PCR . For the detection of F . tularensis and Leishmania a real-time-PCR taqman probe was used . For the identification of Babesia , the conventional PCR was used . All positive samples were sequenced . The primers BJ1 and BN2 amplifying Babesia spp . , detected also Theileria spp . , Hepatozoon spp . and H . mauritanica . The PCR methods , target genes and primer sequences used can be seen in Table 1 [31–41] . PCR positive samples were purified and sequenced in one direction at a commercial sequencing service provider ( Macrogen , Netherlands ) . Nucleotide sequences were analyzed using nucleotide Blast ( National Centre for Biotechnology Information , www . blast . ncbi . nlm . nih . gov/Blast ) . Representative nucleotide sequences from this study were submitted to GenBank under accession numbers MF383491-MF383615 and MF494656-MF494660 . A phylogenetic tree was constructed using the MEGA5 . 1 program .
A total of 322 ticks were collected from humans and identified as Hyalomma marginatum ( n = 164 , 50 . 9% ) , Hyalomma excavatum ( n = 5; 1 . 5% ) , Hyalomma aegyptium ( n = 1; 0 . 31% ) , Hyalomma spp . ( n = 46; 14 . 3% ) , Haemaphysalis parva ( n = 41; 12 . 7% ) , Haemaphysalis punctata ( n = 6; 1 . 8% ) , Haemaphysalis sulcata ( n = 1; 0 . 31% ) , Rhipicephalus turanicus ( n = 34; 10 . 5% ) , Rhipicephalus bursa ( n = 3; 0 . 93% ) , Dermacentor marginatus ( n = 17; 5 . 2% ) and Ixodes ricinus ( n = 4; 1 . 24% ) . Overall , 37 . 2% of the examined ticks were infected with at least one pathogen; 3 . 7% of which with two different pathogens . The infection rate was 100% in Dermacentor spp . , 89% in Haemaphysalis spp . , 75% in Ixodes spp . , 37% in Hyalomma spp . and 27% in Rhipicephalus spp . A total of 17 microorganism species were identified ( Table 2 ) . The most prevalent Rickettsia spp . being R . aeschlimannii ( 19 . 5% ) , R . slovaca ( 4 . 5% ) , R . raoultii ( 2 . 2% ) , R . hoogstraalii ( 1 . 9% ) , R . sibirica subsp . mongolitimonae ( 1 . 2% ) , R . monacensis ( 0 . 31% ) , and Rickettsia spp . ( 1 . 2% ) . In addition , the following pathogens were identified: Borrelia afzelii ( 0 . 31% ) , Anaplasma spp . ( 0 . 31% ) , Ehrlichia spp . ( 0 . 93% ) , Babesia microti ( 0 . 93% ) , Babesia ovis ( 0 . 31% ) , Babesia occultans ( 3 . 4% ) , Theileria spp . ( 1 . 6% ) , Hepatozoon felis ( 0 . 31% ) , Hepatozoon canis ( 0 . 31% ) , and Hemolivia mauritanica ( 2 . 1% ) . Table 3 shows the presence of bacterial pathogens according to the tick species , while in Table 4 the distribution of protozoan pathogens can be seen . All samples were negative for Francisella tularensis , Coxiella burnetii , Bartonella spp . , Toxoplasma gondii and Leishmania spp .
Recently , a lot of attention is being given to ticks and tick-borne diseases in Turkey , were many individuals died as a result of CCHF [1 , 3 , 4] . Table 5 summarizes the studies done on ticks and their pathogens in the seven main regions of Turkey ( Fig 2 ) [8 , 12 , 14 , 24–27 , 42–83] . In Corum province , 10 tick species infesting humans were identified , the most prevalent being H . marginatum , Hae . parva , R . turanicus and D . marginatus . Similar results from the same region has been obtained by Keskin et al . , [84 , 85] , who , in addition to the tick species found in the present study , also reported the infestation of humans with Haemaphysalis erinacei taurica and Ixodes laguri . In their study the most prevalent tick species isolated from humans were H . marginatum , D . marginatus , R . turanicus and R . bursa . The differences could be explained with the changes in tick abundance according to climatic conditions , host factors , socio-demographic factors , deforestation , as well as agricultural and wildlife management [86] . In the present study all D . marginatus specimens were infected with at least one pathogen , while the infection rate was high also in Haemaphysalis spp . Orkun et al . who investigated tick pathogens in Ankara province found high infection rate of Rickettsia spp . , Babesia spp . , and Borrelia spp . in the same tick species [26] . Rickettsia spp . was identified as the most prevalent tick-borne pathogen in this study ( 31% ) . Other studies reported an average infection rate of 41 . 3 in Istanbul [24] , while in Yozgat province the rate was 10 . 5% [56] , and in Ankara province 27 . 2%[26] . Rickettsia aeschlimannii is commonly transmitted by Hyalomma and Rhipicephalus spp . [2] . In Turkey , R . aeschlimannii was detected in H . marginatum , H . aegyptium , H . excavatum , R . bursa and R . turanicus ticks [24 , 26 , 56 , 87 , 88] . In our study , this pathogen was found in all tick species examined with the exception of H . excavatum and R . bursa . To the best of our knowledge , this is the first report that R . aeschlimannii was found in Haemaphysalis spp . , Dermacentor spp . , and Ixodes spp . ticks , indicating that the pathogen might be transmitted also by other tick species . According to nucleotide Blast and phylogenetic analysis ( ompA ) ( Annex 1 ) , R . aeschlimannii strains in our study is closely related with R . aeschlimannii isolate BB-35/Camli-H . marg ( 99–100% identity , accession number KF791251 ) . Rickettsia aeschlimannii was the most prevalent ( 19 . 5% ) pathogen among Rickettsia-positive ticks in this study . In an investigation which was performed in 2009 in Corum province , R . aeschlimannii was found in 5% of the ticks [87] , while in Ankara and Yozgat provinces , where similar climatic conditions prevail , this pathogen was detected in 4 . 7% and 4 . 3% , respectively of ticks examined [26 , 56] . It was reported that R . aeschlimannii infections exhibited symptoms similar to Mediterranean spotted fever ( MSF ) [89] , and potentially lead to severe symptoms resembling to those of viral hemorrhagic fever [17] . Accordingly , R . aeschlimannii infection should be included in the differential diagnosis , especially in endemic regions of MSF . Rickettsia slovaca is usually transmitted by Dermacentor ticks and is associated with symptoms characterized by inoculation eschar on the scalp , necrosis erythema and cervical lymphadenopathy [2 , 24 , 56 , 88 , 90] . This disease is either called tick-borne neck lymphadenopathy ( TIBOLA ) or Dermacentor-borne necrosis erythema and lymphadenopathy ( DEBONEL ) [90] . Incidence of R . slovaca infections is likely underestimated . Parola et al . reported that in 49 out of 86 ( 57% ) TIBOLA/DEBONEL cases the etiologic agent was R . slovaca [90] . Throughout Europe , Dermacentor marginatus and Dermacentor reticulatus ticks are responsible from transmission of this pathogen [90] . In our study , in addition to Dermacentor spp . ticks , this pathogen was for the first time also detected in H . marginatum , Hyalomma spp . nymphs and Hae . parva ( Table 3 ) . Nucleotide Blast and phylogenetic analysis ( ompA , ) of R . slovaca Corum strains were 99% identical to R . slovaca isolate BB-51/Akyurt-D . marg ( accession number KF791235 ) ( Annex 1 ) , while the gltA gene of R . slovaca Corum strains ( Annex 2 ) , showed a 99% identity to R . slovaca strain PotiR30 ( accession number DQ821852 ) . In the present study R . slovaca was detected in 4 . 6% of the ticks . In similar studies conducted earlier , R . slovaca was found in 0 . 3% of ticks in Corum [87] , in 4 . 8% in Yozgat province [56] , and in 9 . 4% in Ankara province [26] . Similar to R . slovaca , R . raoultii is also the etiological agent of TIBOLA/DEBONEL and this Rickettsia seems to be less pathogenic and less frequent than R . slovaca [90] . Parola et al reported that in 7 out of 86 ( 8% ) TIBOLA/DEBONEL cases the etiologic agent was R . raoultii [90] . Dermacentor ticks are known vectors of R . raoultii [24 , 56 , 88] . In the present study , in addition to Dermacentor spp . , R . raoultii was also found in H . marginatum and Hyalomma spp . nymphs ( Table 3 ) . The nucleotide Blast and phylogenetic analysis of gltA gene of Corum R . raoultii strains ( Annex 2 ) share a 99% sequence identity to R . raoultii clone Ds1 ( accession number KF003009 ) and accordingly to ompA genes ( Annex 1 ) . In addition , a 99% similarity was found to R . raoultii strain WB16/Dm Monterenzio ( accession number HM161789 ) . Rickettsia raoultii was detected in 2 . 2% of the ticks examined . Earlier studies from Corum reported that the percentage was 0 . 3% [27] and in Yozgat province 0 . 4% [56] , while this rickettsia was not detected in ticks from the Ankara region [26] . In Corum province , the rate of R . slovaca and R . raoultii in ticks infesting humans increased in comparison to 2009 , and it seems that these pathogens are extending their vector diversity . Rickettsia hoogstraalii has an unknown pathogenicity and it is transmitted by Hae . Parva [26 , 56 , 88] , however , we found it in Hae . parva and Hae . punctata ticks . The nucleotide Blast and phylogenetic analysis of gltA gene of Corum R . hoogstraalii strains ( Annex 2 ) have a 99% similarity to R . hoogstraalii strain RCCE3 with accession number EF629539 . In our study the prevalence of R . hoogstraalii was 1 . 9% , while in Yozgat was 0 . 87% [56] , and in Ankara 13% [26] . Rickettsia sibirica subsp . mongolitimonae , symptoms are characterized by fever , eschar and lymphadenopathies [91] and it is transmitted by ticks such as Hyalomma asiaticum , Hyalomma truncatum , H . excavatum and R . bursa [2 , 91–93] . We found this pathogen in H . marginatum , H . excavatum , R . bursa , and Hae . parva ticks . To the best of our knowledge this is the first detection of this pathogen in Hae . parva ticks . Nucleotide Blast and phylogenetic analysis of R . sibirica subsp . mongolitimonae Corum strains ( ompA ) ( Annex 1 ) , showed a 99% identity to R . sibirica subsp . mongolitimonae Bpy1 ( accession number KT345980 ) . In this study this Rickettsia species was detected earlier in 1 . 2% of the ticks , while it was reported in 0 . 3% of H . marginatum ticks in Corum [87] and in 0 . 25% of ticks in Tokat province [71] . Rickettsia monacensis infection shows flu-like symptoms , eschar and rash , the main vector of this pathogen being Ixodes ricinus [91] . In Anatolian region of Turkey this tick species is rare [3] . The ompA genes of Corum R . monacensis , which was detected also in our study in I . ricinus ticks , showed 99% identity with R . monacensis isolate Est1623 ( accession number KT119437 ) ( Annex 1 ) . In previous studies this pathogens was not found in the Ankara and Yozgat provinces [26 , 56] , whereas the infection rate was 30 . 5% in ticks infesting humans in Istanbul [24] Ehrlichia spp . effect both humans and animals such as dogs and domestic ruminants with symptoms like fever , malaise , leucopenia , thrombocytopenia , and abnormal liver function [94] . The vectors of this pathogen are Amblyomma , Dermacentor , Rhipicephalus , Ixodes and Haemaphysalis ticks [2 , 94] . In this study , Ehrlichia spp . were detected in 0 . 93% of H . marginatum , Hyalomma spp . nymphs and Hae . parva . Nucleotide Blast and phylogenetic analysis of groEL genes of Corum Ehrlichia spp . strain ( Annex 3 ) was 99% identical to Ehrlichia ewingii isolate AaFT81 GroEL . In Turkey , bovine anaplasmosis was detected in I . ricinus ticks which were collected from cattle in the cost of Black Sea [67] . In the present study , Anaplasma spp . was found in Hae . parva ticks . Nucleotide Blast and phylogenetic analysis of groEL genes of Corum Anaplasma spp . strain shared an 81% identity to Anaplasma phagocytophilum isolate Omsk-vole52 with accession number KF745743 , ( Annex 3 ) . Coxiella burnetii is the etiological agent of Q-fever with flu-like symptoms and is considered as a zoonotic disease . The role of ticks in the transmission of C . burnetii to humans is low [95] . In present study this pathogen was not detected in ticks infesting humans . Borrelia afzelii is the pathogenic agent of Lyme disease transmitted mainly by ticks belonging to the genus Ixodes . This pathogen is known from Europe , western parts of the former USSR and Northern Africa [2] . We detected it in one I . ricinus specimen . According to flagelline gene sequence analyses B . afzelii Corum strain was 100% identical to B . afzelii strain S60 with accession number KM198345 ( Annex 4 ) . Orkun et al . reported the presence of Borrelia burgdorferi sensu stricto in 3 . 5% of Hyalomma spp . and Hae . parva in Ankara province [26] . Lyme disease pathogens are prevalent in Istanbul region which has a moderate and wet climate and the infection rate in ticks collected from different areas was 38 . 7% [47] . Francisella tularensis is the causative agent of tularemia a severe zoonotic diseases affecting animals and humans . This pathogen was isolated from the bird-rabbit tick , Haemaphysalis leporispalustris [95] and from Dermacentor reticulatus infesting red foxes [96] . In Turkey , tularemia cases were generally transmitted as water-borne but there are few tick-borne cases [46 , 57 , 97] . F . tularensis was neither found in ticks collected from several barns , cattle and people [98] , nor in the ticks of the present study . Bartonella spp . are zoonotic vector-borne infection agents of humans . One of them , B . henselae is the pathogenic agent of cat-scratch disease , the main vector being the cat flea ( Ctenocephalides felis ) [12] , however a direct link between tick bites , B . henselae and disease symptoms was reported in humans [99] . In the present study B . henselae was not detected in any of the ticks examined . Babesia spp . are the pathogenic agents of babesiosis in humans and animals , which are considered as emerging diseases worldwide [86] . In Europe , infection rates of Babesia spp . in ticks ranges from 0 . 9 to 20% [100] . B . microti is pathogenic to humans causing malaria-like symptoms . The geographical distribution of this pathogen is USA , Canada , and Europe while the main vector is Ixodes spp . [2 , 100] . In USA , the prevalence of B . microti in ticks was 8 . 4% [101] , while in ticks collected from vegetation in Poland was 2 . 8% [102] . In addition to Ixodes spp . , B . microti was also detected in 0 . 7% of Dermacentor reticulatus in Switzerland [39] . In Turkey , B . microti was for the first time detected in one I . ricinus tick collected from a ruminant [63] . In Sinop province of Turkey , the sero-prevalence of B . microti in humans was 6 . 23% [64] , while in the present study , the prevalence of B . microti in H . marginatum ticks was 0 . 93% . According to 18SrRNA gene nucleotide Blast and phylogenetic analysis , B . microti Corum strains were 100% identical to B . microti isolate RUS/Nov15-2950-Ipr with accession number KX987864 ( Annex 5 ) . This is the first report showing the presence of B . microti in H . marginatum infesting humans , which is the most prevalent tick species in Corum province and is the main vector for B . microti . Babesia occultans is a bovine parasite with high prevalence in South Africa , the vectors being Hyalomma spp . [2] . In Turkey , presence of B . occultans was reported by Aktas et al . in H . marginatum and R . turanicus collected from the vegetation , agricultural fields and grazing cattle , with a prevalence rate of 7%; transstadial and transovarial transmission of B . occultans were also demonstrated [103] . Orkun et al . reported this pathogen in 0 . 6% of H . marginatum infesting humans [26] . In our study B . occultans was present in 3 . 4% of H . marginatum , strongly supporting the presence of this pathogen not only in ticks infesting animals but also humans . The 18SrRNA genes of Corum B . occultans strains showed a 99% similarity to B . occultans isolate Trender1with accession number KP745626 ( Annex 5 ) . Babesia ovis is the causative agent of sheep babesiosis and mainly prevalent in Africa , Asia , and Europe , the vectors of this pathogen are R . bursa and R . turanicus [2] . In Turkey , in ticks collected from sheep and goats the prevalence was 16 . 37% [79] . B . ovis was detected by us in one R . bursa infesting a patient . According to 18SrRNA gene nucleotide Blast and phylogenetic analyses ( Annex 5 ) , B . ovis Corum strains was 99% identical to B . ovis isolate tick20 . 3D with accession number KT587794 ( Annex 5 ) . Recent studies show that ticks collected from cats and dogs may be responsible for the transmission of Toxoplasma gondii [21] . Leishmania infantum was also found on ticks infesting dogs [22] . In our study , these agents could not be detected . Hepatozoon canis and Hepatozoon felis are the causative agents of hepatozoonosis in dogs and cats . These pathogens are transmitted by Rhipicephalus sanguineus , Hae . longicornis , and R . turanicus [2] . In Turkey , H . canis and H . felis were for the first time identified in R . sanguineus ticks removed from dogs [83] , while H . canis infection was also reported in dogs [104] . We demonstrated the presence of H . canis in D . marginatus and of H . felis in R . turanicus . The 18SrRNA genes of Corum H . canis strain showed a 99% similarity to H . canis isolate 204B/13b ( accession number KP216425 ) , while the Corum H . felis strain showed a 99% similarity to H . felis , clone 8533 , accession number KC138533 ( Annex 5 ) . Theileria spp . are the pathological agents of theileriosis of ruminants , equids and felids , the vectors being ticks from the genera Hyalomma and Rhipicephalus [1 , 2] . A transstadial but not transovarial transmission was reported in these ticks [105] . In our study Theileria spp . was demonstrated in Hyalomma spp . infesting humans and the prevalence rate was 1 . 6% . According to 18SrRNA genes , the Corum strain of Theileria spp showed a 92% similarity to Theileria youngi ( accession number AF245279 ) ( Annex 5 ) . Hemolivia mauritanica is a pathogen of tortoises and transmitted by H . aegyptium [106] . In the present study , this pathogen was found only in Hyalomma spp . nymphs infesting humans and the prevalence rate was 2 . 1% . According to 18SrRNA genes , Corum H . mauritanica strains showed a 99% similarity to H . mauritanica isolate SY-45-10 ( accession number KF992707 ( Annex 5 ) . In conclusion , ticks in Corum province carry a large variety of human and zoonotic pathogens . There are indications showing that there is an increase in the rate of ticks carrying spotted fever group and lymphangitis-associated Rickettsiae , while Ehrlichia spp . and Anaplasma spp . were reported for the first time in the region . To the best of our knowledge B . microti was detected for the first time in H . marginatum infesting humans . The presence of pathogens such as B . occultans , B . ovis , Hepatozoon spp . , Theileria spp . and H . mauritanica show the role of ticks for diseases of veterinary importance . Pathogens are detected not only in ticks known as vectors but in a variety of other ticks , indicating wider vector diversity . Patients with a tick bite history in Corum region should be followed not only for CCHF but also for other pathogens of medical importance . | Ticks are important vectors for different kind of pathogens , both of medical and veterinary importance , while tick-borne diseases ( TBDs ) are increasing all over the world . In Turkey , many important human and zoonotic TBDs such as , Lyme borreliosis , rickettsiosis , anaplasmosis , ehrlichiosis , tularemia , bartonellosis , babesiosis , theileriosis , and hepatozoonosis have been reported . Nonetheless , there is lack of research-based information concerning the epidemiology , ecology , and vector diversity of these tick-borne pathogens . In this study , we aimed to investigate broad-range bacterial and protozoan vector-borne pathogens by PCR/RT-PCR and sequencing , those ticks infesting humans in the Corum province . Spotted fever group rickettsiae and lymphangitis-associated rickettsiae , Borrelia afzelii , Anaplasma spp . , Ehrlichia spp . were detected . Babesia microti was detected in Hyalomma marginatum infesting humans . Interestingly zoonotic pathogens like Babesia ovis , Babesia occultans , Theileria spp , Hepatozoon felis , Hepatozoon canis , and Hemolivia mauritanica were also detected , showing the role of ticks for diseases also of veterinary importance . This study provides important data for understanding the epidemiology of tick-borne pathogens and it is hoped that these results will challenge clinicians and veterinarians to unify their efforts in the management of TBDs . | [
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"... | 2018 | Bacterial and protozoal pathogens found in ticks collected from humans in Corum province of Turkey |
Erythema nodosum leprosum ( ENL ) is a common immune-mediated complication of lepromatous ( LL ) and borderline lepromatous ( BL ) leprosy . Most patients experience chronic or multiple acute ENL over many years during an economically active period of their lives . Understanding the economic burden of ENL is essential to provide effective patient support , yet this area has not been investigated . Ninety-one patients with LL or BL leprosy attending a leprosy hospital in Purulia district of West Bengal , India , were interviewed using a structured questionnaire . Cases ( n = 53 ) were identified as those who had one or more episodes of ENL within the last 3 years . Controls ( n = 38 ) had LL or BL leprosy but no history of ENL . Data were collected on household income , direct and indirect costs , and coping strategies . The total household cost was Rs 1543 per month or 27 . 9% ( IQR 13 . 2-52 . 6 ) of monthly household income for cases , and Rs 237 per month or 4 . 9% ( IQR 1 . 7-13 . 4 ) of monthly household income for controls . Indirect costs accounted for 65% of total household costs for cases . Direct costs accounted for the remaining 35% of household costs , and resulted almost entirely from treatment-seeking in the private sector . Total household costs exceeded 40% of household income for 37 . 7% of cases ( n = 20 ) and 2 . 6% of controls ( n = 1 ) [1 USD = 59 INR] . Households affected by ENL face significant economic burden and are at risk of being pushed further into poverty . Health policy should acknowledge the importance of private sector provision and the significant contribution to total household costs of lost productivity ( indirect cost ) . Further work is needed to explore this area and identify solutions .
Leprosy is a disease of poverty , affecting poor and marginalised communities in tropical countries throughout the world . [1] More than 200 , 000 new cases of leprosy are registered worldwide annually , with 60% in India . Leprosy reactions occur in up to 50% of patients with multibacillary leprosy and cause nerve damage and disability . [2 , 3] Two types of reaction can occur: type 1 reaction ( T1R ) and type 2 reaction ( T2R ) or erythema nodosum leprosum ( ENL ) . At the end of the first quarter of 2014 there were 180 , 618 leprosy patients on record for treatment globally ( estimated prevalence rate 0 . 32 per 10 , 000 population ) [4] however this does not include those patients who have completed treatment and are at risk of developing reactions . The lepromatous ( LL ) and borderline lepromatous ( BL ) forms of leprosy are characterised by low cell-mediated immunity to the causative organism Mycobacterium leprae . There is uncontrolled proliferation and dissemination of leprosy bacilli resulting in extensive infiltration of the skin and other organs . Erythema nodosum leprosum ( ENL ) is an immune-mediated complication affecting patients with LL and BL leprosy . Patients with ENL have widespread crops of tender , erythematous swellings in the skin , and other organs are often inflamed . The physical impact of ENL is significant—patients suffer repeated episodes of ill health over many years and many develop impaired organ function , deformity and disability . [5] Patients need prolonged courses of high-dose steroids or thalidomide to control inflammation , and this can cause adverse effects including steroid dependency . [6] Families with ENL are affected by out of pocket expenditure for treatment-seeking ( direct cost ) and loss of income resulting from reduced productivity ( earning potential ) of household members ( indirect cost ) . There are some data on the economic burden of malaria and visceral leishmaniasis on households in low-income countries , [7–11] however none for leprosy . Understanding the economic burden of ENL is essential to provide effective patient support and to inform researchers , healthcare professionals , health policy makers and managers of control programmes . Qualitative research has provided some insight into the social and economic consequences of leprosy; however quantitative data on the economic impact of leprosy and ENL do not exist . The Social Welfare Department of the Government of Delhi , India , operate financial assistance schemes for the families of patients affected by leprosy . This financial support is helpful for eligible families , however the schemes are based on a limited understanding of the costs affecting households , have no impact on preventing or reducing these costs , are neither suitable nor efficient long-term solutions , and may not necessarily be available to the families who are most in need of support ( many do not have bank accounts ) . Socio-economic rehabilitation ( SER ) and microfinance programs can provide much needed financial support for leprosy patients and their families . [12 , 13] The International Federation of Anti-Leprosy Associations ( ILEP ) has produced guidelines which identify the broad principles of effective SER programmes , however the design of such programmes would greatly benefit from detailed quantitative data on household costs . Understanding the specific problems faced by families affected by ENL and how costs arise will allow the development of solutions that are appropriate to the needs of the target population . Understanding the magnitude of economic burden on households will predict the degree of financial support required by families and ensure that interventions for ENL are socially viable ( any costs to the patient are financially acceptable ) . In this study we determined the economic impact of having a family member with erythema nodosum leprosum ( ENL ) on households in rural India . We estimated direct and indirect costs associated with ENL and expressed these in relation to household income , and investigated the coping strategies used by households . We hypothesised that patients with lepromatous ( LL ) and borderline lepromatous ( BL ) leprosy complicated by ENL would incur greater household costs than those without ENL .
Ethics approval was obtained from the London School of Hygiene and Tropical Medicine ( LSHTM ) MSc Research Ethics Committee ( ref 012–275 ) and from the ethics committee of The Leprosy Mission ( TLM ) Trust India , for this study to be carried out in TLM hospital Purulia , West Bengal , India . Informed written consent was provided by all adult patients or the parent or guardian if the patient was a child . The perspective of this study was microeconomic ( households ) . We aimed to determine the economic impact of ENL on families . We assessed losses associated with the market economy only , providing estimates of direct costs and indirect costs ( lost market production ) . These costs were combined to provide an estimate of the net impact of ENL on households’ opportunities to consume non-health goods and services ( the primary outcome of interest ) . An appropriate counterfactual ( control patients with LL/BL leprosy but no history of ENL ) was used for comparison of cost data . The broader effects of ill-health on economic welfare , including reductions in health status and leisure time , were not considered in this analysis . The cost to the rest of the society of ENL was not explored . All patients with a diagnosis of lepromatous ( LL ) or borderline lepromatous ( BL ) leprosy were eligible for inclusion in the study . Cases had LL or BL leprosy and one or more episodes of ENL within the last 3 years . Controls had LL or BL leprosy but no history of ENL . Consecutive patients attending the hospital outpatient department and those admitted to the hospital during the study period ( 19th June—27th July 2013 ) were recruited . Published case definitions for ENL were used . [5] Erythema nodosum leprosum ( ENL ) was defined as an eruption of tender erythematous skin nodules; fever and evidence of other organ inflammation may be present . Single acute ENL was one episode lasting less than six months with no recurrence of ENL while receiving prednisolone , no increase in severity requiring an increased steroid dose and no recurrence after the prednisolone has stopped . Multiple acute ENL was more than one episode with the same characteristics as acute single ENL . Chronic ENL was an episode lasting more than six months and could include single and multiple episodes . Data were obtained using a structured questionnaire , administered to the patient or to the parent or guardian if the patient was a child . All interviews were conducted in a private and informal setting in the hospital outpatient department , by the first author ( DC ) . Translation services were provided by an existing member of staff at TLM Purulia ( BM ) . Patients were approached in the hospital waiting room , in between appointments with different members of hospital staff , and were recruited and interviewed with minimal delay . The objectives of the study were outlined to patients before starting the questionnaire , and it was explained that there would be no financial reward or adjustment to treatment costs based on the responses provided during the interview . Patients were generally enthusiastic to participate and help with the study . The questionnaire was piloted on 8 eligible patients ( 5 cases , 3 controls ) allowing improvements to be made to questionnaire content . The results from the pilot interviews were not included in the analysis . Patients were interviewed once only . The questionnaire collected data on clinical history , household income and socioeconomic status , treatment-seeking behaviour , direct and indirect costs , and coping strategies . Direct and indirect costs were specific to leprosy; the presence of other comorbidities and their associated costs was not ascertained . Data were gathered on the number of episodes and type ( chronicity ) of ENL . [5] The clinical history provided the duration of ill-health ( time since diagnosis of leprosy for controls , time since first episode of ENL for cases ) and in doing so defined the time dimension or recall window for which cost and income data were captured . These data were collected retrospectively for the full duration of ill-health , up to a maximum of 3 years . Total costs for the whole recall window were converted to an average cost per month and presented as a proportion of monthly household income . This allowed costs to be compared between patients with varying duration of ill-health . The occupations of all economically active household members and their daily cash earnings were reported by the patient , including regular cash income from family members not living at home . We defined the household as all persons living in the housing unit and those persons not living at home but who contribute to the generation and/or consumption of household wealth . All agricultural output was considered to contribute to income regardless of whether the products were sold or consumed within the household . All government income support ( pension and disability allowance ) was included in estimates . Socioeconomic status was assessed using place of residence ( urban or rural ) and household asset data . The household assets were the same as those recorded in the National Family Health Survey ( NFHS-3 ) India , 2005–2006 . [14] Data were gathered on treatment-seeking behaviour during the period of ill-health . Medical and non-medical costs were recorded per visit , for all healthcare providers visited . Medical direct costs included provider fees ( for consultations and hospital admissions ) and payment for investigations , medicines and other treatments . Multidrug therapy ( MDT ) was available to patients free of charge from all health providers , however not all providers received a free supply of MDT . The cost to these providers of procuring MDT was not determined . Non-medical direct costs included transport costs to and from the health facilities and any additional expenditure on food and other non-medical goods or services . Invoices were available electronically for all inpatient and outpatient episodes at TLM Purulia , and provided an exact breakdown of the medical costs ( as listed above ) for each episode . Invoices also included the total medical cost per episode ( unadjusted cost ) and the amount the patient could afford to pay ( adjusted cost ) . The difference between these two values was the subsidy provided ( cost incurred ) by the hospital for that episode . Cost data presented in this paper are adjusted unless otherwise specified , as these represent the actual costs incurred by the family ( the outcome of interest ) . Unadjusted costs are included for completeness and these represent the amount the family should have paid if they were able to ( without subsidised treatment costs at TLM Purulia ) . We defined catastrophic health expenditure as adjusted direct costs in excess of 40% of total household income . Indirect costs refer to the reduction in household productivity resulting from the interruption of normal or preferred activities of household members . We used an output-based approach to value the time diverted as a result of ill-health in all persons contributing to the economic productivity or wealth of the household . For those with formally-paid wages , the indirect cost was the daily earnings multiplied by the number of days lost . The contribution of individual household members to agricultural output was not determined . Study participants were asked to report the overall loss of income from agriculture during the period of ill-health . When provided with information on the reduction in agricultural output local market prices were used to determine the monetary value . The market values reported by patients were remarkably consistent . The use of an output-based approach was appropriate in this setting , allowing us to account for the significant fluctuations in work intensity that occurred during the year and the coping strategies used to minimise productivity losses , for example recruitment of additional labour to minimise farming losses . The cost of hiring external labour was valued using average local wage rates for males and females . Coping strategies used to generate money , for example selling assets or taking a loan , were identified but considered separately and not factored into cost data . Time diverted by those engaged in unpaid production ( children , the elderly , jobseekers and women engaged in domestic work ) was considered valueless for the purpose of this analysis and excluded from estimates of indirect costs . Data were analysed using Minitab 17 . Household income and cost data are reported using medians and interquartile ranges . The Mann-Whitney ( Wilcoxon ranksum ) test was used to compare differences in costs between cases and controls . 1 USD = 59 INR; Bloomberg 19 June 2013 .
Ninety-one patients ( 53 cases and 38 controls ) were identified and all agreed to be interviewed for the study . There were no drop outs at any stage . All patients had lepromatous ( n = 53 ) or borderline lepromatous ( n = 38 ) leprosy . Clinical details are shown in table 1 . The median time since the first episode of ENL in cases was 24 months ( IQR 14–36 ) . Seventy-seven percent of cases ( n = 41 ) had chronic ENL and 23% ( n = 12 ) experienced multiple acute episodes . Cases had 2 hospital admissions ( IQR 1–3 ) each lasting an average 35 days ( IQR 14–130 ) whilst controls had 1 admission ( IQR 1–1 ) . Monthly household income was Rs 6000 ( IQR 3600–8100 ) and 6000 ( IQR 3895–8370 ) for cases and controls respectively . Household size ranged from 2 to 18 persons ( median 5 persons , IQR 4–7 ) and was similar for cases and controls . Daily manual labour was the major source of income and generated a median Rs 5100 ( IQR 3525–7925 ) per month . Small-scale agriculture was a source of income in 37 households , generating a median Rs 750 ( IQR 540–1600 ) per month . Three households received a regular cash income from a family member not living at home , and three received government support ( disability allowance or pension ) . Most patients ( 92% ) lived in a small village . There was no correlation between household assets and household income . The total direct cost per month ( adjusted ) was Rs 465 ( IQR 217–738 ) for cases and Rs 175 ( IQR 92–386 ) for controls , or 7 . 5% and 3 . 7% of monthly household income for cases and controls respectively . The monthly total direct cost , as a percentage of household income , was 3 . 2% higher in cases than controls ( Mann-Whitney test; 95% CI 1 . 2–5 . 8 , W = 2814 . 5 , p = 0 . 0025 ) . The proportion of households facing catastrophic health expenditure was 11% ( n = 6 ) for cases and 0% ( n = 0 ) for controls . The unadjusted direct cost was Rs 587 ( IQR 235–1651 ) for cases and Rs 204 ( IQR 104–248 ) for controls , or 10 . 1% and 3 . 7% of monthly household income for cases and controls respectively . TLM Purulia incurred an average cost per patient of Rs 5026 ( IQR 180–27079 ) for cases ( n = 31 ) and Rs 200 ( IQR 45–965 ) for controls ( n = 17 ) by providing subsidised service to patients ( see Fig . 1 ) . This illustrates the extent to which families were unable to cope with the increased treatment costs associated with ENL . Treatment-seeking in the private sector caused 96% and 99% of total direct costs for cases and controls respectively . Outpatient services at private health providers other than TLM Purulia were sought by 64% of cases ( n = 34 ) and 47% of controls ( n = 18 ) . Traditional healers were consulted by 43% of cases ( n = 23 ) and 29% of controls ( n = 11 ) . The median distance from household to traditional healer was 10km ( IQR 2–30 ) , compared with 45km ( IQR 25–78 ) for all other private health providers at which outpatient services were consumed . In 41% of patients visiting a traditional healer ( n = 14 ) transport was by foot or bicycle . Medical and non-medical costs for inpatient and outpatient episodes at TLM Purulia are shown below in table 2 . Invoices containing these data were not available for episodes with other healthcare providers . The market economy losses resulting from reduced household productivity due to ENL were significant . The median reduction in monthly household income was Rs 969 ( IQR 227–1873 ) in cases and Rs 60 ( IQR 9–186 ) in controls , or 18 . 2% ( IQR 5 . 3–33 . 7 ) and 1 . 4% ( IQR 0 . 1–2 . 6 ) of monthly household income in cases and controls respectively . On average the reduction in monthly household income was greater in cases than controls by 13 . 3% ( Mann-Whitney test; 95% CI 6 . 0–21 . 7 , W = 3175 , p<0 . 0001 ) . The causes of reduced household productivity included physical illness ( unwell at home ) , hospital admission and attendance at outpatient appointments . Physical illness resulted in a loss of productivity in 94% of cases ( n = 50 ) and accounted for a median 55% ( IQR 27–80 ) of the total indirect cost incurred by these households . Hospital admission and attendance at outpatient appointments caused 33% ( IQR 12–51 ) and 15% ( IQR 5–60 ) of total indirect costs respectively . Seventy-six percent of cases ( n = 38 ) received informal care from an average of 1 . 2 household members . The time diverted by providing care for cases caused a median loss of income of Rs 725 ( IQR 233–1500 ) per household over the duration of ill-health . These data show that ENL affects the productivity of patients and other household members and has a significant impact on household earning potential . Having a family member with ENL impacted on household consumption opportunities . The total household cost per month was Rs 1543 ( IQR 681–3175 ) for cases and Rs 237 ( IQR 124–656 ) for controls , or 27 . 9% ( IQR 13 . 2–52 . 6 ) of monthly household income for cases and 4 . 9% ( IQR 1 . 7–13 . 4 ) for controls . On average the total household cost per month , as a percentage of household income , was 20 . 8% higher in cases compared with controls ( Mann-Whitney test; 95% CI 12 . 6–29 . 4 , W = 3143 , p<0 . 0001 ) . Fig . 2 displays the total household cost for cases and controls . The proportion of households with total household costs in excess of 40% monthly household income was 37 . 7% ( n = 20 ) for cases and 2 . 6% ( n = 1 ) for controls . Two cases incurred total household costs per month in excess of household income ( cost/income >1 ) . Total household costs consisted of 65% indirect costs and 35% direct costs in cases compared with 21% indirect costs and 69% direct costs in controls ( see table 3 ) . All patients used cash savings on one or more occasions to pay for treatment . Other coping strategies included selling assets , borrowing money and being gifted money; these were used in 69 . 8% of cases ( n = 37 ) and 55 . 3% of controls ( n = 21 ) . Selling assets to generate cash was a strategy used by 32% of cases ( n = 17 ) and 16% of controls ( n = 6 ) . Cases sold assets to a median value of Rs 6000 ( IQR 2850–17000 ) compared with Rs 1850 ( IQR 825–3125 ) for controls . Forty-two percent of cases ( n = 22 ) took a loan of median value Rs 5000 ( IQR 2000–10250 ) and incurred costs ( interest on the loan ) ranging from Rs 0 to 11700 . Thirty-two percent of controls ( n = 12 ) took a loan of median value Rs 1000 ( IQR 850–4250 ) and incurred costs ranging from Rs 0 to 18000 .
Capturing an appropriate time dimension for the illness was difficult . Our aim was to assess the economic impact of ENL , as a cause of chronic ill-health , on households over many years . We used 3 years as the maximum time dimension for which cost data were collected , acknowledging the risk of recall bias . The accuracy of estimates was aided by the availability of detailed clinical information and cost data from the hospital records at TLM Purulia . TLM Purulia is a specialist service provider , therefore referral bias may have produced an atypical study population . The high prevalence of ENL overall ( 58% ) and particularly among those with BL leprosy ( 47% ) is unlikely to be representative of the target population . It is likely that patients with severe ENL were overrepresented in this population and the estimates of the economic burden presented in this study are also therefore not representative . However it is possible that patients recruited from this hospital had a higher than average household income ( transport costs may prevent poorer patients from gaining access to the hospital ) thus lessening the perceived economic impact of ENL . Further work is needed to evaluate the economic consequences of leprosy and to validate these findings on a larger scale . Future studies could eliminate selection bias by recruiting patients from the community . Estimates of household costs could be improved if cost data were collected prospectively , although data collection would take a long time and the process may be less efficient with more drop-outs and missing data . In future studies it would be useful to collect data on other comorbidities and their associated costs and in particular to identify any health conditions that might complicate the treatment of leprosy/ENL . Catastrophic health expenditure is common in low- and middle-income countries , [17] and the high rate of catastrophic spending observed in households affected by ENL in this setting was not surprising . Leprosy is associated with a high level of poverty , and patients with ENL face the double insult of high levels of ( predominantly private sector ) healthcare use and poor access to financial risk protection ( prepayment ) mechanisms . Increasing the availability of prepayment mechanisms is needed to reduce the level of out-of-pocket expenditure and protect households from catastrophic payments . [18 , 19] Indirect costs account for the majority of total household costs for ENL . Many households in rural India rely heavily on manual labour ( agriculture ) to provide cash income on a day to day basis and these households are vulnerable to significant reductions in income resulting from ill-health of household members . Thus strategies that aim to develop more robust and sustainable sources of income and protect against income loss would be an important component of reducing household costs . These findings are relevant to many people . Doctors should advise patients to present early to an established leprosy centre and avoid seeking costly treatments with other private providers . Local providers of alternative and complementary therapies should be engaged and encouraged to refer patients for specialist treatment promptly . Other healthcare professionals including social workers should be aware of the economic burden of ENL so they can fully understand the problems faced by patients and provide appropriate support . These findings are important to academics across a range of disciplines from health economics to clinical medicine . This study paves the way for research to better understand how leprosy impacts on the consumption choices made by families over time . Future cost-effectiveness analyses of interventions for leprosy and ENL that make use of these data on household costs will usefully contribute to discussions on resource allocation . Funding bodies should be made aware of the burden of ENL and the importance of conducting research to better understand this neglected complication of leprosy . [20] Charities and donors need to understand the magnitude of economic burden so that leprosy programmes can be adequately funded . Specifically if control strategies and interventions for leprosy are to be socially viable , it is essential that policy makers acknowledge the significant contributions to household costs of private sector provision and lost productivity . Since household costs of ENL are very high , interventions to prevent or reduce the duration of ENL could be very cost-effective . | Erythema nodosum leprosum ( ENL ) is a common complication of leprosy and an important cause of nerve damage and disability . In most cases , ENL causes chronic or recurrent episodes of ill-health over many years . In this study , we show that having a family member affected by ENL places considerable financial burden on households in rural India . Household costs resulted predominantly from the impact of ENL on the productivity ( ability to earn money ) of household members . Out of pocket expenditure on treatment-seeking in the private sector accounted for the remaining costs . Leprosy affects poor and marginalised communities in low- and middle-income countries across the world; households affected by ENL are at risk of being pushed further into poverty . The findings of this study support the need to better understand ENL and develop improved strategies for the prevention and management of ENL . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Household Costs of Leprosy Reactions (ENL) in Rural India |
One hallmark of pathogenic yersiniae is their ability to rapidly adjust their life-style and pathogenesis upon host entry . In order to capture the range , magnitude and complexity of the underlying gene control mechanisms we used comparative RNA-seq-based transcriptomic profiling of the enteric pathogen Y . pseudotuberculosis under environmental and infection-relevant conditions . We identified 1151 individual transcription start sites , multiple riboswitch-like RNA elements , and a global set of antisense RNAs and previously unrecognized trans-acting RNAs . Taking advantage of these data , we revealed a temperature-induced and growth phase-dependent reprogramming of a large set of catabolic/energy production genes and uncovered the existence of a thermo-regulated ‘acetate switch’ , which appear to prime the bacteria for growth in the digestive tract . To elucidate the regulatory architecture linking nutritional status to virulence we also refined the CRP regulon . We identified a massive remodelling of the CRP-controlled network in response to temperature and discovered CRP as a transcriptional master regulator of numerous conserved and newly identified non-coding RNAs which participate in this process . This finding highlights a novel level of complexity of the regulatory network in which the concerted action of transcriptional regulators and multiple non-coding RNAs under control of CRP adjusts the control of Yersinia fitness and virulence to the requirements of their environmental and virulent life-styles .
Pathogenic bacteria have evolved a wide range of regulatory mechanisms to adjust their virulence strategies and biological fitness in response to fluctuating environmental conditions during the infection process . Expression of virulence-associated determinants is strictly and coordinately controlled by a set of transcriptional regulators and complex signal transduction cascades are responsible for sensing and integrating the environmental information into the virulence regulons [1–4] . In addition to the transcriptional control there has been increasing recognition that post-transcriptional mechanisms are used to fine-tune the management of virulence determinants and achieve a rapid and more refined control of the bacterial response [5–9] . Post-transcriptional regulation can be accomplished by ( i ) RNA thermosensors and riboswitches , i . e . regulatory elements of an mRNA , that undergo a structural alteration upon a thermal shift or binding of a small molecule [10–14] , and ( ii ) antisense and trans-encoded small regulatory RNAs of which a subset bind to their target mRNAs by the help of the RNA chaperone Hfq [15] . The regulatory outcomes generally include alterations of the translation initiation efficiency or mRNA stability . In recent years a plethora of sensory and regulatory RNA elements has been identified in many human pathogens , including the genus Yersinia [16–20] , and there is increasing evidence that they serve as crucial players in regulatory circuits adjusting cellular physiology , metabolism and virulence [6 , 21] . Yersinia pseudotuberculosis is a common foodborne pathogen that infects wild and domestic animals , as well as humans [22] . It is a very closely related ancestor of Y . pestis , the causative agent of plague , which has evolved from Y . pseudotuberculosis about 1 , 500 to 20 , 000 years ago [23 , 24] . Although both pathogens are genetically very similar ( >97% nucleotide identity over 75% of the protein-coding genes ) , they differ significantly in their pathogenesis and exhibit very different infection and disease patterns [25 , 26] . In contrast to Y . pestis , Y . pseudotuberculosis causes a range of mild gut-associated diseases such as enteritis , watery diarrhea and mesenterial lymphadenitis , called yersiniosis . The intestinal diseases are usually self-limiting , but in rare cases Y . pseudotuberculosis can also trigger autoimmune responses [27 , 28] . Unlike Y . pestis , which typically colonizes fleas , Y . pseudotuberculosis is able to survive for considerable time periods in the soil and other environmental reservoirs [29] . Based on the distinct phases of their life-style , it is not surprising that sudden temperature and nutrient changes experienced upon entry from external/vector reservoirs into a warm-blooded host are the most important signals for Y . pestis and Y . pseudotuberculosis to trigger virulence gene expression and adjust their host survival program [30] . Thermal/nutrient shifts influence expression of multiple virulence-associated processes of Yersinia , e . g . host cell interaction , intracellular persistence , host immune defense , motility , and host-adapted metabolism . Recent work on individual virulence factors has illustrated that this is achieved by a joint interplay of transcriptional and post-transcriptional mechanisms , including cis-acting RNA elements and non-coding RNAs , that allow a more refined management of the bacterial response [6] . In this study , we used transcriptional profiling by RNA-seq to determine the transcriptional landscape of Y . pseudotuberculosis YPIII , identify thermal and nutrient control mechanisms on a global level , and elucidate the magnitude and regulatory architecture of the CRP regulon linking nutritional status to virulence . The CRP protein , a crucial global transcriptional regulator that interacts with cAMP and controls a plethora of genes in Enterobacteriaceae in response to the supply of glucose or other efficiently utilizable sugars [31 , 32] , was previously shown to play a crucial role for the pathogenicity of Yersinia [33–36] . CRP modulates a large subset of virulence-relevant genes [34 , 37–39] , and comparative metabolome and fluxome studies further revealed that absence of CRP strongly perturbs the carbon core metabolism at the level of the pyruvate-tricarboxylic acid cycle ( TCA ) node [34 , 39] . Transcriptional profiling of Y . pseudotuberculosis in this study further revealed a comprehensive remodelling of the CRP-controlled network in response to temperature and uncovered CRP as transcriptional master regulator of regulatory RNAs .
Y . pseudotuberculosis strain YPIII is a widely distributed virulent isolate which played an important role for the analysis of Yersinia infection [34 , 35 , 40–45] . Many of its virulence factors have been characterized in detail and our knowledge of virulence-relevant gene regulation and networks was mainly derived from this strain . In order to obtain a comprehensive image of the transcriptome , we used rRNA-depleted total RNA of YPIII grown to exponential or stationary phase at 25°C or 37°C resembling alterations in temperatures and nutrient limitations encountered in the different life-styles . To produce a detailed map and identify active transcriptional start sites ( TSSs ) at a single nucleotide resolution , we employed a global differential RNA-seq approach by comparing sequence reads from different strand-specific barcoded cDNA libraries [46] . Libraries denoted with +TAP were generated from RNA pools treated with tobacco acid pyrophosphatase ( TAP ) to allow 5’ adapter ligation to primary transcripts , and-TAP labelled libraries were generated from RNA pools , which were not treated with TAP ( S1 Dataset; S1 Fig ) , similar to what has been described previously [47 , 48] . From each library between 2 . 3–12 . 9 million cDNA reads were generated and mapped to the YPIII genome sequence ( NC_010465 ) and the pYV virulence plasmid ( NC_006153 ) . Our RNA-seq approach confirmed earlier findings that expression of the virulence plasmid pYV-encoded genes is induced at 37°C [30 , 45 , 49–52] , relative to the expression of the chromosome . In this context , enhanced expression of pYV at 37°C is pronounced for stationary phase cultures ( Fig . 1A; S1 Dataset ) . In addition , we observed that the ratio between the intergenic region ( IGR ) and mRNA reads was considerably higher during stationary phase compared to exponential phase ( Fig . 1B ) , indicating a pronounced expression of trans-encoded regulatory RNAs under nutrient-deprived growth conditions . Along with this observation we found that the intracellular level of Hfq , an RNA chaperone , which is often involved in the interaction of trans-encoded sRNAs with their target mRNAs [53] , is substantially increased under nutrient starvation ( Fig . 1C ) . This differs from Escherichia coli in which the abundance of Hfq varies only slightly and is maximal during log phase [54] . Annotation and comparison of the 5’-ends in the ( + ) vs ( - ) TAP cDNA libraries revealed a redistribution of the genome coverage profile towards an elevated sharp-edged 5’ flank , as illustrated for rovA , hfq , katY and crp ( Fig . 2A ) . By a bioinformatic approach and other criteria ( see Materials and Methods ) , we were able to identify 1151 individual TSSs in proximity to annotated open reading frames ( ORFs ) under all tested growth conditions , out of which 24 were mapped on the pYV virulence plasmid ( S2 Dataset ) . In total , for 815 ORFs a single TSS was mapped , while 155 ORFs harbor at least one alternative TSS . The highest number of alternative TSSs was identified for rpoD and gapA for which transcription occurs from at least 5 different start sites ( S2 Dataset ) . To validate the TSSs , we compared predicted TSSs of the RNA-seq analysis with TSSs previously published for genes/operons of Y . pseudotuberculosis or close relatives and found that the vast majority of the identified TSSs start within 2 nt or are identical ( S2 Dataset ) . For example , the identified TSS of katY was identical with the katY TSS identified in Y . pestis [55] , and the two proximal TSSs of rovA matched perfectly with published start sites [56] ( Fig . 2A; S2 Dataset ) . Interestingly , an additional rovA TSS was mapped in our approach , which is in perfect agreement with a third more proximal TSS identified within the rovA regulatory upstream region in Y . enterocolitica [57] . For hfq three individual TSSs were identified ( Fig . 2A; S2 Dataset ) . Two TSSs reside within the coding region of the upstream gene miaA , similar to hfq from E . coli [58] . The most proximal hfq TSS at position -90 relative to the annotated hfq translational start site is in perfect agreement with a TSS recently determined for hfq of Y . pestis and Y . pseudotuberculosis [59] , yet the TSS with the highest accumulation of cDNA reads under all tested conditions is the most distant one at position -509 ( Fig . 2A; S2 Dataset ) . We further validated newly identified Y . pseudotuberculosis TSSs by 5’ RACE and found that 14 of the identified 16 TSSs in the RNA-seq approach matched with high accuracy ( S2 Dataset ) . To further strengthen our genome-wide analysis of TSSs , we investigated the nucleotide preference from position -3 to +4 surrounding the identified Y . pseudotuberculosis TSSs and generated a sequence logo using the WebLogo software ( Fig . 2B ) [60] . Our analysis revealed a significant dinucleotide preference at positions -1 and +1 while adjacent positions did not show any nucleotide preference . Similar to what has previously been reported for E . coli , Salmonella enterica serovar Typhimurium and Klebsiella pneumoniae [61 , 62] , the nucleotide at position -1 is dominated by a pyrimidine base ( 37 . 01% C , 35 . 01% T ) , and transcription is preferentially initiated at a purine base ( position +1; 42 . 75% A , 35 . 27% G ) ( Fig . 2B ) . We further used our global TSS map to determine a motif for the -10 and -35 regions of Y . pseudotuberculosis promoters ( Fig . 2C ) . To compute conserved sequence motifs , we performed de-novo motif discovery within the -10 region ( position -15 to -3 ) and the -35 region ( position -45 to -25 ) using MEME [63] . The detected conserved sequence motifs in the -35 ( TTGc/a ) and -10 promotor region ( TAtaaT ) are highly similar to those identified in other related Enterobacteriaceae [61 , 62] . Our global TSS annotation further allowed us to define and analyze the untranslated regions ( 5’-UTRs ) from the TSS to the start codon of the immediate downstream genes for putative cis-regulatory RNA elements , i . e . RNA riboswitches , known to control the translational efficiency of messengers [10–13] . Inspection of the 5’-UTRs of all 1151 defined transcriptional units of Y . pseudotuberculosis YPIII showed that the majority of 5’-UTRs is 20–40 nt in length ( Fig . 2B; S2 Dataset ) similar to other bacteria [64–66] . Notably , 155 genes are transcribed as mRNAs with 5’-UTRs longer than 200 nt , and a subset of 12 mRNAs had 5’-UTRs longer than 500 nt ( S2 Dataset ) . Computational analysis using RibEx riboswitch explorer [67] predicted 4 known riboswitch-like elements ( RLEs ) among the long 5’-UTRs , e . g . cobalamin and FMN riboswitches , the yybP/ykoY element and the threonine operon leader ( S2 Dataset ) . Moreover , a conserved RNA motif was predicted within the 217 nt 5’-UTR of moaA , encoding the molybdenum cofactor ( Moco ) biosynthesis protein A . Studies in E . coli demonstrated that the highly conserved RNA motif functions as Moco riboswitch [68] . RibEx predicted 17 additional RLEs; the respective candidates are mainly involved in metabolism and gene expression ( S2 Dataset ) . For instance , the 5’-UTR of the corA mRNA of Y . pseudotuberculosis , encoding a magnesium/nickel/cobalt transporter ( YPK_4002 ) , harbors multiple RLEs . It likely represents a cation-responsive riboswitch similar to mgtA in Salmonella , encoding an Mg2+ transporter which is important for replication within macrophages [69 , 70] . We further report a small number of leaderless mRNAs with a 5’-UTR <10 nt ( S2 Dataset ) . All 14 annotated leaderless mRNAs lack a classical ribosome-binding site and use an AUG as translation start codon , which was shown to be essential for stable ribosome-binding to leaderless transcripts [71] . Moreover , we identified 24 TSSs downstream of the annotated translational start site , indicating incorrect translational start site annotation . We compared the predicted sites with the start position of equivalent genes in other sequenced Y . pseudotuberculosis strains , including IP32953 and IP31758 , and Y . pestis CO92 ( S2 Dataset ) . For most of these genes , the annotated translational start site is located downstream of the translational start site given for YPIII in at least one of the other Yersinia genomes . One prominent example includes the virulence plasmid-encoded gene pYV0047 for the effector protein YopM . The annotation of the orthologous yopM gene on the PB1/+ virulence plasmid indicates a start codon that is located several nucleotides downstream of the TSS annotated for pYV ( S2 Dataset ) . A putative ribosomal binding site ( 5’-AGGCA-3’ ) is located 6 nt upstream of the yopM translational start site annotated for the PB1/+ virulence plasmid , but it is lacking upstream of the translational start site annotated on pYV . Several trans-encoded small RNAs ( sRNAs ) of different Yersinia strains have been recently discovered by deep sequencing of cDNA libraries enriched for small transcripts [16–20] . Our analysis of the Y . pseudotuberculosis YPIII transcriptome now allows us to investigate expression of sRNAs in relation to other transcripts under the different growth conditions . For identification of candidate regulatory RNAs a conservative strategy was used which defines non-coding RNAs as generally small transcripts ( >40 nt and <500 nt ) expressed from intergenic regions or the antisense strand of defined mRNAs . Besides the stable tRNAs and rRNAs also the predicted transfer messenger RNA ( tmRNA , SsrA ) , RNase P ( M1 RNA ) and signal recognition particle ( SRP RNA ) were found to be expressed ( S3 Dataset ) . We further identified 78 putative trans-encoded RNAs ( S3 Dataset ) and 80 putative antisense RNAs ( S3 Dataset ) . All trans-encoded sRNA candidates are encoded on the YPIII chromosome . Conspicuously , 19 putative antisense RNAs are encoded on the virulence plasmid , a rather dense population compared to the low number of antisense RNAs identified for the YPIII chromosome . A comparison between trans-encoded sRNAs identified in this and previous studies [16–20] revealed a surprisingly low overlap of 38 sRNAs . We identified 42 new putative trans-encoded sRNAs for Y . pseudotuberculosis YPIII ( S3 Dataset ) , whereas 143 previously described sRNAs were not detectable . This discrepancy is likely to be caused by low expression under the tested growth conditions , strain differences , RNA/library preparation protocols as well as bioinformatic criteria and pipelines applied for the RNA-seq approaches . Notably , several of the previously reported sRNAs were also classified as 5’-UTRs in our study , e . g . sR028 , sR041 , sR050 , sR066 and sR070 which were identified in very close proximity to adjacent ORFs [20] ( S2 Dataset ) . Potentially , those putative sRNAs are expressed as a premature transcript and result from processing of the 5’-UTR . This might account at least for sR066 , for which a distinct short transcript was detected by Northern blotting [20] . In total , 36 of the non-coding RNA candidates were confirmed by Northern blotting ( Figs . 3A , S2 ) . This included 12 conserved sRNAs previously identified in other Enterobacteriaceae , 2 non-validated sRNAs ( Ysr100 and Ysr103 ) and 1 validated sRNA ( Ysr164 ) also identified in sRNA libraries of Y . pestis Kim6+ and Y . pseudotuberculosis IP32953 [16 , 18] , 11 newly identified sRNAs and 10 antisense RNAs . Some of the validated antisense RNAs were identified as trans-encoded RNAs in Y . pseudotuberculosis IP32953 ( Ysr93 , Ysr15 , Ysr114 ) [16] . Ysr15 and Ysr93 were grouped as antisense RNAs in this study , as they are encoded antisense to genes of hypothetical proteins of YPIII , which are not annotated in the IP32953 genome , and Ysr114 was found to largely overlap with the 3’-coding region of trxB ( YPK_2687 ) . How these antisense RNAs exert their function remains to be shown . For instance , the non-coding RNA McaS of E . coli , which regulates motility and biofilm formation is trans-acting , although it is encoded antisense to an annotated ORF [72] . To improve our understanding of the complexity of the regulatory networks controlling the multiple life-styles of yersiniae , we used our RNA-seq approach to identify coordinately regulated genes in response to temperature and nutrient limitation . Comparative RNA-seq analysis using DESeq [73] from triplicate experiments revealed a total of 324 ORFs of YPIII that were differentially regulated by at least 4-fold ( p-value ≤0 . 05 ) in response to temperature ( Fig . 3B; S4 Dataset ) . Growth phase had an even higher impact on global gene expression . Nearly 25% of the genome ( 983 genes ) was differentially expressed ( Fig . 3B; S4 Dataset ) . In accordance with previous data [30 , 45 , 52 , 74–76] , many of the temperature-regulated genes encode well-known ( i ) regulators , effectors or components of the T3SS machinery on virulence plasmid pYV ( of note , the components displayed a wide range of thermoregulation , of which the lcrGVHyopBD , ypkA , yopE and yopH transcripts were highly abundant even at 25°C ) , ( ii ) classical pathogenicity factors such as the adhesins AilA , PsaA , InvA , and the CNFY toxin , or ( iii ) other virulence-associated traits , e . g . urease , iron sequestration systems , motility/chemotaxis machinery and genes related to oxidative stress . The majority of the virulence-associated traits is also subject to growth phase control ( S4 Dataset ) . This overlap is not solely restricted to the virulence-related attributes . Remarkably , 47% of the temperature-regulated genes encode metabolic functions , mainly enzymes of the carbon/energy metabolism , of which most are also subject to growth phase control ( S4 Dataset ) . This observation and validity of our approach was confirmed by the strong correlation between comparative RNA-seq and qRT-PCR data of 24 randomly selected temperature- and/or growth phase-regulated genes encoding hypothetical proteins and proteins implicated in metabolism and virulence-relevant traits ( S3 Fig ) . In summary , this indicated a clear link between both regulons in the control of Yersinia virulence . Among the metabolic genes which are significantly upregulated at 37°C are genes encoding numerous sugar and amino acid transporters , enzymes involved in the catabolism of various carbohydrates and amino acids , β-oxidation of fatty acids , and energy production/conversion , indicating that terminal oxidation of many different substrates is favored at 37°C to allow rapid proliferation within the host ( Fig . 4; S4 Dataset ) . Concomitant changes in oxidative catabolism , implicating a subset of these genes , were also found during temperature transition of the conditionally virulent Y . pestis strain KIM5 [77] . Most likely , these metabolic functions are part of the core regulon of thermoregulated genes in Yersinia . One other striking observation which has not been observed in Y . pestis , is the existence of a thermoregulated ‘acetate switch’ ( Fig . 4 ) . This physiological switch occurs when bacteria transit from a program of rapid growth that produces acetate from acetogenic carbon sources , e . g . glucose , serine ( dissimilation ) , to a program of slower growth facilitated by the import and utilization ( assimilation ) of acetate [78] . Similar to E . coli grown on high concentration of acetate , Y . pseudotuberculosis upregulates genes encoding enzymes with assimilation functions ( PoxB , ActP , Acs ) , the glyoxylate bypass , the TCA cycle and gluconeogenesis ( MaeB , PckA ) at 37°C , while genes encoding PykF , AceEF , the Pta-AckA pathway and AdhE , converting acetyl-CoA to acetate or ethanol , were repressed ( Fig . 4; S4 Dataset ) . A stronger induction of the acetate switch and the glyoxylate pathway was also observed under nutrient limiting condition during stationary phase , although expression of multiple enzymes of the TCA cycle remained unchanged ( S4 Dataset ) . To support this observation , we compared the intracellular amount of two key metabolites of the acetate switch , acetate and acetyl-CoA , in Y . pseudotuberculosis grown at 25°C or 37°C , and found that the level of both metabolites was significantly reduced during growth at 37°C ( S4 Fig ) . Why flipping the switch in response to temperature ? One reason could be that bacteria are primed for growth in the intestine . The mammalian intestinal tract is rich in short-chain fatty acids , of which acetate predominates . These volatile fatty acids are produced by the intestinal microbiota through consumption of available polysaccharides from diet or the host-secreted mucus and could constitute an important carbon/energy source for Y . pseudotuberculosis within the digestive tract [78 , 79] . Temperature-induced reprogramming to facilitate utilization of polysaccharide-derived degradation products is further supported by the fact that expression of several transport and catabolic genes of simple sugars are also thermally induced ( Fig . 4 ) . It is also possible that the acetate metabolism of Yersinia is influenced by the low availability of oxygen in the intestine . Studies with E . coli have shown that the absence of oxygen inhibits the expression of many TCA cycle enzymes which affects the pyruvate-acetate-acetyl-CoA flow [78] . Our global and quantitative RNA-seq approach further revealed a strikingly large number of growth phase- and/or temperature responsive trans-encoded sRNAs ( 74%; Fig . 3A , C; S3 and S4 Datasets ) . The vast majority ( 86% ) of the growth phase-controlled sRNAs are present at higher abundance during stationary phase of which about half also respond to temperature ( Fig . 3A , C; S3 Dataset ) . Among them are most conserved sRNAs , e . g . RybB and many novel sRNAs , e . g . Ysr220 . However , there are also prominent exceptions to this trend , e . g . GcvB and Ysr197 , which are strongly repressed during stationary phase ( Fig . 3A; S3 Dataset ) . The large number of temperature- and growth phase-responsive sRNAs is likely to contribute an important additional control layer to adjust and fine-tune the pathogen’s fitness and virulence in the response to the dramatic changes associated with the transmission of yersiniae between heterothermic environments and homeothermic mammals . The physiological relevance of these sRNAs for pathogenesis is further supported by a previous study , reporting the importance of the RNA chaperone Hfq for the virulence of Y . pseudotuberculosis [80] . One crucial global regulator previously shown to link the nutritional status of Yersinia to virulence is CRP . CRP controls the transcription of multiple genes and operons in Enterobacteriaceae in response to certain carbon sources ( in particular simple sugars ) , which are scarse during stationary phase . Loss of CRP was found to affect transcription of many genes involved in the primary metabolism and pathogenicity in Y . pestis , Y . enterocolitica and Y . pseudotuberculosis and led to a strong attenuation of their virulence [33 , 34 , 36–38 , 81] . This tight link between catabolite repression and virulence motivated us to include a crp deficient mutant of Y . pseudotuberculosis YPIII ( YP89 ) into our transcriptomic approach to ( i ) compare the influence of CRP at temperatures mimicking environmental/vector or mammalian host niches , and ( ii ) address global impact of CRP on the expression of sRNAs . Our analysis revealed 741 CRP-dependent genes ( S5 Dataset ) of which 228 were previously identified by a microarray approach ( 62% overlap ) which addressed expression solely at 25°C during late stationary phase [34] . Strikingly , only a small proportion of genes ( 142 genes , 19% ) is shared between the CRP regulons determined at 25°C or 37°C , as exemplified by the acs and mdh genes , while the majority varies significantly . For instance , aceBAK transcript abundance is negatively affected by CRP at 37°C , but not at 25°C ( S5 Dataset; S5 Fig ) . This is also reflected by the finding that the composition of temperature-regulated genes is dramatically altered in the crp mutant . Loss of crp had only a minor influence on the expression of the pYV-encoded virulence genes , whereas the majority of the temperature-dependent chromosomal genes ( 191 genes , 75% ) , including numerous metabolic , virulence and stress adaptation genes , did not respond to temperature in the absence of CRP ( S6 Dataset ) . In contrast , 247 genes are solely temperature-regulated in the crp deletion strain , the majority of which is negatively affected by CRP . In summary , our data reveal a profound reprogramming of the CRP regulon by temperature ( S6 Dataset ) . How this is accomplished is unclear , but the large number of transcriptional regulators which are members of the two distinct CRP regulons ( S5 Dataset; S5 Fig ) indicates that the switch is supported by CRP-dependent , thermo-controlled regulators such as RovA [34 , 82 , 83] . This global virulence regulator is only expressed and positively controlled by CRP at 25°C , but not at 37°C due to the fact that RovA is a protein thermometer which is partially defolded and rapidly degraded at 37°C [82] . The accumulation of regulatory RNAs during stationary phase suggested that they might also be part of the CRP regulon . Our investigation of transcriptomic changes revealed 53 trans-encoded RNAs of Y . pseudotuberculosis ( 65% ) which are up- or down-regulated in the absence of crp at 25°C and/or 37°C ( S3 and S5 Datasets ) . Many conserved trans-encoded RNAs , but also multiple newly identified sRNAs were identified as members of the CRP regulon , and their intracellular level is strongly affected by temperature . CRP-dependent regulation of all 15 randomly selected trans-encoded sRNAs was confirmed by Northern blotting ( Fig . 5A ) . Furthermore , we validated one newly identified trans-encoded RNA Ysr206 , as well as two antisense RNAs Ysr232 and Ysr114 , which were exclusively detected in the crp mutant ( Fig . 5A ) . Both antisense RNAs constitute the most abundant antisense RNAs identified by our approach ( S3 Dataset ) . The huge subset of CRP-regulated non-coding RNAs strongly indicates that CRP is a master regulator of regulatory RNAs in Y . pseudotuberculosis . Recent identification of four CRP-dependent riboregulators in Y . pestis ( CyaR , sR065 , sR066 , sR084 encoded on pPCP [20] ) further indicate that this might also account for other Yersinia species . Furthermore , CRP-mediated control seems to account for the accumulation of many sRNAs during stationary phase , and it also explains downregulation of a subset of sRNAs ( e . g . GcvB ) , which are negatively affected by the regulator ( Figs . 3A , 5A ) . A manual search and a string-based prediction of CRP binding motifs ( TGTGA-N6-TCACA ) within the regulatory region of all 54 CRP-dependent sRNA genes identified one or more putative CRP binding sites within 18 of these sRNA genes ( S3 Dataset ) . Direct and specific CRP binding in a cAMP-dependent manner was found for cyaR , omrA , sraH , Ysr100 , 191 , 206 , 212 , 215 , 218 , 226 , and a manually identified sRNA , Ysr304 ( Figs . 5B , S6 ) . Cooperative interactions were observed with the regulatory region of Ysr206 and Ysr226 , for which more than one CRP binding site was predicted ( Figs . 5B , S6; S3 Dataset ) . The validity of our data was reinforced by the fact that CRP did not interact with the regulatory regions of ysr204 , rybB and ysr232 for which no CRP binding site was predicted ( Figs . 5B , S6 ) . To confirm these results we also selected two CRP-dependent promoter regions of sRNA genes ( sR018 and ysr212 ) , which have previously been identified to be CRP-dependent ( Figs . 5B , S6; S3 Dataset ) and performed DNase I footprinting experiments . Incubation of the fragments with increasing CRP concentrations revealed that CRP binds specifically to the promoter regions at the predicted CRP-binding sites ( S7 Fig ) . This demonstrates that a large proportion of the Yersinia sRNAs are under direct control of CRP . Catabolite repression of the remaining sRNAs might be mediated indirectly by other transcriptional regulators ( e . g . RpoN for GlmY [84] , and RpoE for RybB [85 , 86] identified as part of the CRP regulon; S5 Dataset; S5 Fig ) . The tight link between catabolite repression and thermal control in Yersinia , in particular when one considers the expression of non-coding RNAs , suggested that synthesis and/or activity of CRP itself might be subjected to thermal and growth phase control . Indeed , we found that expression of the cAMP-producing adenylate cyclase gene cyaA was 2 . 8-fold increased during stationary phase ( S4 Dataset ) . Moreover , CRP levels are strongly increased during stationary phase , whereby the amount of the global regulator is slightly but significantly higher at 25°C compared to 37°C ( Fig . 6A ) . Interestingly , the crp transcript levels quantified by RNA-seq do not increase during transition from exponential to stationary growth ( S4 Dataset ) , indicating that environmental control in response to growth phase occurs on the post-transcriptional level . Post-transcriptional control of CRP was also reported in Y . pestis CO92 in the context of the control of the plasminogen activator protease Pla [36] . In Y . pestis , loss of the RNA chaperone Hfq was found to decrease CRP levels . The molecular mechanism is not yet known , but it was found to involve the crp 5’-UTR [36] . We further tested whether loss of Hfq has also an influence on the amount of CRP in Y . pseudotuberculosis ( Fig . 6B ) . However , no significant difference of CRP levels was detectable between the YPIII wild-type , the isogenic hfq mutant and the in trans-complemented hfq mutant during stationary phase at 25°C and 37°C ( Fig . 6B ) . One the other hand , the intracellular level of Hfq , shown to be CRP-dependent in other related Enterobacteriaceae [87] , remained unchanged in the absence of crp ( Fig . 1C ) . Together , this demonstrated that CRP acts as a global sRNA regulator in Y . pseudotuberculosis YPIII in an Hfq-independent manner . Many years of research on the pathogenicity mechanisms of Yersinia provided great insights into the plethora of virulence and fitness factors required for the successful establishment of an infection . However , we are far from understanding the precise choreography and concerted action of transcriptional and post-transcriptional factors controlling Yersinia virulence . In this work , we successfully applied RNA-seq to obtain a valuable data resource that provides detailled information about the expression of the majority of genes of the enteric pathogen Y . pseudotuberculosis . The high-resolution transcriptome presented here constitutes the first single nucleotide resolution TSS map of a member of the genus Yersinia . In addition to creating a valuable tool for the Yersinia research community , it provides a framework for better analysis of identified sensory and regulatory non-coding RNAs and for studying the complex regulatory mechanisms and networks that allowed pathogenic yersiniae to become successful human pathogens . The complex expression and processing patterns show that many RNA-mediated regulations are yet to be discovered in Yersinia . The ongoing analysis of strain- and species-specific differences of these regulatory elements will further help us to identify how they manipulate the transcriptional output of the pathogens and contribute to phenotypic variations and/or specific adaptation to certain hosts . We further obtained a detailed snapshot of the abundance of coding and non-coding transcripts and their sites of initiation taken at different virulence-relevant conditions . The comparative analysis of these data revealed global reprogramming of the transcriptional units controlled by CRP when growth conditions change from moderate environmental to human body temperatures , which appear to prime the bacteria for growth in the digestive tract . This includes a large set of catabolic and energy production genes that facilitate consumption of various carbon sources , e . g . dietary fibers-derived simple sugars and uncovered the existence of a thermo-controlled ‘acetate switch’ which may allow preferential growth on acetate , a predominant endproduct of the intestinal microbiota . This observation is supported by another study of our group , which demonstrated that absence of CRP perturbs in particular the fluxes of the carbon core metabolism at the level of the pyruvate-acetyl-CoA-TCA cycle node [88] . The thermally induced expression switch of virulence-relevant metabolic genes is most likely to be mediated by CRP-dependent transcriptional regulators and small non-coding RNAs ( Fig . 6C ) . In fact , we found that CRP affects the abundance of more than 50% of all identified sRNAs , most of which are under thermal control and induced during stationary phase . Based on our data we postulate that the transcription factor CRP acts as master regulator of non-coding RNAs in Yersinia . The identified CRP-dependent sRNAs are likely to contribute to virulence and host adaptation by modulating metabolic pathways , which are important for efficient host colonization by Yersinia . Furthermore , use of non-coding RNAs to link the nutritional status of Yersinia to virulence may allow the pathogen to increase its energy budget upon nutrient limitation by replacing proteinaceous regulators by regulatory RNA elements . Considering the wide range of reservoirs , including the different niches in the human body , a global switching between regulons and regulatory elements seems advantageous to rapidly and efficiently readjust gene expression during transmission between the different challenging external reservoirs and host environments . We anticipate that a comprehensive detailed analysis of the global impact of transcriptional regulators important for bacterial fitness and virulence , coupled with the identification of post-transcriptional control mechanisms by sensory and regulatory RNA elements during growth in environmental reservoirs , transmission vectors and during the infection of animal hosts and humans will herald a new exciting era for future research . This will not only help us to elucidate the complex regulatory network adjusting gene expression to the rapidly changing demands during the process of an infection , it will also allow us to identify crucial control mechanisms which can be further exploited as potential drug targets .
All DNA manipulations , restriction digestions , ligations and transformations were performed using standard genetic and molecular techniques [89] . The plasmids used in this work are listed in S1 Table . Oligonucleotides used for PCR and sequencing were purchased from Metabion and are also listed in S1 Table . Plasmid DNA was isolated using Qiagen plasmid preparation kits . DNA-modifying enzymes and restriction enzymes were purchased from New England Biolabs . PCRs were performed in a 100 μl volume for 29 cycles using Phusion High-Fidelity DNA polymerase ( New England Biolabs ) . Purification of PCR products was routinely performed using the QIAquick kit ( Qiagen ) . The constructed plasmid was sequenced by the in-house facility . To generate plasmid pBW131 , the entire hfq gene along with 847 nt upstream region and 42 nt downstream region was amplified by PCR using primers V75 and V80 and cloned into the BamHI site of pHSG575 . Y . pseudotuberculosis YPIII and the isogenic crp mutant YP89 were grown in LB medium to exponential phase ( OD600 0 . 5 ) and stationary phase ( 16 h ) at 25°C and 37°C ( S8 Fig ) . Total bacterial RNA was isolated by a hot phenol extraction protocol [89] , DNA was digested using the TURBO DNase ( Ambion ) , purified with phenol:chlorophorm:isopropanol and the quality was assessed using the Agilent RNA 6000 Nano Kit on the Agilent 2100 Bioanalyzer ( Agilent Technologies ) ( S9 Fig ) . From 8 μg of total RNA the rRNA was depleted using MICROBExpress ( Ambion ) . 1 μg of rRNA depleted RNA was treated with TAP for +TAP libraries ( Epicentre Biotechnologies ) ( S1 Fig ) . Strand-specific RNA-seq cDNA library preparation and barcode introduction was based on RNA adapter ligation as described previously [90] , omitting double strand specific nuclease normalization ( DSN ) . In brief , the rRNA-depleted +/-TAP treated RNA was fragmented by sonication to a median size of 200 nt . The fragments were 5’-phosphorylated and ligated to 3’- and 5’-RNA-adapter oligonucleotides ( S1 Fig ) . After reverse transcription , cDNA libraries were PCR amplified ( 15 cycles ) . Quality of the libraries was validated using Agilent 2100 Bioanalyzer ( Agilent Technologies ) following the manufacturer’s instruction . Cluster generation was performed using the Illumina cluster station . Single-end sequencing on the HiSeq2500 and Genome Analyzer IIx followed a standard protocol . The fluorescent images were processed to sequences and transformed to FastQ format using the Genome Analyzer Pipeline Analysis software 1 . 8 . 2 ( Illumina ) . The sequence output was controlled for general quality features , sequencing adapter clipping and demultiplexing using the fastq-mcf and fastq-multx tool of ea-utils [91] . Quality of the sequencing output was analyzed using FastQC ( Babraham Bioinformatics ) . All sequenced libraries were mapped to the YPIII genome ( NC_010465 ) and the pYV plasmid ( accessions NC_006153 ) using Bowtie2 ( version 2 . 1 . 0 ) [92] with default parameters . After read mapping , SAMtools [93] was employed to filter the resulting bam files for uniquely mapped reads ( both strands ) , which were the basis for downstream analyses . In summary , between 36–95% and 63–97% of the cDNA reads in the +TAP and-TAP libraries could be mapped to the YPIII sequence , of which 3–22% of +TAP reads and 2–23% of-TAP reads mapped uniquely to the genome . In a preliminary step , sample libraries were normalized to million uniquely mapped reads and for every base the coverage and the number of reads starting at the respective position were calculated . Then , biological replicates were combined/merged by averaging coverage and read starts data . For detection of TSSs that are associated with mRNAs an adapted method of Schlüter et al . [94] was applied on YPIII +TAP and YP89 +TAP libraries . Initially , the genome was scanned for potential TSSs . A TSS was defined as the starting position of at least 10 cDNA reads which map to the same genomic position at their 5’-end . Moreover , the read coverage of these reads had to be at least 2-fold higher than the coverage of overlapping reads . In the next step , the potential TSSs were extended in downstream direction to transcripts of continuous coverage and classified according to the minimal transcriptional unit ( MTU ) model [94] . The MTU model extends the given annotation of protein coding genes by virtual 5’- and 3’-UTRs of lengths 54 nt and 20 nt for genes where 5’-UTRs are not annotated . Transcripts were retained if they could be assigned to one of three classes: mTSS ( TSS of messenger RNA ) , lmTSS ( TSS of leaderless mRNAs ) , rmTSS ( TSS of mRNAs to be reannotated ) . A TSS was associated with an mRNA and classified as mTSS , if the TSS transcript started upstream of the corresponding gene and overlapped with its MTU . lmTSS represent the TSS of leaderless transcripts . In this case the 5’-UTR of the transcript is <10 nt . In some cases TSS candidates could not be identified upstream of genes , but in close proximity downstream to the start codon . TSS transcripts with a maximal distance of 70 nt were classified as rmTSS and indicate genes , which need to be reannotated . Following classification , adjacent TSS with distance ≤ 2 nt were clustered and the TSS with the highest number of read starts counted was reported as the primary TSS . Recently , an automated approach for the detection of TSS in dRNA-seq data was proposed by Schmidtke et al . [95] , which exploits the difference of read starts distributions between a library from untreated total RNA ( - ) and a library enriched for primary transcripts ( + ) . Based on the assumption that the number of mapped reads in a defined genomic interval follows a Poisson distribution , the Skellam distribution is used to detect genomic positions , where the difference of observed read starts significantly exceeds the difference of expected read starts . These positions are then defined as TSS . We further applied TSSAR [96] , an implementation of the dRNA-seq TSS detection method , on our RNA-seq data . Different to the standard dRNA-seq experimental setup , the background distribution of read starts was obtained from-TAP libraries , which are specifically depleted for cDNAs derived from fragments containing the 5’-end of primary transcripts , while the corresponding replicate of the +TAP sample represents the unbiased library ( S1 Fig ) . p-values of TSSAR TSS predictions were added to the set of manually curated TSS and in case of matching positions ( S2 Dataset ) . The newly identified TSSs were labelled to following conventions , x_TSS_n , where “x” indicates the YPIII genomic replicon ( i . e . YPK , chromosome; YPKp , pYV virulence plasmid ) . To investigate potential sequence conservation at and around the newly determined TSSs , a sequence logo for the -3 to +4 neighborhoods of all 1151 TSSs was generated using the WebLogo software [60] . To compute conserved sequence motifs in the -10 and -35 promotor region we performed de-novo motif discovery using the MEME software [63] . As input for motif detection in the -10 region served the subsequences starting at position -15 and ending at position -3 ( relative to the TSS ) of all 1151 TSSs determined in this study . For the -35 region , we used the subsequences starting at position -45 and ending -25 . We ran MEME in one occurence per sequence ( OOPS ) mode and searched for motifs between length three and eight for the -10 region and between length three and five for the -35 region . To identify expressed small regulatory RNAs , a global screen in all +TAP samples for unannotated trans-encoded sRNAs and cis-encoded antisense RNAs was performed . For this , transcripts were assembled from reads and subsequently classified . For non-coding RNA classification , TSS data were included in the YPIII annotation . In a first step , transcripts seeds , which correspond to genomic regions of minimal length of 40 nt and a continuous coverage of at least 30 reads , were identified . The resultant transcripts were extended on both ends until the coverage dropped to 10% of the transcript’s maximum coverage or if the coverage was lower than 5 reads . Finally , transcripts located in intergenic regions without overlapping UTRs were classified as trans-encoded sRNAs , while transcripts found on the strand opposite to a protein-coding gene were defined as cis-encoded antisense RNAs . Finally , all TSS and non-coding RNA candidates were inspected manually and reported if they passed this last filter . The novel non-coding RNAs were labelled according to the common convention ( Ysr_n ) with ongoing numbers ( n ) . To detect ORFs and trans-encoded sRNA that are differentially expressed in YPIII and the crp mutant YP89 , we used DESeq ( version 1 . 12 . 1 ) [73] for all differential expression ( DE ) analyses following the default analysis steps described in the package’s vignette . For each comparison HTSeq in union count mode was used to generate raw read counts required by DESeq as basis for DE analysis . In this study , a string-based prediction of conserved CRP binding site motifs was performed for trans-encoded sRNA which were classified as CRP-dependent by differential gene expression analysis . The CRP consensus sequence ( TGTGA-6 nt-TCACA ) was used to scan regions 200 nt upstream and 100 nt downstream of the sRNA 5’-end for putative CRP binding sites , allowing a maximum of two mismatches . Candidate mRNAs with 5’-UTRs ≥ 200 nt were screened for riboswitch-like elements ( RLEs ) . For this purpose the entire 5’-UTR along with 50 nt of the coding region were scanned for RLEs using the RibEx Riboswitch Explorer [67] . The high-throughput read data is deposited at the European Nucleotide Archive ( ENA ) with the accession no . PRJEB7268 ( http://www . ebi . ac . uk/ena/data/view/PRJEB7268 ) . A complete list of the TSSs , antisense and trans-encoded sRNAs is provided in the S2 and S3 Datasets . The comparative transcriptome analyses are given in S4–S6 Datasets . The annotation of global TSSs , reannotated ORFs , trans- and cis-encoded RNAs are provided as TXT files ( S1–S7 Files ) . Total RNA was isolated using the SV Total RNA Isolation System ( Promega ) as described [52] . 5’ RACE was performed using the 5’/3’ RACE Kit ( Roche ) . For Northern blotting 10–20 μg total RNA were separated using 7 M urea/10% acrylamide/0 . 6 x TBE gels . The RNA was transferred onto positively charged membranes ( Roche ) by semi dry blotting in 1 x TBE and UV crosslinked . For radioactive labeling 20 μCi γ-ATP32 ( Hartmann Analytic ) were mixed with 10 U T4 Polynucleotide kinase ( Fermentas ) and 4 pmol oligonucleotide and incubated at 37°C for 1 h . The reaction was stopped by addition of STE buffer ( 100 mM NaCl , 10 mM Tris , pH 8 , 1 mM EDTA ) and the labeled oligonucleotides were purified using MicroSpin G-25 columns ( GE Healthcare ) . The probes were mixed with 500 μg yeast tRNA ( Invitrogen ) and 250 μg salmon sperm DNA ( Invitrogen ) to reduce unspecific probe binding . Denaturation was carried out for 10 min at 95°C . Hybridization was performed in hybridization buffer ( 0 . 5 M Na2HPO4 , pH 7 . 2 , 1 mM EDTA pH 7 . 5 , 7% [w/v] SDS ) over night at 68°C . The blots were washed in washing buffer ( 40 mM Na2HPO4 , pH 7 . 2 , 1 mM EDTA , pH 7 . 5 , 1% [w/v] SDS ) and exposed to a Phosphorimaging screen and analyzed with a Typhoon FLA-9000 ( GE Healthcare ) . qRT-PCR was performed with DNA digested total RNA ( 1 ng/μl ) using the SensiFastNoRox Kit ( Bioline ) . Primers employed for analyzing relative gene expression are listed S1 Table . The 5S rRNA gene was used for normalization and relative gene expression was calculated as described earlier [97] . Primer efficiencies were determined experimentally using serial dilutions of genomic Y . pseudotuberculosis YPIII DNA . Primer efficiencies are: 5S rRNA: 2 . 22; acnA ( YPK_2030 ) : 2 . 21; actP ( YPK_3923 ) : 2 . 17; ail ( YPK_1268 ) : 2 . 15; gntP ( YPK_0762 ) : 2 . 33; katY ( YPK_3388 ) : 2 . 40; sodC ( YPK_3445 ) : 1 . 91; uxaC ( YPK_0554 ) : 2 . 15; yopD ( pYV0054 ) : 2 . 20; yopE ( pYV0025 ) : 2 . 07; yopP/J ( pYV0098 ) : 1 . 89; yscW ( pYV0075 ) : 2 . 3; adhE ( YPK_2072 ) : 2 . 03; fliA ( YPK_2380 ) : 1 . 91; fliC ( YPK_2381 ) : 2 . 03; glnA ( YPK_4189 ) : 2 . 02; glpD ( YPK_0152 ) : 2 . 22; invA ( YPK_2429 ) : 2 . 10; pykF ( YPK_1855 ) : 1 . 91; fumC ( YPK_1985 ) : 2 . 12; glmS ( YPK_4229 ) : 2 . 21; YPK_1731: 2 . 17; YPK_2197: 2 . 19; YPK_2200: 2 . 10 . Y . pseudotuberculosis YPIII , the isogenic mutants YP89 ( Δcrp ) and YP80 ( Δhfq ) were grown in LB medium to exponential phase ( OD600 0 . 5 ) and stationary phase ( 16 h ) at 25°C and 37°C ( S8 Fig ) . For immunological detection of Hfq , H-NS and CRP , cell extracts of equal amounts of bacteria were prepared and separated on a 15% polyacrylamide SDS gel [89] . Proteins were transferred onto an Immobilon-P membrane ( Millipore ) and probed with polyclonal antibodies directed against Hfq , H-NS or CRP as described [56 , 98] . CRP-His6 was purified as described [34] . For the EMSAs , the DNA fragment harboring the potential CRP binding site ( s ) was mixed with a control DNA fragment ( csiD gene fragment of E . coli ) in equimolar amounts . Increasing amounts of the CRP protein in 1 x binding buffer ( 10 mM Tris-HCl pH 7 . 5 , 1 mM EDTA , 5 mM DTT , 5% glycerol , 10 mM NaCl , 1 mM MgCl2 , 0 . 1 mg/ml BSA ) containing 0 . 2 mM cAMP were added and the mixture was incubated at 30°C for 20 min . Subsequently , the reactions were separated on a 4% native polyacrylamide gel and the DNA fragments visualized with ethidium bromide [34] . For DNase I footprinting , segments of the sR018 and ysr212 promoters harboring the predicted CRP binding site were amplified by PCR using a digoxigenin ( DIG ) -labelled primer and a non-labelled primer ( S1 Table ) . PCR fragments were purified , incubated with the purified CRP protein in the presence or absence of cAMP as described for the EMSAs . The PCR products were digested with DNase I of an appropriate dilution and the resulting products were separated and visualized as described previously [56] . The protected nucleotides were identified by comparison with a sequence ladder generated with the same DIG-labelled primer used for the amplification of the fragment by PCR . For the detection of acetate and acetyl-CoA we used an enzymatic assay system ( BioAssay Systems ) and followed the instructions of the manufacturers . | Many bacterial pathogens cycle between environmental sources and mammalian hosts . Adaptation to the different natural habitats and host niches is achieved through complex regulatory networks which adjust synthesis of the large repertoire of crucial virulence factors and fitness determinants . To uncover underlying control circuits , we determined the first in-depth single-nucleotide resolution transcriptome of Yersinia . This revealed important novel genetic information , such as global locations of transcriptional start sites , non-coding RNAs , potential riboswitches and provided a set of virulence-relevant expression profiles , which constitute a valuable tool for the research community . The analysis further uncovered a temperature-induced global reprogramming of central metabolic functions , likely to support intestinal colonization of the pathogen . This is accompanied by a major reorganization of the CRP regulon , which involves a multitude of regulatory RNAs . The primary consequence is a fine-tuned , coordinated control of metabolism and virulence through a plethora of environmentally controlled regulatory RNAs allowing rapid adaptation and high flexibility during life-style changes . | [
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] | [] | 2015 | Transcriptomic Profiling of Yersinia pseudotuberculosis Reveals Reprogramming of the Crp Regulon by Temperature and Uncovers Crp as a Master Regulator of Small RNAs |
The molecular mechanism by which non-enveloped viruses penetrate biological membranes remains enigmatic . The non-enveloped polyomavirus SV40 penetrates the endoplasmic reticulum ( ER ) membrane to reach the cytosol and cause infection . We previously demonstrated that SV40 creates its own membrane penetration structure by mobilizing select transmembrane proteins to distinct puncta in the ER membrane called foci that likely function as the cytosol entry sites . How these ER membrane proteins reorganize into the foci is unknown . B12 is a transmembrane J-protein that mobilizes into the foci to promote cytosol entry of SV40 . Here we identify two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) as B12-interaction partners . Strikingly , SV40 recruits B12 to the foci by inducing release of this J-protein from Erlin1/2 . Our data thus reveal how a non-enveloped virus promotes its own membrane translocation by triggering the release and recruitment of a critical transport factor to the membrane penetration site .
Membrane penetration represents a decisive event during virus infection . For enveloped viruses , fusion between the viral and host membranes delivers the core viral particle into the host cytosol [1 , 2] . By contrast , because a non-enveloped virus lacks a surrounding lipid bilayer , its membrane transport process must be distinct from an enveloped virus . Indeed , membrane penetration by the non-enveloped virus families is not fully understood to date [1–3] . A central enigma that challenges this field is whether a non-enveloped virus hijacks pre-existing channels in the limiting membrane in order to enter the host , or if it generates a membrane transport portal de novo and subsequently crosses this structure to reach the host cytosol . Intriguingly , recent reports suggest that the non-enveloped polyomavirus ( PyV ) creates its own membrane transport structure to enter the host cell and cause infection [4–6] , although aspects of this process remain to be clarified . PyV is a non-enveloped DNA tumor virus known to cause debilitating human diseases especially in immunocompromised individuals . For instance , the human JC PyV is responsible for the fatal demyelinating central nervous system disease progressive multifocal leukoencephalopathy , the BK PyV for BK-associated nephropathy and hemorrhagic cystitis , and the Merkel cell PyV for the aggressive skin cancer Merkel cell carcinoma [7 , 8] . Structurally , a PyV particle is composed of 72 pentamers of the coat protein VP1 , with each pentamer harboring either the internal hydrophobic protein VP2 or VP3 [9–11] . The VP1 pentamers form the outer shell of the virus which in turn encases the 5 kilobase viral DNA genome . A complex network of disulfide bonds , in concert with VP1 C-terminal arms emanating from a pentamer that invade neighboring VP1 pentamers , stabilize the overall viral architecture [10 , 11] . Because simian PyV SV40 displays both structural and genetic similarities to human PyVs such as JC and BK PyVs , studies of the SV40 infection pathway have historically illuminated human PyV infection pathways . To cause infection at the cellular level , SV40 binds to the ganglioside GM1 receptor on the plasma membrane [12 , 13] , entering the host cell via receptor-mediated endocytosis . Upon entry , the virus is initially sorted to endolyososomes [14 , 15] before being routed to the endoplasmic reticulum ( ER ) [13 , 16 , 17] . Here the virus undergoes conformational changes imparted by ER-resident protein disulfide isomerase ( PDI ) family members [18 , 19] . These reactions expose VP2 and VP3 , generating a hydrophobic particle that physically binds to and integrates into the ER membrane [5] . The membrane-embedded virus is initially recognized by the ER membrane protein BAP31 [5] , transiently stabilized by the transmembrane protein EMC1 [20] , and eventually ejected into the cytosol by the Hsc70-SGTA-Hsp105 cytosolic extraction machinery [6 , 21] . This machinery is associated with the cytosolic side of the ER membrane via interaction with three different ER membrane J-proteins called DNAJB12 ( B12 ) , DNAJB14 ( B14 ) and DNAJC18 ( C18 ) [4 , 6 , 21 , 22] . A J-protein typically binds to Hsp70 family proteins and stimulates their ATPase activity , enabling the Hsp70 proteins to efficiently capture their clients [23] . From the cytosol , the virus is further transported to the nucleus to cause lytic infection or cell transformation . Strikingly , prior to ER-to-cytosol membrane penetration , SV40 dynamically recruits B12 , B14 , and C18 ( as well as other membrane components ) into discrete puncta in the ER membrane called foci [4 , 6 , 21] . The kinesin-1 motor promotes maturation of the foci [24] . Because the foci structures are postulated to function as the viral cytosol entry sites [5 , 6 , 21] , SV40-triggered foci formation represents the first example of a non-enveloped virus creating its own membrane transport portal . What remains a major conundrum is how SV40 induces the reorganization of these membrane J-proteins into the foci . Using an unbiased proteomics approach , we identify two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) , which preferentially localize in detergent-resistant membranes and hetero-oligomerize to form a massive protein complex [25–28] , as B12-interacting partners . Erlin1/2 support SV40-induced foci formation , and promote virus ER-to-cytosol transport , and infection . However , Erlin1/2 themselves do not mobilize into the foci . Instead , our results indicate that these ER membrane proteins bind to and likely anchor B12 in the ER , releasing B12 into the foci upon SV40 infection . Our study thus pinpoints Erlin1/2-dependent release as a key regulatory event enabling B12 to efficiently reorganize into the foci , where this J-protein facilitates virus ER membrane penetration leading to successful infection .
Three closely related ER membrane J-proteins called B12 , B14 and C18 play critical yet non-overlapping functions in promoting ER-to-cytosol transport and infection of SV40 [4] . We previously identified novel B14- and C18-binding factors that execute distinct functions in supporting this viral membrane transport process [6 , 20 , 21] . However , potential B12-interacting proteins that may also play roles during SV40 ER membrane penetration have yet to be identified . To identify possible B12-interacting components , we used Flp-in 293 T-REx cells and generated a cell line stably expressing B12 that is tagged with 3xFLAG at the amino-terminus ( 3xFLAG-B12 stable ) ; the parental Flp-in 293 T-REx cells not expressing 3xFLAG-B12 were used as the control . When cell extracts derived from the parental Flp-in 293 T-REx and 3xFLAG-B12 stable cells were analyzed by SDS-PAGE , immunoblotting revealed that a lower level of 3xFLAG-B12 was expressed when compared to the endogenous B12 level ( Fig 1A , compare lanes 1 and 2 ) . The cell extracts were then subjected to FLAG affinity purification using the schematic depicted in Fig 1B . The FLAG peptide-eluted materials were either analyzed by silver staining or subjected to “shot gun” mass spectrometry . By silver staining , we found the appearance of many distinct bands in the sample derived from the 3xFLAG-B12-expressing cells , while fewer bands of lesser intensity were observed in the sample derived from the parental control cell ( Fig 1C ) . By mass spectrometry , peptides corresponding to more than 1 , 500 proteins were identified in samples derived from the 3xFLAG-B12 cells , but with only 58 of these proteins having at least 20 peptides ( Table 1 ) . Of the 58 proteins , the ER membrane proteins BAP31 , EMC1 , and Sel1L were present in this list . Importantly , these proteins were previously demonstrated to participate in ER-to-cytosol membrane penetration of SV40 [5 , 19 , 20] . Also included in this list are Erlin1 and Erlin2 ( Erlin1/2 ) , two closely related ER membrane proteins that hetero-oligomerize to form a massive protein complex [25 , 26 , 28] . Because Erlin1/2 have been implicated in an ER-to-cytosol transport-dependent protein quality control process called ER-associated degradation [27 , 29 , 30] , we decided to focus on these proteins since SV40 similarly undergoes ER-to-cytosol transport . To validate the mass spectrometry data and to assess the specificity of the B12-Erlin1/2 interactions , extracts derived from cells stably expressing 3xFLAG-B12 or 3xFLAG-B14 , along with the parental cells , were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . The precipitated material was subjected to SDS-PAGE followed by immunoblotting . Using this approach , we found that precipitating 3xFLAG-B12 but not 3xFLAG-B14 pulled down endogenous Erlin1/2 ( Fig 1D , first and second panels , compare lane 5 to 6 ) . As expected , Hsc70 co-precipitated with 3xFLAG-B12 and 3xFLAG-B14 ( Fig 1D , third panel , lanes 5 and 6 ) . These results demonstrate that Erlin1/2 interact specifically with 3xFLAG-B12 , in agreement with the mass spectrometry data . We next examined if Erlin1/2 play roles during SV40 infection . Simian CV-1 cells , which are typically used to study SV40 infection , were transfected with siRNA targeting either Erlin1 , Erlin2 , or both ( PAN-Erlin ) . The resulting cell extracts were subjected to SDS-PAGE followed by immunoblotting with antibodies against Erlin1 and Erlin2 . We found that Erlin1 siRNA downregulated Erlin1 expression ( Fig 2A , first panel , compare lane 2 to 1 ) without affecting Erlin2 expression ( Fig 2A , second panel , compare lane 2 to 1 ) . Erlin2 siRNA silenced Erlin2 expression ( Fig 2A , second panel , compare lane 3 to 1 ) while also moderately reducing Erlin1 expression ( Fig 2A , top panel , compare lane 3 to lane 1 ) , suggesting that this siRNA may target Erlin1 to some extent . As expected , the PAN-Erlin siRNA decreased both Erlin1 and Erlin2 levels ( Fig 2A , first and second panels , compare lane 4 to 1 ) . To determine if severe ER stress is induced upon depletion of the Erlins , we monitored splicing of the XBP1 mRNA by RT-PCR analysis . XBP1 splicing typically ensues when misfolded proteins massively accumulate in the ER , and is considered the most sensitive indicator of ER stress induction [31] . We found that when compared to cells treated with the chemical ER stress inducer dithiothreitol ( DTT ) , neither the Erlin1 , Erlin2 , nor PAN-Erlin siRNA triggered significant ER stress ( Fig 2B , compare lane 5 to lanes 1–4 ) . Under these conditions , we assessed SV40 infection by scoring cells for expression of the virally encoded large T antigen , a hallmark for SV40 infection , in the host nucleus using an immunofluorescence-based approach . While depleting either Erlin1 or Erlin2 individually blocked infection moderately ( Fig 2C , compare second and third to first bar ) , simultaneous knockdown of Erlin1/2 potently reduced SV40 infection ( Fig 2C , compare fourth to first bar ) . To verify that this decreased infection is indeed caused by the knockdown of Erlin1 and Erlin2 and not due to unexpected off-target effects , we employed a rescue experiment by transfecting siRNA-resistant FLAG-tagged Erlin1 ( Erlin1*-FLAG ) , Erlin2 ( Erlin2*-FLAG ) , or the control GFP-FLAG construct in cells simultaneously depleted of Erlin1/2 . Only cells expressing the FLAG protein were scored . Using this approach , we found that add-back of either Erlin1*-FLAG or Erlin2*-FLAG fully restored the block in SV40 infection imposed by Erlin1/2 depletion ( Fig 2D , compare third and fourth to first and second bars ) . These findings demonstrate that the robust decrease in SV40 infection due to the PAN-Erlin1/2 siRNA results from silencing Erlin1/2 , thus unambiguously revealing a critical function of Erlin1/2 in SV40 infection . Importantly , as reconstituting either Erlin1 or Erlin2 fully restored the infection block caused by silencing Erlin1/2 , these related membrane proteins likely execute overlapping functions during SV40 infection . Because ER-to-cytosol transport is essential for successful SV40 infection , we asked if Erlin1/2 promote this membrane transport process using a cell-based , semi-permeabilized transport assay that we previously developed [32] . Briefly , cells are treated with a low concentration of digitonin that selectively permeabilizes the plasma membrane without damaging internal membranes . The sample is then centrifuged to generate two fractions , a supernatant fraction that contains cytosolic proteins and SV40 that has penetrated the ER membrane to reach the cytosol ( cytosol ) , and a pellet fraction that contains membranous organelles , including the ER , as well as any ER-localized virus ( membrane ) . Cells transfected with scrambled , Erlin1 , Erlin2 or PAN-Erlin siRNA were infected with SV40 , and subjected to this fractionation procedure , with the resulting cytosol and membrane fractions analyzed by immunoblotting . We found that while the majority of the cytosolic marker Hsp90 was detected in the cytosol fraction ( Fig 3A , compare second to fifth panel ) , the ER marker BiP was found only in the membrane fraction ( Fig 3A , compare sixth to third panel ) , thus verifying the integrity of the fractionation procedure . Importantly , under this condition , we found that although silencing either Erlin1 or Erlin2 moderately decreased the SV40 VP1 level in the cytosol fraction ( Fig 3A , top panel , compare lanes 2 and 3 to 1; the VP1 level is quantified in Fig 3B ) , simultaneous knockdown of Erlin1/2 potently impaired VP1 appearance in the cytosol fraction ( Fig 3A , top panel , compare lane 4 to 1; quantified in Fig 3B ) . These findings strongly suggest that Erlin1/2 support SV40’s ER membrane transport event . We next used a biochemical fractionation protocol based on Triton X-100 extraction to isolate ER-localized SV40 from the membrane fraction [32] ( see also Materials and Methods ) . We found that the level of ER-localized SV40 did not decrease by depleting Erlin1 , Erlin2 , or both Erlin1/2 ( Fig 3C , third panel , compare lanes 2–4 to 1 ) , whereas it did significantly decrease when cells were treated with brefeldin A ( BFA ) which blocks SV40 trafficking from the plasma membrane to the ER ( Fig 3C , third panel , compare lane 5 to 1 ) , as expected . In fact , knockdown of Erlin1/2 moderately increased the level of ER-localized SV40 when compared to the control condition ( Fig 3C , third panel , compare lane 4 to 1 ) , suggesting SV40 is trapped in the ER in the absence of Erlin1/2 . This modest increase was again evident when a lesser amount of the samples was analyzed and quantified ( Fig 3D , top panel , compare lane 2 to 1; quantified in Fig 3E ) . Because only a small fraction of total ER-localized SV40 reaches the cytosol ( see Fig 3A , compare VP1 level in first versus fourth panels ) , the virus level that accumulates in the ER due to a block in cytosol entry is expected to be modest relative to the total amount of virus in the ER . Thus , the decreased VP1 level in the cytosol fractions observed when Erlin1 , Erlin2 , or Erlin1/2 were depleted is not due to defective ER arrival of SV40 from the cell surface . These data establish Erlin1/2 as being critical for supporting SV40 ER-to-cytosol transport , consistent with the importance of these factors during SV40 infection ( Fig 2 ) . During infection , SV40 triggers mobilization of select ER membrane proteins including B12 , B14 , C18 , and BAP31 into distinct puncta on the ER referred to as foci . The virus-induced foci structures have been proposed to function as SV40’s cytosol entry sites from the ER [4–6 , 21] . To begin to address the molecular basis by which Erlin1/2 promote ER membrane penetration of SV40 , we asked if these ER membrane proteins support foci formation . Accordingly , cells transfected with scrambled or PAN-Erlin siRNA were infected with SV40 , and subjected to immunofluorescence using antibodies against B12 and BAP31 to score for appearance of B12 and BAP31 double-positive foci . Our epifluorescence microscopy-based analyses revealed that silencing Erlin1/2 decreased formation of SV40-induced , B12/BAP31-containing foci by approximately 60% ( Fig 3F , compare top and bottom panels; the level of B12/BAP31-containing foci was quantified in Fig 3G ) . We conclude that Erlin1/2 play a pivotal function during SV40-triggered foci formation–this likely explains why Erlin1/2 are crucial for SV40’s ER-to-cytosol membrane transport ( Fig 3A and 3B ) leading to successful infection ( Fig 2 ) . Since Erlin1/2 were initially identified as B12-interacting partners , we asked if B12’s ability to interact with Erin1/2 enables B12 to mobilize into the foci in order to support viral infection . To evaluate this idea , we first sought to identify a B12 mutant that cannot bind to Erlin1/2 . Since Erlin1/2 have a short cytosolic tail followed by single transmembrane domain and an extended ER luminal region , the B12-Erlin1/2 interaction is likely mediated in the ER . In addition , the C-terminal luminal domains of B12 and B14 display a high degree of sequence variations . Because B12 but not B14 interacts with Erlin1/2 ( Fig 1D ) , we reasoned that B12’s C-terminal domain is likely responsible for engaging Erlin1/2 . To test this , we constructed WT B12 and a B12 mutant lacking the last 20 amino acids of its C-terminus , with both constructs containing a single FLAG tag appended at the N-terminus ( FLAG-B12 [WT] and FLAG-B12 [1–355]; Fig 4A ) . As control constructs , in addition to GFP-FLAG , we generated a B12 point mutant in which a mutation is introduced within its cytosolic J-domain ( FLAG-B12 [H138Q]; Fig 4A ) ; while this mutation renders the protein incapable of binding to cytosolic Hsc70 , its luminal C-terminus should remain intact . In cells expressing either GFP-FLAG , FLAG-B12 [WT] , FLAG-B12 [H138Q] , or FLAG-B12 [1–355] , we found that only pull down of FLAG-B12 [WT] or FLAG-B12 [H138Q] but not GFP-FLAG or FLAG-B12 [1–355] co-precipitated endogenous Erlin1/2 ( Fig 4B , first and second panels , compare lanes 2 and 3 to lanes 1 and 4 ) . These findings demonstrate that FLAG-B12 [1–355] cannot interact with Erlin1/2 , indicating that B12’s last 20 amino acids are required for binding to Erlin1/2 . Importantly , in contrast to FLAG-B12 [WT] , FLAG-B12 [1–355] inefficiently mobilizes into the BAP31-containing foci during SV40 infection ( Fig 4C , compare top to bottom row; the level of BAP31 and FLAG double-positive foci is quantified in Fig 4D ) . These data suggest that B12’s interactions with Erlin1/2 are important for the efficient mobilization of B12 into the foci during virus infection . To probe if FLAG-B12 [1–355]’s inability to mobilize into the foci affects virus infection , we generated a B12 CRISPR knockout ( KO ) cell line ( Fig 4E , top panel , compare lane 2 to 1 ) , which was sequenced to confirm the mutation ( S1 Fig ) . SV40 was then incubated with the parental cell line transfected with GFP-FLAG , or the CRISPR B12 KO cell line transfected with either GFP-FLAG , FLAG-B12 [WT] , or FLAG-B12 [1–355] . Virus infection was assessed by scoring for the expression of large T antigen in only FLAG-expressing cells . Using this strategy , we found that SV40 infection was potently blocked in the CRISPR B12 KO cell line when compared to the parental control cells ( Fig 4F , compare second to first bar ) , consistent with our previous studies using an RNAi-mediated knockdown strategy [4 , 22] . Strikingly , addback of FLAG-B12 [WT] but not FLAG-B12 [1–355] into the B12 KO cells fully restored infection ( Fig 4F , compare third to fourth bar ) , indicating that FLAG-B12 [1–355] cannot support SV40 infection . Thus , as a mutant B12 that cannot bind to Erlin1/2 nor efficiently mobilize into the foci also fails to restore infection , these data strongly suggest that B12’s interaction with Erlin1/2 is essential for its reorganization into the foci where B12 facilitates SV40 ER membrane penetration leading to successful infection . The observation that B12’s association with Erlin1/2 is critical for B12 to reorganize into the foci raises the possibility that Erlin1/2 might also mobilize into the foci . However , we found that neither endogenous Erlin1 ( Fig 5A ) nor transfected Erlin2*-FLAG ( Fig 5B ) mobilized into the BAP31-positive foci during SV40 infection by epifluorescence microscopy . Confocal microscopy analyses similarly revealed that neither transfected Erlin1*-FLAG nor Erlin2*-FLAG reorganize into the SV40-induced BAP31-positive foci ( S2 Fig ) . These results demonstrate that , despite the importance of the B12-Erlin1/2 interaction in supporting B12’s mobilization into the foci , Erlin1/2 themselves do not reorganize into these structures . As B12 reorganizes into the foci during SV40 infection , we reasoned that this J-protein must move laterally along the ER lipid bilayer to reach the foci during virus infection . Because Erlin1/2 do not reorganize into the foci , we hypothesize that B12’s mobilization is initiated when it is released from Erlin1/2 . To test this , we used a co-immunoprecipitation approach . Cells transfected with GFP-FLAG or Erlin2*-FLAG were mock-infected or infected with SV40 . The resulting whole cell extracts were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads , and the precipitated proteins analyzed by SDS-PAGE and immunoblotting . We found that Erlin2*-FLAG but not GFP-FLAG co-precipitated endogenous B12 in mock-infected cells ( Fig 6A , top panel , compare lane 2 to 1 ) , as expected ( Fig 1 ) . However , SV40 infection significantly reduced the B12-Erlin2*-FLAG interaction ( Fig 6A , top panel , compare lane 3 to 2; quantified in Fig 6D ) , suggesting that the virus triggers B12 release from Erlin2 . SV40’s ability to reduce this interaction requires its presence in the ER because blocking virus trafficking to the ER ( by incubating cells with BFA ) prevented SV40 from releasing B12 from Erlin2 ( Fig 6A , top panel , compare lane 4 to 3; quantified in Fig 6D ) . SV40 regulates the B12-Erlin1-FLAG interaction in a similar manner ( Fig 6B; quantified in Fig 6E ) . Virus-induced release of B12 from Erlin1/2 is specific because SV40 does not affect binding of Erlin2*-FLAG to TMUB1 ( Fig 6C , top panel , compare lane 2 to 1; quantified in Fig 6F ) , an established ER membrane partner of Erlin2 [33] . Our binding analyses thus demonstrate that SV40 specifically promotes release of B12 from Erlin1/2 , likely to initiate B12’s mobilization and recruitment to the foci where it supports virus ER membrane penetration . We previously found that an SV40 mutant lacking VP3 ( SV40 ( ΔVP3 ) , but not VP2 , can traffic from the cell surface to the ER [32] , although this mutant virus cannot induce foci formation [4] nor penetrate the ER membrane [32] . To test if SV40 ( ΔVP3 ) induces release of B12 from Erlin2 , cells transfected with Erlin2*-FLAG were mock-infected , infected with WT SV40 , or infected with SV40 ( ΔVP3 ) ( Fig 6G ) . Cells were then harvested 16 hpi , and the resulting whole cell lysates subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . We found that while WT SV40 markedly induced dissociation of endogenous B12 from Erlin2-FLAG , SV40 ( ΔVP3 ) did so more modestly ( Fig 6H , top panel , compare lanes 2 and 3 to 1 ) , raising the possibility that VP3 may be involved in triggering B12 release from Erlin2 . To unambiguously demonstrate that B12 release from the Erlins is necessary for B12 to reorganize into the foci to support SV40 infection , we employed a chemical-induced dimerization strategy to trap the B12-Erlin interaction in the presence of SV40 . As depicted in Fig 7A , we generated a WT B12 construct containing an FRB domain and an S-tag at its C-terminus ( B12 [WT]-FRB-S ) , and an Erlin2 construct containing an FKBP domain and a FLAG-tag at its C-terminus ( Erlin2-FKBP-FLAG ) . As the rapamycin-1 analogue ( rapalog-1 ) dimerizes FRB and FKBP [34] , this drug should induce dimerization of B12 [WT]-FRB-S and Erlin2-FKBP-FLAG , thereby forcibly trapping B12 to Erlin2 . To verify the integrity of this system , B12 CRISPR KO cells transfected with B12 [WT]-FRB-S with or without Erlin2-FKBP-FLAG were infected with SV40 or mock-infected in the absence or presence of rapalog-1 . The resulting cell lysates were subjected to immunoprecipitation using FLAG antibody-conjugated agarose beads . Consistent with the endogenous B12-Erlin2-FLAG interaction ( Fig 6A ) , precipitating Erlin2-FKBP-FLAG specifically pulled down B12 [WT]-FRB-S ( Fig 7B , top panel , compare lane 1 to 4 ) . SV40 infection decreased the B12 [WT]-FRB-S- Erlin2-FKBP-FLAG interaction ( Fig 7B , top panel , compare lane 2 to 1 ) . Importantly , adding rapalog-1 prevented the virus-induced decrease in interaction between these two proteins and appeared to have strengthened it ( Fig 7B , top panel , compare lane 3 to 1 and 2 ) . These results demonstrate that addition of rapalog-1 indeed induced hetero-dimerization of B12 [WT]-FRB-S and Erlin2-FKBP-FLAG , as described in Fig 7A . We reasoned that if SV40-dependent B12 release from Erlin2 is required for B12’s reorganization to the foci to support virus ER membrane penetration leading to infection , artificially force-trapping B12 to Erlin2 should prevent B12 from mobilizing into the foci and thus preclude successful infection . To test this , B12 CRISPR KO and its parental CV-1 cells were transfected with Erlin2-FKBP-FLAG and either the control GFP-S , B12 [WT]-S , or B12 [WT]-FRB-S construct , and the experiments conducted with or without adding rapalog-1 . Only cells expressing the S-tagged construct was scored for the expression of large T antigen . As expected ( Fig 4F ) , B12 knockout significantly blocked SV40 infection ( Fig 7C , compare second to first bar ) . Importantly , although expressing B12 [WT]-S completely restored infection regardless of the presence of rapalog-1 ( Fig 7C , compare third and fourth to second bar ) , expressing B12 [WT]-FRB-S can only restore infection in rapalog-1’s absence ( Fig 7C , compare fifth to sixth bar ) . Thus , an exogenously expressed B12 cannot rescue SV40 infection in B12 knockout cells if the B12 protein is force-trapped to Erlin2 . These results predict that exogenously expressed B12 [WT]-FRB-S , in the presence of the Erlin2-FKBP-FLAG binding partner , would be restricted from mobilizing into the foci upon addition of rapalog-1 during SV40 infection . Indeed , B12 [WT]-FRB-S can largely mobilize to the BAP31-positive foci during SV40 infection in the absence of rapalog-1 , similar to B12 [WT]-S ( Fig 7D and S3 Fig , compare first rows in both Figs; quantified in Fig 7E , compare third to first bar ) . By contrast , whereas B12 [WT]-S can reorganize into the virus-induced foci even in the presence of rapalog-1 ( S3 Fig , second row; quantified in Fig 7E , second bar ) , B12 [WT]-FRB-S inefficiently mobilizes into this structure in the presence of rapalog-1 ( Fig 7D , second row; quantified in Fig 7E , fourth bar ) . Hence , artificially entrapping B12 to Erlin2 prevents B12 from moving into the foci structure . These data are in agreement with the infection experiments , further supporting the hypothesis that SV40-induced release of B12 into the foci is essential for promoting virus ER membrane penetration leading to successful infection . Because B12’s release from Erlin1/2 is critical during SV40 ER membrane transport , we hypothesize that part of Erlin1/2’s function is to position B12 in the ER so that B12 can be properly released upon SV40 infection . Accordingly , we probed the fate of endogenous B12 in the absence of Erlin1/2 by epifluorescence microscopy and found that a fraction of B12 mislocalizes to small aggregates that co-localize with the nucleus ( Fig 8A , compare top and bottom panels; quantified in Fig 8B ) . These small B12 aggregates were not artifacts caused by epifluorescence microscopy because confocal microscopy analyses demonstrated that B12 does not normally form such small aggregates ( S4 Fig ) . These findings suggest that Erlin1/2 normally anchor B12 in the ER . In fact , when SV40 infection was evaluated in PAN-Erlin siRNA-transfected cells where B12 displays an obvious nuclear aggregation phenotype ( as oppose to total cells transfected with the PAN-Erlin siRNA ) , the infection block was more severe ( Fig 8C ) . Thus , when Erlin1/2 depletion can lead to mislocalization of B12 to the nucleus , SV40 infection is severely compromised . The idea that B12 requires binding partners to anchor it in the ER is supported by a previous report demonstrating that overexpressed WT B12 localizes to the nucleus [35] , presumably because the overexpressed B12 lacks appropriate levels of corresponding binding partners such as Erlin1/2 that would normally restrict it in the ER . To further support this view , we used a truncated CMV promoter that retains a very low transcriptional activity [36] to drive the expression of either FLAG-B12 [WT] or FLAG-B12 [1–355] . Our results revealed that FLAG-B12 [1–355] displayed an increased mislocalization to the nucleus when compared to FLAG-B12 [WT] ( Fig 8D ) , likely because FLAG-B12 [1–355] is inefficiently anchored in the ER since it cannot bind to Erlin1/2 ( Fig 4 ) .
Membrane penetration by non-enveloped viruses remains an enigmatic process . One key question is whether the viral particle hijacks pre-existing protein-conducting channels embedded in the limiting membrane that would support the transport process , or if the virus creates de-novo membrane transport structures that facilitate membrane translocation . In the case of the non-enveloped SV40 , where translocation across the ER membrane represents the decisive infection step , increasing evidence suggest that this virus does not exploit a pre-existing channel such as the ER membrane-bound E3 ubiquitin ligase Hrd1 [5 , 37 , 38] . Hrd1 is the central component of the retro-translocation channel that translocates misfolded ER proteins to the cytosol for proteasomal degradation in a pathway called ER-associated degradation ( ERAD; [39] ) . Instead , SV40 induces reorganization of select ER membrane components into distinct puncta in the ER membrane called foci that are postulated to serve as the cytosol entry sites [4–6 , 21] . This represents the only example of a non-enveloped virus constructing its own membrane transport portal during entry . The J-protein B12 is one of the ER membrane proteins that mobilizes into the foci during SV40 infection [6] . In the foci , B12 assists in the recruitment of a cytosolic chaperone complex that ejects the virus into the cytosol in coordination with the two other transmembrane J-proteins B14 and C18 [4 , 6 , 21] . However , the molecular basis by which B12 mobilizes into the foci and the host factors co-opted to regulate this process remain unknown . In this study , we identified two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) as cellular components that bind to B12 and facilitate B12’s reorganization into the SV40-induced foci . As proposed in a model depicted in Fig 9 , we envision that Erlin1/2 , which normally hetero-oligomerize , restrict B12 in the ER membrane by associating with B12 . Upon arrival of SV40 to the ER lumen from the cell surface , it induces the release of B12 from the Erlin1/2 complex ( step 1 ) . The disengaged B12 in turn reorganizes into the foci in order to support ER-to-cytosol transport of SV40 ( step 2 ) . At present , how SV40 triggers B12’s release from Erlins is unclear . It is possible that SV40 directly interacts with and induces conformational changes to either B12 or Erlin1/2 , causing these components to lose affinity for each other . Alternatively , during SV40 infection , B12 might attract other ER or cytosolic factors that competitively interfere with binding to the Erlin proteins . Future experiments are needed to clarify this important point . Regardless of the mechanism , the release step is required for B12’s mobilization into the foci where it promotes virus infection as artificially locking B12 to Erlin2 prevents these downstream events from occurring . When Erlin1/2 is depleted , we found that endogenous B12 can mislocalize to the nucleus ( Fig 9 ) , suggesting that Erlins act as anchors , restricting B12 in the ER . Because Erlin1/2 have been shown to hetero-dimerize and form massive megadalton protein complexes [26–28] , this intrinsic property may enable them to function as efficient anchoring factors . Consistent with this , another group demonstrated that overexpressed WT B12 similarly mislocalizes to the nucleus and forms nuclear globular structures termed DJANGOS [35] , presumably due to insufficient levels of Erlins that are required to restrict the overexpressed proteins in the ER . The observation that an ER membrane protein such as B12 can localize incorrectly to the nucleus is not surprising since the ER membrane is topologically contiguous with the outer nuclear membrane . B14 and C18 also promote SV40 ER membrane penetration and infection [20 , 22] . Intriguingly , both of these proteins reorganize into the foci in a virus-dependent manner [4 , 6] . Because B14 does not bind to Erlin1/2 , the mechanism by which it is recruited into the foci must be different than B12 . However , as we previously demonstrated that C18 engages Erlin1/2 [20] , C18’s mechanism of release/recruitment into the foci might be similar to B12 . Erlin1/2-dependent restriction of B12 in the ER is highly reminiscent of the mechanism by which Erlin1/2 restricts the ER membrane protein sterol-regulated element binding protein ( SREBP ) in the ER through a direct interaction with the SREBP-SCAP complex [40] . SREBP is normally kept in the ER but must mobilize to the Golgi when the cholesterol level is low . In the Golgi , SREBP is cleaved to generate an active transcription factor , which in turn moves to the nucleus to upregulate genes responsible for cholesterol synthesis [41] . The parallel finding that Erlins anchor yet another transmembrane protein in the ER suggests that Erlins might function as a general ER-anchoring factor . Moreover , given that Erlins have been shown to play a poorly-defined role in ERAD [39] , the possibility that Erlins might act to restrict various ERAD membrane components in the ER should now be explored .
To construct a transfer vector in order to introduce 3xFLAG-B12 into Flp-in 293 T-REx cells , the 3xFLAG tag sequence was attached to the B12 cDNA by PCR using oligos containing the corresponding sequence and the resulting PCR product inserted into pcDNA5 FRT/TO . The human Erlin1 cDNA was amplified from the HEK 293T cDNA library and the human Erlin2 cDNA was a generous gift from Dr . R . DeBose-Boyd ( University of Texas Southwestern Medical Center ) . To generate siRNA-resistant Erlin1 and Erlin2 constructs , the following silent mutations were introduced into each cDNA using an overlapping PCR method: Erlin1: 248 GGG-GTT-ATG-ATT-TAC-ATC-GAT-AGG 272 , and Erlin2: 241 GGT-GTG-ATG-ATT-TAC-TTC-GAT-CGG 265 , where the underlines denote the introduced silent mutation . The resulting PCR products were inserted into pCDNA3 . 1 ( - ) in frame with the FLAG tag sequence by standard cloning methods . FLAG-B12 [WT] and FLAG-B12 [H138Q] were previously described [22 , 25] and FLAG-B12 [1–355] was constructed by amplifying the corresponding DNA sequence and sub-cloning it into pcDNA3 . 1 ( - ) . For the S-tagged constructs used in this study , PCR-amplified cDNA sequences were inserted into pcDNA3 . 1 ( - ) in frame with the S tag sequence by standard cloning methods . To express low level of B12 constructs , the enhancer region of the full length CMV promoter in pcDNA3 . 1 ( - ) ( 234–773 ) was deleted by an inverse PCR method [36] . To fuse FKBP to Erlin2-FLAG , a PCR-amplified FKBP sequence was introduced between the Erlin2 and FLAG-tag DNA sequences using standard cloning methods . Similarly , a PCR-amplified FRB sequence was introduced between the B12 and S-tag DNA sequences . To construct vectors co-expressing CMV-driven Erlin2-FKBP-FLAG and either truncated CMV-driven GFP-S , B12 [WT]-S , or B12 [WT]-FRB-S , an expression cassette containing the truncated CMV promoter , the S-tagged gene , and the polyA sequences ( with BglII and MluI sites attached at 5’ and 3’ ends , respectively ) was amplified by PCR and inserted into BglII- and MluI-digested pcDNA/Erlin2-FKBP-FLAG . Flp-in 293 T-REx cell stably expressing 3xFLAG-B12 were harvested and semi-permeabilized in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 0 . 02% digitonin , and 1 mM PMSF at 4°C for 10 min . Following centrifugation at 16 , 100x g for 10 min at 4°C , the resulting pellet fraction was further lysed in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% deoxy Big CHAP and 1 mM PMSF at 4°C for 10 min and centrifuged at 16 , 100x g for 10 min at 4°C . The resulting supernatant fraction was further centrifuged at 50 , 000 rpm for 10 min at 4°C in a TLA-100 . 3 rotor ( Beckman Coulter , Brea , CA ) to remove residual detergent-insoluble materials . The cleared lysates were incubated with anti-FLAG M2 antibody for 2 h at 4°C , mixed with Protein G magnetic beads , and further incubated for 2 h at 4°C . After the magnetic beads were extensively washed with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , and 0 . 1% deoxy Big CHAP , bound materials were eluted with 0 . 1 mg/ml 3xFLAG peptide or SDS sample buffer . SV40 was prepared as described previously in Inoue and Tsai , 2011 [32] . 24–48 h post siRNA transfection , CV-1 cells were incubated with SV40 ( MOI ~20 ) at 4°C for 1 h , washed , and incubated at 37°C for 12 h . Cells were harvested with a scraper , semi-permeabilized with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 0 . 1% digitonin , and 1 mM PMSF at 4°C for 10 min , and centrifuged at 16 , 100x g for 10 min . The resulting supernatant is referred to as the ‘cytosol’ fraction , while the resulting pellet ( resuspended in SDS sample buffer ) is referred to as ‘membrane’ fraction . Where indicated , the membrane fraction was further treated with a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% Triton X-100 , and 1 mM PMSF at 4°C for 10 min , and centrifuged at 16 , 100x g for 10 min . This second supernatant is the Triton X-100-soluble membrane fraction harboring ER-localized SV40 . Approximately 1x106 HEK 293T cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated constructs using PEI . 24 h after DNA transfection , cells were harvested and lysed in a buffer containing 50 mM Hepes ( pH 7 . 5 ) , 150 mM NaCl , 1% Triton X-100 , and 1 mM PMSF . Following centrifugation at 16 , 100x g for 10 min , the resulting supernatant fractions were incubated with FLAG antibody-conjugated agarose beads at 4°C for 2 h . After the beads were extensively washed with lysis buffer , bound proteins were eluted from the beads with SDS sample buffer and subjected to SDS-PAGE followed by immunoblotting with the indicated antibodies . To analyze SV40-induced B12 release from Erlin1/2 , approximately 3x105 CV-1 cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated DNA constructs using Fugene HD . 24 h after DNA transfection , cells were infected with SV40 ( MOI~100 ) for 16 h in the absence or presence of BFA , harvested , and analyzed as above . The B12 [WT]-FRB-S-Erlin2-FKBP-FLAG interaction was analyzed similarly . Briefly , approximately 3x105 CRISPR B12 KO CV-1 cells were plated on a 6-cm plate , incubated for 24 h , and transfected with the indicated DNA constructs using PEI . 24 h after DNA transfection , cells were infected with mock or SV40 ( MOI~100 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 10 h , harvested , and analyzed as above . The target sequences of the siRNAs and their final concentrations used in this study are as follows: Erlin 1 siRNA: Thermo Fisher Predesigned Silencer siRNA , ID# 1473100 ( 50 μM ) , Erlin 2 siRNA: GAACUAUACUGCUGACUAU[dT][dT] ( 10 μM ) , PAN-Erlin siRNA: Thermo Fisher Predesigned Stealth RNAi siRNA , ID# HSS116356 ( 50 μM ) . Allstar negative control siRNA ( Qiagen , Hilden , Germany ) was used as a scrambled control siRNA . CV-1 cells were reverse-transfected with each siRNA at the indicated concentration using Lipofectamine RNAi MAX and incubated for 24 h ( Erlin1 and PAN-Erlin siRNAs ) or 48 h ( Erlin 2 siRNA ) prior to SV40 infection . Evaluation of XBP1 splicing was described previously in Ravindran et al . , 2015 [21] . CV-1 cells transfected with siRNAs were infected with SV40 ( MOI~0 . 3 ) for 24 h and fixed with 1% paraformaldehyde . Following permeabilization with 0 . 2% Triton X-100 , fixed cells were stained with a mouse monoclonal SV40 large T antigen antibody , followed by Alexa Fluor 488-conjugated secondary antibodies . In each experiment , approximately 1 , 000 cells were counted to assess the extent of large T antigen expression . For the Erlin1/2 rescue experiments , cells were initially transfected with siRNA , incubated for 24 h , and further transfected with FLAG-tagged constructs using Fugene HD . 24 h post DNA transfection , cells were infected with SV40 , fixed , and permeabilized as described above . Fixed cells were stained with a mouse monoclonal SV40 large T antigen antibody and a rabbit monoclonal FLAG antibody followed by Alexa Fluor 594-conjugated anti-mouse IgG and Alexa Fluor 488-conjugated anti-rabbit secondary antibodies . Large T antigen expression was evaluated in cells expressing the FLAG-tagged protein . At least 100 cells for each sample were counted to assess the extent of large T antigen expression . B12 rescue experiments were similarly performed , except that parental CV-1 or B12 CRISPR KO cells were used without siRNA transfection . As indicated in the result and discussion sections , overexpressed B12 often mislocalized in the nucleus . To avoid artifacts caused by B12 mislocaliztion , only cells expressing FLAG-B12 [WT] or [1–355] that display a clear ER-localization patterns were counted as “FLAG-B12 [WT] or [1–355]-expressing” cells , unless otherwise noted . CV-1 cells transfected with the indicated siRNAs or DNA constructs were infected with SV40 ( MOI~30–50 ) for the indicated time . Cells were fixed and subjected to an immunofluorescence-based method as described in the SV40 infection assay , except that a rat monoclonal BAP31 antibody , the indicated primary antibodies , Alexa Fluor 594-conjugated anti-mouse IgG , and Alexa Fluor 488-conjugated anti-rabbit IgG were used . For triple staining , a rat monoclonal BAP31 antibody , a rabbit polyclonal B12 antibody , and a monoclonal mouse FLAG antibody were used as primary antibodies , while Alexa Fluor 594-conjugated anti-rat IgG , Alexa Fluor 488-conjugated anti-rabbit IgG , and Alexa Fluo-350-conjugated anti-mouse IgG secondary antibodies were used as secondary antibodies . Parental and B12 CRISPR KO CV-1 cells were plated on coverslips in a 24-well plate at a density of approximately 2 . 5x104 cells/well , incubated for 24 h , and transfected with DNA constructs using Fugene HD . 24 h after DNA transfection , cells were infected with SV40 ( MOI~0 . 3 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 18 h . 24 hpi , cells were fixed and subjected to an immunofluorescence method using a mouse monoclonal SV40 large T antigen antibody and a rabbit polyclonal S-tag antibody as described above . Fixed cell samples were imaged using Nikon Eclipse TE2000-E equipped with a 40x objective and a 12-bit CCD camera . To avoid potential overexpression-induced artifacts , cells were counted and examined for the large T antigen expression when expression of B12 [WT]-S or B12 [WT]-FRB-S was localized in the ER without forming nuclear aggregates and that their signal values range from 200 to 1000 under 12-bit resolution . At least 100 cells were counted to assess the extent of large T antigen expression . For SV40-induced foci formation assay , cells were transfected in the same manner , infected with SV40 ( MOI~30–50 ) for 6 h and further incubated in the absence or presence of 0 . 5 μM rapalog-1 heterodimerizer for 10 h . Following the immunofluorescence procedure using anti S-tag and BAP31 antibodies , cell images were taken as in the infection assay , except that instead of assessing the large T antigen expression , BAP31 images were captured . At least 100 cells were counted to assess the extent of BAP31 and S double-positive foci formation in each experiment . Cells transfected with truncated CMV promoter-driven FLAG-B12 [WT] or FLAG-B12 [1–355] were fixed , subjected to the above-mentioned immunofluorescence method using a rabbit monoclonal FLAG antibody and Alexa Fluor 488-conjugated anti-rabbit IgG , and the samples were imaged as described above . To compare the expression level of FLAG-B12 [WT] and FLAG-B12 [1–355] , all images were taken using the same exposure time . Due to the varied expression level of FLAG-tagged proteins among cells , cells were counted and assessed for mislocalization of FLAG-tagged B12 constructs only when the signal values of the ER-localized FLAG-tagged B12 constructs ranged from 500 to 1500 under 12-bit resolution and/or the FLAG-tagged B12 constructs formed small nuclear aggregate structures . By contrast , cells were considered to have overexpressed exogenous B12 and removed from analysis when the signal values of ER-localized FLAG-tagged B12 constructs were above 1500 under 12-bit resolution and/or the FLAG-tagged B12 constructs formed massive nuclear aggregated structures . At least 100 cells were counted to assess mislocalization of FLAG-tagged B12 constructs in each experiment . Flp-In 293 T-REx cells ( Thermo Fisher Sciences ) were co-transfected with pOG44 ( Thermo Fisher Sciences ) and pcDNA5 FRT/TO encoding 3xFLAG-B12 [WT] using Lipofectamine 2000 . 24 h post transfection , cells were split and cultured in DMEM medium containing 100 μg/ml hygromycin and 5 μg/ml blasticidin for 10–15 days . Hygromycin-resistant colonies were cloned . PX330 and PX459 used for generating B12 KO cells were gifts from Dr . F . Zhang ( Addgene plasmid # 42230 and 62988 ) [42] . The following two oligonucleotides containing the +325 to +344 sequence from the transcription start site of the human DNAJB12 ( GI: 194306639 ) were annealed and inserted into PX459 . Fw: CACCGAG GAAGCGGAGCG CCCGGTC . Rv: AAACGACCGGGCGCTCCGCTTCCTC . The PX459 encoding the guide RNA or control PX330 was transfected into CV-1 cells using Fugne HD and 24 h post transfection , the medium was replaced with fresh medium containing 6 μg/ml puromycin . After most control cells have died off , the surviving cells transfected with the PX459 construct were re-plated for single colony isolation . When single colonies were fully grown and became visible , they were isolated with cloning cylinders . One of the established cell lines was further grown , and the resulting whole cell lysate was subjected to immunoblotting using anti-B12 antibodies . The genome was also isolated from this cell line and served as a template to amplify a region corresponding to the +273 to +619 sequence from the transcriptional start site of the human DNAJB12 ( GI: 194306639 ) using the PCR primers: Forward: CTCACACTCA CCGCGAACTC and Reverse: GCACCATTATTGACACCTAC . The amplified PCR product was subjected to agarose gel electrophoresis , and the corresponding band was excised from the gel and purified . The isolated product was analyzed by Sanger sequencing using the following primer: 5’-GACATACGGTAGCACTTCCAC-3’ . Because only one sharp peak was observed by Sanger analysis in which an “A” insertion was detected between the +341 and +342 positions , we concluded that both alleles have the same mutation . | Polyomavirus ( PyV ) is a non-enveloped DNA tumor virus that causes debilitating human diseases especially in immunocompromised individuals . At the cellular level , PyVs such as the simian PyV SV40 must enter a host cell and penetrate the ER membrane to reach the cytosol in order to cause infection . Prior to ER membrane transport , SV40 reorganizes select ER membrane proteins including the J-protein B12 to potential membrane penetration sites on the ER membrane called foci where B12 facilitates virus extraction into the cytosol . How B12 reorganizes into the foci is unclear . Here we find that two closely related ER membrane proteins Erlin1 and Erlin2 ( Erlin1/2 ) bind to B12 . During infection , SV40 induces release of this J-protein from Erlin1/2 to enable B12 to reorganize into the foci . Our data reveal how a non-enveloped virus mobilizes a specific ER membrane component to a membrane penetration structure to promote its own membrane transport . | [
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... | 2017 | Regulated Erlin-dependent release of the B12 transmembrane J-protein promotes ER membrane penetration of a non-enveloped virus |
DNA hydroxymethylation has recently been shown to play critical roles in regulating gene expression and terminal differentiation events in a variety of developmental contexts . However , little is known about its function during eye development . Methylcytosine dioxygenases of the Tet family convert 5-methylcytosine ( 5mC ) to 5-hydroxymethylcytosine ( 5hmC ) , an epigenetic mark thought to serve as a precursor for DNA demethylation and as a stable mark in neurons . Here , we report a requirement for Tet activity during zebrafish retinal neurogenesis . In tet2-/-;tet3-/- mutants , retinal neurons are specified but most fail to terminally differentiate . While differentiation of the first born retinal neurons , the retinal ganglion cells ( RGCs ) , is less affected in tet2-/-;tet3-/- mutants than other retinal cell types , the majority of RGCs do not undergo terminal morphogenesis and form axons . Moreover , the few photoreceptors that differentiate in tet2-/-;tet3-/- mutants fail to form outer segments , suggesting that Tet function is also required for terminal morphogenesis of differentiated retinal neurons . Mosaic analyses revealed a surprising cell non-autonomous requirement for tet2 and tet3 activity in facilitating retinal neurogenesis . Through a combination of candidate gene analysis , transcriptomics and pharmacological manipulations , we identified the Notch and Wnt pathways as cell-extrinsic pathways regulated by tet2 and tet3 activity during RGC differentiation and morphogenesis . Transcriptome analyses also revealed the ectopic expression of non-retinal genes in tet2-/-;tet3-/- mutant retinae , and this correlated with locus-specific reduction in 5hmC . These data provide the first evidence that Tet-dependent regulation of 5hmC formation is critical for retinal neurogenesis , and highlight an additional layer of complexity in the progression from retinal progenitor cell to differentiated retinal neuron during development of the vertebrate retina .
In vertebrates , the majority of CpG sequences in the genome are characterized by addition of a methyl group to the 5th carbon of cytosine residues , 5mC [1] . Hypermethylation within promoters/enhancers is associated with reduced gene transcription [2] , while gene body methylation directly correlates with expression [3] . Indeed , DNA methylation is critical for silencing imprinted genes and transposons [4–6] . Subsets of genes are differentially methylated according to tissue and cell type , and DNA methylation is thought to be a mechanism whereby cell type-specific expression patterns are set during terminal differentiation [7 , 8] , and by which some somatic progenitor cell populations are maintained [9–12] . Three main biochemical events orchestrate DNA methylation . First , de novo methylation , mediated by Dnmt3-family proteins , functions to methylate regions of hypomethylated DNA and is required for tissue-specific differentiation during development [8 , 13–15] . Second , maintenance methylation , mediated by DNA methyltransferase-1 ( Dnmt1 ) , copies the methylation pattern from existing DNA strands on to nascent daughter strands during DNA replication , a process that is important for maintaining the identities of actively proliferating cell populations [10 , 16] . Third , demethylation is the mechanism by which 5mC is removed from the genome . Far less is known about DNA demethylation but several biochemical pathways have been proposed to be involved and these include: replication-dependent passive dilution , direct base excision by the DNA repair machinery , and active enzymatic conversion of 5mC ( reviewed in [17] ) . Of these pathways , most evidence supports the latter and a role for members of the ten-eleven translocation ( Tet ) family of methylcytosine dioxygenases . These enzymes mediate the conversion of 5mC to 5-hydroxymethylcytosine ( 5hmC ) , which can then be converted to non-methylated cytosine [18–20] . Recent studies have identified roles for Tet proteins and DNA hydroxymethylation during vertebrate development , stem cell maintenance and in diseases such as cancer . In mouse , Tet1 and Tet2 knockouts are viable and fertile , while Tet1-/-;Tet2-/- double knockouts show a partially penetrant perinatal lethality associated with imprinting abnormalities [21] . A recently generated Tet1 knockout mouse showed forebrain defects at late gastrulation and high mortality [22] . Tet1/2/3 triple knockout ES cells possess a massive loss of 5hmC , deregulated gene expression , and an impaired ability to differentiate [23] . In frog , depletion of Tet3 by a translation-blocking morpholino ( MO ) affects early neural development and causes an eyeless phenotype [24] . tet2 MO-based knockdown in zebrafish results in mild defects in erythropoiesis [25]; however , mutations in tet2 do not cause any overt embryonic phenotype , although tet2-/- adults develop progressive age-related clonal myelodysplasia [26] . More recently , overlapping roles for tet2 and tet3 during hematopoietic stem cell differentiation have been identified [27] . Likely related to their functions during hematopoiesis , Tet proteins are also associated with a number of hematological malignancies in humans ( reviewed in [28] ) . Within the nervous system , Tet expression and 5hmC enrichment is detected in the developing mouse brain [29 , 30] . 5hmC levels increase during neuronal differentiation , with enrichment at enhancers and also within gene bodies of neuronal genes [30] . This enrichment is interesting because it is not associated with subsequent demethylation , in agreement with a study that shows biochemical stability of 5hmC marks within the genome [31] . Beyond loss-of-function experiments , Tet3 overexpression in mouse olfactory neurons results in an increase in 5hmC levels , altered gene expression , and defects in axon targeting [32] , and tet3 activity is upregulated in dorsal root ganglia neurons during axon regeneration [33] . More recent work has shown that Tet proteins regulate both intrinsic and extrinsic pathways during development . Intrinsically , Tet activity is required during hematopoiesis [34] and B-cell development [35] . Tets also modulate the activity of extrinsic signaling pathways in a variety of contexts . Tets suppress Wnt pathway activity during early mouse development to balance mesoderm and neuroectoderm fates [36] , while at later stages in the intestinal epithelium , Tet1 is required for Wnt pathway activation [37] . During gastrulation , Tet activity regulates the Nodal pathway by suppressing the expression of Nodal inhibitors [38] . Although DNA methylation and hydroxymethylation have been studied extensively in the context of stem cell programming and disease , far less is known about their roles during development and in regulating organogenesis to create a complex structure like the retina . The vertebrate retina consists of seven main cell types that perform distinct functions in phototransduction and visual signal transmission ( reviewed in [39] ) . These cells are organized in the three retinal layers: the ganglion cell layer ( GCL ) , outer nuclear layer ( ONL ) , and inner nuclear layer ( INL ) . The retina develops from a common pool of seemingly indistinguishable multipotent retinal progenitor cells ( RPCs ) , which ultimately give rise to all retinal cells in a stereotyped time order ( reviewed in [40] ) . While much has been learned regarding the specification events that direct RPCs to distinct retinal cell fates , we know little about the epigenetic regulation of these events , or the mechanisms underlying terminal differentiation and morphogenesis of retinal neurons and glia . Indeed , no studies have determined whether Tet function or DNA hydroxymethylation are required during retinal development . To address this topic we functionally inactivated the tet2 and tet3 genes in zebrafish and identified defects in retinal development that resulted from deregulated gene expression in tet2-/-;tet3-/- mutants . This is the first detailed analysis of Tet function during vertebrate retinal development , and our data support a model in which Tet-mediated regulation of 5hmC levels is critical for retinal neurogenesis .
Several studies have reported the expression of Tet-family genes in developing embryos and in tissues , including the eye ( e . g . [24 , 29] ) . Zebrafish possess three Tet-family genes ( tet1 , tet2 and tet3 ) , and these are orthologous to mouse and human Tet1-3 ( S1A Fig ) . At 24 hours post fertilization ( hpf ) all three tet genes are expressed broadly throughout the embryo , including the eye ( S1B–S1D Fig ) . At 48 and 72hpf , tet1 expression is faint in the head and is expressed at comparatively lower levels in the eye than tet2 and tet3 ( S1E and S1H Fig ) . At 48 and 72hpf , tet2 and tet3 are strongly expressed in the anterior of the embryo and , in the eye , transcripts are enriched in the GCL and INL , and are more faintly detected in the ONL ( S1F , S1G , S1I and S1J Fig ) . Because tet2 and tet3 were the only Tet-family genes that showed prominent retinal expression past 24hpf , and a recent report demonstrated that tet1 was dispensable for normal zebrafish development and for DNA hydroxymethylation [27] , we focused on tet2 and tet3 for functional perturbations . Mutant alleles were created by designing transcription activator-like effector nucleases ( TALENs ) targeting the first exon of tet2 and the sixth exon of tet3 [41] . Injected mosaic founders were screened for germline transmission , and mutant alleles were detected and sorted by restriction fragment length polymorphisms ( RFLP ) and then sequenced ( Fig 1A and 1B; S2 Fig ) . Two alleles were identified and maintained: tet2au59 mutants possess a 10bp deletion which is predicted to cause a frameshift beginning at amino acid ( aa ) 407 , inserting 37 incorrect amino acids and truncating the protein at aa 444 out of 1 , 716 [c . 1 , 219_1 , 229delATAGATTTAA , p . Ile407Thrfs*37] , and tet3au60 mutants possess a 22bp deletion mutation , which is predicted to cause a frameshift beginning at aa 1 , 212 , inserting 92 incorrect aa and truncating the tet3 protein at aa 1 , 304 out of 2 , 052 [c . 4 , 860_4 , 881delGAGATAAACTGTACAGAGAAGT , p . Gly1 , 212Alafs*92] . tet2-/- and tet3-/- mutants develop normally with no visible phenotype and they are homozygous viable ( S2 Fig ) . This is an unsurprising result given their close phylogenetic relationship and overlapping expression domains , and it is consistent with recent reports [26 , 27] . Thus , tet2-/-;tet3-/- mutants were generated by in-crossing double heterozygous adults ( tet2+/-;tet3+/- ) . tet2-/-;tet3-/- mutants were recovered at an expected Mendelian ratio ( 6 . 62%; n = 103 mutants/1 , 554 embryos ) . At 36hpf , tet2-/-;tet3-/- mutants displayed a distinct morphological phenotype where the anterior portion of the brain was enlarged and kinked when compared to wild-type siblings ( Fig 1D ) . At 2 days post-fertilization ( dpf ) , tet2-/-;tet3-/- mutants were microphthalmic , mildly hypopigmented , and displayed deformed craniofacial features , cardiac edema , and blood pooling , phenotypes that perdure through 4dpf ( Fig 1G , 1J and 1M ) . tet2au59 and tet3au60 mutations are predicted to truncate the proteins upstream of the C-terminal catalytic domain [20] and therefore encode null or severe loss of function alleles . At 2dpf and 5dpf , tet2 transcripts were detectable in phenotypically wild-type siblings , but not in tet2-/-; tet3-/- mutants , indicating that tet2 transcripts are degraded through nonsense-mediated decay ( NMD ) ( S3A Fig ) . tet3 transcripts were still present in both sibling and tet2-/-;tet3-/- mutants ( S3B Fig ) . To assess tet3 protein , Western blots were performed on 3dpf phenotypically wild-type siblings and tet2-/-;tet3-/- mutants ( S3C and S3D Fig ) . tet3 protein was undetectable in tet2-/-;tet3-/- mutants . These data indicate that tet2au59 and tet3au60 are null alleles . To experimentally validate the loss of catalytic function in tet2-/-;tet3-/- mutants , we utilized a sensitive enzyme-linked immunosorbent assay ( ELISA ) to quantify whole embryo 5hmC levels . At 2dpf , genetically wild-type and phenotypically wild-type siblings possessed genome-wide 5hmC levels of 0 . 30% and 0 . 28% , respectively , consistent with published levels in various isolated mouse tissues [22 , 42] . By comparison , tet2-/-;tet3-/- mutants possessed a significant reduction in 5hmC levels ( 0 . 03% , p<0 . 005 ) indicating that tet2au59 and tet3au60 mutants lack almost all 5hmC in their genome ( Fig 1C ) . To analyze retinal development in tet2-/-;tet3-/- mutants , we performed histology from 36hpf to 4dpf and assessed overall retinal structure . At 36hpf , no obvious differences between retinal morphology in tet2-/-;tet3-/- mutants and wild-type siblings were evident ( Fig 1E and 1F ) . At 2dpf , tet2-/-;tet3-/- mutant retinae appeared progenitor-like in morphology , as little evidence of lamination or neuronal differentiation was evident ( Fig 1H and 1I ) . At 3dpf , tet2-/-;tet3-/- mutant retinae displayed some evidence of lamination , though this was reduced when compared to that in phenotypically wild-type siblings . Moreover , while cells populated the inner retina , and a GCL and INL were discernable , all tet2-/-;tet3-/- mutants lacked a morphologically obvious optic nerve ( Fig 1K and 1L ) . Retinal defects remained prevalent at 4dpf with tet2-/-;tet3-/- mutant retinae continuing to possess lamination defects and no apparent formation of an optic nerve , despite the presence of cells in the inner retinal area normally occupied by RGCs ( Fig 1N and 1O ) . Because retinal cells in tet2-/-;tet3-/- mutants appeared to be progenitor-like in morphology at 2dpf , and lamination defects persisted at 3dpf and 4dpf , a phenotype associated with elongated proliferation and/or defects in cell cycle exit in zebrafish [43–45] , we next determined cell cycle dynamics in tet2-/-;tet3-/- mutant retinal cells . First we assayed the S to M phase progression by utilizing the percent labeled mitoses ( PLM ) assay [43 , 46 , 47] . Embryos were treated with a 15-minute bromodeoxyuridine ( BrdU ) pulse at 32hpf , washed , fixed at 30 , 60 , 90 , and 120 minutes post-treatment , and immunostained for BrdU and phosphohistone H3 ( pH3 ) ( Fig 2A ) . Cells in S-phase during the BrdU pulse ( BrdU+ ) and cells in late G2/M-phase at the time of fixation ( pH3+ ) were quantified . Cells that were proliferative during the BrdU pulse and then undergo mitosis are double positive ( BrdU+ , pH3+ ) . In contrast , cells that were not in S-phase during the BrdU pulse , but still undergo mitosis , are only pH3+ . Thus , the proportion of these cells { ( BrdU+pH3+ ) /pH3+} represent the ‘labeled’ mitotic events . In tet2-/-;tet3-/- mutants , the percent labeled mitoses are significantly lower than sibling at all time points examined ( Fig 2B–2D ) . Nearly 100% of ‘labeled’ RPCs in wild-type embryos completed the S to M phase transition by the end of 120-minute time window , while only 50% of tet2-/-;tet3-/- mutants competed this transition . This indicates that between 32-34hpf , tet2-/-;tet3-/- RPCs are progressing from S to M at a slower rate than wildtype RPCs . At later time points , BrdU incorporation assays over 2-hour pulse windows showed that , although tet2-/-;tet3-/- mutant eyes were smaller and contained fewer cells ( Fig 2E–2K ) , they contained a significantly higher percentage of BrdU+ cells at both 2dpf and 3dpf ( Fig 2L ) . At 2dpf , central retinal cells of wildtype embryos have exited the cell cycle and differentiated , while in tet2-/-;tet3-/- mutant eyes the central retina remained proliferative ( Fig 2E and 2F ) . By 3dpf , cells of the wildtype retina have exited the cell cycle and differentiated except for those in the ciliary marginal zone ( CMZ ) at the retinal periphery which remains proliferative throughout the life of the animal ( Fig 2G and 2H ) [48] . In tet2-/-;tet3-/- mutants , most cells within the central retina were no longer BrdU+ , but the peripheral ( CMZ ) domain was significantly expanded ( Fig 2M ) . Moreover , ectopic proliferative cells were detected in the inner retina , outside of the tet2-/-;tet3-/- CMZ ( Fig 2H ) , a phenotype not observed in wildtype siblings . The identity of these cells remains unclear , although we have ruled out the possibility of these being Müller glia ( MG ) cells , as they do not co-stain with the MG-specific marker , zrf-1/gfap ( Fig 3J ) . By 5dpf , a distinct zone of proliferation is present in tet2-/-;tet3-/- mutants , indicating that the mutant RPCs eventually completed cell cycle exit , and that ectopically proliferative cells are no longer present ( Fig 2I and 2J ) . While the PLM and BrdU incorporation results suggest that elongation of the cell cycle during early retinal development underlies microphthalmia in tet2-/-;tet3-/- mutants , apoptosis of RPCs or newly differentiated neurons could also contribute . To determine whether apoptosis plays a role in tet2-/-;tet3-/- mutant retinal defects , we performed terminal deoxynucleotidyl transferase dUTP nick-end labeling ( TUNEL ) assays . No increase in apoptotic cells was detected in tet2-/-;tet3-/- mutant until after 3dpf ( S4 Fig ) , indicating that apoptosis is unlikely to account for microphthalmia in tet2-/-;tet3-/- mutants . Given the impaired retinal lamination and cell cycle progression defects in tet2-/-;tet3-/- mutants , we next asked whether tet2-/-;tet3-/- retinal cells differentiate into neurons and Müller glia . In wild-type embryos , all retinal neuron and glial cell types are differentiated by 72hpf [i . e . [43 , 49 , 50]] . An early neuronal marker , HuC/D , detects RGCs and amacrine cells ( ACs ) [51 , 52]; in tet2-/-;tet3-/- mutants , while HuC/D+ cells were detected in the GCL and INL , their number in the INL was significantly lower and the few HuC/D+ cells present were restricted to the central-most region of the retina ( Fig 3A and 3F; S5 Fig ) . tet2-/-;tet3-/- mutants almost entirely lacked zpr1-positive red/green double cones ( Fig 3B and 3G ) , zpr-3-positive rods ( Fig 3C , 3D , 3H and 3I ) , and zrf-1/gfap-positive Müller glia ( Fig 3E and 3J ) . tet2-/-;tet3-/- mutants possess positive immunoreactivity to Zn8/neurolin , which detects differentiated RGCs [53] , although the region of Zn8+ RGCs does not extend as far to the retinal periphery as in wild-type siblings ( Fig 3K and 3P ) . For each of the markers tested , immunoreactive cells were restricted to the central-most retina and none were detected more peripherally , corresponding with the expanded zone of proliferation detected at 3dpf in BrdU assays ( Fig 2 ) . In sections of tet2-/-;tet3-/- mutant eyes , no optic nerve was detected ( Figs 1–3 ) . It was formally possible that an optic nerve was present , but that axons were misrouted inside of the retina , a phenotype associated with defects in axonal pathfinding [54] . To exclude this possibility , we performed whole-mount chromogenic labeling using the Zn8/neurolin antibody to label RGC axons [54] . In 3dpf phenotypically wild-type siblings , Zn8 labeled intra-retinal RGC axons that extend along the vitreal surface of the eye to generate the optic nerve , as well as the optic nerve itself as it passes through the choroid fissure ( CF ) and into the optic chiasm ( Fig 3L ) . In tet2-/-;tet3-/- mutants , Zn8 was only detected within the GCL and in a few axons within the choroid fissure ( Fig 3Q ) . As a more sensitive optic nerve labeling assay , we utilized a transgenic reporter , Tg ( isl2b:GFP ) zc7 , which expresses GFP in RGCs , and a subset of developing photoreceptors ( PRs ) [55] . Sibling embryos possessed a strong GFP signal in RGC cell bodies and axons along the entire length of the optic nerve , clearly visible in both section and whole-mount preparations ( Fig 3M–3O ) . In tet2-/-;tet3-/- mutants , while 12% of 41 mutant embryos lacked an optic nerve entirely , the remainder possessed an optic nerve , formed either bilaterally ( 39% ) or unilaterally ( 49% ) ; however , in these embryos , the optic nerve was thin and appeared to be composed of very few axons ( Fig 3M and 3R ) . For the few axons that did leave the eye , retinotectal projections appeared to be normal , even in embryos with unilateral optic nerves ( Fig 3R ) . Taken together , these data indicate that the terminal differentiation and morphogenesis of RGCs is affected in tet2-/-;tet3-/- mutants , and not axon pathfinding . Photoreceptors ( PRs ) undergo terminal differentiation and morphogenesis to form outer segments rich in photoreceptive molecules for phototransduction; this starts in the ventro-nasal patch and spreads through the retina ( Fig 3B–3D ) [50 , 56 , 57] . Sporadic rods and cones are detected in the central region of the retina in tet2-/-;tet3-/- mutants , likely the earliest born PRs , but none are detected more peripherally ( Fig 3G–3I ) . Moreover , in sibling embryos , while outer segments are well formed by 72hpf and highly immuoreactive to zpr-1 ( arrestin3a ) and zpr-3 ( rhodopsin ) , the few PRs that differentiate in tet2-/-;tet3-/- mutants possess little to no outer segment material ( Fig 3D and 3I ) . This is further highlighted by staining isl2b:GFP embryos with zpr-3 . Only a few isl2b:GFP+ PRs express zpr-3 and of those that do , they have nearly undetectable outer segments ( Fig 3O and 3T ) . Given the dramatic reductions in optic nerve size and in PR outer segment formation , we conclude that RGCs and PRs of tet2-/-;tet3-/- mutants , despite expressing some markers of terminal differentiation , do not complete morphogenesis . To begin to determine the molecular mechanism responsible for the reduction of terminally differentiated retinal neurons in tet2-/-;tet3-/- mutants , we next asked if neuronal specification factors were properly expressed ( Fig 4 ) . At 36hpf , vsx2 is expressed in proliferative RPCs , and turned off as these cells exit the cell cycle and begin to differentiate ( Fig 4A , dotted area ) . In tet2-/-;tet3-/- mutants , this zone of differentiation is noticeably smaller ( Fig 4F ) . At 48hpf , vsx2 expression is localized in proliferative cells at the periphery of the retina ( Fig 4K ) [58] . This zone of vsx2 expression was expanded in tet2-/-;tet3-/- mutants when compared to wild-type siblings ( Fig 4P ) , corresponding to the expanded zone of proliferation observed in the BrdU labeling assay ( Fig 2 ) . pax6a is normally expressed in RPCs , in addition to RGCs and ACs [59]; neurod4 is expressed in ACs , horizontal and bipolar cells [60]; atoh7 is expressed in the committed precursors undergoing specification to become RGCs [52] , and crx is expressed in specified PRs [61] ( Fig 4B–4E and 4L–4O ) . Despite terminal differentiation defects observed in retinae of tet2-/-;tet3-/- mutants , they retain relatively normal spatial and temporal expression of specification markers at 36hpf and 48hpf ( Fig 4G–4J and 4Q–4T , respectively ) , suggesting that RPC specification is unaffected during retinal neurogenesis in the absence of tet2 and tet3 function . Tet proteins are known to regulate both intrinsic and extrinsic pathways during development and differentiation events in a range of tissues . For example , tet activity is required intrinsically during hematopoiesis [34] and B-cell differentiation [35] . Recent studies have shown that Tet activity also modulates extrinsic pathways during development . In mouse embryos and embryonic stem cells , Tets function to negatively regulate Wnt pathway activity to balance mesoderm vs neuroectoderm fate choices [36] , and during mouse gastrulation , they modulate Nodal pathway activity by controlling the expression of Lefty1 , a nodal inhibitor [38] . Retinal cell type specification and differentiation depend on a multitude of intrinsic and extrinsic pathways [reviewed in [39 , 62]] , and given that Tet activity can modulate both intrinsic and extrinsic pathways during development , we next sought to determine whether tet2 and tet3 activities were required cell autonomously or cell non-autonomously during retinal neurogenesis . Chimeric embryos were generated by blastomere transplantation [63] to generate embryos whose retinae were composed of clones of wild-type and tet2-/-;tet3-/- mutant cells . Donor embryos were injected with fluorescent dextran and clones were transplanted from labeled donors into unlabeled hosts at shield stage , targeting specifically to the retinal field where cells later develop into the retina [64] . At 3dpf , host embryos were analyzed through a combination of HuC/D and zpr-3 staining , to detect differentiated RGCs , ACs and/or rods ( Fig 5A ) . Sibling to wild-type transplants yielded clones of cells that differentiated normally ( Fig 5B ) . tet2-/-;tet3-/- mutant cells transplanted into genetically wildtype hosts also differentiated normally into retinal neurons including RGCs , ACs , and rods , and the regions of the retina containing mutant clones were also properly laminated ( Fig 5C ) . Genetically wildtype cells transplanted into tet2-/-;tet3-/- mutant host retinae failed to undergo neurogenesis and remained undifferentiated ( Fig 5D ) . Thus , the wild-type retina was able to support normal neurogenesis of tet2-/-;tet3-/- mutant cells , while wild-type cells in a tet2-/-;tet3-/- mutant retina did not differentiate properly . Taken together , these data demonstrate that tet2 and tet3 activity regulates retinal neuron differentiation via cell non-autonomous pathways , potentially by modulating the expression or activity of cell-extrinsic signaling molecules . Because tet2 and tet3 appear to regulate retinal cell differentiation in a cell non-autonomous fashion , we next sought to identify potential factors responsible for these effects , utilizing both a candidate gene approach and an unbiased transcriptomic analysis . The Notch and Wnt pathways regulate retinal neurogenesis in mouse , zebrafish and Xenopus [65–68]; upregulation of these pathways prevents neuronal differentiation in a variety of contexts and resultant phenotypes resemble those in tet2-/-;tet3-/- mutants [45 , 68–70] . Thus , to determine whether the expression of Notch and Wnt pathway components , or the overall activities of the pathways , are regulated by tet2 and tet3 , we first performed in situ hybridizations using probes specific to candidate genes in each pathway . In wild-type sibling embryos at 36hpf , notch1a , deltaA , and ascl1a are expressed in the eye but excluded from the inner part of central retina where RGCs , ACs and PRs are differentiating ( Fig 6A–6C ) . However , in tet2-/-;tet3-/- mutants , these genes are expressed uniformly throughout the retina , without a clear ‘zone’ of differentiation ( Fig 6D–6F ) . Similarly , lef1 , a downstream readout of the Wnt pathway [71] , is expressed in the peripheral part of the retina of wild-type embryos at 36hpf ( Fig 6I ) , and this zone of expression is expanded in tet2-/-;tet3-/- mutants ( Fig 6L ) . These expression patterns suggest that the Notch and Wnt pathways could have elevated activity in the tet2-/-;tet3-/- mutant retina . To complement these candidate gene studies , we performed RNA-seq using dissected tet2-/-;tet3-/- mutant and phenotypically wildtype sibling eyes at 36hpf . Approximately 450 million reads were generated and mapped to GRCz10 [72 , 73] at 85 . 1% mapping efficiency . In tet2-/-;tet3-/- mutants , 278 genes were downregulated and 489 genes were upregulated ( log2 fold-change above 2 ) as compared to wild-type siblings ( S1 Table ) . Gene ontology ( GO ) analysis for biological pathways categorized many of the differentially expressed genes in the development of the visual perception , GPCR and cytokine-mediated signaling pathways , and ion transport ( Fig 6M and 6N ) . Notably , the highest upregulated gene was wnt9b ( log2 fold-change = 9 . 89 ) , and many other members of the Wnt family were also upregulated ( wnt1 , wnt3 , wnt11r , wnt10a ) in tet2-/-;tet3-/- mutant eyes ( S1 Table ) . In situ hybridization using antisense probes for wnt1 and wnt9b revealed that at 36hpf , while both genes showed faint expression at the peripheral edge of the retina in sibling embryos ( Fig 6G and 6H , n = 7 for wnt1 , n = 10 for wnt9b ) , in tet2-/-;tet3-/- mutants , both genes were expressed in expanded domains ( Fig 6J and 6K , n = 7 for wnt1 , n = 8 for wnt9b ) , and at higher relative intensities , consistent with the RNA-seq data . When combined with mosaic analyses , these data support a model in which extrinsic signals , likely including Notch and Wnt-related pathways , are regulated by tet2 and tet3 activity during retinal development and in their absence , these pathways are overactive and impair terminal differentiation of retinal neurons . To further test this model , we utilized pharmacological manipulations to determine if blocking Notch or Wnt activity could restore retinal neuron differentiation and morphogenesis in tet2-/-;tet3-/- mutants . DAPT is a γ–secretase inhibitor that blocks the proteolytic cleavage of the intracellular domain of Notch , thus blocking its downstream signaling [74] , and has been used extensively in zebrafish [66 , 67 , 74 , 75] . Inhibitor of Wnt Response ( IWR-1-endo ) stabilizes Axin2 and promotes β-catenin degradation , effectively inhibiting Wnt signaling [76] . We exposed tet2-/-;tet3-/- mutant and sibling embryos carrying the isl2b:GFP reporter to 50μM DAPT or 5μM IWR from the onset of neurogenesis ( 24hpf ) until fixation at 3dpf . Interestingly , tet2-/-;tet3-/- mutants treated with DAPT showed a significant increase in both the percentage of isl2b:GFP+ RGCs in the retina , and in optic nerve diameter , when compared to DMSO-treated controls ( p = 0 . 0044 and p = 0 . 0010 , respectively , 2-way ANOVA , Fig 7A–7H ) . No significant increases were detected in wild-type sibling embryos treated with either vehicle or DAPT ( Fig 7G and 7H ) . Similarly , tet2-/-;tet3-/- embryos treated with IWR also showed a significant increase in percentage of isl2b:GFP+ RGCs and optic nerve diameter ( p = 0 . 0039 and p = 0 . 0002 , respectively , 2-way ANOVA , Fig 7A–7H ) . Neither treatment rescued expression of isl2b:GFP+ PRs however , suggesting that other extrinsic pathways are likely regulated by tet2 and tet3 activity . These data support a model in which overactive Notch and/or Wnt signaling are partially responsible for neuronal differentiation/morphogenesis defects in tet2-/-;tet3-/- mutants . To further test this model , we upregulated Wnt signaling by treating wild-type embryos from 24hpf to 3dpf with 2μM BIO , a GSK3β inhibitor [71 , 77] . BIO-treated embryos showed dramatically reduced lamination , and decreases in the percentage of isl2b:GFP+ RGCs , and in optic nerve diameter relative to DMSO-treated controls , phenotypes that recapitulate those in tet2-/-;tet3-/- mutants ( p = 0 . 0024 and p<0 . 0001 , Fig 7I–7L ) . Taken together , these data demonstrate that Wnt and Notch signaling pathways act downstream of tet2 and tet3 activity to regulate RGC differentiation and morphogenesis during zebrafish retinal neurogenesis . Despite early specification markers being expressed relatively normally during neurogenesis at 36 and 48hpf ( Fig 4 ) , retinal neurons still do not terminally differentiate . Thus , we next sought to determine the gene expression signatures of retinal cells at 72hpf as a means to infer their identities , and we again utilized RNA-Seq to assess these . Similar to 36hpf assays , total RNA was extracted from dissected 72hpf tet2-/-;tet3-/- eyes and used for RNA-seq . Sixty-six million reads were generated and mapped to GRCz10 at 88 . 7% mapping efficiency . Comparisons of wild-type to tet2-/-;tet3-/- mutants resulted in the identification of 212 upregulated genes and 451 downregulated genes that passed a threshold of at least a 2 log2 fold-change ( Fig 8A and 8B; S2 Table ) . GO analysis was performed and , as expected , downregulated genes in tet2-/-;tet3-/- mutants are members of pathways tightly linked to differentiated PRs; i . e . GPCR signaling , membrane transport and ion transport . Indeed , the most downregulated genes were those encoding proteins required for PR function such as opsins ( e . g . opn1mw1 , opn1sw1 ) and components of the visual cycle ( e . g . guca1c , guca1g , gnat2 , grk1b ) ( S2 Table ) , an unsurprising result given the near absence of terminally differentiated PRs in tet2-/-;tet3-/- mutants ( Fig 3 ) . While the highest number of upregulated genes constituted the regulation of transcription category , other upregulated GO terms included skeletal muscle contraction and cardiac muscle contraction . These GO categories included genes that are not normally expressed in the eye; these included nppa , vmhcl , chrng , and ucp1 , each of which encodes a protein involved in heart and muscle development and/or function ( Fig 8B; S1 Table ) . To verify RNA-seq results , we performed in situ hybridization using probes specific to selected differentially expressed genes . Both opn1mw1 and opn1sw1 were expressed in PRs at 72hpf of wild-type sibling embryos , and expression was largely absent in tet2-/-;tet3-/- mutants , except in a small patch of cells in the ventral retina that corresponds to the region where differentiated PRs are detected ( Fig 8C–8H ) . nppa ( natriuretic peptide precursor a ) was the most highly upregulated gene in tet2-/-;tet3-/- mutant eyes ( log2 fold-change = 6 . 3 ) . nppa encodes the precursor to a peptide required for cardiovascular function and is normally only expressed in the embryonic heart [78] . All wild-type and tet2-/-;tet3-/- mutant embryos showed normal heart expression of nppa ( Fig 8I ) . Interestingly , however , in tet2-/-;tet3-/- mutants nppa was ectopically expressed in the retina and brain of all embryos ( n = 16/16 embryos ) , while no nppa expression was detected in the retina or brain of wild-type embryos ( n = 0/7 embryos ) ( Fig 8I–8K ) . To gain molecular insight into the epigenetic regulation of the differentially expressed genes in tet2-/-;tet3-/- embryos and whether these expression changes correlated with changes in 5mC or 5hmC deposition , we performed locus-specific methylation analyses , using bisulfite conversion followed by sequencing for 5mC + 5hmC , and a glucosylation-digestion assay for 5hmC specifically . We targeted regions surrounding the transcription start sites ( TSSs ) of opn1sw1 , opn1mw1 , a CpG island near a gene cluster that contains multiple opsins , including opn1mw1 , as well as the TSS and gene body of nppa ( S3 Table ) . Out of eleven targets selected for bisulfite sequencing , we observed no difference in methylation status at any CpG sites . In both wild-type and tet2-/-;tet3-/- mutants , all TSSs and the nppa gene body were fully methylated , and the opsin cluster CpG island was fully unmethylated ( Fig 8L and S4 Table ) . Bisulfite sequencing , while providing a nucleotide-resolution view of the methylation status of each CpG analyzed , is incapable of detecting differences between 5mC and 5hmC , meaning any CpG that appeared methylated in bisulfite assays could be either 5mC or 5hmC , or a mixture of both . To distinguish between these two epigenetic marks , we utilized glucosylation-digestion-based ( Quest ) assay to probe the presence of 5hmC at a glucosyl-sensitive restriction site , MspI . We selected targets that were located within or adjacent to the bisulfite-probed regions , due to the relatively sparse occurrence of MspI sites . Out of nine sites selected for analysis , only one , located in the gene body of nppa , showed a significant difference in 5hmC levels ( S4 Table ) . Interestingly , 5hmC was reduced to a nearly undetectable level at this site in tet2-/-;tet3-/- mutants , when compared to WT siblings where ~25% of the residues were hydroxymethylated ( Fig 8M ) . These data demonstrate that tet2 and tet3 mutations result in defects in 5mC to 5hmC conversion within the nppa gene body , which could contribute to misregulated expression of the locus .
DNA hydroxymethylation and demethylation remain somewhat enigmatic processes in the field of epigenetics , with Tet protein function having only been identified recently ( reviewed in [20] ) . Tet proteins are the main drivers of 5mC to 5hmC conversion and thereby key regulators of DNA demethylation [79] . However , in recent years , it has also become evident that they play roles in tissue-specific regulation of gene expression during development [24 , 25 , 27 , 32 , 34] . Despite these studies , we still know little about their developmental functions , and we know virtually nothing about Tet function during eye development . Here , we demonstrate that tet2 and tet3 play a critical role during development of the zebrafish retina . Our data indicate that tet2 and tet3 function redundantly in zebrafish to generate 5hmC , consistent with a recent report [27] . We demonstrate that tet2 and tet3 are critical regulators of retinal cell differentiation and morphogenesis , and that they act during early retinal development by modulating cell non-autonomous pathways . Loss of tet2 and tet3 function resulted in specific defects in retinogenesis , where the RPC population was transiently expanded . Despite relatively normal specification events , retinal cells failed to differentiate and , in the case of RGCs and PRs , failed to undergo terminal morphogenesis . These defects also correlated with mis-regulated gene expression and locus-specific defects in 5hmC formation in subsets of retinal cells at later stages of development . Based on these results , we propose a model wherein Tet proteins function to regulate gene expression during the differentiation of retinal cell types . In the absence of tet2 and tet3 function , gene expression is misregulated and differentiation and terminal morphogenesis of retinal neurons is perturbed ( Fig 9 ) . Similar to the cellular differentiation defects identified here in tet2-/-;tet3-/- retinae , loss of Tet2 and/or Tet3 function results in differentiation defects in the hematopoietic system of zebrafish [25 , 27] and humans [34] . During hematopoiesis , Tet proteins turn on genes involved in erythropoiesis by hydroxymethylating and/or demethylating the their promoters , thereby enabling expression and ultimately , triggering differentiation . Conversely , it is well established that Tet1 is a critical player in stem cell maintenance where it functions to inhibit differentiation potential [80 , 81] , and loss of Tet1 impairs ESC self-renewal [18] . Therefore , Tet proteins play distinct roles in different contexts: they stimulate differentiation in tissue specific contexts ( e . g . retinal , blood cells ) , and suppress differentiation in stem cells . In tet2-/-;tet3-/- mutants , early-born retinal cell types , RGCs and ACs , were less affected than later-born ones ( cones , rods and Müller glia ) , which were almost completely absent . RGCs in tet2-/-;tet3-/- mutants were isl2b:GFP+ and expressed Zn8 , a marker of terminal differentiation [53] . However , Zn8+ RGCs were restricted to the central retina , and in a subset of mutants , no optic nerve ( ON ) was present , while in the remainder , a severely attenuated ON formed . In these latter embryos , we speculate that the RGCs generating axons are most likely the ‘pioneer’ axons [55] that originate from the few early-born RGCs that undergo terminal differentiation and morphogenesis , while the majority of RGCs fail to complete morphogenesis to form an axon . ACs were detected in fewer number and located in an even more limited zone within the central retina of tet2-/-;tet3-/- mutants , and the few differentiated red/green double cones , rods or Müller glia detected in tet2-/-;tet3-/- mutant retinae were always located in the central retina . Specification and differentiation in the zebrafish retina initiates in the ventronasal patch , adjacent to the optic nerve , and proceeds in a central to peripheral gradient [82] . That differentiated cells in tet2-/-;tet3-/- mutants reside in these retinal regions strongly suggests that they represent the first born cells of each retinal cell type . Zebrafish embryos are also endowed with a maternally-derived supply of mRNA and protein ( reviewed in [83] ) . Therefore , it is possible that the centrally located and partially differentiated early born cell types in tet2-/-;tet3-/- mutants reflect a “maternal rescue” , and that defects in later born cell types result from depletion of maternally supplied tet2 and/or tet3 . However , tet transcripts are not maternally deposited in zebrafish [29] , and our 36hpf RNA-Seq analysis from tet2-/-;tet3-/- mutants detects no expression of wild-type ( maternally derived ) tet2 or tet3 transcripts , making this scenario unlikely . Alternatively , tet activity could become progressively more important in RPCs as they transition from producing early born cell types to later born ones , and as the specified cells undergo terminal differentiation and morphogenesis . Moreover , that the first born neurons of each class appeared to partially differentiate , tet2 and tet3 activity could become more important over time in each class of retinal neuron , where the first born neurons of the class develop independent of tet2 and tet3 function , while subsequent neurons require it . None-the-less , in this scenario , early born cells ( and cell types ) still require tet activity for terminal differentiation , because the majority of RGCs in tet2-/-;tet3-/- mutants , though properly specified , do not form axons , and similarly in PRs , centrally-located cells are specified ( crx+ ) and begin to differentiate ( isl2b:GFP+ , zpr-1+ or zpr-3+ ) , but do not complete outer segment morphogenesis . These data suggest that while RGCs and early born PRs may be refractory to the absence of tet activity during the earliest phases of differentiation , tet activity is still required for terminal morphogenesis . Our speculation that epigenetic regulation plays an important role in terminal differentiation of retinal neurons is also supported by recent evidence in mouse where disruption of Dnmt1 , 3a and 3b resulted in severe retinal defects in which some PRs were present , but they appeared disorganized and failed to form outer segments [84] , defects reminiscent to those in tet2-/-;tet3-/- mutants . Mosaic analyses reveal that tet2-/-;tet3-/- retinal phenotype occurs cell non-autonomously , and thus , that the effects of tet2 and tet3 loss of function during early retinal development are mediated by cell extrinsic events . Through a combination of candidate gene assays , transcriptomics and pharmacological manipulations , we demonstrate that elevated Notch and Wnt pathway activity is partially responsible for defects in retinal neurogenesis in tet2-/-;tet3-/- mutants . However , because blocking these pathways only provided partial rescue of retinal defects in tet2-/-;tet3-/- mutants , other signaling pathways are likely to be involved and modulated by tet activity during early retinal development . Hedgehog-PKA , TGFβ/BMP , and FGF are all cell-extrinsic pathways known to contribute to retinal neurogenesis , making these attractive candidates [85–87] . While surprising , these cell non-autonomous results are consistent with those from several other recently published studies on Tet function . Indeed , Tet activity was demonstrated to modulate Nodal activity during mouse gastrulation by intrinsically regulating the methylation status and expression of the Nodal inhibitors , Lefty1 and Lefty2 [38] . Tet activity has also recently been shown to modulate Wnt ligands or Wnt target gene activity in several contexts , either directly or indirectly [36 , 37] . Perhaps the most interesting of these recent studies showed that in mouse ESCs and early embryos , Tet activity is required to balance neuroectoderm vs . mesoderm fates and to inhibit Wnt signaling [36] . In Tet1/2/3 deficient ES cells and embryos , neural cell fates were lost and instead , mesodermal fates like cardiac muscle were detected . These fate changes correlated with increased promoter methylation and decreased expression of the Wnt inhibitor , Sfrp4 and hyperactive Wnt pathway activity . This parallels what we observe in the tet2-/-;tet3-/- mutant zebrafish retina , and is exciting , because it suggests that Tet-mediated modulation of the Wnt pathway , and possibly other cell-extrinsic signaling pathways , may be a conserved function for Tets during embryonic development and organogenesis . Finally , Tet function may also influence chromatin accessibility at the genomic regions surrounding Notch and Wnt genes , enabling other transcriptional regulators to access these loci . Tets have been shown to function in regulating local chromatin environments [30 , 88–90] . From RNA-seq analysis , we detected a suite of cardiac and muscle genes ectopically expressed in the retinae of 72hpf tet2-/-;tet3-/- mutants . Further analysis of one of these , nppa , revealed ectopic expression in the brain and eyes of tet2-/-;tet3-/- mutants . This expression change correlated with an almost complete loss of gene body 5hmC deposition at the nppa locus . Locus-specific effects like this indicate that tet2 and tet3 may also function intrinsically during retinal development , in addition to modulating cell extrinsic pathways . Tet-mediated formation of 5hmC serves as a precursor to demethylation , or as an activating mark in its own right [30] . Tet-mediated 5hmC formation could play a direct role in silencing ectopic gene expression for genes like nppa during retinal development . Gene body 5mC methylation positively correlates with expression [3]; therefore , in this scenario , Tet-mediated conversion to 5hmC likely serves as a precursor for subsequent demethylation and silencing . In tet2-/-;tet3-/- mutants , 5mC is not converted to 5hmC , and the target locus ( nppa ) is ectopically expressed by retinal and brain cells . However , Tet-activity is also required for gene body 5hmC formation that is thought to serve as an active mark [30] . Thus , an alternative model can be envisioned wherein ectopic nppa expression also reflects indirect consequences of loss of tet2 and tet3 activity . In this model , an intermediate silencer/repressor gene is not properly expressed by tet2-/-;tet3-/- mutant retinal cells due to the absence of activating 5hmC marks . This scenario is not unprecedented; Li et al recently demonstrated that tet2-/-;tet3-/- mutants possess defects in hematopoiesis , but the mutants showed no changes in methylation or hydroxymethylation at key genes in the hematopoietic network; instead , in this context , Tet-mediated effects are likely the result of mis-regulation of the Notch pathway [27] . Similarly , mutation of the de novo DNA methyltransferase dnmt3bb1 results in significantly altered expression of many hematopoietic and endothelial genes , although very few of these showed any changes in DNA methylation [91] . Therefore , genes identified as differentially expressed in tet2-/-;tet3-/- mutants that displayed no changes in 5mC or 5hmC may not be direct targets of tet2 and tet3 , but rather , reflect complex intrinsic or extrinsic regulatory pathways modulated by tet2 and tet3 activity . Testing this prediction will require genome-wide profiling of 5mC and 5hmC in the eye over multiple developmental time points and correlating these data with gene expression in a gene-by-gene fashion , as well as generating conditional loss of function tet2 and tet3 alleles such that their functions during later retinal development can be elucidated . CRISPR/Cas9 technology now makes this possible in zebrafish [92] . It is known that CXXC4/IDAX , a protein with homology to the tet3 N-terminal domain , functions as a direct inhibitor of Wnt signaling by competitively binding with Axin to Dvl [93] . In tet2-/-;tet3-/- mutants , we detected no tet3 protein and 5hmC was almost completely absent from the genome , supporting a catalytic role for tet2 and tet3 during development . Significant changes in the expression of several Wnt ligands was detected in tet2-/-;tet3-/- mutants , and these changes could result from this lack of catalytic activity ( i . e . directly , from the lack of 5hmC formation at the loci , or indirectly , from the lack of 5hmC at loci encoding modulators of expression the Wnt ligands ) . However , alternatively , changes in Wnt ligand expression could also result from loss of the tet3 N-terminal CXXC domain , which functions independently of tet3 catalytic activity . Our data cannot yet differentiate between these possibilities . Importantly , this highlights the need to better understand the catalytic vs non-catalytic functions of tet proteins in specific tissues and organs , where tet proteins could regulate gene expression in several different ways . Finally , in addition to DNA modifying enzymes like the Tets , chromatin regulators such as histone deacetylases and histone demethylases have also been shown to modulate cell extrinsic pathways during early retinal development [45 , 94] . When combined with our work , these studies highlight that the epigenetic regulation of signaling events during development is likely to be a more significant and complex layer of regulation than previously realized . Deciphering this complex epigenetic regulation will require a comprehensive , genome-wide approach encompassing multiple profiling strategies ( e . g . bisulfite sequencing , oxidative bisulfite sequencing , RNA-seq , ChIP-seq , and ATAC-Seq ) in pure , isolated RPCs and differentiated retinal cell types from both wild-type embryos , as well as embryos deficient in key enzymes operating in these epigenetic pathways , like tet2 and tet3 .
Zebrafish were maintained at 28 . 5°C on a 14/10 light/dark cycle and treated in accordance with the University of Texas at Austin and University of Pittsburgh IACUC regulations governing animal research . Euthanasia utilized tricaine , following procedures standard in the field and as approved by the IACUC . Lines utilized in this study are: tet2au59 , tet3au60 , and Tg ( isl2b:GFP ) zc7 [55] . Embryos were incubated in the dark at 28 . 5°C and staged according to [95] . To generate the tet2-/- and tet3-/- mutant lines , TALEN constructs were generated using Golden Gate assembly [41 , 96] targeting the following sequences ( spacer in bold and restriction endonuclease recognition sites underlined ) : CATCCCAGATGGAATGGATAGATTTAAACTCAACTTCTGCTTCAAC for tet2au59; GCTCTGGGAGATAAACTGTACAGAGAAGTCACAGAAACCATCACCAAAT for tet3au60 . Embryos at the 1-cell stage were injected with in vitro synthesized ( Ambion ) mRNA encoding the TALEN constructs ( left and right arms ) targeting tet2 and tet3 , separately , and raised to adulthood . At breeding age , potential founders were screened for germline transmission of mutations by sperm genomic PCR , followed by whole amplicon Sanger sequencing . Genotyping primers were: CACAAACCTCTCAGACAGGTCAGT ( tet2 forward ) , TCTCTGTTGACTTTCAGGGGCAG ( tet2 reverse ) , CAATGCCTAGATCAACCACTTAGTGTC ( tet3 forward ) , GTATCAGGAATGTGCAAACATCTCATTTG ( tet3 reverse ) . Founders with deletions that resulted in frameshifts and premature stop codons were outcrossed to wildtype females and embryos reared to adult . Potential heterozygotes were then screened for the desired mutation by restriction fragment length polymorphism ( RFLP ) using DraI for tet2 and RsaI for tet3 . RFLP fragments were resolved on a 1% agarose gel and mutant fragments were detected by the resistance to DraI and/or RsaI digestion ( S2 Fig ) . To generate the double mutant line , heterozygotes carrying tet2 or tet3 mutations were crossed , and offspring were screened for the presence of both mutations by RFLP . tet2+/-;tet3+/- fish were then incrossed to obtain tet2-/-;tet3-/- embryos . Because homozygous mutation in either tet2 or tet3 alone does not affect viability , the remaining embryos survived to adult and a normal Mendelian distribution was obtained ( S2 Fig ) . RT-PCR for tet2 and tet3 was performed using exon-spanning primers listed in S3 Table . Embryos at 2dpf and 5dpf ( n = 20 per genotype per condition ) were euthanized and RNA extracted using RNeasy kit ( Qiagen ) . cDNA libraries were generated using iScript cDNA synthesis kit ( BioRad ) . Western blot analyses was performed essentially as described [16] with slight modifications . At 3dpf , 40 embryos per condition were euthanized , de-yolked , and protein extracted . Samples were separated by electrophoresis on 4–12% bis-tris gel with NuPage MOPS SDS running buffer ( Invitrogen ) and transferred to PVDF membrane at 30V for 2 hours , then at 12V overnight at 4°C . Membranes were blocked in 1%BSA , 5% non-fat milk in TBST for 2 hours at RT and incubated in anti-TET3 rabbit polyclonal antibody ( ab139311 , Abcam ) overnight at 4°C , then washed , incubated with HRP-conjugated donkey-anti-rabbit antibody ( 711-035-152 , Jackson Immuno Research ) , rinsed , and incubated with substrate solution ( Super Signal West Femto , Thermo Fisher ) . Images were acquired and band intensity quantified using ChemiDoc XRS+ system ( BioRad ) . For normalization , membranes were stripped for 12 minutes in Restore Western Blot stripping buffer ( Thermo Fisher ) , rinsed , re-blocked , probed with anti-actin mouse monoclonal antibody ( CP01 , Millipore ) followed by HRP horse-anti-mouse secondary ( 7076 , Cell Signaling ) and imaged as above . Whole mount in situ hybridization experiments were performed essentially as described [97] . DIG-labeled RNA probes for notch1a , deltaA , ascl1a , vsx2 , pax6a , neurod4 and atoh7 were described previously [43] . Probes for tet1 , tet2 , tet3 , lef1 , wnt1 , wnt9B , opn1sw1 , opn1mw1 , and nppa were cloned from zebrafish cDNA using primers listed in S3 Table . PCR fragments were cloned into pGEM-T-Easy vector ( Promega ) , sequence verified , linearized , and transcribed using SP6 and T7 polymerases with DIG RNA labeling mix ( Roche ) . Synthesized RNA probes were purified using RNeasy kit ( Qiagen ) , mixed 1:200 with hybridization buffer ( 50% formamide , 5xSSC , 0 . 1%tween , 5mg/ml yeast tRNA , 50μg/ml heparin ) , and heated to 68°C before use . Amino acid sequences were downloaded from NCBI , using the following accession numbers: NP_085128 . 2 ( human TET1 ) , NP_001240786 . 1 ( mouse Tet1 ) , AHE93329 . 1 ( zebrafish tet1 ) , NP_001120680 . 1 ( human TET2 ) , NP_001035490 . 2 ( mouse Tet2 ) , AHE93330 . 1 ( zebrafish tet2 ) , NP_001274420 . 1 ( human TET3 ) , NP_898961 . 2 ( mouse Tet3 ) , AHE93331 . 1 ( zebrafish tet3 ) . Alignments and phylogenetic trees were constructed using Geneious Tree Builder software with standard neighbor-joining method ( Biomatters ) . 5-bromo-2-deoxyuridine ( BrdU ) incorporation was performed using a 15-minute pulse for PLM assays , and a 2-hour time window for 48hpf-5dpf assays . Embryos were treated in 0 . 3% BrdU , fixed in 4% PFA in PBS , embedded , and cryosectioned at 12μm . Sections were treated with 4M HCl at 37°C for 10min , blocked in block solution ( 5% normal goat serum , 0 . 1% tween , 1% DMSO , in PBS ) , incubated with anti-BrdU ( 1:250; Abcam ) in block overnight at 4°C , stained with anti-rat Cy3 secondary ( 1:250 ) and counterstained with Sytox green at 1:10 , 000 ( Molecular Probes ) . Cells undergoing mitosis were detected using anti-phospho histone H3 ( ser10 ) ( 1:250 ) ( Millipore 06–570 ) , stained with anti-rabbit Cy2 secondary ( 1:250 ) , and counterstained with DAPI ( 1:500 ) . Embryos at 36hpf , 3dpf , 4dpf , and 5dpf were fixed in 4% PFA 1xPBS at 4°C overnight , cryosectioned at 12μm , and processed for TUNEL using TMR Red in situ cell death detection kit ( Roche ) per manufacturer’s protocol . Immunohistochemistry was perform as described [98] , with the following antibodies: zpr-1 ( cones; ZIRC ) , zpr-3 ( rods; ZIRC ) , zrf-1/gfap ( Muller glia cells; ZIRC ) , Zn8 ( ganglion cells; ZIRC ) , and HuC/D ( ganglion and amacrine cells; Molecular Probes ) . Embryos were cryosectioned at 12μm and incubated with primary antibodies diluted at 1:200 in block overnight at 4°C , then incubated with secondary antibody ( anti-mouse Cy3 ) for 2hrs . Sections were counterstained with Alexa Fluor-633 Phalloidin at 1:100 and Sytox green at 1:10 , 000 ( Molecular Probes ) or mounted using Vectashield with DAPI ( Vector Labs ) . Images were acquired using Zeiss LSM5 and/or Olympus FV1200 confocal microscopes , and analyzed using ImageJ with Cell Counter plug-in ( imagej . nih . gov ) . Embryos were fixed in 4% PFA 1x PBS at 4°C overnight , rinsed once in PBST ( 0 . 1% tween-20 , 1xPBS ) , once in water , and treated with 100% acetone for 7min at -20°C to permeabilize the tissue , then rinsed one time each in water , PBST and PBDTX ( 1%BSA , 1%DMSO , 0 . 1% TritonX , 1xPBS , pH = 7 . 3 ) . Embryos were blocked for 1hr ( 2%NGS in PBDTX ) , incubated in Zn8 primary antibody ( ZIRC ) at 1:200 dilution 4°C overnight , washed 4 x 20min in PBDTX , and incubated in secondary horse anti-mouse HRP-tagged secondary ( Cell Signaling ) at 1:1 , 000 dilution for 2hrs . Embryos were then washed in PBSTX ( 0 . 5% Triton-X , 1xPBS ) 4 x 20min , incubated in DAB working solution ( Vector Labs ) for 2-10min until staining was visible , rinsed in water , and stored in PBS before imaging . One hundred zebrafish eyes were dissected at either 36hpf or 72hpf using a flame-sharped tungsten wire , and RNA extracted using Qiagen RNeasy kit as described [67] . For 36hpf , library preparation with polyA mRNA capture and sequencing was performed using Illumina NextSeq 500 paired-end 75bp reads . 450 million reads were generated . Raw FASTQ sequences were quality checked , trimmed , and mapped using CLC Genomic Workbench 9 . 0 . 1 to zebrafish reference genome GRCz10 at 85% mapping efficiency . Transcript abundances were calculated and differentially expressed genes ( DEGs ) were identified using CLC Genomic Workbench 9 . 0 . 1 . For 72hpf , library was prepared as above and sequenced on an Illumina HiSeq 2500 PE2x125 . 66 million reads were generated . FASTQ sequences were quality checked using FastQC ( Babraham Bioinformatics ) , mapped to GRCz10 using TopHat , and DEGs were identified using Cufflinks package from Tuxedo suite [72] . Genes with expression values above log2 fold-change of 2 are considered differentially expressed . All computational analyses utilized the Texas Advanced Computing Center and University of Pittsburgh Center for Simulation and Modeling . Raw and processed data are publicly available through NCBI Gene Expression Omnibus ( accession number GSE80134 ) . Functional annotation was done using DAVID Bioinformatics 6 . 8 ( https://david . ncifcrf . gov ) . Differentially expressed gene lists from RNA-seq were filtered for log2 fold-change of 2 or higher and submitted to DAVID Gene Ontology for biological pathways ( GOTERM_BP_DIRECT ) . Shield-stage transplantation experiments were performed essentially as described [63] . Embryos were injected with Alexa Fluor 488 dextran ( 10 , 000 MW , anionic , fixable ) diluted at 1% in 0 . 2M KCl . Cells were transplanted from labeled donor embryos into unlabeled host embryos at the shield stage , targeting the presumptive retinal field [64] . Approximately 10 cells were transplanted per host embryo to minimize the ‘community’ effect resulting from clones that are too large . Embryos were sorted at 24hpf for donor clone contribution , and fixed at 72hpf for sectioning and immunohistochemistry . Bisulfite sequencing was performed using EZ DNA Methylation-Direct kit ( Zymo Research ) . Eye tissues at 72hpf were dissected ( n = 7 per condition ) and immediately processed through proteinase K digestion and bisulfite conversion . Converted DNA was purified and amplified using hot-start ZymoTaq and bisulfite-specific primer pairs ( S3 Table ) . PCR amplicons ( ~300bp ) were either directly sequenced or sub-cloned for sequencing . Sequencing traces were analyzed using QUMA ( RIKEN , Japan ) . Our bisulfite treatment procedure generally yielded ~98% conversion . Clones that contained low quality sequences were manually excluded from the analysis . Locus-specific 5hmC quantification was performed using the Quest 5hmC Detection kit ( Zymo Research ) . Briefly , genomic DNA was extracted at 72hpf using Purelink Genomic DNA purification kit ( Invitrogen ) . Genomic DNA was divided into three groups: 1 ) Glycosylated and digested with a glucosyl-5hmC sensitive endonuclease , MspI [+GT]; 2 ) Unglucosylated and digested with MspI [-GT] ( negative control ) ; 3 ) unprocessed genomic DNA [untreated] ( positive control ) . All DNA samples were purified , and equal amounts used as templates for quantitative real-time PCR . Quantitative real-time PCR was done using SYBR green master mix in 10ul volume , and reactions run in a CFX384 detection system ( BioRad ) . Cq values were first evaluated by comparing the difference between the +GT and -GT , and between +GT and untreated . If Cq+GT is close to ( <1 Cq difference ) Cquntreated , the locus is considered fully hydroxymethylated . Conversely , if Cq+GT is close to Cq-GT , the locus is considered non-hydroxymethylated . For each locus with Cq values that pass these criteria , they are considered partially hydroxymethylated , and percent 5hmC was calculated as follows: %5hmC = {[Cq-GT−Cq+GT] / [Cq-GT−Cquntreated]} *100 . Sandwich-based 5hmC ELISA was performed according to the manufacturer’s protocol ( Zymo Research ) . Genomic DNA samples were extracted at 5dpf using Purelink Genomic DNA extraction kit ( Invitrogen ) , and diluted to 1ng/μl in water , denatured by heating at 98°C and cooling on ice , and 5hmC DNA was bound to the ELISA plate coated with anti-5hmC polyclonal antibody ( 1:1 , 000 ) . Bound DNA was detected with anti-DNA HRP antibody ( 1:100 ) , and was allowed to develop for 20min . 410nm absorbance was measured by a plate reader ( BioTek ) , and a standard curve generated using linear regression from five DNA samples with known concentrations of 5hmC . Percent 5hmC was calculated as follows: %5hmC = ( absorbance—y-intercept ) /slope . Note that percent 5hmC in ELISA is based on the total number of hydroxymethylated cytosines , calibrated to standards ( set of DNA with known 5hmC% ) . For example , 0 . 1% 5hmC means 1 of every 1 , 000 cytosines is 5hmC . Percent 5hmC in the site-specific glucosylation/digestion ( Quest ) assay represents the relative amount of ‘protected’ 5hmC at each MspI ( CCGG ) site analyzed , compared to the two internal controls for each locus: fully digested DNA ( representing 0% 5hmC ) and undigested DNA ( representing 100% 5hmC ) . Thus , these two numbers are not directly comparable , but should be in agreement with each other . tet2-/-;tet3-/- mutants and sibling embryos carrying isl2b:GFP transgene were dechorionated and incubated from 24hpf to 72hpf in embryo medium with 50μM DAPT ( N-[N- ( 3 , 5-difluorophenacetyl ) -L-alanyl]- S-phenylglycine-t-butyl ester; InSolution γ-secretase inhibitor IX , 565784 , Calbiochem ) , 5μM IWR-1-endo ( 5 . 04462 . 0001 , Calbiochem ) , or 1% DMSO as vehicle control . For BIO treatment , wildtype embryos carrying isl2b:GFP transgene were incubated in 2μM BIO ( 2’Z , 3’E-6-Bromoindirubin-3’-oxime , B1686-5MG , Sigma-Aldrich ) from 24-72hpf . All embryos were fixed at 72hpf in 4%PFA 1xPBS , sectioned , and processed for immunostaining . Optic nerve diameter measurements were done in 5–7 embryos per condition at optic nerve head , using FluoView software ( Olympus ) . P-values were calculated using two-way ANOVA with multiple comparison ( for DAPT and IWR ) and two-tailed unpaired t-test ( for BIO ) using Prism GraphPad . | Tet enzymes function to convert methylated cytosines ( 5mC ) to hydroxymethylated cytosines ( 5hmC ) , an epigenetic mark associated with active transcription or as a precursor to demethylation . Here , we generated zebrafish tet2-/-;tet3-/- mutants , which are deficient in the ability to convert 5mC to 5hmC . We identified functions for Tet enzymes in regulating gene expression and cell type-specific differentiation of retinal progenitor cells ( RPCs ) into neurons and glia during retinal development . Specifically , in tet2-/-;tet3-/- mutants , despite relatively normal expression of specification markers , the majority of retinal cell types do not express markers of differentiation and they fail to undergo terminal differentiation and morphogenesis . Genome-wide expression profiling identified down-regulation of numerous retinal genes in tet2-/-;tet3-/- mutants , and surprisingly , upregulation of cardiac and skeletal muscle-specific genes that are not normally expressed in the eye . Mechanistically , tet2 and tet3 function upstream of cell-extrinsic signaling pathways to enable specified RPCs to undergo terminal differentiation . This study is the first detailed analysis of Tet function during eye development and identifies an exciting new layer of epigenetic regulation operating during retinal neurogenesis . | [
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... | 2017 | Tet-mediated DNA hydroxymethylation regulates retinal neurogenesis by modulating cell-extrinsic signaling pathways |
Lujo virus ( LUJV ) , a new member of the family Arenaviridae and the first hemorrhagic fever–associated arenavirus from the Old World discovered in three decades , was isolated in South Africa during an outbreak of human disease characterized by nosocomial transmission and an unprecedented high case fatality rate of 80% ( 4/5 cases ) . Unbiased pyrosequencing of RNA extracts from serum and tissues of outbreak victims enabled identification and detailed phylogenetic characterization within 72 hours of sample receipt . Full genome analyses of LUJV showed it to be unique and branching off the ancestral node of the Old World arenaviruses . The virus G1 glycoprotein sequence was highly diverse and almost equidistant from that of other Old World and New World arenaviruses , consistent with a potential distinctive receptor tropism . LUJV is a novel , genetically distinct , highly pathogenic arenavirus .
Members of the genus Arenavirus , comprising currently 22 recognized species ( http://www . ictvonline . org/virusTaxonomy . asp ? version=2008 ) , are divided into two complexes based on serologic , genetic , and geographic relationships [1] , [2]: the New World ( NW ) or Tacaribe complex , and the Old World ( OW ) or Lassa-Lymphocytic choriomeningitis complex that includes the ubiquitous arenavirus type-species Lymphocytic choriomeningitis virus ( LCMV; [3] ) . The RNA genome of arenaviruses is bi-segmented , comprising a large ( L ) and a small ( S ) segment that each codes for two proteins in ambisense coding strategy [4] , [5] . Despite this coding strategy , the Arenaviridae are classified together with the families Orthomyxoviridae and Bunyaviridae as segmented single-strand , negative sense RNA viruses . The South American hemorrhagic fever viruses Junin ( JUNV; [6] , [7] ) , Machupo ( MACV; [8] ) , Guanarito ( GTOV; [9] ) and Sabia virus ( SABV , [10] ) , and the African Lassa virus ( LASV [11] ) , are restricted to biosafety level 4 ( BSL-4 ) containment due to their associated aerosol infectivity and rapid onset of severe disease . With the possible exception of NW Tacaribe virus ( TCRV; [12] ) , which has been isolated from bats ( Artibeus spp . ) , individual arenavirus species are commonly transmitted by specific rodent species wherein the capacity for persistent infection without overt disease suggests long evolutionary adaptation between the agent and its host [1] , [13]–[16] . Whereas NW arenaviruses are associated with rodents in the Sigmodontinae subfamily of the family Cricetidae , OW arenaviruses are associated with rodents in the Murinae subfamily of the family Muridae . Humans are most frequently infected through contact with infected rodent excreta , commonly via inhalation of dust or aerosolized virus-containing materials , or ingestion of contaminated foods [13]; however , transmission may also occur by inoculation with infected body fluids and tissue transplantation [17]–[19] . LCMV , which is spread by the ubiquitous Mus musculus as host species and hence found world-wide , causes symptoms in humans that range from asymptomatic infection or mild febrile illness to meningitis and encephalitis [13] . LCMV infection is only rarely fatal in immunocompetent adults; however , infection during pregnancy bears serious risks for mother and child and frequently results in congenital abnormalities . The African LASV , which has its reservoir in rodent species of the Mastomys genus , causes an estimated 100 , 000–500 , 000 human infections per year in West African countries ( Figure 1 ) . Although Lassa fever is typically sub-clinical or associated with mild febrile illness , up to 20% of cases may have severe systemic disease culminating in fatal outcome [20] , [21] . Three other African arenaviruses are not known to cause human disease: Ippy virus ( IPPYV; [22] , [23] ) , isolated from Arvicanthis spp . and Mobala virus ( MOBV; [24] ) isolated from Praomys spp . in the Central African Republic ( CAR ) ; and Mopeia virus ( MOPV ) that like LASV is associated with members of the genus Mastomys , and was reported from Mozambique [25] and Zimbabwe [26] , although antibody studies suggest that MOPV and LASV may also circulate in CAR [27] where the geographies of these viruses appear to overlap ( Figure 1 ) . Up to present , there have been no published reports of severe human disease associated with arenaviruses isolated from southern Africa . In September 2008 an outbreak of unexplained hemorrhagic fever was reported in South Africa [28] . The index patient was airlifted in critical condition from Zambia on September 12 to a clinic in Sandton , South Africa , after infection from an unidentified source . Secondary infections were recognized in a paramedic ( case 2 ) who attended the index case during air transfer from Zambia , in a nurse ( case 3 ) who attended the index case in the intensive care unit in South Africa , and in a member of the hospital staff ( case 4 ) who cleaned the room after the index case died on September 14 . One case of tertiary infection was recorded in a nurse ( case 5 ) who attended case 2 after his transfer from Zambia to Sandton on September 26 , one day before barrier nursing was implemented . The course of disease in cases 1 through 4 was fatal; case 5 received ribavirin treatment and recovered . A detailed description of clinical and epidemiologic data , as well as immunohistological and PCR analyses that indicated the presence of an arenavirus , are reported in a parallel communication ( Paweska et al . , Emerg . Inf . Dis . , submitted ) . Here we report detailed genetic analysis of this novel arenavirus .
RNA extracts from two post-mortem liver biopsies ( cases 2 and 3 ) and one serum sample ( case 2 ) were independently submitted for unbiased high-throughput pyrosequencing . The libraries yielded between 87 , 500 and 106 , 500 sequence reads . Alignment of unique singleton and assembled contiguous sequences to the GenBank database ( http://www . ncbi . nlm . nih . gov/Genbank ) using the Basic Local Alignment Search Tool ( blastn and blastx; [29] ) indicated coverage of approximately 5 . 6 kilobases ( kb ) of sequence distributed along arenavirus genome scaffolds: 2 kb of S segment sequence in two fragments , and 3 . 6 kb of L segment sequence in 7 fragments ( Figure 2 ) . The majority of arenavirus sequences were obtained from serum rather than tissue , potentially reflecting lower levels of competing cellular RNA in random amplification reactions . Sequence gaps between the aligned fragments were rapidly filled by specific PCR amplification with primers designed on the pyrosequence data at both , CU and CDC . Terminal sequences were added by PCR using a universal arenavirus primer , targeting the conserved viral termini ( 5′-CGC ACM GDG GAT CCT AGG C , modified from [30] ) combined with 4 specific primers positioned near the ends of the 2 genome segments . Overlapping primer sets based on the draft genome were synthesized to facilitate sequence validation by conventional dideoxy sequencing . The accumulated data revealed a classical arenavirus genome structure with a bi-segmented genome encoding in an ambisense strategy two open reading frames ( ORF ) separated by an intergenic stem-loop region on each segment ( Figure 2 ) ( GenBank Accession numbers FJ952384 and FJ952385 ) . Our data represent genome sequences directly obtained from liver biopsy and serum ( case 2 ) , and from cell culture isolates obtained from blood at CDC ( case 1 and 2 ) , and from liver biopsies at NICD ( case 2 and 3 ) . No sequence differences were uncovered between virus detected in primary clinical material and virus isolated in cell culture at the two facilities . In addition , no changes were detected between each of the viruses derived from these first three cases . This lack of sequence variation is consistent with the epidemiologic data , indicating an initial natural exposure of the index case , followed by a chain of nosocomial transmission among subsequent cases . Phylogenetic trees constructed from full L or S segment nucleotide sequence show LUJV branching off the root of the OW arenaviruses , and suggest it represents a highly novel genetic lineage , very distinct from previously characterized virus species and clearly separate from the LCMV lineage ( Figure 3A and 3B ) . No evidence of genome segment reassortment is found , given the identical placement of LUJV relative to the other OW arenaviruses based on S and L segment nucleotide sequences . In addition , phylogenetic analysis of each of the individual ORFs reveals similar phylogenetic tree topologies . A phylogenetic tree constructed from deduced L-polymerase amino acid ( aa ) sequence also shows LUJV near the root of the OW arenaviruses , distinct from characterized species , and separate from the LCMV branch ( Figure 3C ) . A distant relationship to OW arenaviruses may also be inferred from the analysis of Z protein sequence ( Figure S1 ) . The NP gene sequence of LUJV differs from other arenaviruses from 36% ( IPPYV ) to 43% ( TAMV ) at the nucleotide level , and from 41% ( MOBV/LASV ) to 55% ( TAMV ) at the aa level ( Table S1 ) . This degree of divergence is considerably higher than both , proposed cut-off values within ( <10–12% ) , or between ( >21 . 5% ) OW arenavirus species [31] , [32] , and indicates a unique phylogenitic position for LUJV ( Figure 3D ) . Historically , phylogenetic assignments of arenaviruses have been based on portions of the NP gene [1] , [33] , because this is the region for which most sequences are known . However , as more genomic sequences have become available , analyses of full-length GPC sequence have revealed evidence of possible relationships between OW and NW arenaviruses not revealed by NP sequence alone [34] . Because G1 sequences are difficult to align some have pursued phylogenetic analyses by combining the GPC signal peptide and the G2 sequence for phylogenetic analysis [16] . We included in our analysis the chimeric signal/G2 sequence ( Figure 3E ) as well as the receptor binding G1 portion ( Figure 3F ) ; both analyses highlighted the novelty of LUJV , showing an almost similar distance from OW as from NW viruses . Canonical polymerase domains pre-A , A , B , C , D , and E [35]–[37] are well conserved in the L ORF of LUJV ( 256 kDa , pI = 6 . 4; Figure 4 ) . The Z ORF ( 10 . 5 kDa , pI = 9 . 3 ) contains two late domain motifs like LASV; however , in place of the PTAP motif found in LASV , that mediates recognition of the tumor susceptibility gene 101 , Tsg101 [38] , involved in vacuolar protein sorting [39] , [40] , LUJV has a unique Y77REL motif that matches the YXXL motif of the retrovirus equine infectious anemia virus [41] , which interacts with the clathrin adaptor protein 2 ( AP2 ) complex [42] . A Tsg101-interacting motif , P90SAP , is found in LUJV in position of the second late domain of LASV , PPPY , which acts as a Nedd4-like ubiquitin ligase recognition motif [43] . The RING motif , containing conserved residue W44 [44] , and the conserved myristoylation site G2 are present [45]–[47] ( Figure 4 ) . The NP of LUJV ( 63 . 1 kDa , pI = 9 . 0 ) contains described aa motifs that resemble mostly OW arenaviruses [48] , including a cytotoxic T-lymphocyte ( CTL ) epitope reported in LCMV ( GVYMGNL; [49] ) , corresponding to G122VYRGNL in LUJV , and a potential antigenic site reported in the N-terminal portion of LASV NP ( RKSKRND; [50] ) , corresponding to R55KDKRND in LUJV ( Figure 4 ) . The GPC precursor ( 52 . 3 kDa , pI = 9 . 0 ) is cotranslationally cleaved into the long , stable signal peptide and the mature glycoproteins G1 and G2 [51]–[54] . Based on analogy to LASV [55] and LCMV [56] , signalase would be predicted to cleave between D58 and S59 in LUJV . However , aspartate and arginine residues in the −1 and −3 positions , respectively , violate the ( −3 , −1 ) -rule [57]; thus , cleavage may occur between S59 and S60 as predicted by the SignalP algorithm . The putative 59 aa signal peptide of LUJV displays a conserved G2 , implicated in myristoylation in JUNV [58] , however , it is followed in LUJV by a non-standard valine residue in position +4 , resembling non-standard glycine residues found in Oliveros virus ( OLVV [59] ) and Latino virus ( LATV; http://www2 . ncid . cdc . gov/arbocat/catalog-listing . asp ? VirusID=263&SI=1 ) . Conservation is also observed for aa residues P12 ( except Amapari virus; AMAV [60] ) , E17 [61] ( except Pirital virus; PIRV [62] ) , and N20 in hydrophobic domain 1 , as well as I32KGVFNLYK40SG , identified as a CTL epitope in LCMV WE ( I32KAVYNFATCG; [63] ) ( Figure 4 ) . Analogous to other arenaviruses , SKI-1/S1P cleavage C-terminal of RKLM221 is predicted to separate mature G1 ( 162 aa , 18 . 9 kDa , pI = 6 . 4 ) from G2 ( 233 aa , 26 . 8 kDa , pI = 9 . 5 ) [52] , [53] , [64] . G2 appears overall well conserved , including the strictly conserved cysteine residues: 6 in the luminal domain , and 3 in the cytoplasmic tail that are included in a conserved zinc finger motif reported in JUNV [65] ( Figure 4 ) . G2 contains 6 potential glycosylation sites , including 2 strictly conserved sites , 2 semi-conserved sites N335 ( absent in LCMVs and Dandenong virus; DANV [19] ) and N352 ( absent in LATV ) , and 2 unique sites in the predicted cytoplasmic tail ( Figure 4 ) . G1 is poorly conserved among arenaviruses [16] , and G1 of LUJV is no exception , being highly divergent from the G1 of the other arenaviruses , and shorter than that of other arenaviruses . LUJV G1 contains 6 potential glycosylation sites in positions comparable to other arenaviruses , including a conserved site N93HS ( Figure 4 ) , which is shifted by one aa in a motif that otherwise aligns well with OW arenaviruses and NW arenavirus clade A and C viruses . There is no discernable homology to other arenavirus G1 sequences that would point to usage of one of the two identified arenavirus receptors; Alpha-dystroglycan ( α-DG ) [66] that binds OW arenaviruses LASV and LCMV , and NW clade C viruses OLVV and LATV [67] , or transferrin receptor 1 ( TfR1 ) that binds pathogenic NW arenaviruses JUNV , MACV , GTOV , and SABV [68] ( Figure S2 ) . In summary , our analysis of the LUJV genome shows a novel virus that is only distantly related to known arenaviruses . Sequence divergence is evident across the whole genome , but is most pronounced in the G1 protein encoded by the S segment , a region implicated in receptor interactions . Reassortment of S and L segments leading to changes in pathogenicity has been described in cultured cells infected with different LCMV strains [69] , and between pathogenic LASV and nonpathogenic MOPV [70] . We find no evidence to support reassortment of the LUJV L or S genome segment ( Figure 3A and 3B ) . Recombination of glycoprotein sequence has been recognized in NW arenaviruses [14] , [16] , [33] , [34] , [71]–[73] , resulting in the division of the complex into four sublineages: lineages A , B , C , and an A/recombinant lineage that forms a branch of lineage A when NP and L sequence is considered ( see Figure 3C and 3D ) , but forms an independent branch in between lineages B and C when glycoprotein sequence is considered ( see Figure 3D ) . While recombination cannot be excluded in case of LUJV , our review of existing databases reveals no candidate donor for the divergent GPC sequence . To our knowledge is LUJV the first hemorrhagic fever-associated arenavirus from Africa identified in the past 3 decades . It is also the first such virus originating south of the equator ( Figure 1 ) . The International Committee on the Taxonomy of Viruses ( ICTV ) defines species within the Arenavirus genus based on association with a specific host , geographic distribution , potential to cause human disease , antigenic cross reactivity , and protein sequence similarity to other species . By these criteria , given the novelty of its presence in southern Africa , capacity to cause hemorrhagic fever , and its genetic distinction , LUJV appears to be a new species .
Clinical specimens were inactivated in TRIzol ( liver tissue , 100 mg ) or TRIzol LS ( serum , 250 µl ) reagent ( Invitrogen , Carlsbad , CA , USA ) prior to removal from BSL-4 containment . Total RNA extracts were treated with DNase I ( DNA-free , Ambion , Austin , TX , USA ) and cDNA generated by using the Superscript II system ( Invitrogen ) and 100–500 ng RNA for reverse transcription primed with random octamers that were linked to an arbitrary , defined 17-mer primer sequence [74] . The resulting cDNA was treated with RNase H and then randomly amplified by the polymerase chain reaction ( PCR; [75] ) ; applying a 9∶1 mixture of a primer corresponding to the defined 17-mer sequence , and the random octamer-linked 17-mer primer , respectively [74] . Products >70 base pairs ( bp ) were selected by column purification ( MinElute , Qiagen , Hilden , Germany ) and ligated to specific linkers for sequencing on the 454 Genome Sequencer FLX ( 454 Life Sciences , Branford , CT , USA ) without fragmentation of the cDNA [19] , [76] , [77] ) . Removal of primer sequences , redundancy filtering , and sequence assembly were performed with software programs accessible through the analysis applications at the GreenePortal website ( http://156 . 145 . 84 . 111/Tools ) . Conventional PCRs at CU were performed with HotStar polymerase ( Qiagen ) according to manufacturer's protocols on PTC-200 thermocyclers ( Bio-Rad , Hercules , CA , USA ) : an enzyme activation step of 5 min at 95°C was followed by 45 cycles of denaturation at 95°C for 1 min , annealing at 55°C for 1 min , and extension at 72°C for 1 to 3 min depending on the expected amplicon size . A two-step RT-PCR protocol was also followed at CDC using Invitrogen's Thermoscript RT at 60 degrees for 30 min followed by RNase H treatment for 20 min . cDNA was amplified using Phusion enzyme with GC Buffer ( Finnzymes , Espoo , Finland ) and 3% DMSO with an activation step at 98°C for 30 sec , followed by the cycling conditions of 98°C for 10 sec , 58°C for 20 sec , and 72°C for 1 min for 35 cycles and a 5 min extension at 72°C . Specific primer sequences are available upon request . Amplification products were run on 1% agarose gels , purified ( MinElute , Qiagen ) , and directly sequenced in both directions with ABI PRISM Big Dye Terminator 1 . 1 Cycle Sequencing kits on ABI PRISM 3700 DNA Analyzers ( Perkin-Elmer Applied Biosystems , Foster City , CA ) . Programs of the Wisconsin GCG Package ( Accelrys , San Diego , CA , USA ) were used for sequence assembly and analysis; percent sequence difference was calculated based on Needleman-Wunsch alignments ( gap open/extension penalties 15/6 . 6 for nucleotide and 10/0 . 1 for aa alignments; EMBOSS [78] ) , using a Perl script to iterate the process for all versus all comparison . Secondary RNA structure predictions were performed with the web-based version of mfold ( http://mfold . bioinfo . rpi . edu ) ; data were exported as . ct files and layout and annotation was done with CLC RNA Workbench ( CLC bio , Århus , Denmark ) . Protein topology and targeting predictions were generated by employing SignalP , and NetNGlyc , TMHMM ( http://www . cbs . dtu . dk/services ) , the web-based version of TopPred ( http://mobyle . pasteur . fr/cgi-bin/portal . py ? form=toppred ) , and Phobius ( http://phobius . sbc . su . se/ ) . Phylogenetic analyses were performed using MEGA software [79] . | In September and October 2008 , five cases of undiagnosed hemorrhagic fever , four of them fatal , were recognized in South Africa after air transfer of a critically ill index case from Zambia . Serum and tissue samples from victims were subjected to unbiased pyrosequencing , yielding within 72 hours of sample receipt , multiple discrete sequence fragments that represented approximately 50% of a prototypic arenavirus genome . Thereafter , full genome sequence was generated by PCR amplification of intervening fragments using specific primers complementary to sequence obtained by pyrosequencing and a universal primer targeting the conserved arenaviral termini . Phylogenetic analyses confirmed the presence of a new member of the family Arenaviridae , provisionally named Lujo virus ( LUJV ) in recognition of its geographic origin ( Lusaka , Zambia , and Johannesburg , South Africa ) . Our findings enable the development of specific reagents to further investigate the reservoir , geographic distribution , and unusual pathogenicity of LUJV , and confirm the utility of unbiased high throughput pyrosequencing for pathogen discovery and public health . | [
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"epidemiology/nosoc... | 2009 | Genetic Detection and Characterization of Lujo Virus, a New Hemorrhagic Fever–Associated Arenavirus from Southern Africa |
Integration into the nuclear genome of germ line cells can lead to vertical inheritance of retroviral genes as host alleles . For other viruses , germ line integration has only rarely been documented . Nonetheless , we identified endogenous viral elements ( EVEs ) derived from ten non-retroviral families by systematic in silico screening of animal genomes , including the first endogenous representatives of double-stranded RNA , reverse-transcribing DNA , and segmented RNA viruses , and the first endogenous DNA viruses in mammalian genomes . Phylogenetic and genomic analysis of EVEs across multiple host species revealed novel information about the origin and evolution of diverse virus groups . Furthermore , several of the elements identified here encode intact open reading frames or are expressed as mRNA . For one element in the primate lineage , we provide statistically robust evidence for exaptation . Our findings establish that genetic material derived from all known viral genome types and replication strategies can enter the animal germ line , greatly broadening the scope of paleovirological studies and indicating a more significant evolutionary role for gene flow from virus to animal genomes than has previously been recognized .
Viral infection of germ line cells ( i . e . gametes , or cells of the early embryo ) can lead to viral genes or genomes becoming integrated into chromosomes and inherited as host alleles [1] , [2] . These insertions , which we refer to here as endogenous viral elements ( EVEs ) , are usually eliminated from the host gene pool within a small number of generations . However , they can also increase in frequency , and some eventually reach fixation [3]–[11] . In animal genomes , the majority of EVEs are derived from reverse transcribing RNA ( rtRNA ) viruses ( i . e . retroviruses ) [5] , [12] , [13] . Retroviruses are the only animal viruses that integrate into the genome of the host cell as an obligate step in their replication strategy , and are thus predisposed to enter the host germ line ( Figure 1 ) . EVEs derived from viruses that use other genome replication strategies also occur , but are much less common [6] , [7] , [9] , [11] , [14] , [15] . Genomic integration of non-retroviral viruses may be mediated by non-homologous recombination with chromosomal DNA [16]–[18] or by interactions with retroelements in the host cell [11] , [19]–[22] ( Figure 1 ) . EVEs reveal complex evolutionary relationships between viruses and their hosts . For example , endogenous retroviruses have shaped vertebrate genome evolution , not only by acting as genetic parasites [23] , [24] , but also by introducing useful genetic novelty . Indeed , the role of exapted retroviral genes ( i . e . integrated retroviral genes that have adapted to serve a function in the host genome ) in mammalian reproduction [25] , [26] identifies EVEs as a key factor in the evolution of placental mammals from egg-laying ancestors . Similarly , in parasitoid wasps , genes derived from ancestral nudiviruses have been exapted to facilitate a parasitic lifestyle [9] . These remarkable examples demonstrate an important role for gene flow from viruses to hosts in animal evolution . EVEs also constitute an invaluable resource for reconstructing the long-term history of virus and host evolution [27] , [28] . Viruses exhibit the potential for extremely high rates of nucleotide substitution , host switching , and lineage extinction , and this sets limitations on what can be reliably inferred from observations of contemporary isolates [29] , [30] . EVE sequences effectively represent the ‘molecular fossils’ of ancient viral genomes , preserving information about ancient virus and host interactions that would otherwise be difficult , if not impossible , to infer . For example , EVEs are subject to host rates of evolution and can thus be dated relatively reliably with molecular clock-based approaches , in which genetic divergence correlates linearly with time [31] . In contrast , structural constraints in exogenous viruses may lead to the decoupling of short and long-term rates of viral evolution , rendering molecular clock assumptions unusable over longer timescales [30] , [32]–[34] . Furthermore , the identification of orthologous EVE insertions allows the incorporation of independent age estimates based on host species divergences ( see Figure 1 ) [35] . Despite the large quantity of published genome sequence data , the diversity of non-retroviral viruses in animal genomes has not been systematically explored . In this report , we use an in silico approach to screen the genomes of mammals , birds and insect vector species for endogenous sequences derived from non-retroviral mammalian viruses . We identify sequences derived from a very broad range of viruses , revealing an extensive history of non-retroviral genome invasion ranging back to at least the late Mesozoic Era ( ∼93 million years ago ) . We demonstrate that these sequences can be highly informative; ( i ) revealing novel virus diversity; ( ii ) providing a timescale for virus evolution; ( iii ) indicating the likely host range of virus groups , and; ( iv ) identifying rare instances of horizontal transmission . Furthermore , using a novel approach , we show that at least some of the EVE sequences identified here are likely to have been exapted during their evolution . The implications of these findings are discussed .
An algorithm for in silico screening of genomes for endogenous non-retroviral insertions was developed . We selected all non-retroviral virus genera that infect mammals , and constructed a library of representative peptide sequences ( restricted to viruses with typical genome sizes of <100 Kilobases ( Kb ) ) ( Table S2 ) . The tBLASTn program was used to screen low coverage and complete genome assemblies for sequences exhibiting similarity to viral peptides in this library . We screened the genomes of likely reservoirs ( birds , n = 2 ) and vector species ( mosquitoes , n = 3; ticks , n = 1 ) as well as all available mammal genomes ( n = 44 ) ( Table S1 ) . Sequences that matched viral peptides with e-values <0 . 001 were extracted ( along with flanking sequences ) and putative protein sequences were inferred through a combination of automated and manual alignment . These sequences were assigned to taxonomic groups ( family , genus ) based on the most closely related exogenous viral sequences in searches of PFAM and Genbank databases ( Tables S3 , S4 , S5 , S6 , S7 ) . For EVEs that were found to encode uninterrupted open reading frames ( ORFs ) , putative protein sequences were used with the tBLASTn program to search expressed sequence tag ( EST ) databases for the corresponding mRNA . For all EVEs disclosing similarity to contemporary virus isolates , putative EVE protein sequences were aligned with representative viral protein sequences , and maximum likelihood phylogenies were constructed . We identified numerous , highly significant matches ( i . e . e-values <1×10−9 ) to RNA viruses in the genomes of mammals and insect vectors ( Table 1 , Tables S3 , S4 , S5 ) . EVEs related to a total of seven families were identified including double stranded RNA ( dsRNA ) viruses ( Reoviridae ) and positive sense RNA ( RNA+ve ) viruses ( Flaviviridae ) , as well as both segmented ( Orthomyxoviridae , Bunyaviridae ) and non-segmented ( Borna- , Filo- and Rhabdoviridae ) families of negative sense RNA ( RNA-ve ) viruses . Consistent with an integration process involving viral mRNA ( rather than genomic RNA ) , all EVEs derived from RNA viruses had genetic structures that spanned a single viral transcript ( or fragments derived from single transcripts ) . EVEs derived from different genes never occurred as contiguous sequences , and consequently we could not determine whether EVEs derived from distinct genes of a given virus family originated from the same or distinct virus lineages/infections . In mammals , matches to RNA virus proteins that spanned complete genes were typically flanked by target site duplications ( TSDs ) and 3′ poly-A tails , consistent with LINE-mediated retrotransposition of viral mRNAs [36] . In insects , similar features were not apparent for any EVE insertion , even when the boundaries of host and viral sequences were clearly identifiable ( Figure S1 ) . Notably , putative 3′ poly-A tails could be identified in the expected position for some mammal genome sequences that matched only weakly to RNA virus peptides , suggesting the presence of EVEs at the limit of detection to our search strategy . We identified highly significant matches to three families of viruses with DNA genomes in the genomes of mammals and birds ( Table 1 , Tables S6 and S7 ) . These included matches to two single stranded DNA ( ssDNA ) virus families ( Parvoviridae and Circoviridae - the first ssDNA virus EVEs to be described in mammals - and one family of reverse transcribing DNA ( rtDNA ) viruses ( Hepadnaviridae ) - the first rtDNA EVEs to be described . A single match to a double stranded DNA ( dsDNA ) virus family ( Adenoviridae ) was identified in the kangaroo rat genome , but this sequence was unambiguously viral across its entire length ( ∼17 Kb ) , encoding thirteen completely intact viral ORFs ( Figure S2 ) , and is thus likely to have derived from free virus and not an EVE . A subset of parvovirus-related EVEs represented complete or nearly complete viral genomes ( Figure 5a ) . For one insertion in the M . lucifugus genome , we identified putative 5′ and 3′ terminal non-coding regions encoding characteristic inverted terminal repeats ( Figure S3 ) . In general , however , DNA virus EVEs occurred as genomic fragments , with no particular region of the viral genome being obviously favored , with the exception of the circoviruses , for which only the Rep gene was found . We identified a number of EVE insertions that were orthologous between species , allowing minimum ages for families to be inferred from host divergence dates ( see Figure 2 ) . Using previously estimated mammalian divergence dates [42] we obtained minimum ages for the Parvo , Circo and Bornaviridae of 30 , 68 and 93 million years respectively , demonstrating the ancient origins of these families ( Figure 6 ) . During completion of this manuscript , orthologous filovirus EVEs were reported in the mouse and rat genomes [22] . These sequences were identified by BLAST searching using EVEs as probes , and were not picked up in our screen , which relied on matches to exogenous viruses . On the basis of the mammalian divergence dates used here [42] , these EVEs provide a minimum age of 30 million years for the Filoviridae ( Figure 6 ) . The EVEs identified in this study extend the host range of several families ( Parvo- , Circo- , Hepadna , Borna- and Filoviridae ) with respect to their known range as exogenous viruses ( Figure 6 ) . Dependovirus EVEs are particularly widespread and occur in diverse mammalian hosts , despite their apparent low probability of germ line integration in AAV-derived gene therapy vector in vivo models [43] . Filoviruses have only been identified as exogenous infections in bats and primates [44] . However , filoviruses EVEs were identified not only in North American bats ( M . lucifugus ) and Asian primates ( tarsier ) , but also in insectivores , rodents , and in both South American and Australian mammals ( Figure 6 ) . In concordance with the recent identification of Ebola Reston in swine [45] , this unexpected result indicates that the distribution of filoviruses is likely much broader than has previously been recognized . Highly discordant host ranges among closely related EVEs ( or EVEs and exogenous viruses ) can provide information about transmission events . In this regard , we note that a dependovirus EVE in the bottlenose dolphin ( Tursiops truncatus ) genome grouped robustly with avian dependoviruses ( rather than mammalian isolates ) in NS1 trees ( Figure 6d ) , suggesting cross-class transmission of parvoviruses between birds and mammals may have occurred in the past . EVEs that are neutral or only slightly deleterious in their hosts may fortuitously drift or hitchhike [46] to fixation , accumulating mutations at the host neutral rate . Alternatively , EVE insertions may confer an advantageous phenotype on the host and spread through the population by selection . In such exapted sequences , selection will act to maintain the functionality of the EVE sequence . Many of the EVEs identified in this study were highly mutated and/or fragmented and these likely represent non-functional , neutrally evolving pseudogenes . However , several EVEs encoded intact ORFs , and some also express RNA ( Figure 2a , Figure 3a , Figure 5a ) . For most of these EVEs , the time since insertion is unknown , and intact ORFs could reflect recent insertion rather than a long-standing history of purifying selection within the host genome . In primates , however , orthology of the bornavirus-derived insert EBLN-1 , which is intact in several species , demonstrates an insertion date predating the divergence of strepsirhine primates ( ∼54 million years ago ( MYA ) ) ( Figure 7 ) . Simulations in which a consensus derived from all EBLN-1 sequences was allowed to neutrally evolve over this time period indicated the probability of maintaining an intact ORF in the absence of purifying selection was <0 . 00001 ( 100 , 000 replicates , mean number of stop codons = 15 . 57 , 95% confidence range 7 . 9–23 . 3 ) . This analysis provides more robust support for purifying selection than classical tests based on the ratio of synonymous to non-synonymous mutations ( which are weakly significant for EBLN-1 [11] ) , strongly indicating that EBLN-1 has been exapted in the primate genome , at least during part of its evolutionary history . Curiously , however , EBLN-1 has not retained coding capacity in all primate species . Perhaps selection to maintain it has recently been lost across all primates , and all the inserts may become inactivated in future .
In this report , systematic screening revealed that sequences derived from a broad range of non-retroviral mammalian virus groups occur as endogenous elements in the genomes of mammals , birds and insect vectors . We describe the first EVEs derived from the rtDNA and dsRNA groups , thereby establishing that the complete range of known animal virus replication strategies ( see Figure 1 ) are represented by endogenous elements in animal genomes . Richer sampling of animal genomes is likely to reveal an even greater diversity of EVEs . While EVEs that are very ancient ( i . e . that inserted prior to the divergence of major host lineages ) can be identified by selectively screening a small number of host species , identification of more recent insertions will often require richer sampling within orders and genera . Sampling of mammalian species for whole genome sequencing has generally been across , rather than within orders ( primates are an exception ) . Consequently the majority of mammal species sampled in this study diverged more than 50 million years ago ( Figure 6 ) . Any mammal species that was not sampled , and diverged more recently , could contain uncharacterized EVEs . Sampling of avian and insect vector genomes has so far been quite limited , and these may also harbor a rich virus fossil history . Furthermore , the vast majority of EVE insertions never reach fixation , and there are likely many unfixed EVEs present within species gene pools at a given time ( known examples of unfixed EVEs include Israeli acute paralysis virus ( IAPPV ) insertions in honey bees ( Apis mellifera ) [15] , koala endogenous retrovirus ( KoRV ) in koala bears [47] , and human herpesvirus 6 ( HHV-6 ) and HERV-K HML-2 insertions in humans [18] , [48] ) . Identification of such unfixed EVEs will often require population-level screening . The in silico screening strategy employed here likely underestimates the actual diversity of EVEs for several reasons . Firstly , only low-coverage , incomplete genome data were available for most species . Furthermore , EVEs within the data we screened could have been overlooked because ( i ) screening was based on similarity searches , and is thus dependent on current ( limited ) knowledge of viral diversity , and ( ii ) more ancient EVEs may not be identified due to the divergence in both host and virus lineages subsequent to insertion ( this may also result in a bias toward detecting more conserved genes ) . Certain groups of ( non-retroviral ) viruses appear to be better represented in the genomic fossil record than others ( e . g . Parvoviridae , Mononegavirales ) . This likely reflects a predisposition for germline integration among viruses with particular patterns of replication and infection . Notably , viruses that establish persistent infections and/or replicate within the nucleus are particularly well represented among the EVEs identified in this study . Nevertheless , these characteristics do not appear to be prerequisites for germ line integration ( Table 1 ) . Indeed , since retroelements are ubiquitous in animal genomes , and replication of all known viruses requires the expression of RNA , retroelement activity in germ line cells [49] may present a general mechanism for mediating insertion of virus genes into animal germ lines ( see Figure 1 ) . The discovery that a broad range of viruses are represented by EVEs in animal genomes indicates that viral ‘molecular fossils’ can provide the basis for robust , time-scaled , macroevolutionary studies across a range of animal and virus groups . For example , EVE sequences can be combined with phylogenetic data of extant host species to reveal patterns of inter-class virus transmission ( Figure 5 ) [50] . In this study , orthologous EVEs derived from the Borna- , Filo- Circo- , and Parvoviridae provided direct evidence for the ancient origins of these families ( Figure 6 ) . These findings also indicate that more recent dates of origin obtained for other virus families using molecular clock-based extrapolations are artifacts [30] . The diversity represented by known virus isolates represents a tiny fraction of the total viral diversity . Indeed , given their likely ancient origins , many virus families may be broadly distributed across mammalian hosts . This was reflected in viral phylogenies containing a mixture of EVEs and exogenous viruses - closely related exogenous relatives could often not be identified , or had only been recently characterized [37] , [38] , [51] ( Figure 2 , Figure 3 , Figure 5 ) . These findings suggest that EVEs can inform viral surveillance efforts by revealing novel virus diversity and indicating the likely host range of virus groups ( particularly if they inserted relatively recently ) . For example , a strong association between filoviruses and marsupials ( Table 1 , Figure 2 ) unexpectedly highlighted this group as a potential filovirus reservoir . The potential presence of EVEs may also be an important consideration in studies where bulk sequencing of environmental samples is used to identify novel virus groups [51]–[53] . EVEs that reach fixation in the host germ line may do so fortuitously , or because they are exapted by the host genome . Monte Carlo simulations provided robust statistical support for a history of purifying selection in the primate EVE EBLN-1 , indicating this sequence has been exapted by the primate genome . However , selection on EBLN-1 has clearly relaxed in some primates and may also have relaxed in humans ( Figure 7 ) . Such transient co-option may be expected for EVEs that function as restriction factors in their hosts by conferring resistance to infection by exogenous viruses . Several examples of this phenomenon have been described in animals [15] , [54] , [55] , and it is likely one of the most common exaptations of viral genes by host organisms [56] , [57] . In these cases , counter-adaptation in a rapidly evolving virus population may eventually render the EVE restriction mechanism non-functional [55] , causing selection to relax . Importantly , the rate at which EVEs are exapted as restriction factors in animals could greatly exceed their rate of fixation in animal genomes . The diverse EVE sequences described in this report demonstrate an extensive history of gene flow from virus to animal genomes . Animal genomes are a living document of virus and host interaction , and genomic studies have an important role to play in advancing understanding of virus and host evolution .
Chromosome assemblies and whole genome shotgun assemblies of 44 species ( Table S1 ) were screened in silico using tBLASTn and a library of representative peptide sequences derived from mammalian virus groups with genomes <100 Kb in total length ( selected from the 2009 International Committee on Taxonomy of Viruses ( ICTV ) master species list ( Table S2 ) ) . Host genome sequences spanning high-identity ( i . e . e-values <0 . 0001 ) matches to viral peptides were extracted , and a putative viral ORF was inferred using BlastAlign [58] and manual editing . Putative EVE peptides were then used to screen the Genbank non-redundant ( nr ) database in a reciprocal tBLASTn search . Matches to retroviruses , viral cloning vectors , and non-specific matches to host loci were filtered and discarded . The remaining sequences were considered viral if they unambiguously matched viral proteins in the Genbank and PFAM databases as shown in Tables S3 , S4 , S5 , S6 , S7 . Genetic structures for these elements were determined by comparison of the putative EVE peptide sequence to the nucleotide sequence of a viral type species representing the most closely related viral genus recognized by ICTV . Boundaries between viral and genomic regions were identified by analysis of sequences flanking matches to viral peptides , the genomes of the host species , and closely related host species . Sequences that flanked viral insertions were considered genomic if they; ( i ) were present as empty insertion sites in a related host species; ( ii ) disclosed highly significant similarity ( i . e . e-values <1×10−9 ) to host proteins; or ( iii ) non-viral and highly repetitive ( >50 copies per host genome ) . Insertions were considered endogenous when >100 bp of genomic flanking sequence could be identified either side of a viral match . Insertions for which >100 bp of unambiguous ( i . e . >80% nucleotide identity ) flanking sequence was identified in host sister taxa were considered orthologous insertions . PERL scripts were used to automate BLAST searches and sequence extraction . Putative EVE peptide sequences , and alignments of EVEs and exogenous retroviruses , are available online ( http://saturn . adarc . org/paleo/ ) . Putative EVE sequences inferred using BlastAlign were aligned with closely related viruses using MUSCLE and manually edited [59] . Maximum likelihood ( ML ) phylogenies were estimated using amino acid sequence alignments with RAXML [60] , implementing in each case the best fitting substitution model as determined by ProtTest [61] . Support for the ML trees was evaluated with 1000 nonparametric bootstrap replicates . The best fitting models for the datasets were: Parvoviridae: dependovirus NS1 gene ( JTT+Γ , 332 amino acids across 17 taxa ) , Parvoviridae: parvovirus NS1 gene , ( JTT+Γ , 293 amino acids across 13 taxa ) , Circoviridae: Rep gene ( Blosum62+Γ+F , 235 amino acids across 14 taxa ) , Hepadnaviridae: polymerase gene ( JTT+Γ+F , 661 amino acids across 9 taxa ) , Orthomyxoviridae: GP gene ( WAG+Γ+F , 482 amino acids across 5 taxa ) , Reoviridae: VP5 gene ( Dayhoff+Γ+F , 171 amino acids across 4 taxa ) , Bunyaviridae: phlebovirus NP gene ( LG+Γ , 247 amino acids across 12 taxa ) , Bunyaviridae: nairovirus NP gene ( LG+Γ , 446 amino acids across 5 taxa ) , Flaviviridae: mostly NS3 gene ( LG+Γ+F , 1846 amino acids across 8 taxa ) , Filoviridae: NP gene ( JTT+Γ , 369 amino acids across 29 taxa ) , Filoviridae: L gene ( LG+Γ+F , 517 amino acids across 9 taxa ) , Bornaviridae: NP gene ( JTT+Γ , 147 amino acids across 73 taxa ) , Bornaviridae: L gene ( JTT+Γ+F , 1243 amino acids across 12 taxa ) , Rhabdoviridae: NP gene ( LG+Γ , 220 amino acids across 34 taxa ) , Rhabdoviridae: L gene ( LG+Γ+F , 383 amino acids across 26 taxa ) . A Monte Carlo simulation procedure was employed to determine the probability that the bornavirus-derived element EBLN-1 has retained coding capacity over 54 . 1 million years under neutral evolution ( i . e . not under purifying selection ) . A consensus EBLN-1 sequence was inferred , and the effects of neutral evolution were simulated using seq-gen [62] for a branch length equivalent to the minimum amount of time that EBLN-1 orthologs have resided in primate genomes , based on the primate divergences estimated by Bininda-Emonds et al [42] , and given a neutral rate of evolution of 2 . 2×10–9 [12] . The number of stop codons accrued was counted for 100 , 000 iterations of the simulation . The probability that the reading frame could have remained open under neutrality is given by the number of replicates under which no stop codons have evolved , divided by the number of iterations . Parvoviridae; AAV2 ( NC_001401 ) ; Minute virus of mice ( NC_001510 . 1 ) ; AMDV ( NC_001662 ) ; Goose parvovirus ( EU583390 . 1 ) ; Muscovy duck parvovirus ( X75093 . 1 ) ; Porcine hokovirus ( EU200671 . 1 ) ; Snake parvovirus ( AY349010 . 1 ) ; Avian AAV ( AY629582 . 1 , AY629583 . 1 , GQ368252 . 1 ) ; AAV1 ( AF063497 . 1 ) ; AAV4 ( U89790 ) ; AAV2 ( AY695375 . 1 ) ; Bovine AAV ( AY388617 . 1 ) ; Caprine AAV ( DQ335246 . 2 ) ; Bocavirus ( M14363 . 1 ) ; Erythrovirus ( AB126265 . 1 ) ; Aleutian mink disease virus ( M20036 . 1 ) ; Porcine parvovirus ( EU790642 . 1 ) ; Feline panleukopenia virus ( EF988660 . 1 ) ; Canine parvovirus ( EU310373 . 2 ) ; Rat parvovirus ( AF036710 . 1 ) ; Hamster parvovirus ( U34255 . 1 ) ; Minute virus of mice ( DQ196317 . 1 ) ; Kilham rat virus ( U79033 . 1 ) ; Circoviridae; Porcine circovirus 1 ( NC_006266 ) ; Porcine circovirus 2 ( GU325757 ) ; Cyclovirus PK5006 ( GQ404856 . 1 ) ; Cyclovirus NG14 ( GQ404855 . 1 ) ; Human stool-associated circular virus NG13 ( GQ404856 . 1 ) ; Beak and feather disease virus ( AY450436 . 1 ) ; Columbid circovirus ( AF252610 . 1 ) ; Hepadnaviridae; duck HBV ( NC_001344 ) ; Stork HBV ( AJ251937 . 1| ) ; Heron HBV ( NC_001486 ) ; Ross' Goose HBV ( AY494849 . 1 ) ; Crane HBV ( AJ441113 . 1 ) ; Sheldgoose HBV ( AY494852 . 1 ) ; Snow goose HBV ( AF111000 . 1 ) ; Woodchuck HBV ( AF410861 . 1 ) ; Flaviviridae; Kamiti river virus ( NC_005064 ) ; Aedes flavivirus ( NC_012932 ) ; Quang binh virus ( NC_012671 ) ; Culex flavivirus ( NC_008604 ) ; Nakiwogo virus ( GQ165809 ) . Reoviridae; Liaoning virus ( NC_007736 - NC_007747 ) ; Kadipiro virus ( NC_004199 , NC_004205-NC_004210 , NC_004212-NC_004216 ) ; Banna virus ( NC_004198 , NC_004200-NC_004204 , NC_004211 , NC_004217-NC_004221 ) . Bunyaviridae; Crimean-Congo hemorrhagic fever virus ( NC_005300 , NC_005301 , NC_005302 ) ; Uukuniemi virus ( NC_005214 , NC_005220 , NC_005221 ) ; Uukuniemi virus ( M33551 . 1 ) ; Catch-me-cave virus ( EU274384 . 1 ) ; Sandfly fever Naples virus ( EF201832 . 1 ) ; Massilia virus ( EU725773 . 1 ) ; Punta Toro virus ( EF201834 . 1 ) ; Buenaventura virus ( EF201839 . 1 ) ; Rift Valley fever virus ( DQ380156 . 1 ) ; Phlebovirus sp . ( EF201818 . 1 ) ; Icoaraci virus ( EF076014 . 1 ) . Orthomyxoviridae; Quaranfil virus ( FJ861694 . 1 ) ; Johnston Atoll virus ( FJ861696 . 1 ) ; Thogoto virus ( M77280 . 1 ) ; Dhori virus ( M34002 . 1 ) . Bornaviridae; Borna disease virus ( NC_001607 ) ; Avian BDV ( FJ169441 ) . Filoviridae; Reston ebola virus ( NC_002549 ) ; Zaire ebola virus ( NC_002549 ) ; Lake Victoria marburgvirus ( NC_001608 ) . Rhabdoviridae; vesicular stomatitis virus ( NC_001560 ) ; Wongabel virus ( NC_011639 ) ; Kotonkon virus ( DQ457099 ) ; Adelaide river virus ( U10363 . 1 ) ; Obodhiang virus ( DQ457098 . 1 ) ; Bovine ephemeral fever virus ( AF234533 . 1 ) ; Rochambeau virus ( DQ457104 . 1 ) ; Mount elgon bat virus ( DQ457103 . 1 ) ; Oita rhabdovirus ( AB116386 ) ; Kern canyon virus ( DQ457101 . 1 ) ; Sandjimba virus ( DQ457102 . 1 ) ; Kolongo virus ( DQ457100 . 1 ) ; Tupaia rhabdovirus ( AY840978 . 1 ) ; Spring viremia of carp ( DQ491000 . 1 ) ; Pike fry rhabdovirus ( FJ872827 . 1 ) ; Cocal virus ( EU373657 . 1 ) ; Vesicular stomatitis Indiana virus ( AF473865 . 1 ) ; Isfahan virus ( AJ810084 . 2 ) ; Chandipura virus ( AY614728 . 1 ) ; Ngaingan virus ( FJ715959 . 1 ) ; Wongabel virus ( EF612701 . 1 ) ; Flanders virus ( AF523194 . 1 ) . Nyaviridae; Midway virus ( NC_012702 ) ; Nyamanini virus ( NC_012703 ) . | The presence of retrovirus sequences in animal genomes has been recognized since the 1970s , but is readily explained by the fact that these viruses integrate into chromosomal DNA as part of their normal replication cycle . Unexpectedly , however , we identified a large and diverse population of sequences in animal genomes that are derived from non-retroviral viruses . Analysis of these sequences—which represent all known virus genome types and replication strategies—reveals new information about the evolutionary history of viruses , in many cases providing the first and only direct evidence for their ancient origins . Additionally , we provide evidence that the functionality of one of these sequences has been maintained in the host genome over many millions of years , raising the possibility that captured viral sequences may have played a larger than expected role in host evolution . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"virology/virus",
"evolution",
"and",
"symbiosis",
"evolutionary",
"biology/paleontology",
"computational",
"biology/genomics"
] | 2010 | Endogenous Viral Elements in Animal Genomes |
Heterogametic sex chromosomes have evolved independently in various lineages of vertebrates . Such sex chromosome pairs often contain nonrecombining regions , with one of the chromosomes harboring a master sex-determining ( SD ) gene . It is hypothesized that these sex chromosomes evolved from a pair of autosomes that diverged after acquiring the SD gene . By linkage and association mapping of the SD locus in fugu ( Takifugu rubripes ) , we show that a SNP ( C/G ) in the anti-Müllerian hormone receptor type II ( Amhr2 ) gene is the only polymorphism associated with phenotypic sex . This SNP changes an amino acid ( His/Asp384 ) in the kinase domain . While females are homozygous ( His/His384 ) , males are heterozygous . Sex in fugu is most likely determined by a combination of the two alleles of Amhr2 . Consistent with this model , the medaka hotei mutant carrying a substitution in the kinase domain of Amhr2 causes a female phenotype . The association of the Amhr2 SNP with phenotypic sex is conserved in two other species of Takifugu but not in Tetraodon . The fugu SD locus shows no sign of recombination suppression between X and Y chromosomes . Thus , fugu sex chromosomes represent an unusual example of proto–sex chromosomes . Such undifferentiated X-Y chromosomes may be more common in vertebrates than previously thought .
Diverse systems of sex determination have evolved independently in the animal and plant kingdoms [1] . The most prominent of them involves heterogametic sex chromosome systems where whole or a part of sex chromosomes are heterozygous in one sex ( XY or ZW ) and homozygous in the other ( XX or ZZ ) . In many species , these sex chromosomes show distinctive morphology . It has been hypothesized that these sex chromosomes originated from a pair of autosomes that eventually diverged due to suppression of recombination [2] , [3] . In addition , a master sex-determining ( SD ) gene often resides on one of the sex chromosomes . However , the SD gene varies between organisms , which underscores the independent evolution of SD and sex chromosome systems [1] . In vertebrates , previously four master SD genes were known; Sry [4] in therian mammals; Dmrt1 in chicken [5]; Dmy , a duplicated copy of Dmrt1 in medaka [6]; and Dm-W , a truncated copy of Dmrt1 in Xenopus laevis [7] . All these SD genes code for transcription factors , belonging to either the Sox family ( Sry ) or the DM-domain family . When this paper was under review , two papers that identified non-transcription factors as vertebrate SD genes were published . One of these genes is a male-specific duplicated copy of the anti-Müllerian hormone ( amhy ) gene in the Patagonian pejerrey ( Odontesthes hatcheri ) [8] . The other is an allele of the gonadal soma derived growth factor ( Gsdf ) called GsdfY , located on the Y chromosome of Oryzias luzonensis , a species closely related to medaka [9] . Although studies of SD gene and sex chromosomes in vertebrates have provided significant insights into the evolution of sex-determining systems , our understanding is still very limited , given that sex chromosomes are known to exist in various states of differentiation [1] and that the SD genes remains to be identified in the vast majority of vertebrates . In the present study , we investigated the SD region of the tiger pufferfish , Takifugu rubripes ( fugu ) . Fugu is a large marine teleost and has an XX-XY sex determining system [10] . The availability of the whole genome sequence and a dense genetic map of fugu , combined with its compact genome size ( 400 Mb ) , allowed us to use the power of genetics to search for the SD gene in this wild species [11] , [12] .
Our previous genome-wide linkage mapping in fugu has shown that the fugu SD region is restricted to a small segment of chromosome 19 flanked by large autosome-like regions [10] , [12] , [13] . In the fugu genome assembly ( version 4 ) , this locus includes four scaffolds of total length 5387 kb , containing ∼300 potential protein-coding genes [10] . Since a contiguous physical map of this region was not available , we first increased the resolution of the genetic map to determine the precise positions of the four scaffolds ( Table S1 ) . We next produced large numbers of fugu siblings and performed extensive linkage analyses to reduce the genomic interval of the SD region ( Figure 1A , Table S2 ) . We first narrowed down the SD region by analyzing 411 individuals and then searched for recombinants in the SD region in 1034 additional siblings . Comparison of the genotype of markers in the SD region and the sex of the 23 identified recombinants localized the SD region to a short stretch of 17 . 5 kb in which two protein-coding genes , NFX1-type zinc finger-containing 1 ( Znfx1 ) and anti-Müllerian hormone receptor type II ( Amhr2 ) , are predicted ( Figure 1B ) . Znfx1 is a transcription factor of unknown function that is expressed in a wide range of tissues in mammals including central nervous system , kidney , lung , muscle , ovary , testis , pancreas and thyroid [14] , whereas Amhr2 is a receptor in the anti-Müllerian hormone ( Amh ) pathway that plays an important role in development and maintenance of reproductive organs in mammals and other vertebrates [15] . Since previously known SD genes in vertebrates are specific to one of the sex chromosomes ( Y , Z or W ) that shows varying degrees of sequence differentiation with the other sex chromosome [4]–[7] , we examined whether the fugu SD region contains a distinct male-specific segment by PCR amplification , cloning and sequencing of the entire 17 . 5 kb SD region of two males chosen from experimental families ( Figure 1B ) . We then determined whether the genomic clones were derived from X or Y by using the markers polymorphic in the families . The sequences of the X and Y derived clones covering the 17 . 5 kb SD region were more than 99% identical ( 99 . 4% for SD2–14k region and 99 . 6% for SD3–14K region , Figure S1 ) indicating that the male sex in fugu is determined by a small difference in the genomic sequence . To precisely pinpoint the SD gene , we employed association mapping utilizing ancestral recombination in a wild population of fugu ( Figure 2 ) . SNPs and other polymorphisms were screened by sequencing the entire SD region of seven males from seven experimental crosses , the 5′region of SD3 for X and Y in one male from an eighth family , and the 5′ region of SD3 for only X in a male from the ninth family ( Figure S1 , Table S3 ) . In this analysis , we noted that only one of the variants , SNP 7271 ( C>G ) , residing within exon 9 of Amhr2 is heterozygous in all males ( Figure 3A , Table S3A and S3B ) . This SNP is located within the kinase domain of Amhr2 and changes an amino acid from His384 to Asp384 ( Figure 3 ) . Additional genotyping of this SNP in nine families showed perfect correlation with the male phenotype ( Table S2 ) . While 100% of males ( 41/41 ) were heterozygous ( C and G ) at this position , all females ( 44/44 ) were homozygous for the C allele . Apart from this SNP , we did not find any other SNP or other types of variants ( repeats , indels , etc . ) that was heterozygous in any male ( Table S3 ) . We further investigated this SNP and other SNPs chosen from the 17 . 5 kb SD region and its neighboring regions ( ∼2 kb upstream harboring three SNPs and ∼600 bp downstream containing one SNP ) in a natural population consisting of 58 females and 47 males ( Figure 2 ) . We first calculated linkage disequilibrium ( LD ) between all SNPs examined and found that the overall extent of LD in the SD region is very low ( Figure 2C ) . This pattern suggested that recombination has not stopped in the region containing the fugu SD gene , unlike the previously characterized SD regions in other vertebrates [3] . Indeed , there is no significant LD even between markers that are 1 kb apart , thus providing a remarkably high resolution for the SD region . We then examined the association between each SNP and the sex phenotype . Consistent with the results of the small-scale analysis , SNP 7271 showed perfect association with the male phenotype ( P = 5 . 6×10−31 ) ( Figure 2B ) . In addition , its neighboring variant , SNP 7412 residing in intron 8 also showed a strong association with the male phenotype ( P = 9 . 9×10−19 ) . However , the latter is unlikely to be responsible for SD because 9 out of 105 wild fish showed discordance in their genotypes and phenotypic sex . The distribution of its genotype was 53 GG and 5 CG in females , and 4 GG , 40 CG , 2 CC and 1 undetermined in males . The association of SNP 7412 to phenotypic sex can be explained as an indirect association due to its close proximity to SNP 7271 ( 141 bp apart , r2 = 0 . 7 ) that shows a perfect correlation with the phenotypic sex ( Figure 2 and Figure S2 ) . The results of the association mapping studies together with those of variant screening suggest that the SNP 7271 ( C>G ) is the sole polymorphism in the 17 . 5 kb SD region that shows perfect correlation with phenotypic sex . Conservation of SNPs between species is rare , unless they are under some selective constraint , or that the species are so closely related that they still share their ancestor's variants . Thus , trans-species analysis offers an additional test to verify the correlation of SNP7271 to phenotypic sex in fugu [16] , [17] . To this end , we investigated two other wild species of Takifugu , T . pardalis ( n = 8 for each sex ) and T . poecilonotus ( n = 6 females and 7 males ) , that diverged from Takifugu rubripes approximately 5 million years ago ( mya ) [18] . We PCR amplified and sequenced a part of the Amhr2 gene ( intron 8 , exon 9 and intron 9 ) from them . Comparison of the sequences revealed that while SNP 7271 is present in both species , the intronic SNP 7412 is present only in T . pardalis ( Figure 3B ) . More importantly , examination of all SNPs around exon 9 of Amhr2 from the two species indicated that SNP 7271 is the sole conserved polymorphism that shows perfect correlation with the sexual phenotype ( Figure 3B and 3C and Figure S3 , Table S4 ) . These results strongly suggest that SNP 7271 is a trans-species SNP and is likely to be a causative variant for sex determination in the three species of Takifugu . Interestingly , this SNP is absent in the green spotted freshwater pufferfish , Tetraodon nigroviridis ( n = 4 for each sex ) which diverged from fugu approximately 40 to 70 mya [18] , [19] . This indicates that the sex-determining polymorphism , SNP 7271 in Amhr2 , evolved in a common ancestor of the three Takifugu species after it diverged from the Tetraodon lineage . To confirm the exon-intron organization of the Amhr2 gene predicted in the fugu genome assembly , we amplified full-length cDNA of fugu Amhr2H384 and Amhr2D384 using cDNA from the testis as a template ( accession number AB618627 in DDBJ ) . Sequencing of these products indicated that fugu Amhr2 comprises 11 exons , and encodes a protein of 514 amino acids ( Figure S4A ) . The fugu protein is 28% identical to the human AMHR2 with 41% identity in the kinase domain ( Figure S4A ) . The fugu Amhr2 gene is embedded in a syntenic block of genes that is conserved in Tetraodon , stickleback as well as in human ( Figure S4B ) . This suggests that the fugu gene is an ortholog of Amhr2 in these species and that it was not transposed recently into the fugu SD locus in the Takifugu lineage . To further confirm its identity , we carried out phylogenetic analysis of Amhr2 and its related proteins from various vertebrates . This analysis confirmed that the fugu gene is an ortholog of Amhr2 in other vertebrates ( Figure S4C ) . Searches of the draft genome assemblies of fugu , Tetraodon , stickleback and medaka identified only a single copy of Amhr2 , suggesting that following the fish-specific whole genome duplication event in the teleost ancestor [20] , [21] , the duplicated copy of Amhr2 may have been lost in these teleosts . In mammals , Amhr2 is responsible for the regression of the Müllerian duct in males [15] . Loss-of-function of this gene in male mouse leads to a partial hermaphrodite having a uterus and an oviduct together with the testis [15] . However , in medaka , a homozygous mutation in exon 9 of Amhr2 ( Tyr390Cys ) ( hotei mutant ) results in complete sex reversal in half of the genetic males [22] . These results indicate that although Amhr2 is not essential for formation of the testis in mammals , in fishes Amhr2 has the potential to influence the decision of the bipotential gonad to become either an ovary or a testis . If the Amhr2 gene indeed resides at the top of the sex determination pathway in fugu , it should be expressed before the differentiation of ovary and testis . However , it need not be expressed in a sex-specific manner like the mammalian Sry or medaka Dmy , because the sex-specific signaling can be produced by the male-specific isoform , Amhr2D384 . Nevertheless , to verify if there is differential expression of Amhr2 between males and females , we analyzed Amhr2 expression in fugu by RT-PCR and in situ hybridization . Gonadal sex differentiation in fugu begins at 8–9 weeks after fertilization when juveniles attain a body length of approximately 25 mm [23] . The first sign of the morphological differentiation of ovary and testis is the formation of the ovarian cavity [23] , [24] . The RT-PCR analysis revealed that Amhr2 is expressed in the differentiating ovary as well as the testis of juvenile fugu ( Figure 4A; n = 3 for each sex ) . The in situ hybridization indicated that Amhr2 is expressed in both sexes before the morphological differentiation of gonads ( Figure 4B; n = 3 for female and n = 4 for male ) , and subsequently in somatic cells surrounding the germ cells of the differentiating gonads ( Figure 4B; n = 5 for each sex at 90 dpf and n = 2 for each sex at 126 dpf ) . A similar expression pattern of Amhr2 is also reported in medaka [25] . These results provide further support for a role for Amhr2 in the sex determination of fugu . Amhr2 is a type II receptor for the TGF-ß family of proteins and contains a single transmembrane domain and a serine/threonine kinase domain [26] . Upon binding to Amh , Amhr2 recruits and phosphorylates a type I receptor ( s ) that then transduces signals by phosphorylating Smad proteins which in turn regulate transcription of downstream genes in mammals [26] . SNP 7271 is located within the kinase domain of Amhr2 ( Figure 3C ) that is responsible for the phosphorylation . Five natural mutations in the kinase domain of Amhr2 in human [26] and one induced mutation in medaka ( Tyr390Cys ) [22] ( see Figure S4D ) resulted in a loss-of-function phenotype of Amh/Amhr2 signaling . Thus , the kinase domain is critical for the function of Amhr2 . Since Asp384 is conserved in Tetraodon , stickleback , mouse and human ( in medaka , Asp is replaced with Asn; Figure 3C ) , we propose that Amhr2H384 is a derived allele in Takifugu that causes a female phenotype when homozygous . To determine the effect of Asp to His substitution in the kinase domain of Amhr2 on the activation of Smad proteins in fugu , we first tried an antibody that can recognize the activated Smad 1/5/8 in mammals and zebrafish on sections of the differentiating testis of fugu by immunohistochemistry [27] . However , the antibody was not effective in fugu . We then used a mouse teratocarcinoma cell line , P19 , which contains all the molecules including type I receptor required for Amh signaling except Amhr2 [26] to assay the effect of substitution of fugu Amhr2 on the activation of Smad1 . However , both fugu Amhr2D384 and Amhr2H384 failed to elicit Smad1 activity in this cell line ( presumably due to the incompatibility with the mouse type I receptors ) ( data not shown ) . Because there are no comparable cell lines from fugu or other fishes , we introduced the His384 mutation into human AMHR2 ( His397 ) and tested the human construct in P19 cells . Interestingly , the human mutant hAMHR2H397 mediated significantly less signaling ( Smad1 activation ) compared to hAMHR2D397 ( Figure 5 ) . This result suggests that fugu Amhr2H384 is an allele with reduced function compared to Amhr2D384 and the homozygous form of this less-effective allele facilitates the formation of ovary , whereas the heterozygous form of the alleles , Amhr2H384 and Amhr2D384 , promotes development of testis . This implies that Amhr2D384 is a dominant allele . This interpretation is consistent with the male to female sex-reversal in the medaka Amhr2 homozygous mutant ( hotei ) that has a loss-of-function mutation in the kinase domain ( Tyr390Cys ) [22] , located 13 positions downstream of fugu Asp384 ( see Figure 3C ) . One possible mechanism of sex determination by Amh/Amhr2 signaling is that it decreases the number of germ cells , which in turn promotes the development of testis . This hypothesis is based on the observation that in the medaka hotei mutant over-proliferated germ cells were associated with ovary formation in males [22] . On the other hand , inhibition of primordial germ cell migration into gonads by knockdown of the chemoattractant receptor gene , cxcr4 , resulted in the formation of testis-like structure in females [28] . Consistent with this finding , high temperature-induced germ cell degeneration in fugu is associated with masculinization of ovarian somatic cells [29] . Another possibility is that the Amh/Amhr2 signaling could antagonistically act on the aromatase activity as reported in mammals [30] . Enhanced estrogen synthesis by aromatase is known to function as a natural inducer of ovarian differentiation in fishes [31] . Thus suppression of aromatase activity by Amh/Amhr2 signaling could lead to the formation of testis in fugu . In vertebrates , previously four master SD genes were known: Sry in therian mammals [4]; Dmrt1 in chicken [5]; Dmy in medaka [6]; and Dm-W in Xenopus laevis [7] . They all code for transcription factors and are able to regulate the expression of other genes in the sex-determination pathway . By contrast , the Amhr2 gene associated with the sex determination in fugu codes for a growth factor receptor . This suggests that any gene that lies in the sex-determination pathway has the potential to be recruited for triggering sex determination . Furthermore , unlike the known vertebrate SD genes that reside on only one of the pair of sex chromosomes , fugu Amhr2 is located on both of the homologous chromosomes . This suggests that phenotypic sex in fugu is determined by a combination of the two allelic variants of Amhr2 gene . When this paper was under review , two other novel candidates for vertebrate SD genes were reported , and both are in fishes . The first one is a male-specific , duplicated copy of the Amh gene ( amhy ) implicated in testicular development of the Patagonian pejerrey [8] . Morpholino-mediated knock down of amhy ( and presumably amh ) resulted in male to female sex reversal in 22% of fishes carrying the duplicated copy of Amh . Interestingly , Amh codes for a hormone that is a ligand for Amhr2 . The other SD gene , GsdfY identified in O . luzonensis [9] , is an allele similar to the sex-determining Amrh2 allele in fugu . The promoter analysis of this gene showed that the allelic difference involves 6 to 9 nucleotide difference in the binding site for a steroidogenic factor 1 ( SF1 ) and results in male-specific high expression of the GsdfY allele . By contrast , the polymorphism in fugu is in the coding sequence and affects the function of the protein . Nevertheless , it is interesting that all these three novel fish sex-determining genes are components of the TGF-ß signaling pathway . These findings underscore the critical role of TGF-ß signaling , a hitherto unappreciated pathway , in gonadal sex determination in vertebrates . It is possible that additional members of this pathway may be involved in sex determination in other teleosts and other non-mammalian vertebrates that have experienced a recent turnover of sex chromosomes . Several models have been proposed to explain the evolution of new sex-determination mechanisms . These include random genetic drift [32] , pleiotropic selection favoring new SD alleles [33] and sexually antagonistic selection [34] . The last model begins with an autosomal locus segregating alleles that have different relative fitness in males and females . Selection will favor an increase in the frequency of any new SD locus that is linked to these sexually antagonistic alleles . When the new SD allele is recessive , like the fugu Amhr2H384 , it cannot increase in frequency quickly . However , a recessive allele could first increase in frequency by drift . Once its frequency reaches a certain level , sexually antagonistic selection at the sex-linked locus can lead to the recessive feminizing allele to spread to fixation , resulting in the disappearance of the previous female determiner such as a dominant allele on the W chromosome . This could lead to heterogamety switch from ZW/ZZ to XX/XY . Since Amh/Amhr2 signaling plays a role downstream of the SD gene in many vertebrates including mammals , birds and medaka [15] , [22] , [35] , the fixation of such a downstream factor as the new SD locus might explain the later onset of the gonadal dimorphism , which occurs around 8–9 weeks after fertilization in fugu . The theory of sex chromosome predicts that when an existing SD gene is replaced by a new SD gene , the young sex chromosomes lack a nonrecombining region . Such a region subsequently evolves through suppression of recombination leading to the divergence of the sequences of the homologous sex chromosomes [3] . The suppression of recombination is generally mediated by accumulation of repetitive sequences and/or inversion of chromosomal segments spanning the newly arisen SD gene/locus [36] . The sex chromosome theory thus implies that just after a new SD gene has replaced an existing one , the two homologous chromosomes show little divergence in their sequences even around the SD gene . However , no such instance of highly similar sex chromosome pair is known in vertebrates except in fugu , and the recently reported O . luzonensis [9] . The two youngest known SD genes in vertebrates , medaka Dmy and Xenopus Dm-W , arose by segmental duplications and are embedded within recognizable nonrecombining regions . In medaka , the Y-specific nonrecombining region , originally derived from the duplication of a 43-kb fragment of the Dmrt1 locus , has grown to 258 kb by accumulating 137 kb of repetitive sequences [37] . The W-specific region of Xenopus , likely derived from a duplication of the Dmrt1 locus , is flanked by 3 to 4 kb of nonrecombining regions [7] , [38] . In fugu , although the recombination ratio in males is reduced compared to females even in regions around SD locus ( Table S1 ) , our genetic analysis did not identify any nonrecombining region in the SD locus ( Figure 1 and Figure 2 ) . This is consistent with our previous observation that the suppression of recombination in males relative to females is no greater around the sex-determination locus than on autosomes [12] . Furthermore , sequence comparison between the two SD chromosomes did not reveal any inversions or large scale accumulation of transposons and other repetitive elements ( Figure 1 and Figure S1 ) . Overall , the fugu SD region contains very little repetitive sequences ( 1 . 8% of retroelements and 2 . 3% simple repeats ) . Therefore , the SD chromosomes of fugu provide an unusual example of proto-sex chromosomes . The SD locus in therian mammals , medaka and Xenopus laevis are associated with duplication events , which can explain recombination suppression . However , in the case of Takifugu species , since sex is determined by just a single nucleotide difference , the SD locus might have eluded recombination suppression . Although there is little information regarding recombination in the SD locus of O . luzonensis [9] , it would be interesting to determine if recombination around this SD gene still occurs , as in fugu . Stock et al . [39] have recently shown that three species of European tree frogs that diverged 5 to 7 mya share the same pair of sex chromosomes with complete absence of X-Y recombination in males . Yet , the sequences of sex-linked loci are very similar between the X and Y chromosomes . Since phylogenetic analysis showed that the X and Y alleles clustered according to species rather than gametologs , the authors proposed that the similarity of the sequences at the sex-linked loci is maintained by occasional X-Y recombination , presumably occurring in sex-reversed XY females . Although this model may explain the sequence similarity between X and Y at the SD locus in fugu , the frequency of XY fugu females appears to be very low . We have not encountered any XY female in our genetic experiments . Moreover , attempts at producing XY females by hormonal or temperature manipulation have proven to be unsuccessful [29] , [40] . Thus , the extent of the contribution of sex-reversed XY females in maintaining similarity at the SD locus of fugu is unclear . We investigated the SD locus in fugu by high-resolution genetic mapping and association mapping . We found that a missense SNP in the kinase domain of Amhr2 that changes an amino acid is the sole polymorphism perfectly correlated with phenotypic sex . Our results suggest that a combination of the two alleles of Amhr2 is responsible for sex determination in fugu . The pattern of LD across the fugu SD locus indicates the absence of a nonrecombining region . Thus , the sex chromosomes of fugu represent a unique example of proto-sex chromosomes in vertebrates . Since genetic mapping studies of sex-determination loci in diverse taxa of vertebrates have indicated that vertebrates such as fishes , reptiles and amphibians have experienced a rapid turnover of sex chromosomes [1] , the pre-differentiated sex chromosomes found in fugu may be more common among these vertebrates than previously thought . The successful identification of the candidate sex-determining SNP in this study relied essentially on a sub-gene-level resolution of the association mapping owing to the low degree of LD in a wild population like fugu . In human populations , this method usually provides resolution at the level of only one or a few genes ( tens to hundreds of kb ) [41] . The resolution is even lower in laboratory mouse and domestic animals due to the strong LD across the genomes caused by a small effective population size [42] . By contrast , a wild species with a large effective population size is likely to show a low degree of LD across its genome . Thus the use of association mapping should greatly facilitate the identification of SD genes and chromosomes in the wild populations of non-model vertebrates .
To produce siblings , we crossed a fugu male with a fugu female as described previously [13] . Details of the families used in linkage mapping are shown in Table S2 . Sex was determined by histological examination of the gonads for fishes aged 2–10 months and by visual inspection for older fish . The microsatellite loci were chosen by scanning scaffolds as described previously [13] . Primer sequences for microsatellite markers are listed in Table S5 . Genotyping with microsatellite markers was performed as described previously [13] . This project was conducted in accordance with the Regulation for Animal Experiments of the University of Tokyo . We generated nine families and used males from these pedigrees for generating complete sequences of the SD region from X and Y chromosomes ( Figure S1 , Table S2 ) . Genomic clones covering Znfx1 ( genomic region of SD2–14k ) and Amhr2 ( genomic region of SD3–14k ) were obtained from the XY single male by PCR using KOD FX ( Toyobo ) reagents suitable for amplifying large genomic fragments ( Figure 1B and Figure S1 ) . Approximately 50 ng of genomic DNA was used as a template in 25 µl PCR reaction . PCR amplification was performed using KOD DNA polymerase ( Toyobo ) with the primer pair SD2–14 . 7kF and SD2–14 . 7kR for SD2–14k genomic region , and the primer pair 33–1464k340F and 33–1464k13469R for SD3–14k genomic region . The cycling conditions were 36 cycles of 94°C for 10 s and 70°C for 12 min . Primer sequences are given in Table S5 . DNA fragments were cloned by using TOPO XL PCR cloning kit ( Invitrogen ) or In-Fusion Advantage PCR cloning Kit ( Clontech ) . The clones from X and Y chromosome were distinguished by SNP7271 or satellite marker 1469 K and were subjected to sequencing . To identify candidate polymorphic sites and obtain variants for linkage disequilibrium analysis , we cloned genomic DNA covering Amhr2 gene ( the 5′ and 3′ regions of SD3 , Figure S1 ) from males from seven independent families . In addition , we cloned genomic DNA covering the 5′ region of SD3 from two males from two other independent families . We sequenced theses clones and identified variants among them ( Table S3 ) . To avoid cloning artifacts , we followed the method described by Saitoh and Chen [43] . For variants found in only one clone , two additional clones were sequenced to obtain the consensus sequence . For the region covering Znfx1 ( genomic region of SD2–14k ) , we directly sequenced the PCR products from ten males and determined DNA sequences for at least seven individuals from ten males . We studied a natural population of fugu consisting of 58 females and 47 males from off shore areas around the mid-west part of Japan . Genotyping was done using TaqMan or restriction fragment length polymorphism ( RFLP ) analysis ( Table S4 ) . We calculated association for SNPs having a minor allele frequency ( MAF ) >0 . 1 and a call rate >99% with phenotypic sex in fugu using Haploview program 4 . 1 [44] . Uncorrected P-values and P-values with 1 , 000 , 000 permutations are reported . We also tested the association between genotypes and phenotypic sexes for a recessive model of penetrance in which homozygosity of one of the alleles is required for phenotypic female based on the previous studies that have suggested that the sex of fugu is determined by an XX-XY system [10] . Linkage disequilibrium plot of r2 and D′ was generated using Haploview . cDNA of the fugu Amhr2H384 and Amhr2D384 were cloned from the testis using SMART RACE cDNA amplification Kit ( Clontech ) and sequenced completely . Multiple sequence alignment and NJ tree of fugu Amhr2 and its related proteins from various vertebrates were generated using ClustalW [45] . We obtained T . pardalis ( n = 8 for each sex ) and T . poecilonotus ( 6 females and 7 males ) from Lake Hamana , Japan . To determine exon 9 sequence of Amhr2 and its neighboring regions , we first amplified the genomic region using primer SD3exon8F and SD3exon10R , and sequenced the PCR product directly . Tetraodon nigroviridis specimens ( n = 4 for each sex ) were obtained from a supplier in Philippines , and the sequence of Amhr2 exon 9 and its neighboring regions were determined after PCR amplification with primers Tet-SD3exon8F and Tet-SD3exon10R . See Table S5 for primer sequences . One microgram of total RNA each from the gonad , brain , eye , intestine , heart , liver , spleen , kidney , trunk muscle and skin of fugu at 147 days post-fertilization ( dpf ) was used for synthesizing first-strand cDNA . PCR amplification was performed with the primer pair RT-SD3exon9F and RT-SD3exon9R , and the primer pair EF1α-F and EF1α-R . The cycling conditions were 40 cycles of 94°C for 10 s , 58°C for 5 s , and 72°C for 30 s . See Table S5 for primer sequences . Juveniles at 62 , 90 and 126 dpf from a full-sib family were dissected along the ventral midline and fixed in Bouin's solution or 4% paraformaldehyde at 4°C overnight . Fixed samples were dehydrated in graded ethanol , embedded in paraffin and sectioned serially at 5 µm thickness . After rehydration , sections were subjected to hybridization . The probes were transcribed from a fugu Amhr2 cDNA construct using DIG RNA labeling kit ( Roche ) . Signals were detected by immunoreaction with alkaline phosphatase-conjugated anti-DIG antibody and NBT/BCIP solution ( Roche ) . The genotypic sexes of all fish were determined by SNP7271 while phenotypic sex of fish at 90 and 126 dpf were determined by the formation of the ovarian cavity . The ovarian cavity was not seen in any fish at 64 dpf with the body length ranging from 19 to 22 mm ( n = 4 for XY fish and n = 5 for XX fish ) . The coding region of human AMHR2 was PCR amplified using a cDNA clone ( MHS4426-99239518 , Open Biosystems ) as a template with hAMHRII-F and hAMHRII-R primers . The amplicon was ligated into the pcDNA3 . 1 vector ( Invitrogen ) . Site-directed mutation was carried out using KOD-Plus-Mutagenesis Kit ( Toyobo ) with hAMHRII-D397H-F and hAMHRII-D397H-R primers . The coding region of mutated hAMHRII was amplified by PCR and ligated into the pcDNA3 . 1 . Coding region of human Smad1 was amplified by PCR using Smad1f and Smad1r primers with pCMV5 Smad1 ( Addgene ) as a template . The amplicon and the pBIND vector ( Promega ) were ligated to generate the construct Gal4-Smad1/pBIND . The constructs were confirmed by sequencing . Construction of 5xGal4-tk-luc construct has been reported previously [46] . See Table S5 for primer sequences . Mouse embryonal carcinoma P19 cells were cultured in Minimum Essential Medium ( αMEM , Sigma M8042 ) containing 2 mM L-Alanyl-L-Glutamine , 7 . 5% FCS and 2 . 5% FBS at 37°C under 5% CO2 . P19 was seeded in 96-well plates at 1×104 cells/100 µl medium 24 h prior to transfection . Transfection was conducted using Lipofectamine LTX ( Invitrogen ) . 5xGal4-tk-luc , Gal4-Smad1/pBIND and empty pcDNA3 . 1 , wild-type or mutated human AMHR2 in pcDNA3 . 1 were transfected . After addition of the recombinant human MIS ( rhMIS , R&D Systems , 2 µg/ml ) , the cells were cultured for 24 h . Luciferase activity was determined using Dual Luciferase Kit ( Promega ) . Firefly luciferase activity was normalized to Renilla luciferase activity . Each experiment was done in triplicates and the average of normalized activity was calculated . Three independent transfections were carried out , and values obtained by rhMIS treatment were divided by those obtained without rhMIS treatment . Data were expressed as mean ± SEM , with mean values comprising values from three independent assays: 3 per assay ( 9 per treatment ) . Luciferase activity was compared by One-way ANOVA ( two-tailed , P<0 . 05 ) . The Holm-Sidak test was applied to measure the differences among means for those cell types ( overall significance level P = 0 . 05 ) . | Diverse systems of sex determination have evolved independently in the animal and plant kingdoms . In vertebrates , so far four master sex-determining ( SD ) genes , Sry , Dmrt1 , Dmy , and Dm-W , have been identified . These genes code for transcription factors and are located on only one of the sex chromosomes surrounded by nonrecombining regions . It is hypothesized that these sex chromosomes evolved from a pair of homologous chromosomes that diverged after acquiring the SD gene . We investigated the SD locus in fugu by high-resolution genetic mapping and association mapping . We found that a SNP that changes an amino acid ( His/Asp384 ) in the kinase domain of anti-Müllerian hormone receptor type II ( Amhr2 ) is perfectly associated with phenotypic sex . A combination of the two alleles of the SNP ( homozygous females and heterozygous males ) is likely to be responsible for sex determination in fugu . While these alleles are conserved in two other species of Takifugu , they are absent in the freshwater pufferfish , Tetraodon . Furthermore , Fugu Amhr2 lies in a region that shows no evidence for recombination suppression between X and Y chromosomes . Thus , fugu sex chromosomes represent an unusual example of a pre-differentiated phase of sex chromosomes in vertebrates . | [
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] | 2012 | A Trans-Species Missense SNP in Amhr2 Is Associated with Sex Determination in the Tiger Pufferfish, Takifugu rubripes (Fugu) |
While phosphotyrosine modification is an established regulatory mechanism in eukaryotes , it is less well characterized in bacteria due to low prevalence . To gain insight into the extent and biological importance of tyrosine phosphorylation in Escherichia coli , we used immunoaffinity-based phosphotyrosine peptide enrichment combined with high resolution mass spectrometry analysis to comprehensively identify tyrosine phosphorylated proteins and accurately map phosphotyrosine sites . We identified a total of 512 unique phosphotyrosine sites on 342 proteins in E . coli K12 and the human pathogen enterohemorrhagic E . coli ( EHEC ) O157:H7 , representing the largest phosphotyrosine proteome reported to date in bacteria . This large number of tyrosine phosphorylation sites allowed us to define five phosphotyrosine site motifs . Tyrosine phosphorylated proteins belong to various functional classes such as metabolism , gene expression and virulence . We demonstrate for the first time that proteins of a type III secretion system ( T3SS ) , required for the attaching and effacing ( A/E ) lesion phenotype characteristic for intestinal colonization by certain EHEC strains , are tyrosine phosphorylated by bacterial kinases . Yet , A/E lesion and metabolic phenotypes were unaffected by the mutation of the two currently known tyrosine kinases , Etk and Wzc . Substantial residual tyrosine phosphorylation present in an etk wzc double mutant strongly indicated the presence of hitherto unknown tyrosine kinases in E . coli . We assess the functional importance of tyrosine phosphorylation and demonstrate that the phosphorylated tyrosine residue of the regulator SspA positively affects expression and secretion of T3SS proteins and formation of A/E lesions . Altogether , our study reveals that tyrosine phosphorylation in bacteria is more prevalent than previously recognized , and suggests the involvement of phosphotyrosine-mediated signaling in a broad range of cellular functions and virulence .
Protein phosphorylation is an evolutionarily highly conserved post-translational modification important for signal transduction in living organisms . The ability of bacteria to rapidly adapt to changing environments , crucial for survival and successful infection of the host by bacterial pathogens , relies on an extensive regulatory network also involving protein phosphorylation . Reversible protein phosphorylation targeting arginine , aspartate , histidine , serine , threonine and tyrosine residues is highly integrated in regulatory networks of bacteria . Among these , phosphorylation-mediated signaling through histidine and aspartate in bacterial two-component systems is the best characterized [1] . Phosphorylation on serine/threonine/tyrosine ( Ser/Thr/Tyr ) residues was initially associated with signaling in eukaryotes; however , during the past two decades it has emerged as an important regulatory function in prokaryotes as well . Recent high resolution mass spectrometry-based phosphoproteomic studies have unambiguously identified phosphorylation events in bacteria on Ser , Thr and less frequently on Tyr residues [2] , significantly expanding the repertoire especially of Ser and Thr phosphorylated proteins . Notably , a comprehensive phosphoproteomic analysis of Mycobacterium tuberculosis revealed more than 500 phosphorylation events on Thr/Ser but none on Tyr residues [3] . About 121 phosphotyrosine ( pTyr ) sites have so far been reported on 114 proteins in 11 bacterial species by phosphoproteomics studies [4] . While the role of pTyr modification in eukaryotes is well established in cell growth , proliferation and differentiation [5] , its role is less clear in bacteria . The Gram-negative bacterium Escherichia coli comprises diverse isolates ranging from gastrointestinal commensals to various disease-causing ones including the human pathogen enterohemorrhagic E . coli ( EHEC ) [6] . EHEC serotype O157:H7 , a commonly occurring food-borne pathogen in developed countries , is associated with diarrhea , hemorrhagic colitis and the potentially fatal hemolytic uremic syndrome [7] . Virulence of EHEC O157:H7 is in part attributed to the presence of about 53 pathogenicity islands ( PAI ) containing genes that are absent in non-pathogenic E . coli K12 [8] , [9] . EHEC O157:H7 infection is characterized by an attaching and effacing ( A/E ) histopathological lesion phenotype of infected intestinal epithelial cells , which is due to the activity of a type III secretion system ( T3SS ) , mainly encoded by the locus of enterocyte effacement ( LEE ) PAI [10] . The T3SS apparatus , responsible for the translocation of bacterial effector proteins into host cells , consists of a needle-like structure composed of the EspA filament , and additional translocon components such as EspB and EspD that form a pore in the host cell membrane . Among the proteins translocated by the T3SS are translocated intimin receptor Tir which binds to the outer membrane adhesion intimin [6] , [11] . It is well-established that host cell tyrosine kinases phosphorylate T3SS effector proteins including Tir from enteropathogenic E . coli ( EPEC ) and the mouse pathogen Citrobacter rodentium upon infection to subsequently manipulate host signaling pathways and induce actin rearrangements [12] , [13] . However , phosphotyrosine modification of virulence-associated E . coli proteins by bacterial tyrosine ( BY ) kinases has yet to be demonstrated . There are currently two characterized classical BY kinases in E . coli , Etk and Wzc , which are structurally and functionally different from eukaryotic tyrosine kinases [14] . BY kinases are associated with exopolysaccharide synthesis , antibiotic resistance , phage lysogenization and the heat shock response [15] . They affect virulence mainly through their involvement in capsular synthesis [16] , [17] . Although E . coli is among the best characterized bacterial species , the extent of known phosphotyrosine modifications by bacterial kinases is currently limited to about 32 proteins . A global phosphoproteome analysis of E . coli K12 using a metal oxide affinity-based phosphopeptide enrichment approach revealed 74 unique Ser/Thr phosphorylation sites , whereas only 7 Tyr phosphorylation sites were identified [18] , implying that specifically enriching for phosphotyrosine-modified phosphopeptides could expand the coverage of the phosphotyrosine-modified proteome in E . coli . Our current knowledge on tyrosine phosphorylation suggests biological significance , yet , a comprehensive identification of phosphotyrosine proteins is required to better understand the function of tyrosine phosphorylation in bacteria . To gain further insight into the extent and biological role of tyrosine phosphorylation in E . coli , we took advantage of technological progress in mass spectrometry-based proteomics and immunoaffinity enrichment strategies , recently applied in eukaryotic phosphotyrosine proteome studies , which enable comprehensive identification of phosphotyrosine events and accurate mapping of phosphorylation sites [19] , [20] . Here , we present an in-depth phosphotyrosine proteome profiling of EHEC O157:H7 and E . coli K12 using gel-free mass spectrometry-based phosphoproteomics , which revealed a total of 512 unique sites from which five enriched phosphotyrosine site motifs were identified . We show that tyrosine phosphorylation targets proteins involved in various cellular processes and virulence . We demonstrate the functional importance of a tyrosine phosphorylated residue on the regulator SspA involved in the regulation of T3SS expression . By addressing the functional importance of etk and wzc for tyrosine phosphorylation and their effects on metabolism and virulence-related phenotypes , we demonstrate that the known tyrosine kinases Etk and Wzc do not explain the observed phenotype of the etk wzc double mutant . Thus , in addition to providing novel insights into phosphotyrosine-mediated regulation in bacteria , our findings strongly suggest the presence of hitherto unidentified tyrosine kinases in EHEC O157:H7 .
We used a phosphoproteomic approach to comprehensively define the E . coli phosphotyrosine proteome and accurately map phosphorylation sites as outlined in figure 1 . Protein extracts were prepared from cultures of E . coli K12 strain MG1655 [21] and EHEC O157:H7 strain TUV93-0 , a Shiga toxin-deleted derivative of the strain EDL933 [8] , [22] , which was associated with an outbreak of hemorrhagic colitis in the United States in 1982 [23] . Strain TUV93-0 is hereafter referred to as wild type EHEC O157:H7 . We enriched tyrosine phosphorylated peptides from trypsin-digested cell lysates using anti-pTyr antibodies followed by peptide identification using high-resolution liquid chromatography tandem mass spectrometry ( LC-MS/MS ) analysis on Linear Trap Quadrupole ( LTQ ) -Orbitrap mass spectrometers . The peptides were fragmented in both collision-induced dissociation ( CID ) and higher collision dissociation ( HCD ) modes to increase the confidence and coverage of identified peptides . Stringent data analysis parameters were applied to ensure confident identification of phosphotyrosine peptides . We identified 166 unique pTyr peptides containing 167 unique pTyr sites on 117 proteins from E . coli K12; and 561 unique pTyr peptides containing 416 unique pTyr sites on 287 proteins from EHEC O157:H7 with a false discovery rate of less than 1% ( Table S1 ) . Localization of phosphotyrosine sites assessed using PhosphoRS revealed probability scores of >95% for the majority of identified peptides , reflecting high accuracy of phosphotyrosine site assignments . The presence of diagnostic peaks corresponding to pTyr immonioum ions in MS/MS spectra obtained in the HCD fragmentation mode further confirmed identified phosphotyrosines ( see figure S1 for representative MS/MS spectra ) . The increased number of phosphorylation events in EHEC O157:H7 could be attributed to increased activity of the tyrosine kinase Etk as reflected by a greater extent of phosphorylation of Tyr residues within the carboxyl-terminus Tyr cluster of Etk with 89 unique peptides detected in EHEC O157:H7 and only four in E . coli K12 ( Table S1 ) . This was also evident by higher levels of tyrosine phosphorylated Etk detected in EHEC O157:H7 by western blot analysis ( Figure S2 ) . Notably , the phosphotyrosine proteomes of EHEC O157:H7 and E . coli K12 represent two snapshots since these strains were grown under different conditions as described in Material and Methods to obtain increased coverage of the phosphotyrosine proteome . In all , specifically enriching for phosphotyrosine peptides rather than using the less specific metal affinity-based phosphopeptide enrichment approach used in most phosphoproteomic studies available for bacteria , combined with high resolution mass spectrometry allowed comprehensive identification of tyrosine phosphorylation events in E . coli . Our data revealed an E . coli phosphotyrosine proteome of 512 unique sites on 342 proteins , which is the highest number of tyrosine phosphorylated proteins reported in prokaryotes to date . Of 342 phosphotyrosine proteins , we were able to find published reports pertaining to only 18 . Identified phosphotyrosine proteins comprise 4% and 6% of E . coli K12 and EHEC O157:H7 proteomes respectively , which exceeds tyrosine phosphorylation levels reported for mammalian cells [24] . To determine the conservation of the 512 unique phosphotyrosine sites identified in E . coli , we mapped sequences including five amino acid residues on each side of phosphorylated residues onto the proteomes of 16 evolutionary diverged bacteria . Conservation of E . coli phosphotyrosine sites was generally low ( <10% ) except for in evolutionary closely related bacteria Salmonella enterica , Shigella flexneri , Klebsiella pneumoniae , Yersinia pestis , Vibrio cholerae and Haemophilus influenzae ( Table S2 ) . With phosphotyrosine sites mainly being conserved in the enteric bacteria E . coli , S . flexneri , S . entrica , V . cholerae and K . pneumoniae , our data supports the idea that phospho-mediated regulation reflects adaptation to environmental niches [25] . Notably , at least two of four phosphotyrosine sites mapping to chaperone ClpB , translation elongation factor Tuf and ATP synthase subunit AtpD were present in all proteomes tested , reflecting early acquisition and potential functional importance of these sites . Indeed , the highly conserved ClpB Tyr653 residue identified in this study as phosphorylated was previously shown to be critical for substrate binding , and thereby chaperone function [26] . Whereas definition of tyrosine phosphorylation site motifs in bacteria has so far been limited by the low number of phosphotyrosine sites available , the extensive dataset of 512 unique pTyr sites identified here provided a reliable source for bioinformatics prediction of such motifs . We used the Motif-X algorithm [27] to extract enriched sequence motifs surrounding the identified phosphotyrosine sites by considering 12 residues centered on the phosphorylated tyrosine residue . We defined five statistically significantly enriched phosphotyrosine site motifs for 160 unique pTyr sites comprising 31% of sites identified ( Figure 2A–E and Table S3 ) . For four of these motifs , unambiguous enrichment of a positively charged lysine was observed at positions +3 , +4 , +5 and −6 relative to the phosphotyrosine respectively , suggesting that lysine might be functionally important for phosphorylation , potentially by acting as an electron sink . Moreover , glycine and aspartate were at positions −1 and +1 respectively; indeed , glycine at position −1 was also observed for phosphotyrosine sites in Streptococcus pneumoniae [28] . A general sequence motif derived from the 512 pTyr sites using Phosphosite logo generator [29] revealed significant overrepresentation of glycine and aspartate residues at positions −1 and +1 respectively , and underrepresentation of lysine residues at −1 , −2 , +1 and +2 ( Figure 2F ) , which is consistent with the sequence composition of the five motifs identified . To determine whether the five tyrosine phosphorylation site motifs from E . coli are prevalent in eukaryotes , we analyzed 7551 phosphotyrosine sites from human proteins catalogued in the Human Protein Reference Database [30] by considering 12 residues surrounding the phosphotyrosine using Motif-X . We identified 13 statistically significantly enriched sequence motifs: YSP , YxD , YDxP , YExP , YxSP , YxxxD , YxxxxK , DY , DxxY , ExxY , EDxY , SxxxY and RxxSxxY , where variable residues are indicated as x ( p value<0 . 000001 ) ( Figure S3 and Table S4 ) . None of the human protein phosphotyrosine site motifs were identical to those of E . coli . However , one human protein pTyr site motif was similar to the E . coli motif GYxxxxK but lacks glycine at −1 ( Figure S3 ) , which might provide structural flexibility to the region of the tyrosine phosphoacceptor . The different composition of phosphotyrosine site motifs identified in human and bacterial proteins are consistent with the finding that BY kinases differ structurally from their eukaryotic counterparts [14] , likely also implying different substrate specificities . Functional classification of the 342 phosphotyrosine proteins identified combined from EHEC O157:H7 and E . coli K12 revealed a broad range of fundamental cell processes including cell division , transport , transcriptional and translational levels of gene expression , various metabolic pathways , and virulence ( Figure 3 and Table S5 ) . The representation of proteins belonging to most functional classes is also evident in bacterial serine and threonine phosphoproteomes including that reported for Mycobacterium tuberculosis [3] . Functional group profiles of the two E . coli strains were similar despite a limited overlap of 60 proteins , which might be explained by the fact that the two strains were grown under different conditions as described in Materials and Methods , resulting in two unique snapshots of the E . coli phosphotyrosine proteome . Moreover , E . coli stains have an extensive mosaic genomic structure with only 64% of all proteins being shared between E . coli K12 and EHEC O157:H7 [31] , which further could account for the differences in the phosphotyrosine protein profiles . Proteins identified as tyrosine phosphorylated confer important functions in their respective classes such as cell division ( FtsA , FtsI , FtsK , FtsZ , YihA , MreB , MukB and ZipA ) , DNA synthesis ( HolE ) and DNA metabolism ( GyrB , RecA , RecT , SbcB , Sms , TopA and Tus ) . Tyrosine phosphorylation is evident in translation , where proteins associated with initiation ( InfA , InfB , InfC and YciH ) and elongation ( TufA , TufB and Tsf ) were phosphorylated in addition to 27 ribosomal proteins . Our study identified phosphotyrosines on additional 21 ribosomal proteins compared to a previous proteomic analysis of ribosomal protein phosphorylation , where 12 proteins were identified as tyrosine phosphorylated [32] . Phosphotyrosine proteins implicated in protein turnover included proteases ( ClpP , GlpG , HslU , Lon and Ptr ) and chaperones ( ClpB , DnaK , GroEL , GroS , HtpG and SurA ) . More than one-third of all phosphotyrosine proteins ( 36% ) were associated with metabolic pathways , included those of energy- and central intermediary metabolism , biosynthesis of building blocks ( amino acids , nucleotides , cofactors and fatty acids ) and biosynthesis of macromolecules , most of which are cell surface exopolysaccharides . Moreover , multiple transport proteins including those of ATP-binding cassette family transporters and phosphotransferase systems were identified as tyrosine phosphorylated . Notably , proteins associated with metabolism , gene expression and virulence were tyrosine phosphorylated as discussed below . Another 42 phosphotyrosine proteins were of unknown function with the majority being hypothetical proteins whose expression was confirmed in this study . Overall , our data indicate a central role of tyrosine phosphorylation in most fundamental cellular processes . Identification of phosphotyrosine modifications on at least seven essential E . coli proteins ( Frr , DnaK , FtsZ , InfA , Map , Ppa and ProS ) further suggests a functional importance of tyrosine phosphorylation . Functional enrichment analysis was conducted to determine if phosphotyrosine proteins tend to participate in certain biological processes by contrasting the functional annotations of phosphotyrosine proteins against the total proteomes of EHEC O157:H7 EDL933 and E . coli K12 MG1655 . We observed significant ( p value≤0 . 05 ) enrichment of phosphotyrosine proteins in a wide range of metabolic and regulatory processes including the tricarboxylic acid cycle , glycolysis , gluconeogenesis , purine nucleotide biosynthesis pathways , and post-transcriptional regulation of gene expression ( Table S6 ) . Moreover , a graph-based analysis of the metabolic network revealed that tyrosine phosphorylated proteins are strikingly positioned centrally in this network ( Figure 4 ) , as they show significant higher closeness centrality values than non-pTyr proteins in the network for both E . coli K12 ( p value = 2×10−5 ) and EHEC O157:H7 ( p value = 1 . 5×10−2 ) . Altogether , we demonstrated that phosphotyrosine proteins are enriched in various biological processes and are central in the metabolic network of E . coli . While phosphotyrosine modification is a well-established means to regulate eukaryotic transcription [33] , [34] , it is less common in prokaryotes . Recent phosphoproteomic studies revealed Ser/Thr phosphorylation of RNA polymerase ( RNAP ) core subunits [17] , [28] , whereas no phosphotyrosine events were observed . Here , we identified tyrosine phosphorylation of 22 transcription-related proteins including the RNAP core enzyme subunits α ( RpoA ) , β ( RpoB ) and β′ ( RpoC ) ( Table S5 ) . Whether tyrosine phosphorylation of the bacterial RNAP affects the association with transcription factors as shown for yeast RNAP II [34] or RNAP assembly remains to be determined . Proteins affecting RNAP sigma factor availability and transcription elongation were also tyrosine phosphorylated , suggesting a role of phosphotyrosine-mediated signaling in controlling RNAP promoter specificity and activity . We detected tyrosine phosphorylation of transcriptional regulators of cysteine biosynthesis ( CysB ) , nucleotide synthesis ( PurR ) and major carbon- and energy metabolic pathways ( Cra ) , for which phosphorylated residues located within helix-turn-helix motifs potentially could affect DNA-binding activity as shown for sigma factor RpoH [35] . Also , tyrosine phosphorylation controls binding to DNA of a single-stranded DNA-binding protein in Bacillus subtilis [36] . Additional phosphotyrosine modified regulators include those involved in two-component response systems ( OmpR , PhoP , RcsB and UvrY ) , multidrug resistance ( EmrR ) , glucose-phosphate stress ( SgrR ) , phosphate starvation- and anaerobiosis-associated stress ( AppY ) , stationary phase-related stress ( SspA ) , iron uptake ( Fur ) , and nucleoid structure ( Cnu , Dps , Hha , H-NS and IHF ) . The histone-like protein , H-NS , is a global modulator of stress response and virulence gene expression in bacterial pathogens including EHEC [37] . Interestingly , the phosphorylated Tyr61 and Tyr99 residues of H-NS are located in hydrophobic cores of the functionally important oligomerization and DNA-binding domains [38] , [39] , and could affect H-NS activity . Indeed , tyrosine phosphorylation determines the ability of eukaryotic histones to form protein associations and subsequently modulate gene expression [40] . Further evidence that tyrosine phosphorylation targets functionally important residues includes modification of the response regulator PhoP switch residue Tyr98 [41] and SspA Tyr92 , which protrudes from a surface-exposed pocket and is critical for activity [42] . At least six of these transcription factors ( Cra , Hha , H-NS , IHF , RcsB and SspA ) are known to control gene expression from PAIs in EHEC [43]–[45] . Post-transcriptional regulation is known to be crucial for proper regulation of various fundamental bacterial cell processes , and has recently emerged as an important means to control virulence gene expression in various bacterial pathogens including EHEC O157:H7 . We identified phosphotyrosine modifications of proteins involved in RNA metabolism comprising RNases ( VacB and RNaseE ) , RNA chaperones ( CsrA and ProQ ) and a RNA helicase ( RhlB ) ( Table S5 ) , suggesting that tyrosine phosphorylation potentially also affects gene expression at the post-transcriptional level in E . coli . The expression of the LEE pathogenicity island is regulated post-transcriptionally by RNaseE directly and through Hfq-mediated small RNA regulation in EHEC [46] , [47] , and by CsrA in EPEC [48] , suggesting that tyrosine phosphorylation might control virulence at the post-transcriptional level of gene expression . The possible involvement of phosphotyrosine modifications in post-transcriptional regulation in bacteria is supported by findings demonstrating a functional importance of tyrosine phosphorylation in eukaryotic RNA metabolism [19] , [49] . Although an effect of tyrosine phosphorylation on the identified proteins remains to be directly demonstrated , our data reveal the potential for phosphotyrosine-mediated signaling in bacterial gene expression is much more widespread than previously known . We identified 28 phosphotyrosine proteins expressed from EHEC O157:H7 PAIs ( Table 1 ) . Importantly , T3SS proteins including translocon components ( EspA , EspB and EspD ) , intimin ( Eae ) , chaperones ( CesA and CesT ) , and the effectors EspJ and EspP were among the identified proteins . This is the first demonstration that proteins of a T3SS are tyrosine phosphorylated by bacterial kinases . The phosphorylated EspB residues , Tyr75 and Tyr272 , are located in regions involved in the interaction with EspD [50] , and might affect the association of these two pore-forming proteins . To assess the functional importance of these phosphotyrosine residues of EspB we created single and double mutants , which have the phosphorylated Tyr residues substituted with Phe , and tested their ability to complement A/E lesion formation of an espB mutant strain . The mutant EspB derivatives fully complemented the A/E lesion phenotype , ( data not shown ) , suggesting that Tyr75 and Tyr272 are redundant for EspB activity in vivo . However , we cannot exclude the possibility that potential compensatory protein-protein interactions present between EspB and its target proteins account for the lack of an in vivo phenotype . It remains to be demonstrated whether tyrosine phosphorylation of any other T3SS proteins identified affects T3SS apparatus assembly and the activity of effectors transclocated into host cells . We also identified phosphotyrosine modifications on non-T3SS virulence-associated proteins including the host cell adhesin Iha , and proteins related to tellurite resistance ( TerC ) and iron acquisition ( ChuX ) . Due to limited iron availability in the host intestine , iron acquisition systems are important for EHEC to survive and successfully colonize the host . Interestingly , the phosphorylated residue of ChuX , Tyr86 , is located in close vicinity to a putative heme-binding cleft [51] , and therefore might affect heme uptake and stability . The involvement of tyrosine phosphorylation in extracellular polysaccharide synthesis , important for virulence in vivo [16] , [52] , was further implicated by the identification of at least 13 phosphotyrosine proteins related to lipopolysaccharide synthesis of which four are involved in O157 antigen synthesis ( Glf , Per , WbdP and Wzy ) . About half of the identified PAI-encoded phosphotyrosine proteins are of unknown function but could potentially also be implicated in virulence , given the location of their genes on pathogenicity islands . Our data suggests that phosphotyrosine modifications by BY kinases in addition to host tyrosine kinases could play a central role in controlling EHEC O157:H7 virulence . To directly demonstrate the role of tyrosine phosphorylation in the function of SspA , which is part of the complex regulatory network controlling expression of the T3SS in E . coli [43] , [45] , we mutated the SspA Tyr92 residue identified here as phosphorylated . SspA positively affects the expression of the H-NS regulon including many virulence-associated proteins by negatively controlling H-NS levels [45] , [53] . A surface-exposed pocket of SspA that includes the Tyr92 residue is important for SspA activity [42] ( Figure 5A ) , raising the question of whether phosphorylation of Tyr92 affects SspA activity . To assess the functional importance of Tyr92 phosphorylation we replaced this residue with Phe , which is non-phosphorylatable due to the lack of the hydroxyl group of Tyr that is target for phosphorylation ( SspA Y92F ) . Deletion of the sspA gene results in loss of T3SS-associated proteins and A/E lesion formation ( Figure 5B and C ) . We compared the ability of wild type SspA and the mutant SspA Y92F encoded on plasmids along with the empty plasmid vector to complement the defect of a sspA mutant in these phenotypes . The SspA Y92F mutant exhibited decreased ability to complement the expression of the LEE-encoded proteins EspA , EspB and Tir compared to wild type SspA ( Figure 5B , compare lanes 3 and 4 ) . Moreover , we observed decreased abundance of these T3SS-associated proteins in culture supernatants from the sspA mutant strain expressing SspA Y92F ( Figure 5B , compare lanes 7 and 8 ) . We tested the ability of SspA Y92F to support a sspA mutant in A/E lesion formation on HeLa cells using fluorescence actin staining assay ( FAS ) , where lesions are visualized as condensed FITC-phalloidin stained actin beneath adherent bacteria [54] . The decreased abundance of T3SS-associated proteins was reflected by a less pronounced A/E lesion phenotype when complementing the sspA mutant with SspA Y92F compared to wild type SspA ( Figure 5C , lower panels ) , indicating functional importance of the phosphorylated SspA residue Tyr92 in regulating expression of virulence-associated proteins in EHEC O157:H7 . To further verify the functional role of Tyr92 phosphorylation in SspA activity , we constructed phosphomimetic mutants with Tyr92 replaced with Asp ( SspA Y92D ) and Glu ( SspA Y92E ) , and tested their ability to complement LEE expression and secretion of LEE-encoded proteins in a sspA mutant by western analyses ( Figure S4 ) . We observed partial restoration of LEE expression and protein secretion in the sspA mutant when complementing with the phosphomimetic SspA mutants to a degree higher than the non-phosphorylatable SspA Y92F mutant ( Figure S4 , compare lanes 5–6 with lane 4; and lanes 11–12 with 10 ) but to a lower level than wild type SspA ( Figure S4 , compare lanes 5–6 with lane 3; and lanes 11–12 with lane 9 ) . The SspA Y92D mutant seemed to be more effective than the SspA Y92E mutant in mimicking the effect of a phosphotyrosine ( Figure S4 , compare lanes 5 and 11 with lanes and 6 and 12 ) . The partial complementation of SspA activity by phosphomimetic SspA could be due to the possibility that the Asp and Glu do not completely reproduce the effect of Tyr92 phosphorylation structurally and electrostatically . Strikingly , a multiple sequence alignment revealed that 15 of 50 SspA orthologs contain either Asp or Glu at position 92 instead of Tyr [42] , which further emphasizes the importance of a negative charge at SspA position 92 for activity whether it is permanently present as Asp/Glu residues or introduced by phosphorylation of Tyr as a mean to regulate SspA activity . In sum , our results strongly indicate that phosphorylation of SspA Tyr92 regulates SspA activity , and thereby virulence of EHEC O157:H7 . To address the functional importance of the two currently known BY kinases , Etk and Wzc , in metabolism and virulence , we generated deletion mutants of etk and wzc in EHEC O157:H7 . Since T3SS-associated virulence factors were among identified phosphotyrosine proteins , we tested the ability of wild type and the tyrosine kinase mutants to form A/E lesions on HeLa cells using the FAS assay . Similar A/E lesion phenotypes were observed for wild type , etk , wzc and etk wzc mutant strains ( Figure 6 ) , indicating that Etk and Wzc are dispensable for pedestal formation . Consistent with comparable A/E lesion phenotypes , western blot analysis revealed similar levels of the T3SS proteins EspA and EspB in cell lysates and culture supernatants of etk and wzc mutant derivates and wild type ( Figure S5A ) . Though an EHEC O157:H7 etk mutant was previously shown to exhibit two-fold increased pedestal formation efficiency [52] , we did not observe any obvious effects of deleting etk or wzc in our assay . Our data suggest that tyrosine phosphorylation of T3SS-associated virulence proteins by Etk and/or Wzc either is not functionally important for A/E lesion formation , or else is mediated by other tyrosine kinases . Since the major functional class of tyrosine phosphorylated proteins involves metabolic pathways , we next determined the role of Etk and Wzc in E . coli metabolism by comparing the ability of EHEC O157:H7 wild type and etk wzc double mutant strains to metabolize various carbon , nitrogen , phosphorus and sulfur sources using Biolog Phenotype Microarrays [55] . Surprisingly , the metabolic profiles of wild type and etk wzc double mutant strains were similar ( Figure S5B ) , indicating that tyrosine phosphorylation by Etk and Wzc is dispensable for metabolizing these analytes . The absence of an effect on metabolism and virulence of EHEC O157:H7 when deleting etk and wzc despite the extensive tyrosine phosphorylation observed for proteins involved in these processes suggested that hitherto unidentified tyrosine kinases exist in E . coli to functionally compensate for these kinases . Indeed , such functional redundancy has been reported for Src family tyrosine kinases in eukaryotes [56] . To address this possibility , we carried out a second set of phosphotyrosine profiling experiments , where samples of EHEC O157:H7 wild type and etk wzc double mutant strains were run in parallel using identical experimental conditions for qualitative comparison . Interestingly , the etk wzc double mutant showed residual tyrosine phosphorylation with 115 unique pTyr peptides comprising 103 unique sites identified on 81 proteins , while 279 unique pTyr peptides comprising 237 unique sites on 168 proteins were identified in the wild type ( Table S1 ) . Interestingly , nearly all ( 93 ) of the 103 unique pTyr sites identified in the etk wzc double mutant were also present in the phosphotyrosine proteome of wild type EHEC O157:H7 defined in the first profiling experiment of this study . Our findings strongly indicate that additional tyrosine kinase ( s ) in E . coli provide at least a partial functional redundancy for tyrosine phosphorylation by Etk and Wzc . 37% of the phosphotyrosine sites from the etk wzc double mutant could be assigned to one of the five pTyr site motifs extracted from wild type E . coli ( Table S3 ) , suggesting that ( an ) unknown tyrosine kinase ( s ) possess site specificity that overlaps with Etk and/or Wzc , similar to overlap that is common for serine/threonine kinases [3] . Identified phosphotyrosine proteins of the etk wzc double mutant represent all functional classes described for those identified in wild type including various metabolic pathways , gene expression and virulence . Among the phosphotyrosine proteins identified in the etk wzc double mutant strain were virulence-associated proteins such as the LEE-encoded EspA and CesT , which are part of the T3SS system , as well as ChuX and WbdP involved in iron acquisition and O157 antigen synthesis respectively ( Table S1 ) . Also , the global regulators H-NS and CsrA important for virulence of A/E pathogens were tyrosine phosphorylated in the etk wzc double mutant . Thus , functional redundancy of E . coli tyrosine kinases could in part explain the lack of associated metabolic and virulence-related phenotypes observed for an etk wzc double mutant . The peptide identifications from the EHEC O157:H7 sample included in this second profiling experiment as a positive control for the detection of tyrosine phosphorylation revealed that tyrosine phosphorylation occurs reproducibly at specific sites . This reproducibility was reflected by the finding that 232 of 237 sites identified in the wild type EHEC O157:H7 sample in this second experiment were among the 416 unique sites identified in the EHEC O157:H7 sample analyzed in the first profiling experiment . It is known that repetitive LC-MS/MS runs of a given sample increase the number of peptide identifications resulting in increased proteome coverage [57] . Thus , the decreased number of phosphotyrosine sites identified in this second wild type EHEC sample ( 237 ) compared to that of that of the first experiment ( 416 ) could be due to the fact that these samples were analyzed by respectively three and ten LC-MS/MS runs . Rather than sharing structural similarity to their eukaryotic counterparts most classical BY kinases contain conserved Walker A , and B motifs of P-loop containing nucleotide triphosphate hydrolases and a Walker A′ motif , defining the active site of the catalytic domain , followed by a carboxyl-terminus tyrosine cluster [58] . BY-kinases of Proteobacteria undergo a two-step activation process involving phosphorylation of the catalytic residue Tyr574 of Etk followed by salt bridge formation with Arg617 and autophosphorylation of the carboxyl-terminus tyrosine cluster [59] , [60] . Structure-based in silico sequence analyses involving similarity searches of BY kinase catalytic domains were used to predict tyrosine kinase candidates . PSI-BLAST searches of catalytic domain residues of Etk and Wzc against EHEC O157:H7 and E . coli K12 total proteomes revealed similarity to the plasmid partition protein SopA ( Table S7 ) . Moreover , fold recognition analyses revealed the fold of nucleotide-binding proteins characteristic for Etk and Wzc in the cell division ATPase MinD and the plasmid partition proteins ParA and SopA ( Table S8 ) . This is consistent with previous in silico analyses revealing sequence significant similarity of BY kinases to MinD and ParA family proteins [58] . MinD lacks tyrosine kinase activity [61] and ParA is absent from EHEC EDL933 , and therefore cannot explain the residual tyrosine phosphorylation observed in the etk wzc double mutant . Structure-based sequence alignment of a ParA-based SopA model ( Figure S6A ) and Etk revealed conservation of Walker motifs A , A′ and B . However , the catalytic residues Tyr574 and Arg614 of Etk were substituted with Thr149 and Ser196 , respectively , in SopA ( Figure S6B ) . Potentially , a compensatory interaction between SopA residues Tyr153 and Arg346 , located within a 4 Å range , might mimic that of Tyr574 and Arg614 in Etk ( Figure S6C ) . However , SopA was not among the identified tyrosine phosphorylated proteins , which argues against SopA being a tyrosine kinase since the activity of classical BY kinases usually requires autophosphorylation . Thus , definitive identification of SopA as a tyrosine kinase will require further functional studies which are beyond the scope of this study .
Although tyrosine phosphorylation has become a recognized protein modification in bacteria , the extent of this modification remains uncertain . Using a combined immunoaffinity enrichment and mass spectrometry-based phosphoproteomic approach we established tyrosine phosphorylation in bacteria as far more prevalent than previously known with 512 unique pTyr sites identified on 342 proteins from E . coli . Our results indicate that tyrosine phosphorylation targets a broad range of fundamental cellular processes ranging from control of gene expression to metabolic pathways , a range that is indicative of a global regulatory network that likely affects various aspects of bacterial cell physiology and virulence . In E . coli phosphotyrosine-based regulation appears highly integrated as reflected by our identification of phosphorylated proteins involved in the synthesis , turnover and modification of DNA , RNA and proteins . The complexity of phosphotyrosine-mediated signaling at multiple levels of regulation is exemplified by the identification of tyrosine phosphorylation of Cra , a global transcription regulator controlling the expression of enzymes involved in major carbon and energy metabolism pathways [62] , as well as more than half of the Cra-controlled enzymes involved in these metabolic processes . Additional phosphotyrosine profiling studies of cells grown under different conditions could help unveil the biological role of tyrosine phosphorylation , such as whether , it like acetylation in Salmonella enterica , coordinates carbon source utilization and metabolic flux of the central carbon metabolism [63] . Tyrosine phosphorylation often affects protein activity and interaction properties . Indeed , several identified phosphotyrosine proteins are components of macromolecular complexes such as the transcription and translation apparatus , RNA degradosome , transporters and the T3SS machinery , likely implying a role of phosphotyrosine modifications in protein associations . Moreover , steric and charge effects caused by tyrosine phosphorylation could potentially modulate ligand binding of the ATP and metal ion-binding proteins . In fact , the chaperone ClpB Tyr503 residue , identified here as phosphorylated , is critical for ATP-binding [26] . We detected phosphorylation of many tyrosine residues previously reported as functionally important by other investigators , suggesting a central role of tyrosine phosphorylation in protein function . A functionally important surface pocket residue of the transcription regulator SspA ( Tyr92 ) was among those identified as phosphorylated . We demonstrated that Tyr92 is involved in phosphotyrosine-mediated regulation of SspA since changing this residue to Phe decreased the ability of SspA to induce expression of T3SS proteins , which also was reflected by reduced A/E lesion formation ( Figure 5 ) . Moreover , the phosphomimetic mutants SspA Y92D and SspA Y92E , in contrast to the SspA Y92F mutant , positively affected LEE expression of a sspA mutant strain ( Figure S4 ) . Since SspA Tyr92 is important for SspA regulation of H-NS levels , phosphotyrosine-mediated regulation of SspA could very likely also affect stress responses important for virulence such as acid resistance . Further studies addressing the functional role of phosphotyrosine modifications are required to reveal an effect on other proteins identified in this study . Functional classes of phosphotyrosine proteins involving transcription , translation and central metabolism exhibit a striking overlap with those of Ser/Thr phosphorylated [18] , [28] and lysine acetylated proteins [64] , [65] , raising the possibility of crosstalk between different types of protein modifications . Such crosstalk is common in eukaryotes and was recently reported for M . pneumoniae [66] . For instance , proteins such as RNAP subunits and translation elongation factor EF-G that we identified as being tyrosine phosphorylated are also Ser/Thr phosphorylated and lysine acetylated [18] , [28] , [64] , [65] , which could affect phosphotyrosine-mediated regulation of these proteins . Indeed , the Yersinia T3SS effector YopJ acetylates Ser/Thr residues on target proteins , thereby blocking their phosphorylation [67] . Multiple central metabolic pathway enzymes identified here as tyrosine phosphorylated are also Ser/Thr phosphorylated and acetylated [18] , [28] , [63] , suggesting the concerted action of these protein modifications in regulating metabolism . Ribosomal proteins can be Ser/Thr/Tyr phosphorylated at multiple sites ( Table S1 , [32] ) , probably affecting ribosome assembly and activity . Moreover , phosphotyrosine modification of translation initiation and elongation factors identified in this study along with Ser/Thr phosphorylation of these proteins reported in other studies [18] , [28] further suggests that phospho-mediated signaling is central in regulating prokaryotic translation as reported for eukaryotes [68] . Thus , crosstalk between different types of protein modifications likely adds complexity to phosphotyrosine-mediated regulation , an already dynamic regulatory process , through the concerted actions of tyrosine kinases and phosphatases . Our data indicate the presence of an as-yet undiscovered tyrosine kinase ( s ) in E . coli since tyrosine phosphorylation was present in the etk wzc kinase double mutant , thereby adding further complexity to the E . coli tyrosine kinome . A structure-based in silico search for additional BY kinases harboring Walker motifs revealed structurally highly similar P-loop NTPase family proteins including SopA for which tyrosine kinase activity has yet to be demonstrated . Though SopA could be a third kinase , additional hitherto unknown tyrosine kinases likely exist as the A/E lesion phenotype is intact when deleting sopA in the etk wzc tyrosine kinase double mutant of EHEC ( data not shown ) . Notably , the ribosome-associated GTPase BipA that autophosphorylates on tyrosine positively affects pedestal formation by affecting LEE expression in EPEC [69] , [70] . This could explain the phosphotyrosine modifications of T3SS proteins , although endogenous tyrosine phosphorylation by BipA has yet to be demonstrated . Although tyrosine phosphorylation in bacteria primarily is conducted by classical BY kinases , tyrosine kinases devoid of the canonical Walker motifs including eukaryotic-like and novel BY kinases [71] , [72] could account for tyrosine phosphorylation observed in the absence of Etk and Wzc . Indeed , the Shiga toxin-encoding 933W prophage of EHEC O157:H7 strain EDL933 expresses a eukaryote-like tyrosine kinase , Stk , which is activated upon phage HK97 infection and is involved in phage exclusion [73] . However , Stk exhibits limited if any activity in uninfected cells and is absent from E . coli K12 , thus being unlikely to account for the phosphotyrosine modification events detected in our study . Also , the A/E lesion phenotype of the EDL933 derivative used in this study was unaffected by the absence of stk in the etk wzc kinase double mutant ( data not shown ) , suggesting that Stk along with Etk and Wzc is functionally redundant for virulence . Besides identifying additional BY kinases , further insight into environmental signals controlling the activity of kinases and cognate phosphatases is a prerequisite for understanding the regulatory effect of phosphotyrosine-mediated signaling in bacteria . Tyrosine phosphorylation-dependent capsule synthesis is important for survival of EHEC O157:H7 and Klebsiella pneumoniae during infection [17] , [52] , currently providing the strongest link between BY kinases and virulence . An EHEC O157:H7 etk mutant devoid of group 4 capsule ( G4C ) was attenuated in an infant rabbit model [52] , suggesting that G4C protects the pathogen from the host immune system , or that tyrosine phosphorylation by Etk positively affects additional factors required for colonization . Although the link of Etk to EHEC virulence via capsule formation was previously demonstrated , tyrosine phosphorylation of proteins that are encoded by EHEC O157:H7 PAIs , and thereby likely to be associated with virulence , had yet to be detected prior to this study . In this study , we identified numerous proteins encoded by PAIs as tyrosine phosphorylated such as those involved in O157 antigen synthesis , tellurite resistance , adhesion and iron acquisition . Most notably , we demonstrated that T3SS proteins including translocon components , chaperones and effectors are tyrosine phosphorylated by bacterial kinases . However , it remains to be determined whether tyrosine phosphorylation of T3SS proteins is required for secretion apparatus assembly and regulation of protein secretion as reported for Thr phosphorylation of type VI secretion system proteins in Pseudomonas aeruginosa [74] . Tyrosine phosphorylation by BY kinases could also affect the activity of the effectors EspG and EspP in host cells . Evaluation of this possibility requires experiments testing whether tyrosine to phenylalanine substitutions of the T3SS protein tyrosine residues detected as phosphorylated can affect T3SS function . Identified tyrosine phosphorylated proteins encoded by the E . coli K12 chromosomal backbone such as Cra , CsrA , H-NS , IHF and SspA further contribute to pathogenesis by controlling virulence- and stress response gene expression , thereby promoting survival of the pathogen in host environments . Indeed , we demonstrated functional importance in virulence of the phosphotyrosine residue of SspA . Taken together , our data suggests that phosphotyrosine modifications by bacterial kinases likely play a role in controlling various aspects of EHEC virulence . In conclusion , we demonstrated extensive tyrosine phosphorylation of E . coli proteins involved in a wide variety of cellular processes , strongly suggesting that phosphotyrosine-mediated signaling coordinately regulates major cellular functions , and is thereby likely to be pivotal in controlling E . coli physiology and virulence . Proteins of a T3SS are here shown for the first time as being tyrosine phosphorylated by bacterial kinases . Importantly , our data demonstrate a functional role of phosphotyrosine-mediated regulation in controlling the expression of T3SS proteins associated with virulence . We defined five enriched phosphotyrosine site motifs and provided evidence for the presence of additional tyrosine kinases in E . coli . Altogether , our data provides a strong basis for further studies addressing the regulatory effect of tyrosine phosphorylation in E . coli . | While phosphotyrosine modification is established in eukaryote cell signaling , it is less characterized in bacteria . Despite that deletion of bacterial tyrosine kinases is known to affect various cellular functions and virulence of bacterial pathogens , few phosphotyrosine proteins are currently known . To gain insight into the extent and biological function of tyrosine phosphorylation in E . coli , we carried out an in-depth phosphotyrosine protein profiling using a mass spectrometry-based proteomics approach . Our study on E . coli K12 and the human pathogen enterohemorrhagic E . coli ( EHEC ) O157:H7 , which is a common cause of food-borne outbreaks of diarrhea , hemorrhagic colitis and hemolytic uremic syndrome , reveal that tyrosine phosphorylation is far more prevalent than previously recognized . Target proteins are involved in a broad range of cellular functions and virulence . Proteins of the type III secretion system ( T3SS ) , required for the attaching and effacing lesion phenotype characteristic for intestinal colonization by EHEC , are tyrosine phosphorylated . The expression of these T3SS proteins and A/E lesion formation is affected by a tyrosine phosphorylated residue on the regulator SspA . Also , our data indicates the presence of hitherto unknown E . coli tyrosine kinases . Overall , tyrosine phosphorylation seems to be involved in controlling cellular core processes and virulence of bacteria . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"biology"
] | 2013 | The Escherichia coli Phosphotyrosine Proteome Relates to Core Pathways and Virulence |
Toll-like receptors ( TLR ) are important in recognizing microbial pathogens and triggering host innate immune responses , including autophagy , and in the mediation of immune activation during human immunodeficiency virus type-1 ( HIV ) infection . We report here that TLR8 activation in human macrophages induces the expression of the human cathelicidin microbial peptide ( CAMP ) , the vitamin D receptor ( VDR ) and cytochrome P450 , family 27 , subfamily B , polypeptide 1 ( CYP27B1 ) , which 1α-hydroxylates the inactive form of vitamin D , 25-hydroxycholecalciferol , into its biologically active metabolite . Moreover , we demonstrate using RNA interference , chemical inhibitors and vitamin D deficient media that TLR8 agonists inhibit HIV through a vitamin D and CAMP dependent autophagic mechanism . These data support an important role for vitamin D in the control of HIV infection , and provide a biological explanation for the benefits of vitamin D . These findings also provide new insights into potential novel targets to prevent and treat HIV infection .
As an obligatory intracellular parasite , human immunodeficiency virus type-1 ( HIV ) survival is dependent upon its ability to exploit host cell machinery for replication and dissemination , and to circumvent cellular processes that prevent its growth . One such intracellular process is macroautophagy ( hereafter referred to as autophagy ) . Autophagy is a degradation pathway whereby cytosolic double membrane-bound compartments termed autophagosomes engulf cytoplasmic constituents such as sub-cellular organelles and microbial pathogens . These autophagosomes then fuse with lysosomes , resulting in the degradation of the engulfed components . HIV relies on several components of autophagy for its replication with silencing of autophagy proteins inhibiting HIV replication [1]–[6] . In macrophages , HIV group-specific antigen ( Gag ) -derived proteins colocalize and interact with microtubule-associated protein 1 light chain 3B ( LC3B ) , and are present at LC3B-II enriched membranes suggesting that autophagy may be involved in Gag processing and the production of nascent virions [5] . This is consistent with the hypothesis that HIV is assembled on endocytic membranes that intersect with recycling endosomes [7] , [8] . Despite the requirement for autophagy , HIV actively downregulates autophagy regulatory factors , reducing both basal autophagy and the numbers of autophagosomes per cell [9]–[11] . The HIV negative elongation factor ( Nef ) protein has been shown to protect HIV from degradation by inhibiting autophagosome maturation [5] , and enhances HIV replication through interactions with immunity-associated GTPase family M ( IRGM ) protein . IRGM interacts with the autophagy-associated proteins autophagy related 5 homologue ( ATG5 ) , ATG10 , LC3B and SH3-domain growth factor receptor-bound protein 2-like endophilin B1 , inducing autophagosome formation [6] . However , inducers of autophagy including amino acid starvation , rapamycin , and 1α , 25-dihydroxycholecalciferol ( 1 , 25D3 ) , the active form of vitamin D , overcome the imposed phagosome maturation block leading to inhibition of viral replication [2] , [3] , [11] . Interestingly , the HIV envelope glycoprotein expressed on the surface of infected cells has been reported to induce cell death in uninfected bystander CD4+ T cells through autophagy [12] , [13] . Recent research has focused on the role of autophagy in the innate and adaptive immune systems . Cells use autophagy as a mechanism to detect intracellular pathogens through pattern-recognition receptors ( PRRs ) which recognize signature molecules of pathogens termed pathogen-associated molecular patterns ( PAMPs ) that are essential for their survival . There are several classes of PRRs: Toll like receptors ( TLRs ) , retinoic acid-inducible gene-I-like receptors and nucleotide-binding oligomerization domain-like receptors . These PRRs recognize PAMPs in various cell compartments and trigger the release of inflammatory cytokines and type I interferons for host defense [14] , [15] . Human TLR2/1 recognizes Mycobacterium tuberculosis lipoproteins , and upon activation induces the expression of cytochrome P450 , family 27 , subfamily B , polypeptide 1 ( CYP27B1 ) which 1α-hydroxylates the inactive form of vitamin D3 , 25-hydroxycholecalciferol ( 25D3 ) , into its biologically active metabolite , the steroid hormone 1 , 25D3 . TLR2/1 agonists also induce the activation and upregulation of the vitamin D ( 1 , 25D3 ) receptor ( VDR ) leading to the induction of the human cathelicidin microbial peptide ( CAMP ) , autophagic flux and the killing of intracellular M . tuberculosis [16] , [17] . We have recently demonstrated that 1 , 25D3 inhibits mycobacterial growth and the replication of HIV through the CAMP-dependent induction of autophagy [3] . TLR8 is phylogenetically and structurally related to TLR7 [18] , [19] and is expressed in endosomes of myeloid cells such as monocytes , macrophages and myeloid dendritic cells , and in regulatory T cells [20]–[22] . TLR8 recognizes both uridine-rich single-stranded RNA ( ssRNA ) and imidazoquinoline compounds [23] , [24] . Upon stimulation , TLR8 agonists activate nuclear factor kappa-light-chain-enhancer of activated B cells via the myeloid differentiation primary response gene ( 88 ) adaptor protein that leads to the induction of a cascade of antiviral effector functions including the induction of autophagy in murine cells [25] and proinflammatory cytokines in human cells [21] . HIV ssRNA encodes for multiple PAMPs that can be recognized by TLR8 expressed in macrophage endosomes [23] , [26] and suppresses HIV replication in acute ex vivo human lymphoid tissue of tonsillar origin and renders peripheral blood mononuclear cells ( PBMC ) barely permissive to HIV infection [20] . Interestingly , HIV downregulates interleukin-1 receptor-associated kinase 4 , which is essential for virtually all TLR signaling [27] . Despite the immune defense mechanisms that the host deploys against HIV and improved antiretroviral therapies , the virus persists in long-lived cells including macrophages and dendritic cells . Major questions remain as to the mechanism by which TLR8 agonists inhibit HIV and whether HIV antigens can activate autophagy in human cells through TLR8 . In the present study , we demonstrate that TLR8 ligands , in the presence of 25D3 , inhibit HIV replication in macrophages through a vitamin D and CAMP-dependent mechanism involving autophagy .
Both ssRNA40 and the imiquimod R837 promote autophagic responses in murine RAW 264 . 7 cells [25] through a beclin-1 ( BECN1 ) dependent mechanism . However , the ability of TLR8 ligands to induce an autophagic response in primary human macrophages has not been investigated . Therefore , the ability of TLR8 agonists to induce autophagy in human macrophages was determined in monocyte-derived macrophages cultured in RPMI 1640 supplemented with 10% ( v/v ) charcoal/dextran treated , heat-inactivated fetal bovine serum ( FBS ) , 10 ng/mL macrophage colony stimulating factor and 100 nmol/L 25D3 as described in the Materials and Methods . The effect of ssRNA40 and the imidazoquinoline CL097 on the formation of the class 3 phosphoinositide-3-kinase ( PIK3C3 ) kinase complex was initially assessed . The PIK3C3 kinase complex is essential for the induction of autophagosome formation at the vesicle elongation step and is formed when BECN1 physically interacts with PIK3C3 . Co-immunoprecipitation followed by immunoblotting demonstrated enhanced binding of BECN1 to PIK3C3 forming the PIK3C3 kinase complex following ssRNA40 or CL097 treatment ( Figure 1A ) . During autophagy , cytosolic LC3B-I is converted to LC3B-II by a ubiquitin-like system that involves ATG7 , ATG3 and the ATG12–ATG5 complex . The ATG12–ATG5 complex ligates LC3B-II to the nascent autophagosome membrane through phosphatidylethanolamine with the LC3B-II associated with the inner membrane degraded after fusion of the autophagosome with lysosomes . Therefore , the conversion of LC3B-I to LC3B-II and its turnover is an indicator of autophagy induction and flux [28] . Activation of macrophages with ssRNA40 and CL097 for 24 h led to an increase in LC3B-II similar to that observed with rapamycin , an inducer of autophagy through inhibition of the mammalian target of rapamycin ( MTOR ) complex 1 ( MTORC1 ) , and was increased in the presence of the lysosomal protease inhibitor pepstatin A indicative of autophagic flux ( Figure 1B ) . During the formation of autophagosomes , LC3B redistributes from a soluble diffuse cytosolic pattern to an insoluble autophagosome-associated vacuolar pattern that can be quantified using fluorescence microscopy [29] . Both ssRNA40 and CL097 induced a significant increase in both the quantity of LC3B per cell and the number of cells with increased LC3B puncta formation in the absence of pyknosis , karyorrhexis , or plasma membrane blebbing and was similar to that observed after rapamycin treatment ( Figure 1C ) . To verify that the increase in the number of autophagosomes in TLR8 agonist treated cells versus control cells represents increased autophagic flux rather than an accumulation of LC3B-positive autophagosomes , the degradation of the polyubiquitin-binding protein sequestosome 1 ( SQSTM1 ) was quantified . Inhibition of autophagy leads to an increase in SQSTM1 protein levels while autolysosomes degrade SQSTM1- and LC3-positive bodies during autophagic flux [30] . Both TLR8 activation and rapamycin treatment of macrophages for 24 h led to a decrease in SQSTM1 protein levels corresponding to the stimulation of autophagic flux ( Figure 1D ) . Moreover , the TLR8 ligands also decreased SQSTM1 protein levels in a dose-dependent manner ( Figure 1D ) . TLR8 and TLR7 both contribute to the recognition of viral ssRNA and are both found in human macrophages [23] , [31] . Therefore , the role of TLR8 and TLR7 in the induction of autophagy in macrophages post-ssRNA40 and CL097 was examined . RNA interference ( RNAi ) of TLR8 ( Figure 2A ) significantly inhibited both the ssRNA40- and CL097-mediated LC3B lipidation ( Figure 2B ) and the increase in the number of LC3B-positive autophagic vesicles ( Figure 2C ) . Although TLR7 is involved in sequence-specific sensing of ssRNAs in human macrophages [31] , TLR7 protein expression was undetectable in primary macrophages; therefore , the role of TLR7 in this system is unknown . However , the complete abrogation of TLR8 agonist-induced LC3B lipidation in the presence of RNAi for TLR8 suggests that TLR8 is the mediator of the effect of ssRNA40 and CL097 on autophagy induction . Recent studies have demonstrated that TLR2/1 activation of human monocytes/macrophages upregulates the expression of vitamin D related genes including CYP27B1 [17] and that 1 , 25D3 induces autophagy [2] , [32] . Given this background , the effect of ssRNA40 and CL097 stimulation on the expression of CYP27B1 and the VDR in macrophages and the role of the vitamin D pathway in TLR8-mediated autophagic flux was investigated . The TLR8 agonists induced a dose-dependent increase in both CYP27B1 ( Figure 3A ) and VDR ( Figure 3B ) mRNA and protein expression . Macrophages were then transduced with short-hairpin RNA ( shRNA ) specific to CYP27B1 , VDR or a scrambled non-specific control followed by TLR8 stimulation . Figure 3C shows that CYP27B1 silencing abrogates the lipidation of LC3B in response to TLR8 activation but not in response to rapamycin . Similar results were observed post-VDR silencing . Furthermore , LC3 puncta in CYP27B1 or VDR silenced cells post-TLR8 activation was significantly reduced ( Figure 3D ) . To determine whether differences in vitamin D concentration affect the ability of TLR8 agonists to stimulate autophagy induction , the concentration of 25D3 in the media was reduced to 45 nmol/L reflecting the lower levels observed in vitamin D deficient individuals [33] . At this concentration , the TLR8-mediated induction of autophagy was significantly impaired with little to no LC3B lipidation observed ( Figure 3E ) . CCAAT/enhancer binding protein β ( CEBPB ) activation is thought to be a required transcription factor controlling immune-mediated transcription of CYP27B1 [34] . Therefore , to assess the role of CEBPB in CYP27B1 expression , macrophages were transduced with shRNA specific to CEBPB , followed by TLR8 stimulation . Figure 4 shows that CEBPB silencing significantly reduced the expression of CYP27B1 in macrophages post-TLR8 activation . Previous studies have shown that TLR8 agonists inhibit HIV replication in ex vivo infected lymphoid tissue while inducing virion release from transformed cell lines [20] , [35] . We therefore determined whether the TLR8 agonists influence HIV infection and replication in primary macrophages by comparing the extent to which CL097 and ssRNA40 pre-treatment influenced p24 antigen accumulation in the supernatants of macrophages that were subsequently infected with HIV . Both ssRNA40 and CL097 induced a dose-dependent inhibition of HIV replication . This inhibition became significant across all concentrations tested by day 3 post-infection ( p<0 . 01 ) with the magnitude of the inhibition increasing until cultures were discontinued on day 10 post-infection ( Figure 5 ) . To confirm that the inhibition of HIV observed in macrophages post-CL097 stimulation is predominantly through TLR8 , we employed RNAi for TLR8 . In the scrambled control RNAi treated cells , CL097 inhibited HIV p24 levels by 90% and 74% at 5 and 1 µg/mL , respectively by day 10 post-infection ( p<0 . 029; Figure 6A ) . Conversely , TLR8 silencing reduced the inhibitory effect of CL097 to <6% at both concentrations tested , which was not significantly different to the vehicle control treated cells ( p>0 . 48; Figure 6A ) . Thus , although human macrophages may express low levels of TLR7 [31] , TLR8 is the predominant signaling pathway through which CL097 inhibits HIV . Based on our observations that: i ) 1 , 25D3 inhibits HIV replication through the induction of autophagy [2] , [3] , ii ) TLR8 activation significantly increases the expression of both CYP27B1 and the VDR , and iii ) silencing either CYP27B1 or the VDR inhibits TLR8-mediated autophagy , we sought to determine whether the autophagic response induced by TLR8 through the vitamin D pathway was responsible for the observed inhibition of HIV . Silencing of CYP27B1 resulted in the markedly decreased inhibition of HIV by CL097 to levels that were not significantly different to the vehicle control treated cells ( p>0 . 05; Figure 6A ) . Similarly , silencing the VDR significantly reduced the inhibition to control levels ( p>0 . 1; Figure 6A ) suggesting that the vitamin D pathway is important during the inhibition of HIV by TLR8 agonists . To confirm this , and to determine whether differences in the availability of vitamin D affects the ability of TLR8 agonists to inhibit HIV replication , we reduced the concentration of 25D3 in the media to 45 nmol/L , reflecting the lower levels observed in vitamin D deficient individuals . Under these conditions , we observed a significantly diminished capacity of both ssRNA40 and CL097 to inhibit HIV replication ( Figure 6B ) . To determine whether TLR8-induced autophagy contributes to the CL097-mediated inhibition of HIV by CL097 , we assessed the effect of BECN1 and ATG5 silencing on HIV infection post-TLR8 activation . BECN1 silencing reduced the 5 µg/mL CL097 mediated inhibition of HIV at day 10 from 90% to 50% ( p = 0 . 028; Figure 7A ) . We next assessed the effect of ATG5 silencing . During autophagy , cytosolic LC3B-I is converted to LC3B-II by an ubiquitin-like system that involves ATG7 , ATG3 and the ATG12–ATG5 complex . The ATG12–ATG5 complex ligates LC3B-II to the nascent autophagosome membrane through phosphatidylethanolamine . Therefore , RNAi of ATG5 inhibits autophagosome formation . ATG5 RNAi abrogated the CL097 mediated inhibition of HIV by day 10 ( 90% versus 22% inhibition; p<0 . 028; Figure 7B ) . We next investigated whether autophagosome acidification , a late stage event during autophagy , is required for the TLR8-mediated autophagic inhibition of HIV . During autophagy , lysosomes fuse with autophagosomes to form autolysosomes . Macrophages were treated with bafilomycin A1 , an inhibitor of the vacuolar H+ ATPase and autophagosome-lysosome fusion , and subsequently infected with HIV . Bafilomycin A1 reversed the TLR8-mediated inhibition of HIV ( Figure 7C ) suggesting that the acidic pH of autolysosomes is required for the autophagy-mediated control of HIV . After lysosomes fuse with autophagosomes to form autolysosomes , the sequestered components are degraded by lysosomal hydrolases and released into the cytosol by lysosomal efflux permeases . We investigated whether lysosomal hydrolases are important for TLR8-mediated inhibition of HIV through autophagy using SID 26681509 , a novel thiocarbazate specific inhibitor of the lysosome hydrolase cathepsin L . In the absence of TLR8 ligands , SID 26681509 induced no net inhibition of HIV ( Figure 7C ) . Moreover , in the presence of TLR8 ligands , SID 26681509 abrogated the HIV inhibition ( Figure 7C ) . Previous studies have demonstrated that CAMP expression is upregulated by 1 , 25D3 , that it is required for 1 , 25D3 mediated autophagy [3] , [32] , and that it is involved in the autophagic inhibition of HIV in human macrophages [3] . Moreover , monocytes express CAMP in response to TLR2/1 agonists [16] . Therefore , to determine the role of CAMP in the TLR8-mediated autophagic response , we first investigated whether TLR8 agonists induce the expression of CAMP . Both ssRNA40 and CL097 induced the expression of CAMP mRNA by 13- and 10-fold , respectively over the vehicle control ( p = 0 . 029; Figure 8A ) . These data indicate that TLR8 activation triggers CAMP expression in human macrophages . A functioning vitamin D signaling pathway is required for the expression of CAMP in response to TLR2/1 agonists [36] . To assess whether TLR8 activation of CAMP expression was dependent on the presence of 25D3 , CAMP expression post-TLR8 activation was investigated in macrophages in 25D3 sufficient and deficient media . TLR8-induced CAMP expression was observed in cultures containing 100 nmol/L 25D3 , but not in vitamin D deficient culture medium ( Figure 8B ) . To address the role of CAMP in TLR8-induced autophagy and antimicrobial activity , RNAi for CAMP was employed . Transduction of shCAMP into macrophages significantly blocked endogenous LC3B lipidation post-TLR8 activation , whereas macrophages transduced with a scrambled control ( shNS ) showed increased LC3B-II conversion consistent with autophagosome formation and the induction of autophagy ( Figure 8C ) . Consistent with the findings that autophagy is required for the restriction of HIV replication , CAMP silencing reduced TLR8-mediated inhibition of HIV to insignificant levels ( p>0 . 09; Figure 8D ) . Collectively , these data suggest that 25D3 is required for the TLR8 induced expression of CAMP and that CAMP expression is required for TLR8-mediated antimicrobial activity in human macrophages .
The antimicrobial effects of vitamin D have been well documented and association studies have linked low levels of 25D3 and/or 1 , 25D3 with increased risk of , or severity of infection with HIV [37] , [38] . The present study identifies how vitamin D deficiency may influence innate immunity against HIV infection . Stimulation of human macrophages with TLR8 agonists upregulates the expression of CYP27B1 and the VDR leading to the induction of CAMP and autophagic flux . Moreover , when serum was 25D3 deficient , or when the vitamin D signaling pathway was silenced , TLR8 agonists were unable to induce autophagy . Thus , the presence of 25D3 and a functional vitamin D signaling pathway are required for TLR8-induced autophagy . Previous studies have demonstrated that 1 , 25D3 induces autophagy in primary macrophages through a CAMP dependent mechanism [3] , [32] and inhibits HIV replication in macrophages [2] , [3] . Consistent with published data , CL097 and the guanosine- and uracil-rich oligonucleotide ssRNA40 , but not RNA41 in which all uracils were replaced with adenosines , inhibited HIV replication in primary macrophages [20] . The current study expands on these findings and demonstrates that TLR8 agonists inhibit HIV replication in macrophages through a vitamin D3- and CAMP-dependent mechanism involving autophagy . Indeed , the TLR8-mediated inhibition of HIV replication occurred only in the presence of vitamin D-sufficient media or in cells with an intact vitamin D signaling pathway . The present data demonstrate that in CAMP silenced cells , TLR8 activation failed to induce LC3B-II lipidation and inhibit HIV . Endogenous CAMP has been implicated in a number of cellular functions including the regulation of inflammatory responses [39] and the formation and maturation of autophagosomes [32] . CAMP has also been shown to play an important role in the activation of mitogen activated protein kinases and CEBPB which contribute to the transcriptional activation of BECN1 and ATG5 in response to 1 , 25D3 [32] . Moreover , during autophagy , autophagosomes recruit CAMP through an AMP kinase , Ca2+ and calcium/calmodulin-dependent protein kinase kinase 2 beta dependent mechanism where it is involved in microbial killing [32] . Further work is necessary to determine the precise role of CAMP in TLR-activated autophagy and antiretroviral activity . Vitamin D deficiency is conservatively defined by most experts as <50 nmol/L 25D3 [33]; 52–72 nmol/L 25D3 is considered to indicate insufficiency and >73 nmol/L considered sufficient [33] . In contrast to this , the estimated mean concentration of 25D3 present in people worldwide is just 54 nmol/L [40] . The major source of vitamin D is through the endogenous photochemical conversion of 7-dehydrocholesterol in the skin to pre-vitamin D3 by ultra-violet B light exposure which then undergoes a 1 , 7-sigmatropic hydrogen transfer forming cholecalciferol . This is then transferred from the skin by the vitamin D binding protein and is subsequently 25-hydroxylated by cytochrome P450 , family 2 , subfamily R , polypeptide 1 ( CYP2R1 ) in hepatocytes to form 25D3 in a poorly regulated manner . Lesser amounts of vitamin D3 metabolites are also consumed through fortified dairy products and oily fish . Vitamin D status , therefore , is largely dependent upon the availability of cholecalciferol . Why HIV-infected individuals tend to have lower levels of 1 , 25D3 and/or 25D3 is largely unknown but it is possible that inadequate renal 1α-hydroxylation mediated by pro-inflammatory cytokines and/or a direct effect of antiretroviral drugs play a role [37] . Four genes contribute to the variability of serum 25D3 concentrations: 7-dehydrocholesterol reductase ( involved in cholesterol synthesis and the availability of 7-dehydrocholesterol in the skin ) , 25-hydroxylase CYP2R1 , and CYP24A1 ( cytochrome P450 , family 24 , subfamily A , polypeptide 1 ) ( degrades and recycles 1 , 25D3 ) , and GC ( group-specific component [vitamin D binding protein] ) which encodes for the vitamin D binding protein . Genetic variations at these loci were recently identified to be significantly associated with an increased risk of 25D3 insufficiency [41] . The characterization of the TLR8/vitamin D mediated antimicrobial mechanism in macrophages provides further evidence of the link between vitamin D and the immune system . In a recent study , 25D3 levels were negatively correlated with the expression of TLR8 in human monocytes . In the same study , it was observed that in healthy individuals circulating 25D3 levels and TLR8 expression decreased with age and that this decrease coincided with a decrease in CAMP expression [42] . Unlike the parathyroid-hormone responsiveness of renal CYP27B1 , extra-renal CYP27B1 is not subject to the same feedback control so that the local synthesis of 1 , 25D3 in macrophages probably reflects the availability of 25D3 . Therefore , the intracrine nature of this mechanism suggests that the ability of TLR8 to promote HIV killing could be affected by the availability of 25D3 and the efficiency of the synthesis of 1 , 25D3 by macrophages . TLR7 and TLR8 expression in peripheral blood monocytes decreases with disease progression and monocytes from HIV-infected individuals produce less tumor necrosis factor following TLR8 activation than those from uninfected individuals while successfully inhibiting HIV infection [43] . Moreover , these monocyte responses are negatively correlated with CD4+ T cell count and positively associated with HIV viral load [44] . The ability of cells to respond strongly to a TLR8 agonist in the presence of high HIV viremia means that ongoing chronic immune activation can be continuously driven by HIV-encoded PAMPs . Despite this , there is no tolerance induction towards TLR8 agonists [35] , [44] . Persistent immune activation during HIV infection contributes to the pathogenesis of disease by disturbing the functional organization of the immune system with induction of high levels of cytokines and chemokines . Therefore , chronic stimulation of the innate immune system by TLR ligands may result in the chronic production of proinflammatory cytokines which drive disease progression through generalized immune activation [45] . Supporting this model is the association of a single-nucleotide polymorphism in TLR8 ( TLR8 A1G; rs3764880 ) which confers a significant protective effect against HIV disease progression [46]; however , this same polymorphism increases male susceptibility to pulmonary tuberculosis [47] , [48] . Despite these apparent limitations , TLR8 agonists given as vaccine adjuvants with HIV proteins in non-human primate models enhance the magnitude and quality of the anti-HIV Th1 and CD8+ T cell responses [49] . Finally , as TLR8 activation of the latently infected cell lines U1 and OM10 results in a marked increase in HIV replication [20] , TLR8 triggering of latently infected macrophages may result in the increased release of HIV in vivo . Therefore , it may be possible to use TLR8 agonists to purge latently infected cells while inhibiting new infections . Thus , further research on the effect of TLR8 agonists on latently infected macrophages from HIV-infected individuals is warranted . Collectively , this study demonstrates that TLR8 agonists inhibit HIV replication in macrophages through the induction of autophagy that is dependent upon both available 25D3 and a functioning vitamin D signaling pathway as well as the induction of CAMP . Moreover , this study also expands the known PAMP that induce vitamin D-dependent autophagy to include TLR8 . Well-controlled clinical trials are needed to determine if vitamin D supplementation is of value as adjunctive treatment in HIV-infected persons . Dissecting the molecular mechanisms by which HIV utilizes autophagy has the potential to lead to the identification of novel drug candidates to prevent and treat HIV infection and related opportunistic infections including tuberculosis .
Venous blood was drawn from HIV seronegative subjects using a protocol that was reviewed and approved by the Human Research Protections Program of the University of California , San Diego ( Project 08-1613 ) in accordance with the requirements of the Code of Federal Regulations on the Protection of Human Subjects ( 45 CFR 46 and 21 CFR 50 and 56 ) . Written informed consent was obtained from all blood donors prior to their participation . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood of HIV seronegative donors by density gradient centrifugation over Ficoll-Paque Plus ( GE Healthcare ) . PBMC were then incubated overnight at 37°C , 5% CO2 in RPMI 1640 ( Gibco ) supplemented with 10% ( v/v ) charcoal/dextran treated , heat-inactivated FBS ( Gemini Bio-Products ) and 10 ng/mL macrophage colony stimulating factor ( R&D Systems ) , after which non-adherent cells were removed by aspiration . Monocyte derived macrophages were obtained by further incubating the adherent population in RPMI 1640 ( Gibco ) supplemented with 10% ( v/v ) heat-inactivated FBS and 10 ng/mL macrophage colony stimulating factor ( R&D Systems ) for 10 d at 37°C , 5% CO2 . All experiments were performed in RPMI 1640 supplemented with 10% ( v/v ) charcoal/dextran treated , heat-inactivated FBS , 10 ng/mL macrophage colony stimulating factor and 100 nmol/L 25D3 ( Sigma ) unless otherwise stated . CL097 , ssRNA40 and ssRNA41 were obtained from Invivogen and were described previously [23] . Pepstatin A , bafilomycin A1 , SID 26681509 and rapamycin were purchased from Sigma . Bafilomycin A1 was used at 100 nmol/L , SID 26681509 at 50 nmol/L , and pepstatin A at 10 µg/mL with pretreatment for 1 h before addition of TLR8 ligands or rapamycin . HIVBa-L was obtained through the AIDS Research and Reference Reagent Program , from Dr . Suzanne Gartner and Dr . Robert Gallo [50] , [51] . Virus stocks and titers were prepared as previously described using the Alliance HIV p24 antigen ELISA ( Perkin Elmer ) [52] . Cells were infected with 105 TCID50/mL HIVBa-L per 5×105 cells for 3 h after 24 h pretreatment with TLR8 ligands or rapamycin unless otherwise stated . LC3B ( D11 ) , PIK3C3 ( D9A5 ) , SQSTM1 ( D5E2 ) , BECN1 ( D40C5 ) , and ATG5 antibodies were obtained from Cell Signaling; VDR ( N-terminal ) , TLR8 ( 4C6 ) , and β-actin ( AC-74 ) antibodies were from Sigma; CYP27B1 ( H-90 ) antibody was from Santa Cruz Biotechnology . Cell lysates were prepared using CelLytic M ( Sigma ) supplemented with protease inhibitors ( Thermo Scientific ) . For co-immunoprecipitation , 50 µg anti-BECN1 was immobilized in a coupling gel then 50 µg of the cell lysates were incubated with the antibody-immobilized coupling gel using the ProFound-Co-Immunoprecipitation kit ( Thermo Scientific ) . For immunoblot analyses , cell lysates were resolved using 2-[bis ( 2-hydroxyethyl ) amino]-2- ( hydroxymethyl ) propane-1 , 3-diol buffered 12% polyacrylamide gel ( Novex ) and transferred to polyvinylidene difluoride membranes ( Thermo Scientific ) , followed by detection with the WesternBreeze chemiluminescence kit ( Novex ) as described previously [52] . Relative densities of the target bands compared to the reference β-actin bands were analyzed using ImageJ ( NIH ) . Cells were fixed and permeabilized in Dulbecco's phosphate buffered saline supplemented with 4 . 5% ( w/v ) paraformaldehyde and 0 . 1% ( v/v ) saponin for 30 min , washed , then probed with rabbit anti-LC3B ( D11 ) for 30 mins followed by goat anti-rabbit Alexa Fluor 488 conjugated antibodies ( Molecular Probes ) for 30 mins and counterstained with Hoechst 33342 . Cells and LC3B puncta were imaged and counted using an Olympus IX71 inverted fluorescence microscope as described previously [2] . Lentiviral transduction of macrophages with MISSION lentiviral particles containing shRNAs targeting ATG5 ( SHCLNV-NM_004849/TRCN0000150940 ) , BECN1 ( SHCLNV-NM_003766/TRCN0000033551 ) , CAMP ( SHCLNV- NM_004345/TRCN0000118645 ) , CYP27B1 ( SHCLNV-NM_000785/TRCN0000064365 ) , TLR8 ( SHCLNV-NM_138636/TRCN0000359246 ) , CEBPB ( SHCLNV-NM_005194/TRCN0000007440 ) , VDR ( SHCLNV-NM_000376/TRCN0000277001 ) , or scrambled non-target negative control ( Scr , SHC002V ) was performed according to the manufacturer's protocol ( Sigma ) . Macrophages were transduced with non-specific scrambled shRNA ( shNS ) or target shRNA and selected using puromycin ( Gibco ) . Five days later , cells were analyzed for target gene silencing and used in experiments . mRNA quantification was measured by real time PCR using the LightCycler 1 . 5 Instrument and the FastStart RNA Master SYBR Green I kit ( all Roche Applied Science ) . PCR reactions were carried out in a 20 µL mixture composed of 3 . 25 mM Mn ( CH3COO ) 2 , 0 . 5 µM of each primer , 1 µL sample and 1-fold LightCycler RNA Master SYBR Green I . Primers were synthesized by Integrated DNA Technologies and were CYP27B1 sense 5′-GTTTGTGTCCACGCTG-3′ , antisense 5′-CCCGCCAATAGCAACT-3′; VDR sense 5′-GTTGCTAAACGAGTCAATCC-3′ , antisense 5′-AGTAACGGCACGATCT-3′; CAMP sense 5′-CTCGGATGCTAACCTCT-3′ , antisense 5′-CATACACCGCTTCACC-3′; polymerase ( RNA ) II ( DNA directed ) polypeptide A ( POLR2A ) sense 5′-GCACCACGTCCAATGACAT-3′ , antisense 5′-GTGCGGCTGCTTCCATAA-3′ . Reaction mixtures were initially incubated at 61°C for 20 min to reverse transcribe the RNA . Samples were then heated to 95°C for 30 sec to denature the cDNA followed by 45 cycles consisting of following parameters: CYP27B1 5 s at 95°C , 15 s at 55°C and 16 s at 72°C; VDR 5 s at 95°C , 20 s at 58°C and 25 s at 72°C; CAMP 10 s at 95°C , 10 s at 61°C and 7 s at 72°C each with a single fluorescent reading at the end of each cycle followed by a melting curve analysis . To exclude contamination with DNA , Alu-PCR and minus reverse transcriptase controls were performed . Results were calculated using the Pfaffl method [53] and are expressed as the ratio between the target gene and the reference gene POLR2A and normalized so that CYP27B1 , VDR and CAMP mRNA expression in unconditioned cells equals 1 . 00 . Intracellular staining of endogenous CAMP was performed as previously described [2] using goat anti-LL-37 antibodies ( Santa Cruz Biotechnology ) and Alexa Fluor 647 conjugated donkey anti-goat antibodies ( Invitrogen ) . Comparisons between groups were performed using the nonparametric two-sided Mann-Whitney U test . Differences were considered to be statistically significant when p<0 . 05 . | Cells use macroautophagy ( autophagy - ‘self-eating’ , lysosome-dependent degradation and recycling of intracellular components in response to stress ) as a mechanism to detect intracellular pathogens through pattern-recognition receptors such as Toll-like receptors ( TLRs ) that recognize signature molecules of pathogens that are essential for their survival . One such Toll-like receptor , TLR8 , which is located in human macrophage endosomes , recognizes both imidazoquinoline compounds and uridine-rich single-stranded RNA such as human immunodeficiency virus type-1 ( HIV ) single-stranded RNA . In the present study we report that TLR8 activation in human macrophages induces the expression of the human cathelicidin microbial peptide ( CAMP ) , the vitamin D receptor ( VDR ) , and cytochrome P450 , family 27 , subfamily B , polypeptide 1 ( CYP27B1 ) , which 1α-hydroxylates the inactive form of vitamin D , 25-hydroxycholecalciferol , into its biologically active metabolite . Moreover , we demonstrate that TLR8 activation induces autophagy in human macrophages through a vitamin D and CAMP dependent mechanism , and that the induction of autophagy by TLR8 agonists inhibits HIV . These data support an important role for vitamin D in the control of HIV infection , and provide a biological explanation for the benefits of vitamin D . These findings also provide new insights into potential novel targets to prevent and treat HIV infection . | [
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... | 2012 | Toll-Like Receptor 8 Ligands Activate a Vitamin D Mediated Autophagic Response that Inhibits Human Immunodeficiency Virus Type 1 |
Neurons in different layers of sensory cortex generally have different functional properties . But what determines firing rates and tuning properties of neurons in different layers ? Orientation selectivity in primary visual cortex ( V1 ) is an interesting case to study these questions . Thalamic projections essentially determine the preferred orientation of neurons that receive direct input . But how is this tuning propagated though layers , and how can selective responses emerge in layers that do not have direct access to the thalamus ? Here we combine numerical simulations with mathematical analyses to address this problem . We find that a large-scale network , which just accounts for experimentally measured layer and cell-type specific connection probabilities , yields firing rates and orientation selectivities matching electrophysiological recordings in rodent V1 surprisingly well . Further analysis , however , is complicated by the fact that neuronal responses emerge in a dynamic fashion and cannot be directly inferred from static neuroanatomy , as some connections tend to have unintuitive effects due to recurrent interactions and strong feedback loops . These emergent phenomena can be understood by linearizing and coarse-graining . In fact , we were able to derive a low-dimensional linear dynamical system effectively describing stimulus-driven activity layer by layer . This low-dimensional system explains layer-specific firing rates and orientation tuning by accounting for the different gain factors of the aggregate system . Our theory can also be used to design novel optogenetic stimulation experiments , thus facilitating further exploration of the interplay between connectivity and function .
Understanding the complex computations performed by neural networks in the nervous system is a central challenge in neuroscience . In recent years , research in rodent sensory systems has provided access to many new facts , but has also raised new theoretical questions . In particular , the role of cortical layers [1 , 2] and other distinct subpopulations [3 , 4] has received increasing attention . While the anatomical path of sensory input from the thalamus through cortical layers has been particularly well characterized for the visual system , it provides little explanation yet how the information is processed and transformed in transit . Here , we apply computational neuroscience techniques to gain new insight into the various computational steps within a highly recurrent neural network . From a theoretical perspective , the cortical network constitutes a dynamical system , which operates on different types of sensory input . To study basic properties of such networks , its input has been assumed to be identical for all neurons [5 , 6] . In a sensory system , however , the input varies across neurons , as sensors extract different aspects of the stimulus . The primary visual cortex ( V1 ) represents an interesting example to study such systems [7 , 8] . In this case , non-homogeneous input for example carries information about the orientation of moving gratings or light bars presented to the eye of an animal [9] . A major difference of rodent visual cortex compared to carnivores and primates is the absence of orientation columns and maps [10] . Although the “salt-and-pepper” organization of orientation preferences implies that lateral interactions are quite unspecific [11 , 12] , neurons nevertheless exhibit responses that are strongly tuned with respect to oriented light bars or gratings [1 , 3 , 13] . Interestingly , this output tuning is already present at eye opening in juvenile mice , where recurrent connectivity appears to be functionally random [14] . In recent theoretical studies , this phenomenon could be explained by a strong attenuation of the untuned component of the distributed input , due to the dominance of inhibition in the recurrent network [7 , 8 , 15–17] . While these previous works identified the mechanism underlying strong output tuning in the primary visual cortex of rodents , they provide no explanation for the different degrees of orientation selectivity in different sub-populations , like cortical layers . In the present work , we apply computational network modeling techniques to study the emergence of orientation selectivity in a layered cortical network model , with a focus on the differences between layers and neuronal subpopulations . As a starting point , we adopt the anatomically and physiologically founded network model developed by Potjans and Diesmann [2] and extend it by orientation selective input similar to Sadeh et al . [8] . We model an experiment in which an animal is shown a sinusoidal grating as a visual stimulus . By this approach , we can focus on the influence of synaptic connectivity on neuronal tuning as all other parameters are chosen to be the same over all populations . Without any adaptation or fine-tuning of parameters , the resulting model matches the distributions of orientation selectivity found in electrophysiological recordings , including the weak tuning of inhibitory neurons as well as L5 pyramidal neurons . We then show that these specific properties cannot be attributed to any specific projection in the circuit . Rather , it is an emergent network feature of the entire recurrent microcircuit . Furthermore , we suggest a concept for the design of novel optogenetic stimulation experiments . Applying this concept to the V1 cortical microcircuit , we can make specific predictions for the outcome of experiments to manipulate the orientation selectivity of L5 principal cells . The analysis of such experiments can eventually confirm or refute our analysis of the circuit .
The network model considered throughout our study is , in essence , the same as the recurrent network model developed by Potjans and Diesmann [2] , extended by thalamic input with a weak orientation bias as described in Sadeh et al . [8] . Potjans and Diesmann [2] created a full-scale neural network model , matching as close as possible the nervous tissue underneath 1 mm2 of neocortex . They incorporated a large set of experimental studies , most prominently the ones by Thomson et al . [18] and Binzegger et al . [19] . They derived a generic connectivity map between eight populations of neurons situated in four layers ( L2/3 , L4 , L5 , L6 ) , one excitatory ( e ) and one inhibitory ( i ) population in each layer ( see Fig 1A for an illustration of the model ) . The model network consists of close to 80 000 leaky integrate-and-fire neurons , where the respective size of individual subpopulations varies strongly across layers . While the two largest populations ( L4e and L2/3e ) contain more than 20 000 neurons , L5e comprizes only about 5 000 neurons ( Fig 1D ) . Throughout all layers , inhibitory populations are considerably smaller than the respective excitatory populations , by a factor of four on average . In our work , the leaky integrate-and-fire neurons are all equipped with delta synapses , i . e . with each incoming spike , the membrane potential is instantaneously deflected by a fixed voltage J , decaying with the membrane time constant τ . The synaptic efficacy J varies depending on the source and target population , respectively . Our model has a fixed number of synapses for each projection , i . e . each neuron of the postsynaptic population P receives the same number of synaptic inputs KPT from the presynaptic population T . For any postsynaptic neuron in the target population , the presynaptic neurons are drawn randomly from the source population . The numbers KPT , which are the essential parameters of our model , were derived by Potjans and Diesmann [2] and are graphically represented in Fig 1B . In addition to recurrent synaptic connections , neurons of the model network receive external input from two different sources ( Fig 1A ) . The first type of input , termed “background input” throughout this paper , is targeting all populations . It represents axons from other cortical and subcortical regions , including gray matter and white matter projections ( Fig 1B/1E ) , but excludes those originating from the thalamus . These background inputs are independent of the stimulus . In all simulations , they are modeled as Poisson processes with rate νbg . The second type of input is represented by thalamocortical projections that originate from the lateral geniculate nucleus ( LGN ) ( Fig 1B/1F ) . In primary visual cortex , these projections are the main source of information about a stimulus . This is the component where we made an important addition to the original model of Potjans and Diesmann [2] . We replaced the unspecific thalamic input with an input that depends in a specific way on the stimulus . Throughout this paper , the stimuli considered were oriented moving gratings , which are commonly used in visual neuroscience . Similar to Sadeh et al . [8] , each neuron in one of the two populations that receive input ( i . e . L4 and L6 ) is randomly assigned a preferred orientation θ^i to represent the tuning of the effective compound input from all pre-synaptic thalamic neurons . Specifically , the input to each neuron depends on the orientation θ of the stimulus and varies according to νith ( θ ) =KPthν0th[1+mcos ( 2 ( θ−θ^i ) ) ] . ( 1 ) Here , KPth is the in-degree of this projection , and ν0th is the mean rate of individual thalamic neurons . We only require the compound input to be tuned and do not make any assumptions about the origin of this tuning . An illustration of the input variation is provided in Fig 1C . Note that orientations vary between 0° and 180° as we do not consider direction selectivity in this study . Similar to the background input , the thalamic input was conceived as a homogeneous Poisson process . It is instructive to analyze and compare two different modes of operation . The stimulation condition is the situation described above , emphasizing the presentation of a structured visual stimulus ( here , a moving oriented grating ) to the animal . In contrast , the spontaneous condition emulates the presentation of a homogeneous gray screen to the animal . In this case , the intensity of thalamic inputs is the same as the rate of the background inputs . Moreover , the angular modulation m is set to zero , removing any information about stimulus orientation from the input . The results of this study are based on three different approximations of the network model . Each of them makes additional assumptions about the network dynamics to achieve different levels of abstraction and simplification . At the same time , all renderings are based on the same connectivity parameters and the same single neuron model . In the following , the three models will be briefly described . In the following , the mathematical model implementations are described in detail . While this documentation allows to fully reproduce the model , it is not essential for the comprehension of the results of the study . In the main text of the manuscript we do not refer to these details , and the reader may choose to directly jump to Results .
The results of the spiking neural network simulations performed in NEST and of the detailed data analysis performed on the simulated data are summarized in Fig 2 , both for the spontaneous ( weak , untuned thalamic input ) and stimulated ( tuned thalamic input ) condition . Note that we do not subtract the spontaneous rates in the stimulated condition for our analysis . As expected , the single neuron firing rates ( Fig 2B/2C ) match the values reported by Potjans and Diesmann [2] . Fig 2D/2E compares the firing rates of the model with experimental values obtained by Niell and Stryker [1] from adult mouse visual cortex . In the spontaneous condition ( Fig 2D ) , the model firing rates are somewhat lower compared to experimental values , but are otherwise in good qualitative agreement with them . We find very low activity in L2/3 and higher rates in the granular and infragranular layers , with the exception of population L6e , where rates are also low . For the stimulus condition , Fig 2E compares the evoked rates of the model with the results of Niell and Stryker [1] . The evoked rates are defined as the maximal rate of the individual neurons over all angles . Therefore , they differ from the median values shown in Fig 2C . In this condition , the model rates vary more strongly across layers as compared to the experimental values . The mean and standard deviation ( SD ) of the pairwise spike-count correlations over all populations are ( 0 . 2 ± 10 . 3 ) ⋅ 10−3 and ( 0 . 3 ± 10 . 4 ) ⋅ 10−3 for spontaneous and stimulated conditions , respectively ( 10 ms bin size ) . The mean and SD of the coefficient of variation ( CV ) of the inter-spike intervals are 0 . 9 ± 0 . 1 for both conditions . Both measures show only small variations over different populations ( see S1 and S2 Figs for population-wise quantification ) . The low correlation and Poisson-like irregularity indicate that the network indeed operates in the asynchronous irregular regime [6 , 28] , as illustrated by the raster plot in Fig 2A . The activity of the network is robust to changes in important parameters which are not well constrained by experiments . Although average firing rates vary across parameter sets , their distribution remained qualitatively similar ( S4A/S4B–S9A/S9B Figs ) . Only for very low background input , the network operates in a more synchronous regime with increased correlations ( S3B/S3C and S6D Figs ) . In this case , the network activity also changes qualitatively . Surprisingly , this is only the case for reduced background input . For a two-fold increase , the activity remains stable ( S3B and S5D Figs ) . Our model extends the work of Potjans and Diesmann by giving the thalamic input a weak orientation bias , as described previously [7 , 8] . The macroscopic connectivity pattern of the network ( Fig 1A ) already gives a rough idea of the signal flow through the network . Thalamic afferents project mainly to L4 and to a smaller extent also to L6 . From L4 , the signal is transmitted to L2/3 , which is thought to be the central processing unit of the microcircuit . Signals are then forwarded to higher brain areas as well as to L5 , which is the major output unit for projections to sub-cortical brain areas . In order to dissect the computations performed by the network , we extract tuning curves of all neurons in the model network . As expected from previous studies [7 , 8 , 16 , 17] , during stimulation , the weak orientation bias of the thalamic input is amplified to strongly orientation selective responses in the input population ( Fig 3 ) . Interestingly , L2/3e also shows strong tuning although these neurons only receive input via random connections from L4 neurons . This phenomenon was previously described by Hansel and van Vreeswijk [7] , where projections from L4e to L2/3 were studied independently of other parts of the network . The strong tuning in L2/3e can be explained by a recurrent tuning amplification in inhibition dominated networks [7 , 8] . This effect is enough to create strong output tuning from a small bias in the input , which in turn is due to random sampling of preferred orientations in L4 . In this context it may seem even more surprising that orientation selectivity of L5 neurons appears to be weaker compared to the other layers . This finding , which is a direct consequence of the underlying experimentally determined connectivity , is consistent with several measurements of orientation selectivity performed in different rodent species [1 , 3 , 29–31] ( Fig 4D ) . However , it cannot be easily explained by visual inspection of the connectivity matrix . Even though synaptic connectivity is stronger for the projection to L2/3e , L5e neurons also receive a large fraction of their input from L4e ( Fig 1B , Table 3 ) . Therefore , as the output tuning of L2/3e neurons is essentially inherited from these neurons , one might expect a stronger tuning also in L5e . In order to understand in more detail the differences in orientation selectivity between layers , we extracted the orientation selectivity index ( OSI ) of all neurons in the model network ( Fig 4B/4C ) . We employ a measure based on the circular variance to quantify orientation selectivity ( see Methods for details ) . For each neuron , the OSI is calculated as the ratio of first ( F1 ) and zeroth ( F0 ) Fourier component of the tuning curve ( Fig 4A ) . A stronger modulation of the firing rate over angles ( higher F1 ) , leads to a higher OSI , while a higher mean rate ( higher F0 ) reduces the OSI . Experimental studies often use a different measure for orientation selectivity based on the activity at the preferred and orthogonal orientation . To allow direct comparison with these works , we also calculated this alternative quantity ( S10 Fig ) . While the absolute numbers are generally higher for this measure , the qualitative results are identical . Note that also the distribution of orientation tuning is quite robust against variations in parameters ( S4C–S9C Figs ) . Consistent with experimental findings and throughout all layers , inhibitory neurons have a lower OSI than the corresponding excitatory neurons [1 , 3 , 32] ( Fig 4D ) . While this might be expected for L4 and L6 due to the lower number of thalamic afferents , it is not at all obvious for L2/3 . In this population , it is a consequence of the recurrent network connectivity , as our analysis will show . Note that during spontaneous activity , neurons also exhibit weak apparent orientation selectivity ( Fig 4B ) . This is essentially due to fluctuations in the spiking process . Neurons may randomly fire a few action potentials more for one orientation as compared to another , which results in weak but nonzero orientation selectivity . The magnitudes found here are consistent with a Poisson process with the same rate and recording time . The orientation selectivity is measured as the ratio of first and zeroth Fourier component of the tuning curves ( Fig 4A ) . Analyzing the tuning curves in Fig 3A in more detail , we see that although the F1 component in L5e is stronger than in L2/3e , this is overcompensated by the high F0 component of this population , leading to weaker tuning in L5e . This raises two questions: First , which projections onto L2/3e and L5e lead to the observed difference in activity ? Second , why is the tuned ( F1 ) component of L5e not as strongly amplified as the mean rate ( F0 ) , similar to the situation in L2/3 ? In the following , we will address these questions , employing suitable mathematical and computational methods . While the first question can be answered by analyzing the input currents received by the different populations , the second question requires an approach which takes the strongly recurrent nature of the circuit across layers into consideration . Each neuron in the eight populations of the network receives inputs from several pre-synaptic populations , possibly all with different tuning curves , and forms its own output tuning from those ( Fig 4A ) . It has been shown in experiments that this scenario also reflects the situation in rodents [11] . Which projections are most potent for driving the the target neuron is not immediately obvious from the anatomical connectivity between neurons ( Fig 1B ) . The activity of pre-synaptic neurons is of course also relevant for the total input current to a given neuron . In Fig 5A , the mean input current ( Eq 3 ) for each projection between populations is shown . While the difference between inputs to L2/3e and L5e seems insignificant when looking at the underlying connectivity ( Fig 1B ) , the picture changes completely if the activity of pre-synaptic neurons is taken into consideration ( Fig 5A ) . The input to L2/3e neurons is mostly determined by L2/3i , both L4 populations and the constant background . Input to L5e , on the other hand , is dominated by L5i and background input , but only to a lesser extent by inputs from L2/3i , L4e and L5e . As a result , due to the different excitation-inhibition ( EI ) balance , the net currents imply a higher mean input to L5e compared to L2/3e ( Fig 5C ) . The marked difference between anatomically defined connectivity ( Fig 1B ) and the total input current in the stationary balanced state ( Fig 5A ) highlights the contribution of activity dynamics in recurrent networks [6 , 33 , 34] . While the situation for L5e and L2/3e is very similar when counting the number of excitatory and inhibitory synapses that terminate in each population , the actual current drive they receive is quite different . Since the activity of the whole network autonomously settles in an operating point where the EI balance is dynamically maintained , this can have very different consequences for the various populations , irrespective of the anatomical connectivity . Having demonstrated how the dynamic equilibrium and the resulting operating point of the layered network explains the high firing rate of L5 neurons , we can now ask the same question with regard to orientation tuning in each neuronal population . In combination , these two aspects fully determine the orientation selectivity index ( OSI ) of all neurons . To achieve this , we calculate for each neuron a tuning vector summarizing the information about the stimulus conveyed by the input currents to that neuron . Its magnitude measures the tuning strength of the combined input to the post-synaptic neuron , whereas its direction indicates its preferred orientation ( Fig 5F inset , Eq 4 ) . The total tuning input from one specific source population to a single neuron can then be calculated as the sum of all tuning vectors of neurons in the pre-synaptic population which connect to the post-synaptic neuron . If all tuning curves are cosine-like , this concept exactly corresponds to linear summation of inputs . The magnitudes of the tuning vectors are identical to the first Fourier component ( F1 ) of the tuning curves for the current input . Fig 5B summarizes the mean lengths of all projection tuning vectors , summarizing the information flow in the system . The tuning information enters the cortical network in L4 and L6 and then spreads to the other populations . Although both L4 and L6 neurons show significant tuning to stimulus orientation , due to the low firing rates of L6e neurons it is mostly L4 which provides tuning information to the other populations . In contrast to what would be expected from the connectivity alone ( Fig 1B ) , also L5e neurons receive most of the tuned current from L4 . Comparing the input to L2/3e and L5e neurons in more detail reveals that L2/3e neurons mostly integrate tuned input from both populations in L4 , while L5e neurons receive less tuned input almost exclusively from L4e . Considering only the output modulation of the different populations ( Fig 5D ) , which is defined as the first Fourier component of the tuning curves ( Fig 4A ) , it surprises that despite the less tuned input to L5e , these neurons still show a stronger output modulation than L2/3e . This highlights that , besides the input strength , the input sensitivity of neurons is relevant as well . However , the large modulation component of the output of L5e neurons cannot compensate their high firing rates , leading to a lower OSI ( Fig 5C–5E ) . Comparing the distribution of firing rates and output modulations over the eight populations ( Fig 5C/5D ) , it is evident that these two components are subject to different gain factors . In the following , these dependencies will be studied in more detail . Our analyses so far disentangled the input to neurons in different populations , which explains the observed features of neuronal output . However , the multi-population model is highly recurrent . Therefore , it is not yet clear why the input to all neurons settles in the observed operating point . We face a chicken-and-egg problem here , as in the recurrent network , the output of all populations is simultaneously the input to the same populations , eventually settling in the dynamical operating point . In order to tackle this problem , we now change our perspective from analyzing the input-output relation of single neurons to input-output behavior of the entire network . The idea is to manage the problem by considering the network as a distributed system , which can perform its function only as a whole . This approach is best realized by linearizing the network dynamics about its operating point . We obtain our close to 80000-dimensional linearized network model ( Model C ) by first formulating a single neuron firing rate model based on the single neuron transfer function Fi ( ν , νth ) ( Model B ) . The firing rate model is then linearized about its dynamical operating point , leading to the explicit input-output relation Δν= ( 1−W ) −1Δβ . ( 13 ) Here , Δν is a vector summarizing all single neuron rate changes due to a change in the effective input Δβ , which in turn is the change in thalamic input rate scaled by the input sensitivities of the different populations . These sensitivities can also be understood as the feed-forward gains of the system [8] , see Methods for details . Furthermore , the behavior of the recurrent model is governed by the matrix W , which is the matrix of effective recurrent connections , which are a product of the anatomically and physiologically defined synaptic weights and the input-output sensitivities ( gains ) of individual post-synaptic neurons at their operating point . In combination with the activity at the operating point ν0 , the rate change Δν results in the output activity ν = ν0 + Δν of the linear network model . The explicit form of Eq 13 also supports the idea of considering the network as an integrated system with one single vector-valued input-output relation . Importantly , this relation is governed by the inverse of the effective connectivity 1−W . Because the entries in Wij are a function of Kij , this inversion potentially distributes the influence of each single connectivity parameter Kij over the entire network . This observation also explains why the effects of individual connectivity parameters can be counterintuitive [35] . The firing rate model ( Model B ) as well as the linear network model ( Model C ) can be considered as simplifications of the spiking neural network ( Model A ) , as they make additional assumptions about its dynamics . Therefore , before analyzing these models in more detail , it is important to establish the consistency of their respective behavior . S11 Fig compares the results from a simulation of all three models , for matched network parameters . We found that the single neuron firing rates of the three models are in good agreement . While the similarity between the non-linear rate model ( Model B ) and the linear model ( Model C ) is quite high , both exhibit mild discrepancies from the spiking network model ( Model A ) . In particular , for the larger firing rates of L5e , the non-linear rate model and the linear model tend to slightly underestimate them . Generally , also the preferred orientations and orientation selectivities are in good agreement between the different models . While the match is again excellent for the non-linear firing rate model and linear network model , both slighly deviate from the spiking network model . This can be explained by the role played by activity fluctuations in this model . In particular , the observed orientation selectivities are somewhat higher here , reflecting the same positive bias as the one observed during spontaneous activity . The match between the three models is also very robust with respect to changes in parameters ( S4A/S4B/S4C–S9A/S9B/S9C Figs ) . In fact , even for reduced background input , when there is substantial synchrony in the spiking activity , the non-linear rate and linear models still provide reasonable approximations ( S6A/S6B/S6C Fig ) . Now that the general consistency in the behavior of the different models is established , we can analyze the linearized network and be confident that our conclusions are also valid for the spiking network . In previous work , a separation into two separate , non-interfering pathways were postulated , exploiting that the network can have different gains for different components of its input [8] ( Fig 6A ) . Specifically , the effective input perturbation is split into a baseline and a modulation component such that Δβ = ΔβB + ΔβM . Here , we generalize this decomposition to our present multi-population model . Furthermore , we present a novel analytical approach for their analysis . For each population , the baseline ΔβB is conceived as the mean input these neurons receive during stimulation . Since the mean input is different for each population , this leads to a population-wise constant input vector ( Fig 6B , red curve ) . The modulation ΔβM , in contrast , accounts for all neuron-by-neuron deviations from the population baseline , ΔβM ≔ Δβ − ΔβB , yielding a vector with zero mean in each of the eight populations ( Fig 6B , inset ) . Most importantly , the modulation component includes the tuning information that each neurons receives , based on the neuron-specific orientation bias of the stimulus . In other words , the deviation of ΔβM from the baseline is determined by the orientation of the stimulus , as described by Eq 6 . Note that the baseline and modulation components are both only indirectly related to mean rate ( F0 ) and tuning strength ( F1 ) discussed in the previous section . While the latter are calculated for the tuning curve of each single neuron independently , the baseline and modulation of the input are calculated over all neurons in one population , for a single stimulation angle . Separation of baseline and modulation pathway means that the two input components ΔβB and ΔβM are processed by two independent mechanisms with possibly distinct gains γB and γM . As shown in Methods , the two pathways are governed by ΔνB= ( 1−Q ) −1ΔβBΔνM= ( 1−S ) −1ΔβM , where the decomposition W = Q + S was used . Thereby , Q contains population-wise expectation values , resulting in an 8 × 8 block structure , and S are the individual modulations S = W − Q of single connections in the network . Importantly , there is no cross-talk between the two pathways . A change in the input baseline ΔβB does not lead to change in the output modulation ΔνM , and vice versa . Solving these two systems independently yields two output rate changes ΔνB and ΔνM . If the cross-terms in the calculations are indeed negligible , the solution Δν = ΔνB + ΔνM provides a good approximation to the network behavior ( see Eq 12ff ) . For the network under study here , the mean magnitudes μ of the cross-terms are μ[|QΔνM|] = 0 . 02 and μ[|SΔνB|] = 0 . 09 as compared to μ[|Δβ|] = 3 . 63 , implying that the system can indeed be studied by treating the baseline and the modulation pathway separately . Similar numbers are also obtained for the other parameter sets ( S3E Fig ) . The solutions of the baseline and modulation systems , ΔνB and ΔνM , are shown in Fig 6C in comparison to the solution of the W-system . Throughout all populations , the separation solution is in excellent agreement with the direct solution . The modulation part , which conveys the tuning information of the neurons , matches almost perfectly ( Fig 6C inset ) . The good agreement is further confirmed by the high coefficient of determination ( “variance explained” ) of R2 = 99 . 1% . For the different parameter sets , the lowest R2 is obtained for increased background input with R2 = 98 . 0% ( S3D , S4E–S9E Figs ) . This demonstrates the robustness of the baseline and modulation decomposition also with respect to changes in parameters . In previous sections , we identified the high baseline rate of L5e as the main cause for the low orientation selectivity of its neurons . This becomes manifest in a high baseline output rate ΔνB for this particular population ( Fig 6C ) . While this observation still does not fully reveal the underlying reason for the high rates , it hints at the effective mean connectivity represented by Q . In the following , we will therefore study the baseline pathway in more detail . We start by summarizing the results of Sadeh et al . [8 , 36] , where a similar scenario for a two population EI network was studied . In analogy to the present model , the two population system can also be described by a linear system of the form Δν= ( 1−W ) −1Δβ , where the dimensionality of the system equals the total number of neurons . In order to study the network behavior , it is instrumental to inspect the eigenvalue spectrum of the matrix W . In the two population case , when both populations receive the same input , the eigenvalue spectrum consists of a bulk of known radius localized at the origin and a single exceptional eigenvalue λ [37] . For inhibition dominated networks , this exceptional eigenvalue is real and negative ( Fig 7A ) . Furthermore , the eigenvector Ψ corresponding to that exceptional eigenvalue is the uniform vector , with all entries identical . In this two-population network model , the baseline component of the input perturbation is also proportional to the uniform vector ΔβB∝Ψ∝ ( 1⋮1 ) . Therefore , exploiting the eigenvector property of a baseline perturbation , the exceptional eigenvalue can be transformed into the gain factor of the baseline ΔνB= ( 1−W ) −1ΔβB= ( 1−λ ) −1ΔβB=λ˜ΔβB . For inhibition dominated networks , this gain factor λ˜ has a small magnitude and thus results in a strong attenuation of the baseline component . This , in turn , amplifies the orientation selectivity of the network . Generalizing the two-population scenario to the eight-population network considered here , two major differences in terms of the eigenvalue spectrum of W become apparent . First , instead of a single exceptional eigenvalue , the spectrum of the effective connectivity is more complex ( Fig 7B ) . In addition to the bulk of eigenvalues ( diameter indicated in green in Fig 7B ) , there are seven eigenvalues with a significantly larger magnitude ( blue dots in Fig 7B ) . In a random matrix theory context , it was previously shown that the exceptional and bulk eigenvalues are due to the expectation values and variances of the distribution of the elements of W . Therefore , the exceptional eigenvalues are asymptotically identical to the eigenvalues of Q [38] . Indeed , the first seven eigenvalues are in good agreement with the numerical calculations on W ( crosses in Fig 7B ) . Furthermore , an eighth exceptional eigenvalue is identified , which lies in the middle of the bulk and could therefore not be distinguished from bulk eigenvalues in numerical calculations on W . The second difference compared to the two-population scenario is the fact that the constant vector is not any more an eigenvector of an exceptional eigenvalue . Instead , the eigenvectors of the baseline system Q are only population-wise constant ( cf . Fig 7D inset ) . However , also the input perturbation is not proportional to the constant vector in this case . For each population , it depends on the sensitivity to thalamic input , also resulting in a population-wise constant function . In order to study the input-output behavior , the input can therefore be decomposed into the eight eigenvectors ( cf . Fig 8 left ) ΔβB=∑i=18ξiΨi . Here , Ψi are the eigenvectors of Q and ξi are the coefficients of the modes . The weighted components ξiΨi can be considered as the input modes of the input perturbation ΔβB . The eight modes which represent the perturbation of the stimulation are shown in the inset of Fig 7D . Note that the sum over these modes is identical to ΔβB in Fig 6B . Applying the decomposition to the linear baseline system of Eq 14 results in a decomposition of the output rates of the network , where the eight exceptional eigenvalues define the gain factors of the individual modes by ( Fig 8 right ) ΔνB=∑i=18λ˜iξiΨi . ( 14 ) The transformed gain factors λ˜i are shown in Fig 7C . The individual parts of this decomposition can be considered as output modes of the system , with a direct one-to-one relation to the input modes . The effect of the exceptional eigenvalues λ is quite similar to the two-population scenario described previously , where the single exceptional eigenvalue defined the gain of the homogeneous common mode . In the multi-population case , each input mode is amplified with the respective gain λ˜i to form the output perturbation of the network . The eight output modes are shown in Fig 7D . Note that , similar to the input perturbation ΔβB , the sum of the modes is identical to the baseline component ΔνB shown in Fig 6C . Studying the output modes in more detail , it is apparent that the high rates of L5e neurons , and thus also their low orientation selectivity , are mostly due to one specific mode ( orange line in Fig 7D ) . On the other hand , the same mode has a similar strength as the other components in the input space ( orange line , Fig 7D inset ) . As the corresponding eigenvalue of W has a very small magnitude , however , the gain factor λ˜ of that mode is much larger compared to the other modes ( Fig 7C , orange dot ) . Due to this gain , the mode is dominant in the output rate change , resulting in high firing rates in L5e . The fact that only a single mode is responsible for this strong amplification underlines our conclusion that this effect is a feature of the whole network , which cannot be pinned down to one specific connection in a meaningful way . Importantly , although the eigenvalue spectrum and mode structure can change to some degree , the observation that a single mode with a strong gain is responsible for the high rates of L5e is consistent for all parameter sets considered ( S4F/S4G/S4H–S9F/S9G/S9H Figs ) . Having identified the large gain of a specific mode in the input as the source of the high firing rates in L5e , we now apply our theory to predict the network behavior for a scenario , where that particular mode is absent in the input . For two reasons the mode cannot be directly removed from the input firing rates . First , that mode has non-zero coefficients for all populations , which leads to non-zero thalamic input also to L2/3 and L5 . While it is technically possible to implement this in a computational model , it is infeasible as a biological experiment . The second problem is more fundamental: Subtracting the mode from the input perturbation would lead to negative firing rates , which cannot be realized . Therefore , instead of using altered Poisson input to neurons , we designed an external current stimulation , which has an equivalent effect on the neuronal output rates . It is conceivable to realize such an input , for example by specific optogenetic stimulation . In the linear model ( Model C ) , an external current results in an additional input perturbation Δγ such that Δν= ( 1−W ) −1 ( Δβ+Δγ ) . In order to delete a specific mode Ψk from the input , we can set Δγ = −ξkΨk . As a consequence , when the two inputs Δβ and Δγ are combined , the mode cancels out , resulting in altered output perturbation . The required input current for single neurons in each population are shown in Fig 9A . They are not directly proportional to the deleted mode , due to the scaling by the individual feed-forward gain of each population , as well as due to the highly recurrent processing of the network . As expected , since the rate of L5e neurons should be suppressed , these neurons receive a strongly negative current . More surprisingly , L2/3e requires a positive input current of similar magnitude , although the desired change in rate of that population is much smaller . When the current is applied with an intensity that exactly removes the mode from the input , the change in output exactly matches the sum of the remaining modes ( Fig 9B , relative intensity 1 ) . For stronger stimulation intensities , the input mode is overcompensated . The mean population rates of the linearized network and spiking network model are in good agreement . Also in the spiking neural network model , the mean rates change almost linearly , even if the input mode is overcompensated by a factor of two . As expected , the reduction of L5e firing rates leads to an increase in orientation selectivity of 250% from 0 . 02 to 0 . 07 in the linearized network model ( 75% in the spiking network model , Fig 9C ) . Simultaneously , due to the increase in firing rate in L2/3e , the orientation selectivity of this population decreases by 66% from 0 . 12 to 0 . 04 in the linearized network model ( 25% in the spiking network model ) . The discrepancy between the OSI of the spiking and linearized network model can be partially explained by the bias of random tuning due to the fluctuations of the spiking process , which were also the reason for non-zero OSIs observed in the spontaneous , unstimulated condition in Fig 4B . Other contributions could come from a weak crosstalk between the baseline and modulation pathways , or from deviations from the assumptions of Poisson firing statistics and negligible correlations . Effectively deleting the mode from the input is not the only option to reduce the activity of L5e neurons . From our analytic calculations , all feed-forward and recurrent baseline gains are known . Exploiting this knowledge , we can design yet another current stimulus that exclusively affects L5e neurons , leaving the mean firing rates of all other populations unchanged ( Methods ) . The desired change of rates for this stimulus can be conveniently summarized in a vector ΔνL5e . This vector is zero for all populations except L5e . For this population , it contains the negative of the mean rates in the stimulation condition such that the resulting rates vanish . The required effective external perturbation can then be calculated by Δγ= ( 1−W ) ΔνL5e , which are the external currents scaled with the known neuron sensitivities . The required stimulation currents are shown in Fig 9D . Surprisingly , in this case , the input currents to L5e neurons all need to be positive , although the desired effect on the output rates is negative , and the rates of these neurons are reduced . It is also interesting that input currents to all neurons except L4 are of similar strength . It is clear that the other populations also require substantial stimulation to maintain their original rate , since the altered rate of L5e neurons needs to be compensated . However , the magnitude of these currents surprises as L5 does not serve as a major input to any other population within the network ( Figs 1B and 5A ) . This highlights again the strongly recurrent and often unintuitive nature of such networks . Fig 9E shows that the new current stimulus successfully reduces the rate of L5e neurons , while keeping all other firing rates unchanged . Similar to the first current stimulus discussed above , the two models match quite well . Only for strong stimulation intensities , when the rates of L5e neurons approach zero , the two models show discrepancies . In this regime , the rates of L5e begin to be rectified . Since this is a non-linear effect , it can of course not be captured by the linearized version of the model . Analogously to our first prediction for current stimulation , L5e neurons show a strong increase in orientation selectivity ( Fig 9F ) . In the linear model , the OSI rises very sharply for large current intensities . This is an artifact of the neglected rectification of neuron rates , as already observed above . In the more realistic spiking network model ( Model A ) , the OSI also increases strongly by about 150% ( from 0 . 04 to 0 . 1 ) . However , in contrast to the first current stimulus discussed above , in this case orientation selectivity of L2/3e neurons is not compromised by the additional input currents . The OSIs of the other excitatory and inhibitory populations also remain unchanged .
The goal of this work was to study signal propagation across layers in primary visual cortex of rodents , with a particular focus on the emergence of orientation tuning in layers that have no direct access to thalamic input . We decided to employ a model that was developed previously by Potjans and Diesmann [2] . The synaptic connectivity defining this model was rigorously derived from the results of anatomical and electrophysiological measurements . The parameters describing neuronal connectivity depend on the pre-synaptic and post-synaptic population , but they do not depend on the functional properties ( tuning ) of individual neurons . Furthermore , in order to focus our analysis on the impact of neuronal connectivity on dynamics , single-neuron parameters were chosen the same for all neurons , independently of the population they belong to . Since not all required connectivity data are available , Potjans and Diesmann [2] combined data from different species ( cat and rat ) into one single generic model circuit . In addition , the measurements were performed in several different brain regions , so it is debatable whether this model can account for information processing in mouse visual cortex at all . In view of this , it may be surprising that the predictions of our study with regard to neuronal activity are in very good agreement with direct electrophysiological measurements performed in mouse visual cortex . This finding is in fact compatible with the idea of a universal cortical circuit which is found in different species and in different cortical regions devoted to the processing of sensory information . It will be interesting to see to which degree a neural network model based on the full mouse/rat visual cortex connectome ( when it becomes available ) deviates from the present model . In our work , the model proposed by Potjans and Diesmann [2] was extended by adding neuron-specific thalamic input , which is tuned to the orientation of a visual stimulus , i . e . , a moving grating . This input was provided to both the excitatory and inhibitory populations in L4 and L6 . From experiments , it is known that neurons modulate their firing rate with the temporal frequency of the grating [1 , 3] . However , in order to keep the already complex model and analysis as simple as possible , we did not include this aspect of visual processing into our considerations . In fact , our analysis showed that the cortical network operates in a quasi-linear regime . Therefore , we expect that the additional temporal modulation of firing rates would not have a significant impact on the mean values studied and reported here . Via random connections , the very weak orientation bias in the input was enough to induce strong orientation tuning in L2/3; see Hansel and van Vreeswijk [7] and Sadeh et al . [8 , 16 , 17] for an analysis of this generic effect . Note , however , that we did not make any assumptions about the origin of the orientation bias in the input . It could be a consequence of the alignment of the receptive fields of afferent neurons [39] , or it could be inherited from already tuned thalamic neurons [23 , 40 , 41] . While the input was assumed to convey information about a visual stimulus throughout the present study , our analysis of the layered network does not rely on this interpretation . Our approach could therefore also be applied to study feature selectivity in other sensory modalities , like in somatosensory or auditory cortex . The distributions of orientation selectivity that emerge in our model across the different layers and neuronal populations are qualitatively very similar to experimental recordings [1 , 3 , 29–31] ( see also [42] for some inconclusive results ) . This represents a remarkable result , as the model is based on measured connectivity in different cortical regions in different species , and it was not at all designed and tuned as a specific model of rodent visual cortex . Furthermore , the recordings cited above were performed in adult mice , which are known to have some degree of feature-specific connectivity . Our model , in contrast , entirely lacks specific connectivity , resembling the situation at eye opening [14] . Our analysis demonstrates that well-tuned neuronal responses can emerge in all cortical layers without feature-specific connections between neurons . On the other hand , it is known that specific connectivity can work as a concurrent mechanism to improve orientation selectivity and yield the higher values observed experimentally ( see also [36] ) . While the distribution of firing rates in spontaneous and evoked conditions are qualitatively similar to experimental findings [1 , 3] , there are also discrepancies in several respects ( Fig 2D/2E ) . It is difficult to asses the impact of these differences for the findings of our work . However , our results are generally robust to changes in central network parameters , despite considerable rate changes . Therefore , we expect that the neuronal mechanisms described in our work still apply when more precise and detailed models of the microcircuit are developed . In the present model , the mouse which is subject to visual stimulation is assumed to be non-moving . As shown in a recent study [3] , neuronal responses in primary visual cortex of rodents are very similar in awake and anesthetized animals . In contrast , the responses dramatically change during locomotion behavior [43] . Different inhibitory neuronal subtypes play a central role in this [4] . A future enhanced version of our model might account for such effects as well . While analyzing network activity in terms of firing rates and orientation tuning , it becomes apparent that a direct interpretation of connectivity cannot fully explain all features of the dynamics observed in network simulations . For example , although afferent connectivity originating from L4e is very similar , firing rates and orientation selectivity in L2/3e and L5e are quite different . As the input is comprised of both excitatory and inhibitory projections that partially cancel each other , a small difference in the input may have strong effects on the operating point the two populations eventually settle in [33 , 34] . Such effects are not straight-forward to predict , since in a highly recurrent network neuronal output simultaneously also provides input to the same network . Sometimes this leads to non-intuitive effects like effective inhibitory influence of excitatory neuron populations [35] . To master such difficulties in the analysis , a system-level view of complex microcircuits is inevitable [44 , 45] . Previously , inhibition dominated recurrent networks were shown to exhibit two distinct processing pathways [8] . While the untuned and uninformative component ( baseline ) of the input is strongly attenuated by the recurrent network , the modulated component , which conveys all the information about the stimulus , has a much higher gain . This results in a net amplification of feature selectivity through the input-output transformation exerted by the network . By separating the ( linearized ) layered model into two systems , we were able to analytically calculate the feed-forward and recurrent gains of the baseline pathway also for a multi-population system . In combination , these two gains describe how the input to one population affects the output of any other population . In contrast to our model , neurons in the model of Potjans and Diesmann [2] have binomially distributed in-degrees , leading to a certain degree of cross-talk between the two pathways by non-vanishing interference terms in Eq 12 . While this introduces some discrepancies between the direct and separate solutions of the linear model , it does not impair the described mechanism . It should be emphasized that the separation between baseline and modulation pathways , its different gains , and thus also tuning amplification are purely linear effects . This is possible , as a linear transformation may impose different scaling factors to different input components . Instead of analyzing the operation of a network from the viewpoint of neuronal populations , we chose a basis that is more natural for the network in question . We decomposed the input into eigenmodes of the system , where the eigenvalues of the effective connectivity correspond to the gains of the individual input modes . Interestingly , the high gain of L5e can be attributed to a single mode , which has a much higher gain than all the other modes . Due to this mode , L5e neurons exhibit a high firing rate and a weak selectivity for stimulus orientation . What is the functional significance of having a mode with this exceptionally high gain ? For a preliminary answer , it is important to take into consideration that the different populations in the layered cortical network play different roles for information processing . For instance , L2/3e neurons project to higher brain areas and need to transmit information in a reliable way [46] . Therefore , it is desirable that this population has a strong attenuation of the baseline and , thus , strong tuning amplification . L5e neurons , on the other hand , mainly project to sub-cortical brain regions , including thalamus [47] . It is likely that this population is involved in feedback control mechanisms , where tuning information may be less relevant . Using the theory developed in this work , we are in the position to devise external ( e . g . optogenetically applied ) stimuli which effectively manipulate the orientation tuning of L5e neurons . We designed two different such stimuli which reduce the mean firing rates of these neurons , thus increasing their tuning . Interestingly , although the effect on the rate of L5e is identical , one stimulus requires a positive current , while the other utilizes a negative current . This can be explained by the indirect effect of the stimulus on other populations , propagated via the recurrent network . Our calculations constitute strong predictions of our model , which can be tested with some effort in experiments . Different subpopulations can be stimulated by a combination of channelrhodopsin and halorhodopsin with different wavelength sensitivities , as described by Klapoetke et al . [48] , possibly in combination with locally confined optical stimulation . Throughout our study , and in particular for the derived predictions , the consistency of results between the spiking network and the linear model are remarkable . Only for very strong stimulation , when non-linear rectification sets in , some deviations become evident . This confirms previous findings that a network of highly nonlinear neurons can be effectively linearized about its dynamical operating point , which corresponds to the balanced state of the excitatory-inhibitory network [33 , 34] . In our work , the newly developed mathematical tools were applied to design current stimuli which effectively and selectively suppress L5e activity . In our model , all rate gains are known from the decomposition into baseline and modulation pathways . Therefore , other types of experiments can be performed as well , even for complex multi-population networks different from the primary visual cortex . This makes the suggested method an ideal tool for the design of external current stimuli in a large variety of experiments . In summary , we presented a novel method for analyzing the dynamics of multi-population networks in terms of their input-output relations . We applied the approach to the analysis of a layered model of rodent visual cortex , revealing interesting and to some degree also unexpected and non-intuitive consequences of the underlying connectivity . In addition , we derived predictions for future optogenetic experiments , which could be performed to test the power of our computational analysis . | Understanding the precise roles of neuronal sub-populations in shaping the activity of networks is a fundamental objective of neuroscience research . In complex neuronal network structures like the neocortex , the relation between the connectome and the algorithm implemented in it is often not self-explaining . To this end , our work makes three important contributions . First , we show that the connectivity extracted by anatomical and physiological experiments in visual cortex suffices to explain important properties of the various sub-populations , including their selectivity to visual stimulation . Second , we introduce a novel system-level approach for the analysis of input-output relations of recurrent networks , which leads to the observed activity patterns . Third , we present a method for the design of future optogenetic experiments that can be used to devise specific stimuli resulting in a predictable change of neuronal activity . In summary , we introduce a novel framework to determine the relevant features of neuronal microcircuit function that can be applied to a wide range of neuronal systems . | [
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... | 2019 | Propagation of orientation selectivity in a spiking network model of layered primary visual cortex |
Malaria parasite infection is initiated by the mosquito-transmitted sporozoite stage , a highly motile invasive cell that targets hepatocytes in the liver for infection . A promising approach to developing a malaria vaccine is the use of proteins located on the sporozoite surface as antigens to elicit humoral immune responses that prevent the establishment of infection . Very little of the P . falciparum genome has been considered as potential vaccine targets , and candidate vaccines have been almost exclusively based on single antigens , generating the need for novel target identification . The most advanced malaria vaccine to date , RTS , S , a subunit vaccine consisting of a portion of the major surface protein circumsporozoite protein ( CSP ) , conferred limited protection in Phase III trials , falling short of community-established vaccine efficacy goals . In striking contrast to the limited protection seen in current vaccine trials , sterilizing immunity can be achieved by immunization with radiation-attenuated sporozoites , suggesting that more potent protection may be achievable with a multivalent protein vaccine . Here , we provide the most comprehensive analysis to date of proteins located on the surface of or secreted by Plasmodium falciparum salivary gland sporozoites . We used chemical labeling to isolate surface-exposed proteins on sporozoites and identified these proteins by mass spectrometry . We validated several of these targets and also provide evidence that components of the inner membrane complex are in fact surface-exposed and accessible to antibodies in live sporozoites . Finally , our mass spectrometry data provide the first direct evidence that the Plasmodium surface proteins CSP and TRAP are glycosylated in sporozoites , a finding that could impact the selection of vaccine antigens .
Malaria remains one of the major global infectious diseases , responsible for nearly 438 , 000 deaths and 150 to 300 million new infections annually ( World Malaria Report 2015 , WHO ) . This disease , found in much of the tropical and subtropical regions of the world , is perpetuated through the mosquito-borne transmission of a eukaryotic parasite of the genus Plasmodium . Malaria infection is initiated when an infected anopheline mosquito takes a blood meal , and by doing so deposits the sporozoite form of the parasite into the skin . Sporozoites are motile and leave the bite site by traversing into the vasculature , traveling through the blood stream and ultimately invading hepatocytes in the liver . Liver stage parasites develop for approximately one week and release exo-erythrocytic merozoites into the blood stream to begin the blood stage of infection ( reviewed in [1–2] ) . During the blood stage of infection , the iterative cycles of replication lead to high parasite numbers and to all clinical symptoms of malaria . Targeting the asymptomatic sporozoite and liver stage parasites , a time when parasite numbers are low , can lead to elimination of the parasite before it advances to the symptomatic stage of disease . Malaria remains such a formidable disease in part due to the lack of effective approved vaccines and the ability of the parasite to rapidly evolve drug resistance [3–4] . Major strides have been made with RTS , S , the first malaria vaccine candidate to show efficacy in Phase III clinical trials . This has produced great enthusiasm for eventually meeting the goal of eradication . However , the efficacy and longevity metrics of RTS , S still fall below the community established efficacy goals [5] , providing approximately 50% efficacy in preventing infection and 47% efficacy in protection from severe disease . [6–7] . Follow-up studies with RTS , S and other vaccine approaches on immunized volunteers indicated that antibody titers specific for sporozoites correlate with protection [8–10] , suggesting that targeting the extracellular sporozoite stage may be an effective approach . One obvious shortcoming of the RTS , S vaccine is that it is composed of a single protein found on the sporozoite surface , the circumsporozoite protein ( CSP ) . Conversely , powerful sterilizing immunity can be achieved by immunization with radiation-attenuated sporozoites . A recent study using protein arrays to probe the antibody repertoire of individuals immunized with irradiated sporozoites found 77 parasite proteins were associated with sterile protection against sporozoites [11] , suggesting that a multivalent anti-sporozoite vaccine targeting several surface-exposed antigens will likely induce more potent protection . There is a wealth of studies focused upon identifying additional sporozoite antigens that could ultimately be part of a multivalent subunit vaccine in addition to CSP ( reviewed in [12] ) . Sporozoite antigens currently being assessed in Phase I and Phase II clinical trials include apical membrane antigen 1 ( AMA1 ) , liver stage antigen 1 ( LSA1 ) , circumsporozoite-related antigen ( Exp-1 ) , and thrombospondin-related anonymous protein/sporozoite surface protein 2 ( TRAP ) [13] ( see the WHO Rainbow Table [14] ) . Several of these antigens , when formulated as recombinant proteins with different adjuvants , or when expressed from a viral vector , have conferred protection by inducing antibody and T-cell responses . However , the selection of an optimal antigen cocktail would be greatly advanced by having an extensive , experimentally validated list of surface proteins . Several efforts have been made to identify the repertoire of sporozoite proteins [15–17] . Our recent work has resulted in the most comprehensive sporozoite proteome to date , as well as a preliminary study of the sporozoite surface proteome in which 14 putative surface proteins were identified [18] . In this study , we identify novel putative surface proteins of Plasmodium falciparum salivary gland sporozoites , and confirm that key targets remain surface-exposed in response to treatment with molecular mimics of the host environments that the sporozoite encounters . We additionally provide evidence that components of the inner membrane complex ( IMC ) are in fact surface-exposed and accessible to antibodies , thus opening this protein group up for consideration in vaccine target selection . Finally , we provide evidence that two leading vaccine candidates , CSP and TRAP , are glycosylated in their thrombospondin type 1 repeat ( TSR ) domains . Understanding such protein modifications is crucial in the design of effective antibody-based vaccines .
We used chemical labeling and mass spectrometry-based proteomics to identify putatively surface-exposed proteins of P . falciparum salivary gland sporozoites . Sporozoites were obtained by dissection of salivary glands from infected mosquitoes and then purified twice on an Accudenz gradient as previously described [19] . Live parasites were treated with a cell-impermeable , amine-reactive tag [20] that attached a biotin moiety to surface-exposed lysine residues and N-termini . Subsequently , parasites were lysed and biotin-labeled proteins were purified using streptavidin affixed to magnetic beads . The affinity-purified proteins were eluted and fractionated by SDS-PAGE . Peptides resulting from in-gel digestion with trypsin were analyzed by nanoLC-MS/MS employing an LTQ Velos Pro-Orbitrap Elite . Mass spectrometry data were analyzed with the Trans-Proteomic Pipeline [21] . The data presented here only includes proteins identified with a ProteinProphet probability corresponding to a false discovery rate ( FDR ) less than one percent . A total of 349 Plasmodium proteins were identified from six biological replicates of surface-labeled salivary gland sporozoites ( S2 Table ) . A total of 50 proteins were identified from three biological replicates of unlabeled controls , of which 47 were also found in the labeled samples ( S3 Table ) . The six labeled replicates consisted of two sub-groups representing two different laboratories collecting and preparing the samples . A total of 51 proteins were identified from the group 1 samples , of which 49 were among the 347 proteins identified from the group 2 samples . The laboratory preparing samples , the amount of starting material , and endogenous and exogenous contamination ( e . g . residual mosquito protein , streptavidin and BSA from magnetic dynabeads ) had a large effect on protein recovery , and the number of parasite proteins identified varied widely , ranging from 27 to 313 . Despite this variability , all of the proteins identified from the group 1 samples as highly likely to be surface-exposed on sporozoites were also identified as high-quality candidates from the group 2 samples . As was observed from a similar analysis of the ookinete surface proteome [22] , we identified many intracellular components that are unlikely to be present at the surface of the sporozoite , e . g . , histones and ribosomal proteins . While control experiments with unlabeled sporozoites revealed that non-specific binding of high-abundance intracellular proteins was not entirely prevented in our experiments , the abundance of the proteins identified from controls was quite low , in contrast to the relatively high abundance of proteins identified from labeled sporozoites ( S7 Table ) . It is more likely that the majority of these intracellular proteins originated from parasites with compromised plasma membranes . Indeed , when we examined the permeability of purified sporozoites prior to biotinylation using propidium iodide , a cell-impermeant dye that enters dead or dying sporozoites , we found that approximately 10% of sporozoites were permeable to the dye . Thus , it is not altogether surprising that highly-abundant intracellular proteins would be biotinylated and found in our dataset . Despite this confounding factor , many of the proteins with the strongest evidence for being enriched by the method employed were validated as truly surface-exposed on sporozoites , as we show below . In order to identify high-quality surface antigens , we used experimental and theoretical information to devise a prioritization scheme . We first identified those proteins with the strongest evidence of having been truly enriched by the biotinylation strategy . We employed label-free quantification methods to assess enrichment relative to unlabeled controls , using the program SAINT [23] as well as comparing the spectral abundance factor ( SAF ) [24] by t-test . These two quantitative methods agreed well; SAINT identified as significantly-enriched the same 110 proteins identified by t-test , plus an additional 46 ( S2 Table ) . For some high-abundance proteins ( e . g . CSP ) , addition of the biotin tag to a lysine residue could be directly detected in a portion of the identifying mass spectra , providing direct evidence that the protein was labeled ( S6 Table ) . Failure to detect labeled peptides for a protein does not mean that the protein was not labeled [25] , so we did not consider absence of this evidence as evidence against enrichment . It was then necessary to distinguish truly surface-exposed proteins from intracellular proteins that were likely labeled due to compromised plasma membranes in a small number of sporozoites , as we discuss above . Accordingly , we used established tools using a protein’s primary sequence to predict the presence of surface protein characteristics , i . e . , a signal sequence , transmembrane domain or glycosylphosphatidylinositol ( GPI ) anchor addition sequence . We identified 42 proteins that are highly likely to be present on the surface of salivary gland sporozoites ( Table 1 ) . These proteins , the strongest candidates identified by our prioritization scheme , are known or predicted to have surface protein characteristics and were significantly enriched by the biotinylation strategy . The seven proteins with direct evidence for biotinylation were designated Tier 1 candidates and the other 35 were designated Tier 2 . An additional 58 proteins , designated Tier 3 , had predicted surface protein characteristics but were not significantly enriched compared to unlabeled controls ( S2 Table ) . Although proteins in this category had weaker experimental evidence for enrichment , they may still be viable targets for future studies . Indeed , included in this category are several known surface proteins , e . g . , gamete egress and sporozoite traversal protein ( GEST; PF3D7_1449000 ) and sporozoite protein essential for cell traversal ( SPECT1; PF3D7_1342500 ) . Priority tiers 4 and 5 are comprised of 114 proteins that do not have predicted surface characteristics but were significantly enriched , some with spectral evidence for labeling ( Tier 4 ) . The majority of these are likely intracellular proteins that were labeled due to compromised plasma membranes , as discussed above . However , as we will discuss below , some of these proteins which are thought to be primarily intercellular may in fact be surface-exposed on sporozoites . Not surprisingly , CSP ( PF3D7_0304600 ) was among the strongest of the tier 1 candidates , identified in every biological replicate of labeled sporozoites as the most abundant Plasmodium protein and exhibiting strong evidence for incorporation of the biotin label . Similarly strong evidence ( in terms of enrichment , abundance and labeling ) was found for thrombospondin-related sporozoite protein ( TRSP; PF3D7_0104000 ) and the putative BEM46-like protein PBLP ( PF3D7_0818600 ) . Interestingly , TRAP ( PF3D7_1335900 ) , a known micronemal protein that is translocated to the sporozoite surface during motility , was not nearly so well-enriched by our biotinylation method . Both TRAP and CSP were among the 10 most abundant proteins in our previously-published sporozoite proteome and exhibited nearly identical abundance [18] ( S7 Table ) , but TRAP was over 30-fold less abundant than CSP in this study of surface proteins . This data is consistent with the fact that , unlike CSP , the majority of TRAP is intracellular in salivary gland sporozoites [26] ( Fig 1 ) and suggests that some TRAP secretion was likely induced over the course of sample handling . We assigned each of the proteins in Table 1 to functional categories based on the PlasmoDB annotation ( Version 26; [27] ) as well as their published function in sporozoites or erythrocytic stages when known . If a protein’s function was not known , functional class was assigned based on the literature on other organisms . As would be expected , proteins involved in sporozoite motility , migration through tissue , and invasion were well-represented . Unexpectedly , some chaperone and metabolic proteins were also identified . While these may be experimental artifacts , there is increasing evidence that proteins from these classes are found on the surface of both prokaryotic and eukaryotic pathogens where they function as virulence factors [28–29] . The three chaperone proteins in Table 1 , endoplasmin ( PF3D7_1222300 ) , HSP70-2 ( PF3D7_0917900 ) and PDI8 ( PF3D7_0827900 ) , are known to localize to the endoplasmic reticulum . All three contain predicted signal sequences at their N-termini in addition to endoplasmic reticulum retention sequences at their C-termini [30–32] . Interestingly , homologs of HSP70-2 and endoplasmin ( grp78/BiP and gp96 , respectively ) can be found on the surface of certain cell types in vertebrates [33–34] . Further , endoplasmin was identified as a putatively surface-exposed protein in P . berghei ookinetes [22] . Other work with Plasmodium ookinetes demonstrated that they express enolase ( PF3D7_1015900 ) , GAPDH ( PF3D7_1462800 ) and even actin ( PF3D7_1246200 ) [35–37] on their surface , and that these proteins function during migration through the mosquito midgut [35–36] . Further , recent work demonstrated a critical role for the chaperone HSP20 ( PF3D7_0816500 ) in gliding motility [38] . Enolase , GAPDH , actin and HSP20 were among the Tier 4 and 5 proteins in our data set , identified as significantly-enriched compared to unlabeled controls but lacking predicted characteristics of surface proteins ( S2 Table ) . Taken together , these studies provide support for the notion that the metabolic enzymes and chaperones we identified in our study may have important moonlighting functions in the sporozoite . The invasive stages of apicomplexan parasites have specialized apical organelles termed micronemes and rhoptries whose regulated secretion is required for active migration and ultimately for host cell invasion . This has been best demonstrated with Toxoplasma gondii and Plasmodium merozoites where material is not limiting [39–41] . Sporozoites possess apical organelles and express many of the same microneme and rhoptry proteins found in merozoites . In contrast to merozoites , however , sporozoites have a significant migration phase prior to host cell invasion , and though some apical organellar proteins overlap , others are likely to be unique . In order to determine how the sporozoite surface changes as sporozoites migrate through different environments , such as the skin and hepatic sinusoids , we performed a proteomic analysis of surface-exposed proteins in sporozoites treated with compounds previously reported to be associated with sporozoite migration and invasion . Incubation with bovine serum albumin ( BSA ) mimics arrival in the mammalian host and induces gliding motility [42] . Incubation with heparin induces proteolytic cleavage of the major surface protein , CSP , and mimics arrival at the liver , initiating a switch from migration to cell invasion [43–44] . We analyzed three BSA-treated replicates ( S4 Table ) and three heparin-treated replicates ( S5 Table ) and applied the same criteria for labeling and enrichment as were applied to the untreated dataset ( Table 2 ) . There was large overlap in the proteins identified from untreated and treated sporozoites , though a few proteins were only identified or enriched from treated sporozoites , notably the 6-Cys protein P12p . Importantly , the highest-confidence proteins identified from untreated sporozoites ( i . e . , those identified in nearly every replicate and significantly enriched compared to controls ) were also identified as likely surface proteins in the treated sporozoites . Interestingly , there was more spectral evidence for labeling in the treated sporozoites , especially from BSA treatment ( S6 Table ) . These data suggest the possibility that certain sporozoite surface proteins were more accessible to label upon exposure to chemicals that mimic environments encountered in the mammalian host . In order to provide additional evidence for our list of proteins that are putatively surface-exposed on salivary gland sporozoites , we tagged two whose localization has not been previously investigated . We used the rodent malaria parasite Plasmodium yoelii for these experiments as this species is more amenable to genetic modification than is P . falciparum . Importantly , proteins thus far characterized in both P . falciparum and rodent malaria sporozoites have not differed in their subcellular localization ( apiloc . biochem . unimelb . edu . au/apiloc/apiloc; [45] ) . We tagged the endogenous gene for a putative sugar transporter , PY17X_0823700 ( ortholog of Pf3D7_0919500 ) , and a 6-Cys protein , p38 , PY17X_1108700 ( ortholog of Pf3D7_0508000 ) with a triple HA tag on the C-terminus . Transgenic parasites were produced by single-crossover recombination for p38 or double-crossover recombination for the sugar transporter , and their correct integration was confirmed by genotyping PCR ( S1 Fig and S8 Table ) . Transgenic parasites were fed to An . stephensi mosquitoes and salivary gland sporozoites were isolated and subjected to an indirect immunofluorescence assay ( IFA ) . The staining and localization of the 6-Cys protein p38 was internal to CSP and matched that of TRAP , a known micronemal protein that is secreted during motility and translocated to the sporozoite surface ( Fig 1 ) . In contrast , the staining and localization of the sugar transporter co-localized well with CSP , suggesting surface localization ( Fig 2B ) . We also investigated localization of the sugar transporter during liver stage infection , co-staining with antibodies to the parasitophorous vacuole membrane marker UIS4 ( Fig 2C ) . We observed the localization of the sugar transporter on the parasite plasma membrane located just interior to the PVM . This is most evident at 8 and 16 h post-infection . Further work is needed to determine the function ( s ) of the sugar transporter and other transporters in our dataset . Interestingly , expression of the gene for this sugar transporter is significantly higher in sporozoites than in other parasite stages [46] . It is therefore possible that this transporter is required for importing nutrients while the parasite is in mosquito salivary glands waiting to be inoculated into the next host , a phase that can last days to weeks , or for importing nutrients after their inoculation into the mammalian host , a time when energy resources are needed to fuel migration to the liver . Taken together , the localization data of p38 and the sugar transporter lends credence to the methods and prioritization scheme we employ here for identifying surface-exposed proteins . Among the significantly-enriched proteins identified here were several components of the glideosome that are known to be located between the plasma membrane and the inner membrane complex ( IMC ) , a double-membrane structure consisting of flattened cisternae located beneath the plasma membrane that plays a critical role in motility as well as interacting with the microtubule cytoskeleton and maintaining sporozoite structure [47–48] . Notably , the motor protein myosin A ( MyoA , PF3D7_1342600 ) and its partner actin ( ACT1 , PF3D7_1246200 ) were identified in nearly every biological replicate of labeled sporozoites and exhibited spectral evidence for labeling . These proteins were not anticipated to be on the sporozoite surface . However , they were also identified in the only other surface proteome of a malaria parasite , the ookinete , which also exhibits gliding motility [22 , 49] . To determine if this finding resulted from experimental artifact , or if in fact IMC proteins were truly exposed and more accessible to the external environment than previously considered , we performed IFAs on P . falciparum sporozoites that were spun onto coverslips and allowed to glide for 20 min , then paraformaldehyde-fixed . The sporozoites were then either permeabilized or not prior to staining with antibodies specific for the IMC proteins MTIP [50] and GAP45 [51] ( Fig 3 ) . Surprisingly , between 75 and 85% of the population of non-permeabilized sporozoites was stained by these antibodies , showing a restricted , banded pattern that was observed either at the anterior or posterior ends or in a patch in the middle section of the sporozoite . In contrast , permeabilized sporozoites were stained circumferentially in a pattern typical for IMC proteins [50] ( Fig 3 ) . We also performed MTIP and GAP45 staining of live P . falciparum sporozoites that had been allowed to glide for 20 min and then moved to the cold room and stained with the MTIP and GAP45 antibodies prior to fixation . By this methodology , a smaller proportion of total sporozoites stained with the antibodies , however , 38% and 13% of sporozoites reproducibly stained with MTIP or GAP45 antibodies , respectively ( S2 Fig ) . To determine whether paraformaldehyde fixation led to permeabilization of sporozoites , we performed propidium iodide staining on live and fixed sporozoites . Only 5% of either live or fixed sporozoites incorporated the dye , suggesting that our fixation protocol did not significantly permeabilize the sporozoite . Taken together , these data indicate that either portions of the sporozoite plasma membrane are sufficiently permeable to allow antibody access to the IMC , or that some components of the motility machinery change localization during motility such that they become surface-exposed and accessible to the biotinylation reagent as well as to antibodies [52] . These data suggest that components of the IMC could be considered in the selection of antigen candidates . Our mass spectrometric analysis of Plasmodium surface proteins provides , for the first time , direct evidence of glycosylation on CSP and TRAP in salivary gland sporozoites . Both proteins contain a thrombospondin type 1 repeat ( TSR ) , a cell-adhesion domain found in many proteins that functions in cell-cell interactions and cell guidance [53] . TSRs in other organisms have been shown to be O-fucosylated and C-mannosylated [54–55] . The motif CX2-3 ( S/T ) CXXG in TSR domains can be modified with an O-linked fucose at the Ser/Thr [55] , and this fucose can be further modified with glucose to produce a β1 , 3-linked disaccharide [56–57] . Additionally , the WXXW and WXXC motifs of TSR domains can be modified with a C-linked mannose at Trp [58–59] . These potential glycosylation motifs are present in the TSR domains of both CSP and TRAP in all Plasmodium species . X-ray crystallography and mass spectrometry studies on proteins expressed in mammalian cells showed that PfCSP and PfTRAP were O-fucosylated and that the TRAP homologue MIC2 in T . gondii was C-mannosylated in the predicted fashion in recombinant systems [60–62] . We now present direct evidence that CSP is modified by O-fucosylation and that TRAP is modified by both O-fucosylation and C-mannosylation in P . falciparum sporozoites ( Fig 4 ) . While it is possible to identify peptide modifications using peptide spectrum matching search engines , this approach could not be used to search our data for O-fucosylation . O-linked glycans are highly labile in the gas phase and virtually all of the glycan is lost at the collision energies employed for peptide fragmentation , resulting in unmodified peptide fragments [63–64] . C-linked hexose is more stable , but collisional activation may lead to cross-ring cleavage of the glycan , resulting in a mass defect in the fragment spectra [58] . In order to determine if CSP and TRAP are glycosylated in salivary gland sporozoites , we manually investigated our nanoLC-MS/MS data for the presence of precursor peptide ions with masses corresponding to modification of the peptides in question . Because precursor ion spectra were collected in an Orbitrap , it was possible to match predicted and observed precursor ion masses with less than 2 ppm mass error . The identity of these peptide ions were then confirmed by manual annotation of the low-resolution fragmentation mass spectra collected in the ion trap . The TRAP tryptic peptide TASCGVWDEWSPCSVTCGK , which contains both the O-fucosylation motif as well as two of the C-mannosylation motifs described above , was only identified in its glycosylated form . The TRAP peptide was confidently identified from two ion species with distinct chromatographic retention times , one with a mass matching the addition of one deoxyhexose and one hexose , and the other with a mass corresponding to addition of one deoxyhexose and two hexoses ( Fig 5 ) . Of note , fucose is a deoxyhexose and mannose and glucose are hexoses . The dominant species in the fragment spectra for both of the putatively O-fucosylated peptides matched the mass of the peptide with addition of a single hexose , suggesting the presence of a single C-mannose which was retained at the collision energy employed while the O-linked fucose monosaccharide or glucosylfucose disaccharide was lost . The second most abundant fragment ion in the fragment spectra matched the mass of the hexosylated peptide with a loss of 120 Da , consistent with cross-ring cleavage of C-mannose . Several abundant fragment ions confidently placed the C-mannose at the C-terminal tryptophan of the motif WDEWSPC . No precursor ions were observed to suggest that the peptide was present in unmodified form or O-fucosylated without the presence of C-mannose; in fact , the unmodified peptide was never identified in any of the data sets presented here or in our previous analysis of the sporozoite proteome [18] . These data , combined with chromatographic elution profiles , suggest that TRAP in salivary gland sporozoites is entirely or almost entirely modified with both C-mannose and O-fucose . We observed modification with either a single fucose or with a glucosylfucose disaccharide . Based on chromatographic peak area , it appears that the disaccharide is the more prevalent modification . The CSP tryptic peptide IQNSLSTEWSPCSVTCGNGIQVR contains an O-fucosylation motif and a WXXC C-mannosylation motif , but unlike TRAP , the CSP peptide lacks the preceding WXXW motif . We identified this peptide both with and without modification ( Fig 6 ) . The modified peptide was confidently identified from precursor ions with masses corresponding to addition of a single deoxyhexose or one deoxyhexose and one hexose . Chromatographic peaks showed evidence that the unmodified peptide was also present ( Fig 6 ) . Unlike for TRAP , the fragment spectra of the putatively modified CSP peptides contained only unmodified fragment ions and exhibited no evidence of C-mannosylation . Furthermore , highly abundant fragment ions matched the mass of the intact , unmodified peptide precursor . These data suggest that in salivary gland sporozoites , most but not all CSP is modified with either O-fucose or an O-glucosylfucose disaccharide . Based on chromatographic peak area , it appears that the monosaccharide is the more prevalent modification .
In this study we have used chemical labeling and mass spectrometry to identify proteins on the surface of sporozoites , constitutively and after treatment with compounds that initiate gliding motility and invasion . We also present verification experiments on two of these identified proteins and provide evidence that some of the proteins of the sporozoite’s glideosome may be bona fide surface proteins , at least during a portion of the sporozoite’s life . Although the remainder of these proteins will require validation of their surface localization using orthogonal methodologies , this represents the largest dataset to date focused on the sporozoite surface and should fuel future research in the areas of sporozoite biology and pre-erythrocytic stage vaccine development . Importantly , our prioritized list of surface-expose antigens identifies candidates that should aid current vaccine efforts . The list of putative sporozoite surface proteins we present here greatly expands on previous work from our group , which was preliminary and relied on a single biological replicate of salivary gland sporozoites [18] . In order to directly compare that dataset with our current data set , we reanalyzed the raw mass spectral data from that work using the databases and analysis parameters employed here ( S9 Table ) . Excepting a putative GTPase , all Plasmodium proteins identified from our previous dataset were also identified here , the majority from multiple replicates and with more peptide spectrum matches than previously . Of these 27 proteins , 21 were significantly enriched in labeled sporozoites compared to unlabeled controls in this work , and 11 were among the high-priority proteins listed in Table 1 , including CSP , TRAP , AMA1 , TRSP and the sugar transporter . Importantly , this new work also identifies many putative surface proteins not identified in our preliminary work , and of these , we have validated p38 in this study . The large overlap between the treatment sets ( BSA and heparin ) and the untreated protein dataset reflect our lack of knowledge about how sporozoites change their activation state , and related to this , our inability to synchronize sporozoites in any one state . Thus , the identification of known microneme proteins such as TRAP and TRSP in the untreated dataset is not surprising , as sporozoites isolated from salivary glands and put through further purification procedures are likely encountering signals that they have left the mosquito . On this background , the addition of BSA and heparin , which have observable effects on freshly dissected sporozoites , did not reveal as many differences as we expected to find in the complement of enriched proteins . However , these experiments with treated sporozoites did identify additional targets that were not enriched from the untreated samples , and several high-priority targets from the untreated samples were enriched as well or even better in conditions mimicking those found in the mammalian host . As our knowledge of sporozoite biology increases , these types of studies should yield more informative data on how the sporozoite’s surface changes as it migrates from mosquito midgut to mammalian liver . Our lists of putative surface proteins include several that we did not expect to observe , such as chaperones and proteins that function in cellular metabolism . As stated earlier , a possible source of enriched cytosolic proteins is the small number of dead or distressed sporozoites that cannot be separated from the pool of sporozoites used for these analyses . However , some of these unexpected proteins may have moonlighting functions and actually spend some time on the sporozoite surface . Indeed , an increasing number of studies , with both bacterial and eukaryotic pathogens , suggest that this may be quite common [65–67] . Because the primary objective of this work was to identify proteins that are localized to the parasite surface , it was necessary to devise a means of prioritizing identified proteins for their likelihood of being truly surface-exposed . This was accomplished by combining predicted characteristics of membrane proteins with the experimental evidence for protein enrichment and incorporation of the biotin label . The ranking system we present here provides a scheme for rationally prioritizing the putative sporozoite surface proteins identified for future validation and vaccine development . Indeed , the top tier includes CSP and TRAP , components of vaccine candidates in Phase III and II clinical trials , respectively [7 , 68] , and we believe the other high-priority proteins are quality candidates for combining with CSP and TRAP in a multivalent subunit vaccine . Until recently , it was not clear whether Plasmodium was capable of N- or O- glycosylation of proteins [69] . This lack of knowledge stemmed from the paucity of material available for study and contamination of that material with host proteins due to the obligate intracellular lifestyle of the parasite . Bioinformatic analyses demonstrate that the Plasmodium genome encodes only a few glycosyltransferases and thus , can synthesize only the truncated N-glycans , GlcNAc or ( GlcNAc ) 2 [70] . Using lectins specific for these modifications , N-glycosylated proteins were found on intraerythrocytic stage parasites , though the identities of the proteins and their modifications has not been investigated [71] . To date , O-glycosylation has not been unequivocally demonstrated in Plasmodium , though the presence of specific sugar nucleotides and a protein O-fucosyltransferase in the genome indicate that O-glycosylation is possible [72] . Here we describe , for the first time , examples of O-fucosylated and C-mannosylated proteins in Plasmodium . These data are relevant to the malaria vaccine effort as the carbohydrate modifications we describe were found in CSP and TRAP , two of the leading pre-erythrocytic stage malaria vaccine candidates . Polysaccharides have long been known to be important antigenic determinants in immune responses to pathogens and must be considered when selecting targets for vaccine development . Their importance has been emphasized recently by broadly neutralizing anti-HIV gp120 antibodies that recognize combinatorial oligosaccharide/protein epitopes [73] . The O-fucosylation and C-mannosylation sites in both CSP and TRAP are highly conserved , and the same fucosylation and glucosylation of CSP and TRAP seen in sporozoites was also seen when these proteins were expressed in mammalian cells [60–61] . In contrast , mannosylation showed marked variation . TRAP has an orthologue in T . gondii , MIC2 . The same WXXWXXC mannosylation motif is present in each , yet crystal structures showed that human cells mannosylated the first Trp in MIC2 [62] and neither Trp in TRAP [60 , 62] , while mass spectrometry here showed that the second Trp in TRAP was mannosylated in sporozoites . These differences emphasize the importance of chemical characterization of parasite post-translational modifications in vaccine design . The enzymes necessary for these modifications are expressed in Plasmodium sporozoites . TSR domains can be modified with an O-linked fucose by the O-fucosyltransferase POFUT2 [55] , and this fucose can be further modified by the enzyme β-1 , 3-glucosyltransferase to produce a β1 , 3-linked disaccharide [56–57] . POFUT2 is encoded by a highly conserved Plasmodium gene ( PF3D7_0909200 ) . There is no annotated P . falciparum β-1 , 3-glucosyltransferase; however , BLAST analysis with the human enzyme revealed a protein with 31% identity and 50% similarity . This protein , parasite-infected erythrocyte surface protein or PIESP1 [74] ( PF3D7_0310400 ) , is predicted to have glycosyltransferase activity due to the presence of a domain with similarity to Fringe , a beta1 , 3-N-acetylglucosaminyltransferase that glycosylates O-linked fucose in epidermal growth factor-like domains [57] . Both POFUT2 and PIESP1 are expressed in P . falciparum salivary gland sporozoites [18] . There is no annotated C-mannosyltrasferase in Plasmodium , but BLAST analysis of a recently characterized C . elegans protein Dpy-19 , which adds C-mannose to TSR domains [75] , revealed an uncharacterized P . falciparum membrane protein ( PF3D7_0806200 ) with 26% identity and 47% similarity , including a Dpy-19-like domain annotated by InterPro [76] . This potential glycosyltransferase is also expressed in P . falciparum salivary gland sporozoites [18] . Structural studies suggest that fucosylation and mannosylation in apicomplexans will make important contributions to antigenic determinants . Crystal structures show that these glycans are well-defined in electron density , and thus have structurally constrained orientations [60 , 62] . For example , the glucosylfucose disaccharide is stabilized by its interactions with the disulfide bond , which it shields from solvent [60] . The models of the TSRs of CSP and TRAP based upon crystal structures ( Fig 4 ) suggest that the carbohydrates project prominently above the protein surface , yet do not project nearly as far as , and are unlikely to be nearly as flexible as , N-linked glycans . Fucosylated and mannosylated amino acids may be viewed as surrogate amino acids [77] , so the combinatorial protein and carbohydrate determinants create a well-defined recognition surface . For example , Delta-like ligands are modified by an essentially identical glucosylfucose disaccharide , and the carbohydrate is central in the ligand-binding site for Notch [77] . Vertebrate TSR domains contain identical glycans as those we have found in Plasmodium; however , the surrounding amino acid residues in parasite TSR domains create unique combinatorial epitopes . Notably , previous studies of naturally-acquired T cell immunity to P . falciparum CSP have mapped epitopes , including those that correlate with protection , to peptides N-terminal and C-terminal to the fucosylated threonine residue [78–79] . In conclusion , we have employed mass spectrometry-based proteomics to identify putatively surface-exposed proteins of P . falciparum salivary gland sporozoites . Because the goal of this work was to identify novel antigens for vaccine development efforts , we have combined experimental and theoretical evidence to assign the identified proteins to priority tiers in order to facilitate selection of high-impact targets for follow-up studies . Our investigation included sporozoites treated with molecular mimics of the host environments that sporozoites encounter , providing support that targets of interest are accessible to antibodies throughout the sporozoite’s journey to the liver . Among the more compelling results was the discovery that components of the inner membrane complex appear to be surface-exposed in sporozoites , meaning that this class of proteins may be considered when selecting antigens for vaccine design . Finally , the use of mass spectrometry has enabled us to provide the first direct evidence that the sporozoite surface proteins CSP and TRAP are post-translationally modified with sugars . This information is of critical importance to vaccine design as glycosylation significantly alters recognition epitopes . Taken together , the results that we present here provide significant new information that will aid the development of effective antibody-based therapeutics .
All procedures involving vertebrate animals were conducted in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health with approved OLAW animal welfare assurance ( A3640-01 ) and Institutional Animal Care and Use Committee ( IACUC ) protocols ( SK-02 ) . Standard procedures were used for the culture and transmission of P . falciparum ( NF54 strain ) parasites . Briefly , asexual cultures were maintained in vitro through infections of washed , type O+ erythrocytes grown in RPMI 1640 supplemented with 50 μM hypoxanthine , 25 mM HEPES , 2 mM L-glutamine , and 10% v/v A+ or O+ human serum in a gas mixture consisting of 5% CO2 , 5% O2 , and 90% N2 . Gametocyte cultures were initiated at 5% hematocrit and 1% parasitemia and were maintained for up to 17 days with daily media changes to promote sexual development . Adult female An . stephensi mosquitoes ( 3 to 7 days post-emergence ) were collected into mesh-topped , wax-lined pots and were allowed to feed through a membrane feeding apparatus for up to 20 min upon gametocyte cultures supplemented to 40% hematocrit containing fresh A+ or O+ human serum and O+ erythrocytes . Infected mosquitoes were maintained for 14 to 19 days at 27°C and 75% humidity and were provided with an 8% or 20% w/v dextrose solution and 0 . 05% w/v p-aminobenzoic acid ( PABA ) in water . Salivary glands from P . falciparum-infected mosquitoes were harvested by microdissection and homogenized by grinding . Sporozoite preparations were cleaned with two rounds of purification on an Accudenz discontinuous gradient as previously described [19] , washed once with 1 × PBS and resuspended in 630 μL of cold 1 × PBS ( pH 8 . 0 at room temperature ( RT ) ) . Total sporozoite numbers were counted on a hemocytometer . Between 4 × 106 and 2 × 107 purified sporozoites were used for each biological replicate . For each experiment , sporozoite viability was assessed by incubating 30 μL of this preparation with 3 μL of 100 μg/mL propidium iodide ( PI ) for 10 min at RT , followed by a wash in 970 μL 1 × DMEM; sporozoites were pelleted in a microcentrifuge ( 16 , 000 × g 4 min at 4°C ) and then resuspended in 2 μL 1 × DMEM and PI-stained sporozoites were counted by fluorescence microscopy . Typically , about 10% of sporozoites took up the dye . The remaining 600 μL of sporozoites were used for surface biotinylation . Sporozoites were either left untreated or treated with bovine serum albumin ( 4% w/v final concentration , Sigma Cat# A7906 ) at 37°C for 30 min or heparin ( 0 . 5 μg/mL final concentration , Sigma Cat# H5515 ) at 37°C for 10 min . Following treatment , sporozoites were pelleted and washed three times in 1 × PBS pH 7 . 4 . The following steps were performed at 4°C . Surface-exposed proteins on sporozoites were biotinylated by incubating in 1 . 4 mM EZ-Link Sulfo-NHS-SS-Biotin ( Thermo Scientific , Cat# 21331 ) final concentration for 1 h . The sulfonated-NHS ester is water soluble and cannot penetrate intact cell membranes [20] . The biotinylation reaction was quenched by adding glycine ( 100 mM final concentration ) for 5 min , and sporozoites were then washed twice with 0 . 5 mL of 100 mM glycine in 1 × PBS pH 8 . 0 . Sporozoites were then resuspended in 100 μL of lysis buffer ( 0 . 4% w/v sodium dodecyl sulfate ( SDS ) , 4 M urea , 20 mM Tris-HCl pH 8 . 0 ) containing 1 × protease inhibitors ( Roche cOmplete protease inhibitor , Cat# 04693159001 ) for 30 min on a rotating mixer . Lysate was spun at 16 , 000 × g for 25 min and the supernatant was transferred to a new 2-mL tube and diluted tenfold with 1 × PBS ( pH 7 . 4 at RT ) . One hundred μL of Dynabeads MyOne Streptavidin T1 ( Life Technologies , Cat# 65601 ) were washed three times in 1 × PBS pH 7 . 4 and incubated with the sporozoite lysate and mixed by end-over-end rotation overnight at 4 ˚C . The following wash steps were performed at RT: Dynabeads were washed sequentially using one of the following three protocols ( see S1 Table for sample / wash protocol assignments ) . Protocol 1: 1 ) 0 . 1% w/v SDS in distilled water , 400 mM urea , 150 mM NaCl , 50 mM Tris-HCl pH 8 . 0; 2 ) 0 . 1% w/v SDS in distilled water , 500 mM NaCl , 50 mM Tris-HCl pH 8 . 0; 3 ) 0 . 1% w/v SDS in distilled water , 50 mM Tris-HCl pH 8 . 0 . Protocol 2: 1 ) 2% w/v SDS in distilled water; 2 ) 1% v/v Triton X-100 , 500 mM NaCl , 1 mM EDTA and 50 mM Hepes pH 7 . 5; 3 ) 50 mM Tris pH 7 . 4 and 50 mM NaCl . Protocol 3: 1 ) 2% w/v SDS in distilled water; 2 ) 6 M urea , 2% w/v SDS , 1 M NaCl , 50 mM Tris pH 7 . 4; 3 ) 0 . 2% w/v SDS , 4 M urea , 200 mM NaCl , 1 mM EDTA and 50 mM Tris pH 7 . 4; 4 ) 0 . 2% w/v SDS , 50 mM NaCl and 50 mM Tris pH 7 . 4 . Bound proteins were eluted with 40 μL 2 × sample buffer ( 50 mM Tris pH 6 . 8 , 5% w/v SDS , 5% v/v glycerol , 0 . 16% w/v bromophenol blue ) to which DTT was added ( final concentration of 50 mM ) just prior to heating the tube to 70 ˚C for 7 min . The eluted fraction was transferred to a new tube , snap frozen in liquid nitrogen and stored at -80 ˚C until separated by SDS-PAGE . P . yoelii parasites , 17XNL strain , were maintained in six-to-eight week old female Swiss Webster ( SW ) mice from Harlan ( Indianapolis , IN ) . Transgenic parasites for PY17X_0823700 ( putative sugar transporter ) and PY17X_1108700 ( p38 ) were created by double-crossover and single-crossover recombination , respectively , to append a C-terminal 3 × HA tag to each protein by electroporating linearized derivatives of the pDEF suite of vectors with the Amaxa Nucleofector 2b system as previously described [80] . Confirmation of the recombination events was achieved by genotyping PCR across both targeting sequence regions as previously described [81] . Transmission of P . yoelii parasites to An . stephensi mosquitoes was accomplished by direct feeding on anesthetized , infected mice . Infected mosquitoes were maintained at 24 ˚C and 70% humidity for 14 days . Detection of the expression and localization of proteins of interest by indirect immunofluorescence assay ( IFA ) was performed by fluorescence microscopy as previously described [81–82] . P . yoelii wildtype or epitope-tagged sporozoites and liver stage parasites were collected at the indicated time points and were stained with primary antibodies specific to PyCSP ( mAb Clone 2F6 [83] ) , PyTRAP ( mAb Clone F3B5 ) , PyUIS4 ( rabbit polyclonal antisera [84] ) , HA epitope tag ( rabbit polyclonal antisera , SCBT Cat#Y-11 or mAb Clone 12CA5 , Roche Cat#11583816001 ) . Alexa Fluor 488-labeled secondary antibodies against mouse or rabbit IgG were used to detect the proteins of interest , and then sporozoites were stained with 4’ , 6’-diamidino-2-phenylindole ( DAPI ) to visualize nucleic acid . Slides were mounted with VectaShield antifade reagent ( Vector Laboratory ) and images were acquired at 100× using an Olympus IX70 DeltaVision microscope using the softWoRx software package . P . falciparum salivary gland sporozoites were isolated from An . stephensi mosquitoes 14 to 17 days post blood feeding . Sporozoites were diluted in cold RPMI 1640 containing 1% w/v BSA and spun onto poly-L-lysine coated coverslips at 300 x g for 4 min at 4°C . To allow sporozoites to glide , samples were incubated at 37°C for 20 min . Following this , samples were taken to the 4°C cold room and fixed in 4% v/v paraformaldehyde in PBS for 10 min . Samples were then washed and incubated in DMEM containing 1% w/v BSA and 5% w/v goat serum for 45 min at RT , labeled with rabbit polyclonal anti-PyMTIP antibody 1:400 [50] or rabbit polyclonal anti-PfGAP45 1:200 ( kind gift from Anthony A . Holder , NIMR [85] ) for 45 mins at RT then washed again and labeled with Alexa Fluor 488-conjugated goat anti-rabbit IgG for 45 mins at RT before staining with Hoechst 33342 DNA stain for 5 mins . For permeabilization , 0 . 1% v/v Triton X-100 was included in the blocking and incubation buffers . Live samples were not immediately fixed after transfer to the cold room but instead incubated in DMEM containing 1% w/v BSA and 5% v/v goat serum for 1 h , labeled with anti-PyMTIP antibody 1:400 [50] or anti-PfGAP45 1:200 [85] for 2 h , then washed and fixed in 4% v/v paraformaldehyde in PBS for 2 h . Samples were washed and again incubated in DMEM containing 1% w/v BSA and 5% w/v goat serum for 1 h , and labeled with Alexa Fluor 488-conjugated goat anti-rabbit IgG and Hoechst 33342 DNA stain . Images were acquired on a Zeiss AxioObserver with LSM700 confocal module with the Zeiss ZEN software package or a Nikon Eclipse E600 fluorescence microscope using Nikon Elements software . The proportion of sporozoites displaying strong patches of staining was determined by manual counting of two hundred parasites . Proteins eluted from magnetic dynabeads were electrophoresed through a 4–20% w/v SDS-polyacrylamide gel at 180 V at 22 ˚C . Gels were post-stained with Imperial Stain ( Thermo Scientific ) and destained in Milli-Q Water ( Millipore , USA ) . Each gel lane was cut into four to nine fractions ( S1 Table ) for in-gel tryptic digestion of proteins . Each gel fraction was cut into small pieces ( ~1 mm ) and placed into wells in a 96-well PCR plate . Unless indicated otherwise , all wash and incubation steps were performed at 37°C while agitating the plate at 700 rpm . Gel pieces were destained with three washes of 50 μL of 50 mM ammonium bicarbonate ( ABC ) in 50% v/v acetonitrile ( ACN ) for 10 min . The gel pieces were dehydrated with 2 washes of 50 μL of ACN for five min and allowed to dry . The gel pieces were rehydrated with 50 μL of 10 mM dithiothreitol in 100 mM ABC and incubated for 30 min . The unabsorbed reducing solution was discarded and 50 μL of 50 mM iodoacetamide in 100 mM ABC was added and incubated for 20 min . This alkylating solution was discarded and the gel pieces were then washed three times with 50 mM ABC in 50% acetonitrile and dehydrated with three washes of ACN as above . The dried gel pieces were rehydrated with 50 μL of 6 . 25 ng/μL sequencing grade trypsin ( Promega ) in 50 mM ABC . Additional 50 mM ABC was added to each well as needed to ensure that the gel pieces were submerged in solution . The plate was incubated overnight without agitation in an oven at 37°C . Tryptic peptides were collected with three extractions: 50 μL of 2% v/v ACN/1% v/v formic acid for 30 min , 50 μL of ACN for 30 min , and 50 μL of 2% v/v ACN/1% v/v formic acid for 30 min . The extracted peptide solutions were combined and evaporated to dryness in a rotary vacuum and reconstituted in 20 μL of 2% v/v ACN/0 . 2% v/v trifluoroacetic acid . Nanoflow liquid chromatography ( nanoLC ) was performed using an Agilent 1100 nano pump with electronically controlled split flow . Peptides were separated on a column with an integrated fritted tip ( 360 μm outer diameter ( O . D . ) , 75 μm inner diameter ( I . D . ) , 15 μm I . D . tip; New Objective ) packed in-house with a 20 cm bed of C18 ( Dr . Maisch ReproSil-Pur C18-AQ , 120 Å , 3 μm; Ammerbuch-Entringen , Germany ) . Prior to each run , sample was loaded onto a trap column consisting of a fritted capillary ( 360 μm O . D . , 150 μm I . D . ) packed with a 1 cm bed of the same stationary phase and washed with loading buffer ( 2% v/v ACN/0 . 2% v/v trifluoroacetic acid in water ) . The trap was then placed in-line with the separation column for the separation gradient . The LC mobile phases consisted of 0 . 1% v/v formic acid in water ( solvent A ) and 0 . 1% v/v formic acid in ACN ( solvent B ) . The separation gradient was 5% B to 35% B over 60 min or 90 min , followed by a 10 min ramp to 80% B , a 10 min wash at 80% B , and a re-equilibration step at 5% B . The gradient flow rate was 500 nL/min . Tandem mass spectrometry ( MS/MS ) was performed with an LTQ Velos Pro-Orbitrap Elite ( Thermo Fisher Scientific ) . Precursor MS1 scans over the range of 400–1600 m/z were collected in the Orbitrap with a nominal resolving power of 60 , 000 at 400 m/z . Data-dependent acquisition was employed to select the top 20 doubly- or triply-charged precursors for collision-induced dissociation ( CID ) and analysis in the ion-trap . Dynamic exclusion was employed with an exclusion list of up to 500 precursors . Precursors were excluded with a +/- 5 ppm tolerance for 30 sec after a single observation or after the precursor level was observed a single time at an intensity below twice the signal-to-noise , whichever came first . Precursors were isolated with a 2 . 0 m/z window and fragmented by CID for 10 ms at a normalized collision energy of 35% . Two nanoLC-MS technical replicates were performed for each fraction , with roughly half the available sample injected for each replicate . The mass spectrometry data generated for this manuscript , along with the search parameters , analysis parameters and protein databases can be downloaded from PeptideAtlas ( www . peptideatlas . org ) using the identifier PASS00729 . Raw mass spectrometer output files were converted to . mZML format using MSConvert version 3 . 0 . 5533 [86] and searched with X ! Tandem [87] version 2013 . 06 . 15 . 1 JACKHAMMER and Comet version 2013 . 2 rev . 2 [88] . Spectra were searched against a protein sequence database comprised of the following: P . falciparum 3D7 ( version 10 . 0 , www . plasmodb . org ) ; A . gambiae ( version 3 . 7 , www . vectorbase . org ) ; a modified version of the common Repository of Adventitious Proteins ( version 2012 . 01 . 01 , www . thegpm . org/cRAP , ) with the Sigma Universal Standard Proteins removed; the peptides human angiotensin II and [Glu-1] fibrinopeptide B ( common MS calibration peptides ) ; the antibody against circumsporozoite protein [89]; and streptavidin ( Uniprot ID P22629 ) . Gene annotations of identified P . falciparum proteins were updated with PlasmoDB PF3D7 version 26 . Decoys with randomized sequence were generated using Mimic ( www . kaell . org ) . A wide precursor mass tolerance of 20 ppm was used to improve the performance of the accurate mass binning tool available in PeptideProphet [90] . Fragment ions were searched with a monoisotopic mass error of 0 . 4 Da in X ! Tandem . Fragment ion bins in Comet were set to a tolerance of 1 . 0005 m/z and a monoisotopic mass offset of 0 . 4 m/z . Semi-tryptic peptides and up to 2 missed cleavages were allowed . The search parameters included a static modification of +57 . 021464 Da at C for carbamidomethylation by iodoacetamide and potential modifications of +15 . 994915 Da for oxidation at M and +145 . 019749 Da at K for labeling . ( The biotin label contained a disulfide bond which was cleaved and alkylated in the course of sample preparation . ) Additionally , X ! Tandem automatically searched for potential modifications of -17 . 026549 Da for deamidation at N-terminal Q and -18 . 010565 Da for loss of water at N-terminal E from formation of pyro-Glu , as well as -17 . 026549 Da at N-terminal carbamidomethylated C for deamidation from formation of S-carbamoylmethylcysteine . MS/MS data were analyzed using the Trans-Proteomic Pipeline [91] version 4 . 7 POLAR VORTEX rev 0 . Peptide spectrum matches ( PSM ) generated by each search engine were analyzed separately with PeptideProphet in order to assess spectral matching quality at the spectrum level , and then the analyses were combined in iProphet with the number of sibling peptides ( NSP ) model disabled [92] . Protein identifications were inferred with ProteinProphet [93] . In the case that multiple proteins were inferred at equal confidence by a set of peptides , the inference was counted as a single identification and all relevant protein IDs were listed . Only proteins with ProteinProphet probabilities corresponding to a model-estimated false discovery rate ( FDR ) less than 1 . 0% were reported . Label-free proteomics methods based on spectral counts ( SpC ) were used to identify proteins that were significantly more abundant in labeled samples compared to unlabeled controls . A total of 15 biological replicates were analyzed: six untreated , three BSA-treated , three heparin-treated , and three unlabeled controls ( S1 Table ) . The six untreated replicates consisted of two sub-groups ( three replicates each ) representing two different labs collecting and preparing the samples ( S1 Table ) . For quantitative measurement of protein enrichment by biotinylation , each group of biological triplicates of labeled samples was compared separately against the three control replicates . The SpC for a given protein in a given biological replicate was taken as the number of PSM used by ProteinProphet to make the protein inference . All SpC values were increased by one in order to give all proteins non-zero SpC values for log-transformation [94] . The spectral abundance factor ( SAF ) for a given protein was calculated as the quotient of the SpC and the protein's length and natural log-transformed to ln ( SAF ) [24] . While it is common practice to normalize log-transformed SpC or SAF values in order to minimize technical variance , these transformations are based on the assumption that the majority of protein abundances remain unchanged between the compared samples [95] . These conditions were not met in the experiments described here because the unlabeled controls were expected ( and observed ) to consist of much lower total protein than the labeled samples . Accordingly , we did not normalize the ln ( SAF ) values . The ln ( SAF ) values for each protein were compared between labeled and unlabeled samples by a two-tailed , two sample homoscedastic t-test in Microsoft Excel . The FDR associated with multiple hypothesis testing was assessed by the Benjamini-Hochberg method , and p-values corresponding to an FDR less than 10% were considered significantly enriched . Enrichment relative to unlabeled controls was also quantitatively assessed by the program SAINT v2 . 5 . 0 [23] , which was designed to quantify enrichment in affinity purification experiments using SpC . The following pertinent parameters were used: lowMode = 0 , minFold = 1 , normalize = 0 . Proteins with SAINT probabilities corresponding to a Bayesian FDR less than 10% as calculated by SAINT were considered significantly enriched . For some proteins , labeling with the biotin tag could be directly observed from the mass spectra . A protein was considered to have spectral evidence for labeling if a component peptide displaying the addition of the biotin tag was identified from at least one high-quality spectrum in the same biological replicate in which the protein was inferred by ProteinProphet . For each set of PSMs generated from the Comet or the X ! Tandem search of the data from a given biological replicate , the prevalence of PSMs matching decoys among the putatively-labeled spectra was determined and used to calculate a FDR . Only labeled PSMs with PeptideProphet probabilities corresponding to a decoy-estimated FDR less than 1% were counted . The primary sequences of enriched proteins were analyzed using established tools for predicting the presence of surface protein characteristics . Transmembrane ( TM ) domains and signal peptide predictions were taken from PlasmoDB . org ( P . falciparum 3D7 version 13 ) combined with Signal IP version 4 . 0 ( http://www . cbs . dtu . dk/services/SignalP/ ) and TMHMM version 2 . 0 ( http://www . cbs . dtu . dk/services/ TMHMM-2 . 0 ) . Glycosylphosphatidylinositol ( GPI ) anchors were predicted using GPI-SOM ( http://gpi . unibe . ch/ ) . For high priority targets , these results were also manually validated based on published literature . All Plasmodium proteins identified in each sporozoite treatment condition ( untreated , BSA-treated , or heparin-treated ) , were assigned to tiers in order to prioritize them for follow-up studies . The top tiers consisted of proteins with predicted surface protein characteristics ( i . e . signal peptide , TM domain , and/or GPI anchor ) and were ranked as follows , in order of decreasing priority: 1 ) proteins significantly enriched compared to unlabeled controls ( as assessed by SAINT ) that exhibited spectral evidence for labeling; 2 ) proteins that were significantly enriched but did not have evidence for labeling or were labeled but not enriched; 3 ) proteins that were neither enriched nor labeled . Tiers 4 , 5 and 6 had the same criteria as tiers 1 , 2 and 3 , respectively , but were assigned to proteins without predicted surface protein characteristics . Tiers 1 and 2 were considered most likely to be truly surface-exposed in salivary gland sporozoites . | Malaria remains one of the most important infectious diseases in the world , responsible for an estimated 500 million new cases and 600 , 000 deaths annually . The etiologic agents of the disease are protozoan parasites of the genus Plasmodium that have a complex cycle between mosquito and mammalian hosts . Though all clinical symptoms are attributable to the blood stages , it is only by attacking the transmission stages that we can make an impact on the economic and health burdens of malaria . Infection is initiated when mosquitoes inoculate sporozoites into the skin as they probe for blood . Sporozoites must locate blood vessels and enter the circulation to reach the liver where they invade and grow in hepatocytes . The inoculum is low and these early stages of infection are asymptomatic . Though the small amounts of material available for study has made large scale -omics studies difficult , killing the parasite at this stage would prevent infection and block downstream transmission to mosquitoes , thus preventing spread of disease . Here we use state-of-the-art biochemistry tools to identify the proteins on the sporozoite surface and find that two of the most studied proteins , CSP and TRAP , have post-translational modifications . These studies will aid investigations into the novel biology of sporozoites and importantly , significantly expand the pool of potential vaccine candidates . | [
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... | 2016 | Interrogating the Plasmodium Sporozoite Surface: Identification of Surface-Exposed Proteins and Demonstration of Glycosylation on CSP and TRAP by Mass Spectrometry-Based Proteomics |
Previous analysis of Epstein-Barr virus ( EBV ) persistent infection has involved biological and immunological studies to identify and quantify infected cell populations and the immune response to them . This led to a biological model whereby EBV infects and activates naive B-cells , which then transit through the germinal center to become resting memory B-cells where the virus resides quiescently . Occasionally the virus reactivates from these memory cells to produce infectious virions . Some of this virus infects new naive B-cells , completing a cycle of infection . What has been lacking is an understanding of the dynamic interactions between these components and how their regulation by the immune response produces the observed pattern of viral persistence . We have recently provided a mathematical analysis of a pathogen which , like EBV , has a cycle of infected stages . In this paper we have developed biologically credible values for all of the parameters governing this model and show that with these values , it successfully recapitulates persistent EBV infection with remarkable accuracy . This includes correctly predicting the observed patterns of cytotoxic T-cell regulation ( which and by how much each infected population is regulated by the immune response ) and the size of the infected germinal center and memory populations . Furthermore , we find that viral quiescence in the memory compartment dictates the pattern of regulation but is not required for persistence; it is the cycle of infection that explains persistence and provides the stability that allows EBV to persist at extremely low levels . This shifts the focus away from a single infected stage , the memory B-cell , to the whole cycle of infection . We conclude that the mathematical description of the biological model of EBV persistence provides a sound basis for quantitative analysis of viral persistence and provides testable predictions about the nature of EBV-associated diseases and how to curb or prevent them .
Epstein-Barr virus ( EBV ) is a herpesvirus that benignly infects more than 95% of the world's adult human population [1] , but is occasionally associated with certain tumors including 3 forms of lymphoma [2] . One prominent feature of EBV is that it persists as a lifelong low-level infection in the memory B-cells of healthy carriers [3] , [4] . Our laboratory has measured the level of infection in the peripheral blood memory B-cells of healthy carriers over the course of decades ( [5] , [6] and unpublished observations ) and shown that it remains stable . If there is a real decline ( or expansion ) , it is happening too slowly to detect . Persistent infection is also associated with an active humoral and cellular immune response by the host that is also stable over time [1] , [7] . We see this stability as a balance between infection and the immune response which returns to equilibrium when perturbed . Two biological models have been proposed to account for this persistence: the germinal center ( GC ) model [4] , [8] and the direct infection model [9] , [10] . The GC model proposes that EBV persists by exploiting normal B-cell biology . This involves new latently infected B cells passing through a series of differentiation stages , each employing a discrete viral gene transcription program ( Figure 1 ) . Thus , EBV directly infects naive B-cells , causing their activation into proliferating latently infected Blasts . At this stage the virus expresses all nine known latent proteins , a condition referred to as latency 3 or the growth transcription program . These cells then move into the germinal center ( GC ) to participate in the GC reaction . Here they express a more restricted pattern of latent proteins referred to as latency 2 or the default program . Eventually these cells leave as latently infected memory B-cells that either only express the viral genome tethering protein EBNA1 ( known as the EBNA1 only program or latency 1 ) or no viral proteins at all . The later state is referred to as latency 0 or the latency program . The memory compartment has been considered the site of long-term persistence because the virus is quiescent [11] , and therefore invisible to the immune response . At any time a small subset of latently infected memory B-cells initiates lytic reactivation in association with terminal differentiation signals [12] , [13] . Reactivation of the virus is subdivided into three discrete phases; Immediate early when the transcription factors initiating viral replication are expressed , Early when the proteins involved in viral DNA replication are produced , and Late when viral DNA and structural proteins are assembled into virions [14] . Ultimately this leads to the release of infectious virus that can either be shed or infect new naive B-cells , thus completing the cycle . Each stage of this cycle has been demonstrated experimentally [13] , [15] , [16] and , with the exception of the memory compartment , is potentially regulated by the immune response [7] . Thus , the GC model accounts for all the latent and lytic stages of the virus and thereby provides an explanation for the origin of EBV-associated lymphomas . For example , Burkitt's lymphoma and Hodgkin's disease are believed to descend from latently infected GC B-cells which have failed to successfully differentiate into a resting memory state ( for a detailed discussion of this issue see [2] ) . The second model , proposed by Rajewsky and coworkers [9] , [10] , holds that EBV directly infects memory B-cells . Although proposed over 10 years ago , no evidence has subsequently been evinced to explain the mechanism behind this model . Unlike the GC model it does not account for the four well-defined transcription programs/states of latent EBV infection , intermediate states between newly infected and persistently infected memory B-cells have not been identified in vivo , the model does not account for the origin of EBV-infected tumors and the basis for viral reactivation remains unexplained . Furthermore , predictions of the direct infection model were incorrect when tested experimentally and instead supported the GC model . For example , infected GC B-cells express the viral default transcription program ( latency 2 ) in vivo [16] , [17] ( as predicted by the GC model ) , not latency 3 ( as predicted by the direct infection model [18] ) , and in a transgenic mouse model one of the EBV latent proteins expressed in the GC ( LMP2a ) was able to drive B-cells to form GCs in the absence of antigen [19] . Thus , the direct infection model remains ill-defined and unverified at the biological level , and therefore difficult to test mathematically . Like most biological models the GC model is descriptive and , as such , is not quantitative . However , unlike the direct infection model it is sufficiently detailed for mathematical testing and analysis [20] , [21] , [22] , [23] , [24] , [25] , [26] . Mathematical approaches to studying host-pathogen interactions have increased steadily in the last four decades . ( For entry into the corresponding body of literature , we recommend [27] , [28] , [29] , [30] , [31] ) . Mathematical analysis can be rigorous ( i . e . , comprehensive , thorough and exact ) and a good model should be able to withstand such analysis . Thus , if a biological model is not mathematically consistent , i . e . , not capable of being described mathematically , it cannot be correct . Therefore , one test of the validity of a biological model is to see if it is mathematically sound . If such a mathematical description can be established , it provides a powerful quantitative tool for analyzing the infection process . For the first time this would allow us to quantitatively define the biological constraints on the behavior of the virus , determine just how stable the persistent infection is , and where and how aggressively interventions must be applied to alleviate and/or prevent infection and disease . We have provided such a detailed mathematical description of the GC model [21] . The current paper depends critically on this work where we modeled the interactions between a host and a pathogen , such as EBV , which transits a cycle of antigenically distinct stages . We refer to this model as the cyclic pathogen model ( CPM ) . The paper describing CPM is highly technical and may not be accessible to many working biologists , therefore a review of the relevant material is presented in Boxes 1 , 2 and 3 . The most interesting conclusion of CPM was that while for any given set of parameter values there are many potential equilibrium states , only one has non-negative populations ( and is therefore biologically meaningful ) and is stable . We propose here that this unique , biologically meaningful , stable fixed point corresponds to long-term persistent infection by EBV . If correct , then the essential features of a cyclic host-pathogen interaction like EBV's can be accurately represented mathematically , which in turn shows that the biological model is mathematically consistent . In our application of the CPM to the study of EBV , we assume that persistent infection has reached a stable equilibrium . We also assume that all the infected stages in the cycle possess some level of antigenicity . Whether an immune response arises to a particular infected stage depends in part on the number of cells at that stage . If it is too high , the immune response will drive the number down to the point where it is just sufficient to sustain the response . Conversely , if the number is too low , the cells will fail to establish or sustain an immune response . In this event there are two potential outcomes . In one instance , the infected population rises until it generates and is counterbalanced by an immune response . In this case the stage is regulated by the immune response . Alternatively , if the population is limited , for example by its rate of production from the previous stage ( s ) , it may already be at a steady state level and therefore will not sustain an immune response . In this case the population is not regulated by the immune response but by the rate of follow-on from the previous stage . For any given set of parameters , we use the term “pattern of regulation” as the final outcome of which stages are regulated by the immune response ( regulated stages ) and which are regulated as follow-on populations ( unregulated stages ) for that particular set of parameters . In this paper we have sought to test the hypothesis that the unique biologically meaningful stable fixed point predicted by the CPM corresponds for EBV to long-term persistent infection . To achieve this , we challenged the model with the full range of biologically plausible values for the model parameters and asked if the observed regulation patterns are biologically valid .
We wished to test the hypothesis that the unique stable fixed point predicted by the CPM is a description of EBV infection . To achieve this , we have estimated the range of values for all the parameters needed to compute this stable fixed point ( for details see Methods and Supplementary Table S1 ) . Given a value for each parameter , we can calculate the unique biologically meaningful stable fixed point determined by that particular set of parameters and ask if it is biologically valid . Specifically , we can determine the pattern of regulation , i . e . , which stages fall under direct regulation by the immune response and which do not . For EBV we have a six-stage model of infection ( naive Blast , GC , memory , Immediate early lytic , Early lytic and Late lytic ) where each stage may or may not be regulated by the immune response . Therefore , there are in theory 26 = 64 possible combinations of regulated and unregulated stages . In actual EBV infection , the memory stage does not express CTL targets so presumably is never regulated [4] , [8] , [11] . Analysis of responses to latent and lytic antigens [7] reveals that CTL recognizing antigens expressed: Therefore , of the 64 hypothetically possible patterns of regulation , only 4 are biologically credible . In order of prevalence they are: Our model contains 25 parameters that affect the size of the infected populations ( for a detailed description and discussion see Methods and Supplementary Table S1 ) . We have identified the biologically credible range of values for each of these parameters ( see Methods ) . Together , the combined ranges for these parameters can be thought of as generating a 25 dimensional parameter space . This space consists of all the possible combinations for the plausible values for our 25 parameters; we refer to this as the “parameter cube” . We then tested the validity of our model by sampling 10 , 000 random points in that cube ( i . e . , 10 , 000 randomly chosen combinations of biologically tenable values for the 25 parameters ) and computing the pattern of regulation at the stable fixed point for each set of parameters . A simplistic version of this approach for 2 instead of 25 parameters is shown in Figure 2 . For a typical run of 10 , 000 randomly selected parameter sets , the four most prominent patterns of regulation we found are shown in Figure 3A and B . In order of prevalence they were: Thus a full 55 . 1% gave the most common pattern of regulation seen biologically , i . e . , where all stages are regulated by the immune response except memory . Furthermore , of the 64 possible patterns of regulation , the top four patterns were the 4 biologically credible ones , and they accounted for essentially all of the random sample of parameter sets ( 93 . 1% ) . Of the non-biological patterns , 6 accounted for the remaining 7% of predicted patterns and 54 patterns were never detected . We speculate that these biologically implausible patterns of regulation arise because there may be combinations of plausible values for the parameters that are not consistent with each other . Direct comparison of model predictions with CTL studies are also informative . The model predicts that the Blast , Immediate early and Early stages are almost always regulated ( >95% of the time ) , as is seen experimentally . Similarly the model predicts that the GC is regulated 63% of the time which is very close to the 60% predicted from CTL studies [32] . Taken together , these results provide strong quantitative validation of the model . The case for Late lytic is less convincing since the model predicts regulation 88% of the time , but CTL studies only report detection 28% of the time [33] . Thus the model is qualitatively accurate ( the Late lytic stage is not always regulated ) , but either quantitatively imprecise in this area or the biological data are not accurate . For example , it has been suggested by the original authors that 28% is an underestimate ( Hislop , personal communication ) . Clarification of this point experimentally will provide a test of the accuracy and predictive power of the model . The model's prediction that the Late lytic stage is not always regulated makes an important point in terms of understanding how the model works . The model predicts that at equilibrium , the size of a population regulated by the immune response is inversely proportional to its net antigenicity . This is because the more antigenic a population , the fewer cells it takes to stimulate a controlling T-cell response . Based on the observed population sizes of Immediate early ( ∼50–500 cells ) , Early ( ∼5–50 cells ) and Late ( ∼2–10 cells ) lytic populations in all of the tonsils at equilibrium ( [13] and Supplementary Table S1 ) , i . e . , during persistent infection , the model predicts that net antigenicity should increase across the lytic stages , with the Late lytic population being the strongest . Thus , it is noteworthy that published observations on the avidity of CTLs to these stages demonstrates that Late CTLs are indeed significantly more avid than Immediate early or Early [33] . Since the Late stage has a higher antigenicity than any other stage , a superficial analysis might predict that it should always be regulated by the immune response . However , the CPM model states that , no matter how antigenic a state is , if the size of the population is below the level necessary to trigger an immune response , it will only be regulated as a follow-on population , that is to say the rate at which it is produced from the previous stage combined with its average lifespan . The model predicts that in about 12% of cases there will be insufficient Late lytic cells to stimulate a detectable CTL response . This demonstrates that net antigenicity alone does not predetermine which stages in the model are regulated and which are not . One central premise of the biological model is that EBV can persist because it resides quiescently in resting memory B-cells that cannot be recognized by the immune response . The CPM makes a different prediction , namely that even if the memory compartment was antigenic the virus could persist . However , the structure of persistence in terms of population numbers and responses would be very different from what is seen biologically . An example is shown in Figure 4 . Here the same analysis was performed as in Figure 3 , but with the memory compartment being assigned a much greater antigenicity . The picture that emerges is complex ( Figure 4A ) and dominated by non-biological patterns of regulation ( Figure 4B ) . Perhaps the most striking outcome being that in ∼90% of the cases the Immediate early stage is no longer regulated despite being strongly antigenic . Cleary this is a non-biological result , since we know that biologically the Immediate early stage is always under regulation [7] , emphasizing again the point that net antigenicity alone does not decide if a stage will be regulated by the immune response . This result produces a shift in our understanding of persistence away from relying on the poorly antigenic nature of the memory compartment towards the importance of the whole cyclic nature of the infection . Expressed more generally , it is not the CTL response ( or lack thereof ) to certain , specific stages that explains EBV persistence . EBV could persist no matter the pattern of regulation . Rather , it is the CTL response ( or lack thereof ) to certain , specific stages that defines the way persistence looks . The model produces detailed predictions about the sizes and flow rates through each stage for any given set of parameters ( i . e . , point in the parameter cube ) . A relatively simple way to demonstrate this is with pie charts , where the left half shows the flows into the population and the right half shows the flows out . Since the system is stable , i . e . , at equilibrium , the population size for each stage is constant and the flows in must equal the flows out , i . e . , the size of the two halves of the pie chart must be the same . Net gain can occur either from input from the previous stage or as the end product of cell division , i . e . , proliferation . Net loss can occur via death , killing by CTLs , differentiation to the next stage or as loss to cell division ( for convenience we consider cell division to be the consequence of loss of one cell and the appearance of two new cells ) . The simplest case is the memory compartment , which biologically is never regulated by the immune system . We have proposed previously that the infected memory compartment is regulated by normal memory B-cell homeostasis . In this case , we assume that death and proliferation are exactly balanced , producing the pie chart shown in Figure 5A where net input from the GC stage effectively equals net output to the Immediate early stage ( see Methods for further discussion ) . We can also calculate the predicted population size and the flow rate through the stage ( values shown in the figure come from a particular set of typical parameters ) . In our analysis the memory population is never regulated by the immune response , rather its level is predicted by CPM to be a complex outcome of all the interactions and rates throughout the model and , as such , an emergent property of the whole model , i . e . , not pre-programmed or governed by specific model parameters . Currently , there is no way to measure the actual flow rate through any given stage , but we are able to measure the population sizes . To test how well the range of values predicted by CPM compared to what is seen biologically , we calculated the steady state size of the memory compartment for each of the 10 , 000 randomly chosen parameter sets . The result is plotted as a histogram in Figure 5B ( green bars ) . Superimposed upon it is a histogram of the actual number of EBV infected memory B-cells in Waldeyer's ring for 42 independent tonsils from persistently infected individuals ( purple bars ) . As may be seen , the full range of CPM predicted values falls within the actual range of biological measurements . Furthermore , the predicted values have a similar log mean and log median value ( log mean: biological 4 . 48; CPM 4 . 36; log median: biological 4 . 65; CPM 4 . 31; p-value = 0 . 079 using a two-sided , unpaired , two-sample Mann-Whitney test ) . The major difference is that the biological values have a somewhat wider range ( std . dev . of log values: biological 0 . 83; CPM 0 . 40 ) . Since this distribution of values for the size of the memory population is derived only as an emergent property of the model , we conclude that the accuracy of the predicted range serves as good validation both for the theoretical underpinning of the CPM model and for the credibility of our parameter set . We can apply a similar analysis to the GC compartment . We have seen that CPM predicts , in good agreement with experimental data [32] , that the GC compartment is regulated approximately 63% of the time . Pie charts for both regulated and unregulated conditions are given in Figure 6A and B respectively . These also reiterate the key features of the model . Thus , in Figure 6A ( the regulated condition ) , the rate at which infected GC B-cells are produced from the previous stage and by proliferation is greater than the rate at which they are lost to the next stage . Thus , the size of this stage will increase until it is above the threshold for triggering an immune response . Above this threshold the immune response will expand and drive the infected population back down to the threshold level . So , at equilibrium a regulated population will be at the threshold level where there are just enough infected cells to drive the immune response and just a sufficient number of T-cells to limit the population . The size of the regulated population is a model assumption because the level to which the immune response limits it is a direct function of its net antigenicity which is given by the model parameters ci and b in the equation . However , if the equilibrium population size is not sufficient to induce an immune response , i . e . , below the threshold , then for this unregulated population the rate of production ( from the previous stage and proliferation ) must equal the rate of loss ( to the next stage ) ; this is the condition displayed in Figure 6B . As with the memory compartment , the size of this GC population is emergent , i . e . , a complex outcome of all the interactions and rates throughout the model - not governed by model parameters . As such , it provides a vehicle to further test and validate the CPM . We can ask what the range of these values is for the ∼30% of our parameter sets that produce an unregulated GC population and compare it to the 42 independent measurements we have made on biological samples . Since we know that failure to be regulated by the immune response arises if a population does not achieve a large enough size , we would predict that the range of sizes for unregulated GC populations should reside at the lower end of the range of measured values . The result is shown in Figure 6C . The purple histogram shows the distribution of actual measured values and the green histogram shows the values predicted by CPM for the parameter sets where the GC compartment is unregulated by the immune response . As predicted , the values from CPM lie within those measured biologically , but at the lower end of the range . Once again this provides validation both for the model and the parameter set we have chosen to test it . Since most of the biologically measured values are larger than those predicted for unregulated GC populations , this model states that these must be regulated levels , i . e . , in the majority of individuals the infected population of GC B-cells is regulated by the immune response . The simplest model for reinfection is that each bursting B-cell releases infectious virions that infect adjacent B-cells . Since the avidity of EBV for its receptor on B-cells is so high , it is unlikely that the virus from each lytic cell would travel beyond the first layer of surrounding B-cells , i . e . , infection of approximately 20 B-cells , to become Blasts . An example of the net gains and losses to the Blast stage based on this scenario is shown in Figure 7A . In this case , approximately 10 cells undergo lytic reactivation per day resulting in ∼200 freshly infected cells , a vanishingly small contribution compared to the approximately 12 , 500 daughter cells produced daily by proliferation . In order to balance the two sides ( i . e . , at steady state ) , it is necessary to invoke that approximately 200 Blasts are killed per day . Since we know from the literature that there are approximately 4 . 6e6 to 1 . 4e8 CTLs in Waldeyer's ring directed against the Blast stage ( see Supplemental Table 1 ) , we can estimate the average time between kills for these CTLs to be between 60 and 380 years . ( It is worth noting here that B-cells in lymph nodes are moving rapidly [34] , therefore the number of B-cells that a lytic B-cell could come into contact with may be higher than 20 . Given the burst size for a B-cell of ∼104 virions , the number of infected cells could be 10-fold higher; however that does not significantly affect the conclusions here ) . Under this scenario , therefore , almost the entire CTL population would never see its target again after resolution of the initial acute phase . This is not consistent with published information that ∼10% of CTLs in tonsils against the Blast stage express activation markers [35] . To sustain this response , therefore , requires amplification of the virus beyond those produced by lytic B-cells . We have previously presented biological and modeling data to support the idea that EBV must replicate in epithelial cells to account for the levels of virus shedding we observe in saliva [5] . Figure 7B shows the pie chart where we assume that each bursting B-cell infects epithelial cells , which in turn release virus that infects ∼10 , 000 B-cells . Here , flow in is dominated by infection and flow out by CTL killing , which is close to 100 , 000 cells per day . Under this scenario a CTL encounters a target every 46 days to approximately 3 . 8 years . The lower end of this estimate seems a credible time frame for sustaining the response . It is worth noting that it is well within the capabilities of the 7 . 7e8 macrophages of Waldeyer's ring ( this lab and [36] ) to deal with a daily death toll of this size . Figure 7C shows the predicted mean time between killings for CTLs specific for Blasts in Waldeyer's ring plotted against a wide range of amplification factors . We conclude that amplification of EBV , possibly through epithelial infection , is necessary to account for the observed biology of the CTL population . One concern is that the patterns of regulation described above are somehow intrinsic properties of the model and not dependent on the biological validity of the value ranges we have estimated for the parameters . We have performed several control computational tests to address this . First , we took the values for each parameter and randomly scrambled them between the different stages . For each parameter , there are 6 values , one for each stage . Scrambling these values produces 6 ! = 720 possible permutations for each parameter . This was performed for each of the four parameters: r , f , a and c . Each randomized value for a given parameter is then in turn grouped with randomized values for the other three parameters . Since there are in total 4 parameters with 720 permutations each , there are 7204 ( ∼2 . 7e11 ) possible permutations of this type . We randomly selected 1000 of these permutations , constructed a parameter cube for each , and sampled each as before . Of the 1000 , 13 had a percentage of sampled points with a biologically plausible pattern greater than or equal to that obtained with the unscrambled values ( red line in Figure 8A ) . This gives an empirical p-value of 0 . 013 and allows us to reject the null hypothesis that randomly chosen parameter estimates would perform as well . A further concern is that the patterns of regulation we observe are driven by the values we have derived for the net antigenicities , ci . To address this , we have performed a permutation test in which all parameters , except ci , are permuted as above . Holding the net antigenicities constant and permuting the other parameters degrades performance over 96% of the time ( Figure 8B ) . This gives an empirical p-value of 0 . 033 allowing us to reject the hypothesis that the observed patterns of regulation were determined by the values for net antigenicity . We also examined the degradation in performance when only a single parameter is permuted . Since there are six stages , there are , including the permutation which leaves everything in place . The fraction of the time a permutation performed at the same level or better than the unpermutted cube for r , f , a and c respectively were 0 . 149 , 0 . 0875 , 0 . 25 and 0 . 00278 . In summary , although c is the most critical parameter , it is not sufficient to account for the observed patterns of regulation . The ranges of our parameter values were chosen to be as biologically accurate as possible . If this and the model are correct then we should predict that performance will rapidly degrade if we arbitrarily extend the value ranges . To test this , the ranges were increased by up to 100-fold and the fraction of biologically plausible outcomes measured . As may be seen in Figure 8C , the actual set of parameters used was very close to optimal in terms of producing biologically credible patterns; this success quickly falls off within 0 . 2 logs . This is good evidence for the specificity of the model since it only works optimally at , or close to , a biologically tenable range of values for the parameters and conversely validates the quality of our parameter values . Latently infected memory B-cells produce a single viral protein only when undergoing cell division , which takes place perhaps once every 30 days . Furthermore , this protein is produced in low quantities and is poorly presented , if at all [37] , [38] , [39] . Consequently , these cells are thought to be weakly antigenic . One advantage of a mathematical model is that it can provide quantitative answers to biological questions . In this case , we can ask how weakly antigenic does the memory compartment have to be relative to the other stages in order to produce the biologically expected pattern of regulation ? We took the 10 , 000 random points in the parameter cube that we sampled for Figure 3 where none showed regulation of the memory stage , and for each of these we computed how much the value for memory net antigenicity cmemory would have to increase before the stage became regulated . We found that memory remained unregulated if it is at least two orders of magnitude less antigenic than the other stages .
For the first time we have a detailed , mathematically consistent model of EBV persistence . The advantage of such a model is that it now allows us to make quantitatively precise predictions about infection , i . e . , observe the extent to which the mathematics constrains the biological possibilities . In the results section we have presented such arguments with respect to the size and antigenicity of the memory compartment , whether or not infected GC B-cells come under immune regulation and the possible role of viral amplification in epithelium between the Late lytic and Blast stages . From a practical stand point the model can also be used to make predictions about interventions and how effective they must be to alleviate or prevent EBV infection and associated diseases , and what their probability of success might be . Generally speaking , CPM predicts that it will be extremely difficult to clear EBV infection once established . This is because it is the complete cycle of infection , not any one stage , which is important for persistence . Consequently , any treatment regimen must reduce the value of R0 ( the net amplification achieved by one circuit of the cycle ) to less than 1 . The value of R0 depends critically on the amplification factor at the Late lytic stage which is likely to lie in the range ∼104–106 . This means that persistent infection is not only robust with respect to random variations in populations as we have discussed , it is also robust with respect to large changes in the parameter values , e . g . , those induced by the administration of an anti-viral or vaccine . In order to eliminate EBV infection , such treatment would have to reduce viral production by a factor of 104 or greater . This will be a difficult task given the complicated PK/PD issues involved in administering antivirals and the inability of anti-herpesviral drugs to dramatically reduce EBV production for a sustained period of time [42] , [43] . One consequence of CPM that may not be self-evident to a biologist is that if an infected stage is being regulated by a CTL response , then the level of that stage is controlled solely by its antigenicity and the decay rate of the CTL response - and nothing else ( see also [44] ) . There are several consequences of this . For example: A generalization of our analysis is that if a pathogen can establish a cycle of infection , it has the potential to become persistent . This can be true of any virus . For example , an acutely infecting virus like influenza can be thought of as having a very simple cycle , i . e . , infectious virus cycling through infected cells and back to virus . Why doesn't influenza establish a persistent infection ? The answer is twofold . First , when potent neutralizing antibodies arise they effectively break the cycle of infection by removing all infectious virus . This break is permanent because the antibody response persists long after the antigen is cleared . Second , the virus does not have multiple infected stages that can re-establish the cycle when it is interrupted by the immune response . In the case of EBV , potent neutralizing antibodies arise but are apparently unable to effectively clear all of the virus . We know this because despite the presence of potent neutralizing antibody in the serum , infectious virus can readily be isolated from the saliva , and newly infected naive B-cells are routinely present in the tonsils of healthy carriers of the virus . We have attributed EBV's ability to establish persistent infection to the existence of a cycle of infective stages . We can in turn attribute the existence of that cycle to the failure of antibody to provide a sterilizing response . Why it is not possible to produce a sterilizing level of neutralizing antibody to EBV is unclear , but is crucial in allowing the cycle of infection to proceed . In our model we have encapsulated the steps between lytic infection and blasts because this is an area where we are still lacking detailed information about the intervening steps . We do not know if the virus effectively avoids neutralization because of compartmentalization of infection and serum antibodies , or because virus transmission is through cell-to-cell contact , for example . As we have argued above and previously [5] , it seems inevitable that infectious virus is amplified in the epithelium on the way to infecting new naive B-cells . A complicating but very interesting issue that arises is the possible role of epithelial cells in abrogating the sterilizing effect of neutralizing antibody . It is known that antibody that neutralizes B-cell infection actually favors epithelial cell infection [47] , thus giving a positive feedback loop in response to neutralizing antibody ( for modeling of this effect see [24] ) . It seems , therefore , that modeling has produced several compelling reasons to believe that epithelial infection plays a central role in persistence . It will be important to better understand the exact relationship between the route ( s ) taken by infectious virus between B-cells and epithelium , since it relates directly to the pathways by which EBV must enter the tonsil lymphoepithelium during initial acute infection . The level of infected memory B-cells in the peripheral blood of healthy carriers is stable ( [5] , [6] and unpublished observations ) . There is no detectable decline ( or expansion ) . By comparison levels of shed virus in saliva are extremely variable ( by up to 4 logs ) [5] . If the levels in saliva truly reflected infectious virus for naive B-cells , then we would expect to see a large variation in the ratio of infected Blasts to GC to memory B-cells , with the variation in the level of infection dampening down as the virus traverses the infected stages into memory . However , we see no evidence of this in our analysis of a large numbers of tonsil samples [41] . This suggests that the rate of new infection is relatively constant and independent of the wild fluctuations of virus shedding in saliva . The validation that we have offered in this paper makes a strong case that the CPM is able to capture the gross features of the architecture of persistent EBV infection , and gives a first principled quantitative explanation of how this architecture produces persistent infection . It will be important now to extend this work to the dynamics of acute infection and more detailed description of the biology , particularly of the immune response to EBV .
For CPM , we assume that all of the significant biology for EBV occurs in the lymphoid tissue of Waldeyer's ring . Therefore all CPM parameter values are for the entire Waldeyer's ring . We have not attempted to include the peripheral blood , which contains relatively few infected cells , nor the peripheral lymphoid tissue where the level of infection is markedly lower [41] . We assume , but have not tested , that the dynamics of infection in Waldeyer's ring are representative of the whole body . We have also omitted the naive B-cells that the virus infects because , based on our own calculations on the number of naive B-cells in Waldeyer's ring ( ∼5e9 , see Supplemental Table S1 ) , the supply of new naive target cells is not a limiting factor . That is , EBV infects at most ∼1e6 naive B-cells out of a total of ∼5e9 , and given the fact that immature B-cells can replenish and effectively buffer the naive compartment [48] , we do not expect such a relatively small amount of new infection to significantly reduce naive B-cell numbers . We only consider the CD8/CTL response; we do not take CD4 T-cells into account , nor do we include a humoral or innate response . Furthermore , we do not include discrete CTL sub-populations such as effector , central memory and effector memory , all of which have different life-spans and activation requirements [49] , [50] , [51] . We also do not include any model of CTL exhaustion after chronic stimulation nor of EBV-induced inhibition of antigen processing/presentation , i . e . , the known decrease in presentation of lytic antigens from Immediate early>Early>>Late [33] . This effect is encapsulated into net antigenicity . Traditionally , there are two methods by which the values for the parameters in a mathematical model may be determined . In the first method , one searches for values that give an outcome for the model that most closely approximates what is seen biologically . In essence this is empirical fitting . The second method attempts to directly or indirectly measure the individual parameters experimentally . This typically produces a range of observed values due to technical limitations and variation that is a natural property of the biological system itself for any given human population . In this work we have followed the second approach . We have used our own laboratory's work together with an extensive search of the currently available literature for experiments that either directly measure the parameters we are interested in or allow for an indirect calculation . A complete list of parameter values is given in Supplementary Table S1 . This includes discussion of their origin ( together with references ) and any potential limitations . The parameter rlate lytic encapsulates all of the processes that go on between the burst of a Late lytic cell and the production of new naive Blasts . This includes free virus , the role played by infected epithelium , and the humoral response against free virions . Analysis of this parameter , its size and the implications for viral replication are considered in the results section . The parameter ci or “net antigenicity” encapsulates an overall efficiency of promoting stage-specific CTL activation and proliferation . Because it encapsulates so many biological processes , it is difficult to obtain a single laboratory measurement for it . However , as the model makes clear , the size of a regulated stage is determined by net antigenicity together with the death rate for CTLs; a regulated stage stabilizes at exactly the level where the antigenic population provokes just enough activation and proliferation of CTL to balance losses . Consequently , for regulated stages , we have derived a value for “net antigenicity” from the size of the infected population and our estimate of the CTL decay rate . It is worth noting that the ranking of these derived values for the lytic stages agrees with the published ranking based on avidity of peptide binding [33] . To investigate if the model exhibits the observed regulation patterns when biologically credible values are assigned to the parameters , we developed a parameter cube as follows . The parameters of the CPM are listed in Box 2 . Parameters ri , fi , ai , ci and pi have stage-specific values , that is , the model requires a value for each of these at each of the six stages . The parameter b describes the decay rate for a CTL population in the absence of antigen which we assume is not stage specific and therefore has a single value which is applied to all stages . Thus , the model has 31 distinct parameters . At equilibrium the value of the parameter pi only affects the size of the CTL populations , not the infected populations ( see Box 3 ) . Therefore , the pattern of regulation is determined by the 25 remaining parameters ri , fi , ai , ci and b . We are able to assign precise values for 8 of the 25 parameters . ri gives the gain in proceeding from one stage to the next and thus is equal to 1 for all stages except Late lytic . ai gives the difference between the death and proliferation rates at stage i . Since the memory B-cell compartment is stable , we assume aMemory = 0 , i . e . , the birth and death rate are equal . Further , the net antigenicity of the memory compartment is vanishingly low . Because of this , there was no need to work with a range of values for this parameter . We have therefore assigned a single value which we justify below . We have also assigned alate lytic = 0 under the assumption that once initiated , the Late lytic process always ends in death . This leaves 17 parameters for which we cannot determine the exact values . We have defined the ranges of these parameters using values from the literature and unpublished results from this laboratory ( see Supplementary Table S1 ) . Collectively , these fixed values and defined ranges define our concept of biologically credible values . We are thus concerned with 24 stage-specific parameters , 8 of which are assigned fixed values and 16 of which are allowed to vary over fixed ranges , plus b . This set of values can be thought of as a 25-dimensional space; we refer to this as the parameter cube . Choosing a biologically credible value for each of the parameters is the same thing as choosing a single point in this parameter cube . We are thus able to probe the behavior of the model when confronted with biologically credible parameter values by choosing random points from this cube and running the model using each of these randomly chosen points . | Epstein-Barr virus ( EBV ) is a herpesvirus that establishes a lifelong persistent infection in virtually all human beings . This infection is a risk factor for the subsequent development of certain tumors and possibly also autoimmune diseases . In order to understand the origin of these diseases , it is necessary to first understand how EBV maintains persistent infection . We have used mathematical analysis to study this question . We find that the characteristic cycle of infected stages that EBV establishes in vivo allows it to persist stably at extremely low levels . This represents a consistent mathematical description of EBV infection and allows us to describe what must change to convert benign infection into pathogenic infection , as well as what kind of efficacy drugs and vaccines must have in order to be useful . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2013 | The Cycle of EBV Infection Explains Persistence, the Sizes of the Infected Cell Populations and Which Come under CTL Regulation |
The majority of broadly neutralizing antibodies to hepatitis C virus ( HCV ) are against conformational epitopes on the E2 glycoprotein . Many of them recognize overlapping epitopes in a cluster , designated as antigenic domain B , that contains residues G530 and D535 . To gain information on other regions that will be relevant for vaccine design , we employed yeast surface display of antibodies that bound to genotype 1a H77C E2 mutant proteins containing a substitution either at Y632A ( to avoid selecting non-neutralizing antibodies ) or D535A . A panel of nine human monoclonal antibodies ( HMAbs ) was isolated and designated as HC-84-related antibodies . Each HMAb neutralized cell culture infectious HCV ( HCVcc ) with genotypes 1–6 envelope proteins with varying profiles , and each inhibited E2 binding to the viral receptor CD81 . Five of these antibodies neutralized representative genotypes 1–6 HCVcc . Epitope mapping identified a cluster of overlapping epitopes that included nine contact residues in two E2 regions encompassing aa418–446 and aa611–616 . Effect on virus entry was measured using H77C HCV retroviral pseudoparticles , HCVpp , bearing an alanine substitution at each of the contact residues . Seven of ten mutant HCVpp showed over 90% reduction compared to wild-type HCVpp and two others showed approximately 80% reduction . Interestingly , four of these antibodies bound to a linear E2 synthetic peptide encompassing aa434–446 . This region on E2 has been proposed to elicit non-neutralizing antibodies in humans that interfere with neutralizing antibodies directed at an adjacent E2 region from aa410–425 . The isolation of four HC-84 HMAbs binding to the peptide , aa434–446 , proves that some antibodies to this region are to highly conserved epitopes mediating broad virus neutralization . Indeed , when HCVcc were passaged in the presence of each of these antibodies , virus escape was not observed . Thus , the cluster of HC-84 epitopes , designated as antigenic domain D , is relevant for vaccine design for this highly diverse virus .
Hepatitis C virus ( HCV ) infection continues to be a major health problem worldwide , and is associated with cirrhosis , liver failure and hepatocellular carcinoma . Nearly 170 million people are chronically infected with HCV and the annual increase in the global burden is estimated at two million new infections [1] , [2] . The recent advances in in vitro and in vivo HCV infection systems and increased understanding of HCV biology have led to the development of many HCV-specific small molecules with antiviral activity . There is new optimism in HCV treatment programs with the recent completion of Phase III studies of several protease inhibitors [3] . However , the potential for HCV mutants that escape from these direct-acting antivirals is a source of concern . Additional approaches are clearly needed for treatment and prevention of infection . An effective HCV vaccine has yet to be achieved , despite considerable effort . A required step in the design of a preventive vaccine for HCV is to identify relevant mechanisms of immune protection . For HCV , emerging evidence indicates a protective role for virus-neutralizing antibodies . Animal studies showed that protection from an infectious HCV inoculum with HCV-specific IgG is correlated with antibody titers blocking infection of target cells with pseudotyped retroviral particles expressing HCV E1E2 glycoproteins ( HCVpp ) [4] . Other studies with HCVpp observed a relationship between the control of virus infection and the neutralizing antibody response in single source outbreaks of acute HCV infections [5] , [6] . In addition , antibodies to HCV E2 prevent infection in a murine model with a chimeric human liver [7] , [8] . Finally , a recently developed immunocompetent humanized mouse model for HCV exhibited a robust antibody response to a recombinant vaccinia virus expressing HCV C-E1-E2-p7-NS2 proteins that protected from a heterologous infectious HCV challenge in some of the animals , and correlated with the serum level of antibodies to E2 [9] . Consequently , understanding the antibody-epitope interaction provides a basis for the formulation of a B cell-based vaccine to prevent HCV infection . The HCV envelope glycoprotein E2 is a major natural target for a protective antibody response , although there are a number of limitations . First , a significant challenge is defining conserved epitopes in this highly diverse virus that are capable of eliciting protective antibodies . HCV is classified into seven major genotypes with more than 30% divergence between genotypes , and each genotype containing a large number of related subtypes that differ between 20–25% at the nucleotide and amino acid ( aa ) level [10] , [11] . The virus replicates at a high rate and exists in an infected individual as a swarm of quasispecies [12]–[14] . A rapid rate of quasispecies formation contributes to the emergence of viral variants escaping immune containment . A major region of variability is the first hypervariable region ( HVR1 ) located at the N-terminus of E2 . While HVR1 contains highly immunogenic epitopes that induce neutralizing antibodies , they tend to be isolate-specific , leading to viral escape [15]–[17] . Second , not all antibodies to E2 mediate virus neutralization . We previously described a large panel of human monoclonal antibodies ( HMAbs ) to HCV E2 [18] , [19] . Cross-competition analysis segregated these antibodies into three immunogenic clusters with all of the non-neutralizing antibodies falling into one cluster , designated as antigenic domain A [19] , [20] . Isolation of these antigenic domain A antibodies indicated that they are similar to the non-neutralizing Fabs isolated by phage display [21] , and to the non-neutralizing serum antibodies present in individuals with chronic HCV infections [22] . Third , it has been proposed that a segment of E2 encompassing aa434–446 , “epitope-II , " contains epitopes that are associated with non-neutralizing antibodies; more importantly , these antibodies interfere with the neutralizing activities of antibodies directed at an adjacent E2 segment encompassing aa410–425 [23] , [24] . Fourth , specific N-glycans on E2 negatively modulate neutralizing antibodies to E2 , perhaps by interfering with their binding to nearby contact residues that are part of their epitopes [25]–[28] . Nonetheless , substantial progress has been achieved in identifying broadly neutralizing monoclonal antibodies that are directed at the CD81-binding site . Some of these antibodies are against linear epitopes within aa412–423 [29]–[31] , but the majority of these antibodies are against conformational epitopes on E2 [18] , [27] , [32]–[38] . Cross-competition analysis has revealed that many of these neutralizing HMAbs are directed at overlapping epitopes , which can be grouped into two distinct clusters , and both clusters mediate neutralization by inhibiting E2 binding to CD81 [19] , [27] . One of these clusters , designated as antigenic domain B , contains antibodies displaying varying degrees of broad neutralizing activities against different genotype and subtype HCVpp [27] , [39] , [40] . Epitope mapping studies revealed two E2 residues at G530 and D535 that are required for binding by all antigenic domain B HMAbs [27] , [39] , [40] . Similar studies with other broadly neutralizing HMAbs also recognize epitopes containing G530 and D535 [36]–[38] . Importantly , the residues G530 and D535 are absolutely conserved and shown to participate in the interaction of E2 with CD81 [41] , [42] . Thus , antigenic domain B antibodies broadly neutralize different HCV isolates by competing with CD81 for binding to conserved residues on E2 that are important for viral entry . A key question for vaccine development is whether immune selection by some antigenic domain B antibodies can lead to virus escape . This possibility is suggested by a study of virus neutralization by antigenic domain B antibodies against a sequential panel of HCV variants of a single patient with chronic HCV infection [17] . Some of these antibodies showed varying degrees of neutralization against the variants from different time points . Others showed sustained neutralization against the majority of these variants . Furthermore , we recently reported on three patterns of virus escape under immune pressure by propagating cell culture infectious virus , 2a HCVcc , under increasing concentrations of a neutralizing antibody [43] . Of the three tested antigenic domain B antibodies , one antibody led to escape mutant viruses without affecting viral fitness . A second led to escape but with compromised viral fitness and a third led to complete virus elimination without escape mutants . These findings collectively highlight the rarity of viral epitopes that are both conserved and not associated with virus escape . To identify highly conserved epitopes that will be relevant for vaccine design , we used the information gained from epitope mapping of antigenic domain A and B HMAbs to construct soluble E2 mutants that do not bind to their respective antibodies . By employing yeast surface display of antibodies , HCV HMAbs were isolated that initially bound to a 1a HCV E2 antigenic domain A mutant to minimize the selection of non-neutralizing HMAbs and then bound to a second 1a HCV E2 antigenic domain B mutant to minimize the selection of antigenic domain B HMAbs . We describe in this report a panel of nine HMAbs to overlapping epitopes on HCV E2 , designated as HC-84-related antibodies . Each HMAb neutralized HCVcc of different genotypes with varying profiles and potency , and mediated neutralization by inhibiting E2 binding to CD81 . Epitope mapping revealed distinct contact residue patterns that differ from antigenic domain B . More importantly , when infectious HCVcc was co-cultured with each of the tested HC-84 antibodies , virus escape was not observed .
A yeast display scFv antibody library was constructed from peripheral blood B cells obtained from an individual with asymptomatic chronic HCV genotype 2b infection . The donor was identified after testing nearly 90 different sera from HCV seropositive blood donors for binding and neutralizing antibody titers . The donor's serum contained a high antibody binding titer ( >1∶10 , 000 ) to E2 and similarly high neutralizing titers against genotype 2a JFH1 HCVcc and 1a H77C HCVpp . Immunoglobulin heavy chain variable ( VH ) and light chain variable ( VL ) gene regions from total RNA were amplified and cloned into the yeast vector pYD2 to generate a scFv-expressing yeast display library . The final library size was 2×107 individual clones . Inserts of the correct size were found in 100% of 20 tested clones by PCR and showed 90% diversity by DNA sequence analysis . HCV E2 mutant glycoproteins were designed for the selection of novel HCV HMAbs from the immune library . We have shown that HCV E2 contains at least three antigenic domains with neutralizing antibodies to overlapping conformational epitopes segregating into two antigenic domains ( designated as antigenic domain B and antigenic domain C ) and non-neutralizing antibodies to overlapping epitopes in one domain ( antigenic domain A ) [19] , [20] , [44] . Epitope mapping revealed that distinct but partially overlapping sets of amino acids are critical to the binding of antibodies within antigenic domain B [43] . Similar information was obtained for antigenic domain A antibodies ( not published ) [19] , [20] , [44] . This information led us to engineer E2 mutants to avoid selecting HMAbs to antigenic domain A and B epitopes by substituting with alanine a shared residue in each antigenic domain . Two E2 antigens for antigenic domain A and B knockouts were constructed by introducing mutations at respective positions Y632A and D535A ( designated as E2Y632A and E2D535A ) . The algorithm for isolating novel HMAbs is summarized in Figure 1A . Two rounds of magnetic immunobead , MACS , selection , R1 and R2 , were performed to avoid selection of non-neutralizing anti-HCV HMAbs ( antigenic domain A ) and antibodies not specific to HCV E2 . The yeast display scFv library was incubated with soluble E2Y632A protein immobilized on the immunobeads and then separated . A third round of antigenic domain A depletion was again carried out with soluble E2Y632A protein and the bound yeast cells were separated by fluorescence-activated cell sorting ( FACS ) ( collected cells are as indicated in R3 , Figure 1B ) . E2 bound by scFvs was detected by HC-33 . 1 , an HMAb to an epitope located on E2 in a region encompassing amino acids 410–425 [45] . Correctly displayed scFvs on yeast surface were detected by anti-V5 against the SV5 tag . The next round of FACS selection was performed to deplete antigenic domain B scFv expressing yeast cells by incubating the collected non-antigenic domain A fraction with soluble E2D535A protein and selecting bound scFvs by FACS ( collected cells are as indicated in R4 , Figure 1B ) . A total of 300 monoclonal scFv yeast cells were screened for binding activity to HCV E2 . Fingerprint and DNA sequence analyses from the isolated scFv yeast cells identified 75 unique scFv ( 25% ) . Each monoclonal yeast cell bound to HCV E2 but not to a no-antigen control . We next tested the ability of these scFvs to bind to soluble E2 derived from six different HCV genotypes and subtypes , 1a ( H77C ) , 1b ( UKN1B5 . 23 ) , 2a ( UKN2A1 . 2 ) , 2b ( UKN2B2 . 8 ) , 3a ( UKN3A1 . 9 ) , 4 ( UKN4 . 11 . 1 ) , 5 ( UKN5 . 15 . 7 ) and 6 ( UKN6 . 5 . 8 ) . A final nine scFvs that showed a broad breadth of reactivity and had unique sequence combinations of heavy and light chain CDR1 , 2 and 3 regions ( data not shown ) were converted to full IgG1 molecules and transiently expressed ( Figure 1C ) . These HCV HMAbs are designated as HC-84 . 1 , HC-84 . 20 , HC-84 . 21 , HC-84 . 22 , HC-23 , HC-84 . 24 , HC-84 . 25 , HC-84 . 26 and HC-84 . 27 . Each antibody was tested against a panel of recombinant cell-associated E1E2 proteins ( the same panel used to derive soluble E2 from six different HCV genotypes and subtypes ) by ELISA ( Figure 1C ) . Each HC-84 HMAb bound broadly , with HC-84 . 1 , - . 21 , -25 , - . 26 and -27 binding to all isolates . Having implemented a bias-screening approach to select for novel neutralizing antibodies , we next investigated the neutralizing activities of these HMAbs ( Figure 2 ) . The purified IgG1 HMAbs at 20 µg/ml were first tested for neutralization by FFU reduction assay against 1a H77C and 2a JFH1 HCVcc to assess neutralization activity and whether they were directed to conserved epitopes . Each HMAb neutralized both HCVcc isolates ( data not shown ) . Dose-dependent neutralization from 0 . 005–20 µg/ml was performed against 1a H77C HCVcc and from 0 . 0005–20 µg/ml against 2a JFH1 HCVcc , and ranked ( in the figures ) based on the concentration required to reach 50% neutralization , IC50 , as calculated by nonlinear regression ( Figures 2A and 2B ) . As summarized in Table 1 , HC-84 HMAbs neutralized 1a HCVcc with IC50 ranging from 0 . 08–272 µg/ml and neutralized 2a HCVcc with IC50 ranging from 0 . 003–0 . 020 µg/ml . The panel of antibodies was further tested against a panel of 2a JFH-1 chimeric HCVcc bearing C , E1 , E2 , p7 and NS2 from genotypes: 1a ( strain H77C ) , 2a ( J6 ) , 3a ( S52 ) , 4a ( ED43 ) , 5a ( SA13 ) , and 6a ( HK6a ) at 50 µg/ml ( Figure 2C ) ; all except the 2a virus contained adaptive mutations . All nine HMAbs neutralized 1a , 2a , 4a , 5a and 6a HCVcc by >40% . The only isolate in which no neutralization was observed with some of the antibodies ( as defined by <40% neutralization ) was against the genotype 3a HCVcc . HC-84 . 1 , - . 24 , - . 25 , - . 26 and - . 27 showed >40% neutralization . The remaining antibodies , HC-84 . 20 , - . 21 , - . 22 and - . 23 showed <40% neutralization . R04 , an isotype-matched HMAb to HCMV , exhibited no neutralization . IC50 values against all genotypes were determined with HC-84 . 1 and - . 26 since these two antibodies showed more uniform neutralization ( Figures 2D–2F ) . For HC-84 . 1 , the IC50 ranged from 0 . 043 µg/ml ( against HK6a/JFH1 ) to >50 µg/ml ( against SA13/JFH1 ) ( Figures 2D and 2F ) . HC-84 . 26 IC50 values ranged from 0 . 005–12 . 91 µg/ml ( Figures 2E and 2F ) . R04 , as expected , showed no neutralization ( not shown , except in Figure 2F ) . Of note is the different sensitivity to neutralization between the two genotype 2a isolates , JFH1 and J6 ( Figures 2B and 2E ) . For both HC-84 . 1 and HC-84 . 26 , J6/JFH1 HCVcc required higher levels of antibody concentrations than against JFH1 to achieve similar degrees of neutralization ( Figures 2B and 2E , Table 1 ) . Since the titration studies against different HCVcc genotypes were performed with a second batch of HC-84 . 1 , both batches were compared against the genotype 3a isolate ( Figure S1 ) . The IC50 for the first and second batches were respectively 4 . 3 and 15 µg/ml . A possible explanation is the extent of IgG aggregation between the two batches , which could affect antibody function and stability . Overall , broad patterns of neutralization were observed with the entire panel of HC-84 HMAbs . Differences between breadth of neutralization and breadth of binding to E2 genotypes and subtypes ( Figure 1C ) could be due to different binding affinities or isolates employed for the respective studies . We next examined whether HC-84 HMAbs mediated neutralization by inhibiting the binding of E2 to CD81 . As shown in Figure 2G , pre-incubation of 1a H77C E2 glycoproteins with 1 and 10 µg/ml of each HC-84 HMAb reduced E2 binding to CD81 in a dose-dependent manner , as observed with a control antigenic domain B antibody , HC-11 [27] . Over 90% inhibition of E2 binding to CD81 was achieved with each antibody at 10 µg/ml . The neutralization activities observed with each of the HC-84 HMAbs demonstrated that they are not antigenic domain A antibodies . The employment of E2Y632A efficiently avoided the selection of non-neutralizing antibodies . To evaluate the efficiency of the bias-screening algorithm to avoid selecting antigenic domain B antibodies , we tested the binding of each HMAb to recombinant cell-associated wt H77C E2 and E2D535A mutant by ELISA ( Figure 3A ) . As expected , the nine HC-84 HMAbs bound to both E2 proteins nearly equally except for HC-84 . 20 , which showed significantly greater binding to E2D535A ( 0 . 51 OD ) compared with HC-1 ( 0 . 02 OD ) or R04 ( 0 . 01 OD ) binding to E2D535A ( p<0 . 0001 ) , but with 70% reduction compared to binding against wt E2 ( 1 . 74 OD , Figure 3A ) , suggesting that HC-84 . 20 may partially overlap with antigenic domain B antibodies . As is typical of antigenic domain B antibodies , HC-1 [27] bound to wt E2 and not to E2D535A since D535 is a contact residue . R04 showed no binding to both E2 proteins . The employment of E2D535A in the screening algorithm led to the successful isolation of novel broadly neutralizing HCV HMAbs . All nine antibodies were able to immunoprecipitate recombinant 1a H77C E1E2 from cell lysate ( Figure 3B ) . A slower E2 migration pattern was observed for HC-84 . 23 and HC-84 . 24 , which is likely due to gel distortion by the heavy chains of immunoglobulin . Treatment of the E1E2 by heating to 56°C in the presence of 0 . 5% SDS and 5 mM DTT resulted in the complete abrogation of reactivity for all nine HC-84 HMAbs by ELISA , indicating that these antibodies target conformational epitopes on HCV E2 ( Figure 3C ) . HC-33 . 1 , an antibody directed to a mostly linear epitope on the E2 glycoprotein , was used as a positive control , and as expected retained 80% binding to E1E2 after denaturation ( Figure 3C ) [45] . We next measured the binding affinity of the HC-84 HMAbs by employing purified scFv of each antibody as determined by surface plasmon resonance in a BIAcore 3000 ( Table 2 , Figure S2 ) . Purified 1a H77C secreted E2 was first captured onto a pre-coated sensor chip with CBH-4D , an antigenic domain A HMAb to a conformational epitope [18]–[20] . This step was taken to ensure that native E2 was employed in these measurements since only a fraction of the total purified E2 is functionally active because of the intrinsic deficiency of secreted E2 produced by overexpression in mammalian cells , which includes misfolding , aggregation , and different degrees of glycosylation [46] . Eight of nine HC-84 HMAbs were successfully expressed as scFvs . Figures S2A1–S2A8 show the overlay plots of association and dissociation curves for each of the scFvs to obtain Kon , Koff , and KD values ( Table 2 ) . The ranked order , from the highest-affinity antibody , HC-84 . 23 , to the lowest-affinity antibody , HC84 . 1 , is within a narrow range of no greater than 6-fold difference . This is in contrast to a wider range in IC50 values against 1a HCVcc for these antibodies ( Table 1 ) . In addition , there was no correlation between affinity and neutralization potency suggesting that both affinity and specificity influence the neutralization activities of anti-HCV HMAbs . To assess which of the contact residues bound by the HC-84 HMAbs participate in binding to CD81 , epitope mapping was performed by site-directed alanine substitution studies in defined E2 regions ( Figure 4 ) . Three separate segments of E2 encompassing aa418–446 , aa526–536 and aa611–616 ( respectively designated as regions 1 , 2 and 3 in Figure 4 ) were selected based on previous observations that residues within these regions are essential for E2 binding to CD81 [41] , [42] , [47] , [48] . A series of alanine substitution H77C E1E2 mutants covering the three regions were constructed by site-directed mutagenesis . Binding by each of the HC-84 HMAbs to these mutants was measured by ELISA using lysates of transiently transfected HEK293 cells . The results were normalized according to the E2 abundance in each lysate , as determined by the binding of a non-neutralizing HMAb , CBH-17 , directed at a linear E2 epitope [18] . To confirm that the E2 conformational structure was not altered with each alanine substitution , binding by antibodies representing antigenic domain A ( CBH-4D and -4G ) and antigenic domain C ( CBH-7 and -23 ) was also measured , since they have minimal to no cross-reactivity to antigenic domain B antibodies [44] . Thus , a substitution resulting in reduced binding to the test antibody and to either or both antigenic domain A and C antibodies was interpreted as having a global effect on E2 structure instead of being specific for the test antibody . Two antigenic domain B antibodies , HC-1 and HC-11 , were included in this analysis to determine the extent of overlap between antigenic domain B and HC-84 HMAbs [27] . As shown in Figure 4 , alanine scanning of the three regions of E2 revealed that eight residues located at aa420 , 428 , 437 , 441 , 442 , 443 , 613 and 616 , within regions 1 and 3 , bound ≤20% relative to wt by at least one of the HC-84 HMAbs ( when tested at 2 µg/ml ) , which indicates that these residues are involved in their respective epitopes . Note that binding to the cysteine at aa429 is discounted as a contact residue since a substitution at this site would be expected to have significant structural effects . The exception was HC-84 . 21 , which showed relative to wt >20% binding at all of these sites . Since HC-84 . 21 has similar binding and neutralization profiles , and substantial cross-competition with other HC-84 antibodies ( data not shown ) , a dose-dependent study employing 0 . 005–2 µg/ml was performed against three E2 mutants containing alanine substitutions at aa441 , 442 or 443 ( Figure 5A ) . Substantial difference in binding compared to wt was observed at each concentration . At 0 . 1 µg/ml , binding to aa441 , 442 and 443 mutants was respectively 23% , 17% and 25% of wt , suggesting that these residues participate in the HC-84 . 21 epitope . As a cluster , the HC-84 epitopes are centered at aa441 , 442 and 443 with the majority including a contact residue at aa616 ( Figure 5B ) . The >70–80% reduction in binding observed with each HC-84 HMAb to the identified residues in Figure 5B ( compared to binding to wt ) was confirmed with testing at 0 . 1 µg/ml ( data not shown ) . While <20% binding retention indicate involvement as a contact residue , binding retention between 21–30% suggests probable involvement , as shown in Figure 5B . Two of the antibodies , HC-84 . 22 and - . 23 also contain contact residues at aa420 , 428 and 437 . Epitope mapping of HC-11 revealed contact residues ( <20% binding ) located at aa425 , 428 , 436 , 437 , 438 , 442 , 443 , 530 and 535 ( Figure 4 ) . The shared contact residues between HC-84 antibodies and HC-11 , an antigenic domain B antibody , are aa428 , 437 , 442 and 443 , which indicate a high degree of overlap between antigenic domain B and the HC-84 antibodies . It remains possible that other HC-84-related contact residues could be identified by evaluating the E2 alanine substitution library ( as shown in Figure 4 ) at a lower antibody concentration ( 0 . 1 µg/ml ) since testing at 2 µg/ml is higher than the Kd values for most of these antibodies . Nonetheless , HC-84 epitopes are distinctly separate from antigenic domain B epitopes . Moreover , our findings confirmed the validity of the screening algorithm employing designed E2 mutants to identify novel antibodies . Sequence alignment of the nine amino acids encompassed by the HC-84 epitopes ( as identified in Figure 5B ) with other HCV sequences in an HCV database ( http://hcv . lanl . gov ) revealed that six residues located at aa420 , 428 , 441 , 443 , 613 and 616 are 100% conserved in all genotypes and subtypes . For the remaining three residues , sequence analysis showed that residue W437 , specific to the epitopes of HC-84 . 22 and - . 23 , was 100% conserved in genotype 1 and over 99% conserved in genotype 4 . A different side chain change , F437 , is observed in <1% of genotype 4 . However , F437 is dominant in genotypes 2 , 3 , 5 and 6 , which suggests that a mutation at residue 437 could lead to escape from neutralization by HC-84-related antibodies . Residue F442 , shared in all of the HC-84 epitopes , is 100% conserved in genotypes 1 , 2 , 3 and 4 but M442 or L442 are found at a low frequency in genotypes 5 and 6 . Residue K446 , unique to the epitope of HC-84 . 27 , was 100% conserved in genotypes 1 , 3 and 4 , but not in genotypes 2 , 5 and 6 . Overall , the sequence of each residue within the HC-84 epitopes has remained highly conserved among HCV genotypes and subtypes . To begin to assess the structural and functional constraints for entry of each contact residue within the HC-84 epitopes , virus infectivity was measured using mutant 1a H77C HCVpp bearing an alanine substitution at each of the contact residues of HC-84 HMAbs ( Figure 6A ) . Over 90% reduction in viral entry compared to wt was observed with substitution at aa420 , 437 , 441 , 442 , 613 or 616 , as measured by luciferase readout . This could imply a high degree of structural or functional constraint and was consistent with the observation that these residues were 100% conserved in all genotypes and subtypes , except for F442 . However , in genotype 1a , F442 was 100% conserved . Substitution at aa428 and 443 led respectively to 78% and 82% reduction in viral entry . The alanine side chain replacement at these two sites also has significant structural or functional impact , although less than that of the absolutely conserved residues . The only E2 mutant that maintained moderate entry capacity , with 61% reduction relative to wt , had a substitution at aa446 . This contact residue is restricted to HC-84 . 27 . To rule out the possibility that the lost infectivity was caused by impaired E1E2 assembly affected by introduced mutations in the E2 protein , each mutant HCVpp was partially purified through a 20% sucrose cushion followed by Western blot analysis and probed for E1 and E2 , and p24 to control loading levels for HCVpp ( inset in Figure 6A ) . For the majority of HCVpp mutants and wt , the E2 proteins were probed by HC-33 . 1 . Since the HC-33 . 1 epitope was known to contain a contact residue at W420 [45] , the W420A HCVpp mutant , along with wt , were probed with MAb 6/82a that is directed at an H77C HVR1 epitope [29] . E1 was identified by H-111 , an HMAb to an E1 linear epitope [49] . HIV p24 was identified by an anti-HIV p24 antibody as a loading control . The levels of E1 and E2 incorporated in the HCVpp mutants were similar to the levels observed in wild-type HCVpp . Since each of the amino acids within the HC-84 epitopes led to reduced HCVpp infectivity , their role in E2 binding to CD81 was assessed by a CD81-capture ELISA . Cell lysate of mutant 1a H77 HCVpp bearing an alanine substitution at each of the residues was captured onto GNA-coated microtiter plates . The amount of E2 captured by GNA was normalized by binding to CBH-17 . CD81-LEL at 20 µg/ml was then added and binding to CD81 was detected by an anti-CD81 monoclonal antibody ( Figure 6B ) . Relative CD81 binding to each E2 mutant compared to wt was <10% for each HC-84 contact residue except for the substitution mutants at aa428 ( 10% ) , 443 ( 20% ) and 446 ( 83% ) . The three E2 mutants with substitutions at aa428 , 443 or 446 retained >10% entry capacity compared to wt , and showed ≥10% retention in binding to CD81 . There was a general correlation between reduction in viral entry , as measured by relative HCVpp infectivity , and reduction in binding to CD81 . A segment of E2 encompassing aa434–446 ( “epitope II" ) has been proposed to include residues involved in the epitopes of non-neutralizing antibodies that interfere with the neutralizing activities of antibodies directed at an adjacent E2 segment encompassing aa412–426 ( “epitope I" ) [23] , [24] . The presence of interfering antibodies could be a significant negative modulator of neutralizing antibodies and is highlighted by the detection of antibodies to epitope II in four of nine serum samples from patients with chronic HCV infection [23] , [24] . Since all of the HC-84 epitopes contain contact residues at L441 , F442 or Y443 , and two of the epitopes also contain contact residues at W437 , direct binding assays of each HC-84 HMAb to a biotin-linked synthetic peptide encompassing aa434–446 of H77C E2 was performed ( Figure 6C ) . Four of the nine antibodies , HC-84 . 1 , - . 25 , - . 26 and - . 27 , at 5 µg/ml bound strongly to aa434–446 ( O . D . >1 . 0 ) . The presence of serum antibody to aa434–446 of the donor whose B cells were employed to isolate the HC-84 HMAbs was also tested and found to have significant binding at a 1∶100 dilution . A serum from a normal blood donor ( seronegative to HCV ) and HC-11 , an antigenic domain B HMAb , showed no binding to the synthetic peptide . To confirm the direct binding of HC-84 . 1 , - . 25 , - . 26 and - . 27 to aa434–446 , non-biotinylated aa434–446 was used to inhibit the binding of HC-84 antibodies to recombinant 1a E1E2 lysate ( Figure 6D ) . The synthetic peptide from 2 µg/ml to 40 µg/ml progressively diminished the binding of each of the tested HC-84 antibodies , and not of HC-11 . However , maximum inhibition of binding was at 50% . Since these antibodies are to conformational epitopes , it would be expected that a synthetic peptide would not be able to completely inhibit the binding of these antibodies to native E2 . Collectively , our findings showed that this region is the target of broadly neutralizing antibodies to HCV , although it remains possible that aa434–446 also could be recognized by interfering non-neutralizing antibodies to predominantly linear epitopes . The in vitro selection of monoclonal neutralizing antibody escape mutants , by repetitive neutralization and passage of cell culture infectious HCV in the presence of the antibody , represents a powerful approach to mapping amino acid residues within the viral envelope that contribute to antibody binding [43] , [50] . Our recent study on neutralization escape from three antigenic domain B antibodies revealed that these antibodies bind to at least two discontinuous regions of E2 encompassing aa425–443 and aa529–535 [43] . Furthermore , aa425–443 is a region of variability that is responsible primarily for viral escape from neutralization , with or without compromising viral fitness . The region aa529–535 is a core CD81-binding region that does not tolerate neutralization escape mutations . Identification of contact residues responsible for escape from HC-84 antibodies will clarify which residues within the E2 region encompassing aa425–443 are more invariant , and which residues when mutated lead to escape with or without a cost in viral fitness . We previously designed a viral escape selection protocol to maximize the likelihood of escape variants by subjecting wt HCVcc to increasing concentrations of the selection antibody starting at IC50 ( µg/ml ) value ( Figure S3 ) [43] . At each antibody concentration , the extracellular virus was passaged repeatedly to reach a titer of 1×104 FFU/ml before the virus was subjected to the next higher antibody concentration . This step allows minor variants to be amplified prior to the next round of selective pressure at a higher antibody concentration . As a control virus population , wt HCVcc was subjected to serial passages in increasing concentrations of R04 to provide reference viral variants . This permits specific discrimination between mutations introduced during long-term in vitro propagation of wt HCVcc and those mutations induced under the selective pressure of HC-84 antibodies . A second control was CBH-2 , an antigenic domain B-neutralizing HMAb , which leads to viral escape without a cost in viral fitness [39] , [43] . At each passage of extracellular virus , infected cells were monitored for virus escape by screening with a two-color indirect immunofluorescence assay ( IFA ) that uses both the test antibody , and a second antibody that recognizes virus replication regardless of a change in envelope antigenicity [43] , [50] . In this case , we detected cells infected with an escape mutant by the loss of specific binding by the test antibody , but with retained binding by an anti-NS3 antibody . When escape was detected , RNA from escape mutants was extracted from either cells or culture supernatants , reverse-transcribed , PCR amplified , and subcloned . Genomic residues 1491–2579 spanning the entire E2 coding region were sequenced from selected individual clones . The number of clones that were sequenced and analyzed ranged from 20 to 60 per sample . Five antibodies were selected for escape studies , HC-84 . 1 , - . 20 , - . 23 , - . 24 and - . 25 , three with ( HC-84 . 20 , - . 23 and - . 25 ) and two without ( HC-84 . 1 and - . 24 ) contact residues at aa616 . In addition , the HC-84 . 23 epitope also includes W420 and N428 . W616 is thought to be a contact residue involved in E2 binding to CD81 [47] , [51] . The modest degree of viral entry of 16% and 21% observed respectively with 1a H77C HCVpp mutants with substitution at N428A or Y443A suggested that viral escape could be observed at these residues ( Figure 5B ) . Figures 7A and 7B show neutralization escape profiles for the five tested antibodies , and the control antibodies , R04 and CBH-2 . The concentration of R04 was raised rapidly since this antibody had no effect on HCV and only 1–2 passages at each antibody concentration were needed to reach 104 FFU/ml HCVcc . Spontaneously formed variants containing V402A and N417S , or containing a single mutation at N415D , were identified ( Figure 7B ) , as previously observed [43] . CBH-2 when increased progressively from 0 . 1 to 1 µg/ml required 3–4 passages before the virus reached a titer of 104 FFU/ml . This was consistent with the antibody neutralizing a substantial portion of wt HCVcc . When the concentration increased from 1 to 10 µg/ml and after three passages at 10 µg/ml , 100% of the infected cells were not stained by CBH-2 but were stained by anti-NS3 ( Figure 7A ) . Three sets of escape mutants were identified: a mutant with a single mutation at D431G , a mutant with triple mutations at N415D , A439E and N578D , and a mutant with double mutations at A439E and N578D ( Figure 7B ) . These CBH-2 escape variants are identical to the ones previously observed and escape has been linked to mutations at aa431 and aa439 [43] . Under the conditions tested , selective pressure of each tested antibody , HC-84 . 1 , - . 20 , - . 23 , - . 24 and - . 25 , led to no escape in two independent experiments with 2a HCVcc . The lack of escape is shown for HC-84 . 1 ( Figure 7A ) to represent each of the tested HC-84 antibodies . At low antibody concentrations of <0 . 05 µg/ml , up to four passages at each concentration were required for the virus titer to reach 104 FFU/ml . This indicated some degree of virus neutralization . When HC-84 . 1 reached 0 . 5 µg/ml , after four passages in HC-84 . 1 , the virus was completely eliminated . A similar pattern of virus elimination was observed with HC-84 . 25 , after five passages at 0 . 5 µg/ml of HC-84 . 25 . HC-84 . 20 eliminated the virus after four passages at 0 . 1 µg/ml . HC-84 . 24 was the most efficient; virus elimination occurred at 0 . 05 µg/ml after two passages . HC-84 . 23 was the least efficient . After eight passages in 0 . 5 µg/ml , rare infected cells ( <1% ) , detected by both HC-84 . 23 and anti-NS3 , could still be observed but the virus titer could not be increased . The rare infected cells persisted as the concentration was increased from 1 to 5 µg/ml , even though the virus titer was <104 FFU/ml . After two passages at this antibody concentration , some of the rare infected cells were not stained by HC-84 . 23 but were stained by anti-NS3 , which indicates the possibility of escape . When the concentration was increased to 10 µg/ml , no infected cells were observed corresponding to virus elimination . The concentration associated with virus elimination indicated the following order of potency: HC-84 . 24>HC-84 . 20>HC-84 . 1=HC-84 . 25>HC-84 . 23 . This order of potency is in rough agreement with the narrow range of IC50 for these antibodies against 2a HCVcc ( Table 1 ) , in which the potency ranking is HC-84 . 20>HC-84 . 24>HC-84 . 1>HC-84 . 23>HC-84 . 25 . We attempted to rescue the virus by passaging cultured supernatant when infected cells were not observed with each of the HC-84 HMAbs onto naïve Huh7 . 5 cells in the absence of respective antibody for two additional passages , and no detectable virus emerged from the passaged supernatants . The failure to generate antibody-induced HCV escape mutants could be due to the viral strain employed in these studies . However , it is also possible that the selected antibodies are to highly conserved epitopes such that each contact residue for them is essential and the induction of escape within this epitope leads to a lethal change in virus function or structure . A tertiary model for HCV E2 was recently proposed , based on the 3-domain , β-sheet-rich “class II" fold of the flavivirus fusion proteins [52] . This model took into account the HCV E2 disulfide bonds , the residues that are part of the CD81 binding site and the data on deletion mutants of E2 that still bind CD81 and conformational antibodies [53] . Mapping the alanine scanning results of the HC-84 HMAbs on this model indicates that their corresponding epitopes are on the exposed surface of domain I ( DI ) and also cover a small region of domain III ( DIII ) ( Figure 8A ) . More precisely , in domain I the residues highlighted in this work map to the N-terminal side of the 4-stranded C0D0E0F0 β-sheet ( i . e . , the C0D0 β-hairpin , Figure 8B ) . Both of these regions ( in domains I and III ) are also part of the CD81 binding site , suggesting that both the receptor and the HC-84 antibodies bind at the interface between these two domains . In the viral class II fusion proteins for which structures are available , domains I and III are close in space in the pre-fusion form . Being along a β-strand , it is likely that W420 and I422 are buried ( the sequence is 419-SWHIN-423 , alternating buried hydrophobic and exposed hydrophilic residues , the hallmark of a β-strand ) , and that a mutation at W420A results in a local distortion of the conformation , such that the conformational antibodies binding to other epitopes are not affected . This is likely to also be the case for the 441-LFY-443 sequence , in which at least one residue ( either L441 and Y443 , or F442 , depending on the register of the D0 β-strand ) would be buried in the β-sheet and not exposed for direct contact with the antibody or with the CD81-LEL . Mutation of the concerned residue to alanine could induce only a local distortion at this epitope . Notably , replacement of F442 by tryptophan reduced HCVpp entry to 30% [41] , [42] , [47] , [48] , while an alanine at that position completely abrogated CD81 binding and HCVpp entry , further supporting the requirement for a large hydrophobic side chain at position 442 . The HC-84 epitope mapping findings provide additional information to further refine the available model , since the distance between residues 420 and 428 would be about 24 Å , if they were along a single strand containing a glycosylation site ( 423-NST-425 ) in the middle . If residues 420 and 428 are part of the epitope , a long strand spanning the two would imply that mutating the residues in between would also affect binding , which is not observed . The new data would thus be more compatible with the glycosylation site bulging out , and with the loop connecting the C0 and D0 strands having a somewhat convoluted 3-dimensional conformation , than that suggested by the flat 2D diagram of the E2 model . The tertiary structure model brings residues 428 and 437 close in space , matching with the fact that C429 is disulfide-bonded with C552 , which would be right underneath W428 in the opposite β-sheet ( B0I0H0G0 , facing the viral membrane , Figure 8B ) . This end of DI , opposite to DII , is likely to be in close contact with DIII , as illustrated in Figure 8 , and the interaction with DIII may affect its conformation . This spatial arrangement in the DI-DIII interface may also be affected by the mutation of the DIII residues , which may not necessarily make physical contact with the antibody or with the CD81-LEL .
The HCV E2 glycoprotein encodes clusters of overlapping epitopes that are highly immunogenic with evidence that the more dominant epitopes do not elicit the most broadly protective antibodies . For example , the HVR1 region displays immunodominant epitopes that are mainly targeted by isolate-specific antibodies from which the virus is able to rapidly escape [15]–[17] . A second cluster of highly immunogenic epitopes , designated as antigenic domain B , contains overlapping conformational epitopes that account for the majority of the identified broadly neutralizing antibodies [18] , [32]–[38] . Although antigenic domain B antibodies exhibit broad neutralization against different HCV genotypes and subtypes , it is probable that HCV is able to escape from immune pressure by the majority of these antibodies [43] . A third cluster , designated as antigenic domain A , includes epitopes that induce non-neutralizing antibodies [19] , [20] . It is also probable that antigenic domain A and other non-neutralizing determinants are highly immunogenic and account for a substantial portion of antibody response to E2 [21] , [22] . Taken together , HCV is able to divert the immune response to these highly immunogenic determinants and thereby gains a selective advantage . We implemented a screening approach for novel antibodies that avoided these determinants . Heterologous E2 employed in the screening eliminated antibodies to HVR1 . Information gained from epitope mapping of previously isolated HCV HMAbs led to the development of mutant E2 antigens that minimized the selection of non-neutralizing antigenic domain A antibodies and neutralizing antibodies to antigenic domain B . Based on broad binding patterns to different genotype and subtype E2 proteins , nine scFvs were selected for conversion to IgG1 and were further studied . Surprisingly , all nine antibodies mediated virus neutralization and neutralized both 1a and 2a HCVcc . Our earlier experiences in isolating HMAbs to HCV , using an initial screen by IFA binding to recombinant E2 , yielded nearly 50% of the isolated antibodies that are non-neutralizing [18] , [20] . A possible implication of this finding is that the majority of the non-neutralizing antibody response to HCV E2 is to antigenic domain A epitopes . If this proves to be the case , a vaccine candidate that avoids an antibody response to antigenic domain A could be an approach to focus the immune response to a repertoire of antibodies that eliminates at least a significant portion of non-neutralizing antibodies . As expected , all of these antibodies bound to a 1a D535A E2 mutant , which is not bound by broadly neutralizing antigenic domain B antibodies [27] , [39] , [40] . The nine HC-84-related antibodies , designated as antigenic domain D , broadly neutralized different HCVcc genotypes and subtypes , and many of these antibodies have greater neutralization potency against 1a and 2a HCVcc than two of the more potent antigenic domain B antibodies , HC-1 and HC-11 [27] . The IC50 values of antigenic domain D antibodies are substantially lower against 2a HCVcc ( JFH1 ) than 1a HCVcc ( H77 ) ( Table 1 ) . Subtle differences in the presentation of antigenic domain D epitopes between these two genotypes could account for the different IC50 values . Since these antibodies are derived from B cells of an individual infected with HCV genotype 2b , it is possible that the HC-84 HMAbs are more directed at their respective epitopes as presented in genotype 2 . However , it is also possible that the JFH1 isolate is more sensitive to neutralizing antibodies than the H77 isolate . We previously reported on antigenic domain B antibodies isolated from B cells of an individual infected with genotype 1b having lower IC50 values against 2a HCVcc ( JFH1 ) than 1a HCVcc ( H77 ) [27] . Moreover , the IC50 values for HC-84 . 1 and - . 26 against the 2a HCVcc ( JFH1 and J6 ) isolates are significantly different . Even though their epitopes have complete sequence conservation between these two isolates , it is possible that global conformation with the variation ( approximately 13% ) in E2 glycoproteins of JFH1 and J6 could disturb antibody binding to their respective epitope . This , in turn , will be reflected in the observed differences in antibody-mediated neutralization . Additional studies will be required to link the amino acid ( s ) and precise location ( s ) that contribute to this possibility . Nonetheless , the patterns of neutralization against HCVcc of different genotypes ( Figure 2C ) suggest that these antibodies are directed at highly conserved epitopes . These antigenic domain D antibodies showed more uniform neutralization against different HCV genotypes and subtypes than antigenic domain B antibodies [40] . Based on the observation that each antigenic domain D antibody inhibits E2 binding to CD81 , epitope mapping studies by alanine scanning focused on E2 segments encompassing aa410–446 , aa526–540 and aa611–617 . These regions have been reported to contain contact residues that form the E2 binding site to CD81 [41] , [42] , [47] , [48] . As expected , contact residues were not located within aa526–540 but within aa410–446 and aa611–617 . In the proposed model of the tertiary organization of HCV E2 , domain I is organized such that β-strands C0D0 , as well as E0 and F0 , are consecutive in sequence , spanning aa418–444 and aa526–542 as two β-hairpins , respectively . While antigenic domain B antibodies are localized on the C0D0 and E0F0 β-hairpins , antigenic domain D antibodies are localized on C0D0 and on domain III . The location of antigenic domain D contact residues is in agreement with the model in which domains I and III are close in space . Although antigenic domain D is a distinct cluster of overlapping epitopes , there is some overlap between antigenic domain D and antigenic domain B . Some antigenic domain B antibodies , e . g . , HC-11 , share contact residues within C0D0 , at residues 442 and 443 . The HC-84 epitope mapping data makes possible several adjustments of the tertiary model to accommodate the distance between contact residues and the spatial orientation of a connecting loop between the C0 and D0 β-strands ( Figure 8B ) . One adjustment involves the sequence aa441–443 . This sequence was proposed to be located on β-strand D0 , implying that at least one of the three residues would be buried in the β-sheet and not exposed to participate in binding to either CD81 or antibodies to this region , as discussed above . Of the three amino acids , L441 and Y443 are absolutely conserved but F442 , although 100% conserved in genotypes 1 , 2 , 3 and 4 , has low frequency changes to either M442 or L442 in genotypes 5 and 6 . If F442 is buried and required for the structural integrity of the β-sheet , substitution with a large bulky residue like tryptophan is likely to distort the β-sheet less than substitution with a small side-chain like alanine . Interestingly , CD81 binding experiments showed that when phenylalanine was replaced by tryptophan , HCVpp entry was reduced by 70% , while for a F442A mutant no CD81 binding could be observed [48] . A second adjustment is that W420 and I422 may be buried in the β-sheet , such that a substitution at these residues provides only a local distortion . If that is the case , a question is the accessibility of these residues to participate in binding to CD81 , as well as binding to antibodies directed at this region [41] . Moreover , these residues are absolutely conserved in the entire HCV database indicating functional or structural constraints preventing mutations at this site . Additional studies are required to confirm these modifications to the model . Overall , our findings provide support for this model , in which these antibodies bind to the same tertiary structure that interacts with CD81 . Since all of the antigenic domain D antibodies are affected by substitution to alanine at L441 , F442 or Y443 , we tested the binding of each antibody to a synthetic peptide encompassing aa434–446 . This peptide has been proposed to encode non-neutralizing epitopes [23] , [24] . However , several neutralizing monoclonal antibodies to linear epitopes in this region have been described [54] , [55] . In our study , not only do the neutralizing antigenic domain D antibodies bind to contact residues within this sequence , but four of the nine antibodies , HC-84 . 1 , - . 25 , - . 26 and - . 27 , bound to this peptide indicating that the region aa434–446 forms an integral part of their epitopes . Although HC-84 . 25 , - . 26 and - . 27 HMAbs are antibodies to conformational epitopes , their ability to bind to the aa434–446 synthetic peptide indicates that their epitopes contain a significant linear component . At the same time , binding by these antibodies to denatured E1E2 was significantly reduced ( Figure 3C ) . One explanation could be that the synthetic peptide is sufficiently flexible to be shaped by the interaction with HC-84 . 1 , - . 25 , - . 26 and - . 27 HMAbs leading to binding , but this cannot occur when the aa434–446 region is expressed in the context of denatured E1E2 . Taken together , this region includes residues that are involved in conformational epitopes of potent and broadly neutralizing antibodies , although it remains possible that the E2 region aa434–446 encodes for non-neutralizing antibodies . The initial expectation was for a minor subset of the antigenic domain D epitopes to be invariant because of functional or structural constraints . Surprisingly , when 2a HCVcc was grown in the presence of HC-84 HMAbs , under a viral escape selection protocol to maximize the likelihood of escape variants , five of five selected antibodies led to no escape variants , under the conditions tested . This can be explained in part by the findings in analysis of the effect of each contact residue within the HC-84 epitopes on H77C HCVpp entry ( Figure 7A ) . Seven of ten contact residues , aa420 , 429 , 437 , 441 , 442 , 613 and 616 , when substituted with alanine led to >90% reduction in HCVpp entry compared to wt . Two other residues , aa428 and 443 , when substituted reduced entry by approximately 80% . The only contact residue with moderate reduction was aa446 , which is restricted to HC-84 . 27 . This antibody was not selected for escape study . Reduction in entry correlated with reduction in binding to CD81 with each of these HC-84-related contact residues . The only residue when substituted without significant decrease in binding to CD81 was K446A . Previously studies have identified residues W420 , W437 , L441 , F442 , Y443 , Y613 and W616 on E2 as participants in the interaction with CD81 [41] , [42] , [47] , [48] . Substitution at these sites would be expected to negatively modulate entry . Substitution of the cysteine at aa429 also would be expected to alter the structure required for this interaction . However , it remains possible that escape from HC-84 HMAbs can occur . The leading candidate would be HC-84 . 27 , which includes a contact residue at aa446 . Mutations at this site would not significantly decrease virus entry . Although rare , mutations at W437 and F442 have been documented in the HCV database , but the mutations could be associated with a reduction in virus fitness . Moreover , since the JFH1 2a HCVcc is highly sensitive to antibody-mediated neutralization , escape studies with a less sensitive isolate will need to be performed to confirm our findings . Collectively , the HC-84 cluster of epitopes , designated as antigenic domain D , is highly conserved among HCV genotypes and subtypes , and mediates broad and potent virus neutralization that is not likely to lead to virus escape . Thus , these epitopes are relevant in vaccine design for this highly diverse virus .
Ethical approval was obtained from Administrative Panel on Human Subjects in Medical Research ( protocol number 13860 ) , Stanford University , Stanford , California , USA . Written informed consent was obtained from the participant . HEK-293T cells were obtained from the ATCC . Huh7 . 5 cells ( generously provided by Dr . C . Rice , Rockefeller University ) were grown in Dulbecco's modified minimal essential medium ( Invitrogen , Carlsbad , CA ) , supplemented with 10% fetal calf serum ( Sigma-Aldrich Co . , St . Louis , MO ) and 2 mM glutamine . Yeast strain EBY-100 ( GAL1-AGA1:URA3 ura3–52 trp1 leu2Δ1 his3Δ200 pep4::HIS2 prb1Δ1 . 6R can1 GAL ) ( Invitrogen , Carlsbad , CA ) was maintained in YPD broth ( Difco ) . HMAbs CBH-4D , CBH-4G , CBH-7 , CBH-23 , HC-1 , HC-11 , and H-111 against HCV E1E2 were produced as described [18] , [19] , [49] . HC-33 . 1 is an HMAb that binds to a mostly linear epitope between aa410–425 on the E2 glycoprotein [45] . MAb 6/82a against H77C HVR1 was generously provided by Dr . J . McKeating ( University of Birmingham , UK ) . MAb against HCV NS3 protein was generously provided by Dr . G . Luo ( University of Kentucky ) . MAb against human CD81 ( clone JS-81 ) was purchased from BD Bioscience ( San Jose , CA ) . MAb against V-5 tag was purchased from Invitrogen ( Carlsbad , CA ) . The detection MAbs used in Fluorescence-activated cell sorting ( FACS ) , namely Phycoerythrin ( PE ) -labeled donkey-anti-human IgG ( Fcγ specific ) , FITC-labeled goat-anti-mouse IgG ( Fcγ specific ) and Allophycocyanin ( APC ) -conjugated donkey-anti-human IgG ( Fcγ specific ) were purchased from Jackson ImmunoResearch Laboratories ( West Grove , PA ) . The cell culture infectious virus ( HCVcc ) , 2a JFH1 , was generously provided by Dr . T . Wakita ( National Institute of Infectious Diseases , Japan ) [56] . The 1a H77 HCVcc ( HJ3–5 ) virus is an inter-genotypic chimeric virus produced by replacing the core-NS2 segment of the JFH-1 virus genome [56] with the comparable segment of the genotype 1a H77C recombinant [57] . A molecular clone encoding the CD81 large extracellular loop fused to glutathione S-transferase was generously provided by Dr . S . Levy ( Stanford University ) and affinity-purified over a GSTrap FF affinity column according to the manufacturer's instructions ( GE Healthcare Bio-Sciences AB , Uppsala , Sweden ) . HCV E1E2 constructs , genotype ( gt ) 1b UKN1B5 . 23 ( AY734976 ) ; gt 2a UKN2A1 . 2 ( AY734977 ) ; gt 2b UKN2B2 . 8 ( AY734983 ) ; gt 3a UKN3A1 . 9 ( AY734985 ) ; gt 4 UKN4 . 11 . 1 ( AY734986 ) ; gt 5 UKN5 . 15 . 7 ( AY894682 ) and gt 6 UKN6 . 5 . 8 ( EF427671 ) were generously provided by Dr . J . K . Ball ( University of Nottingham ) . The yeast display vector pYD2 was kindly provided by Dr . J . D . Marks ( UCSF ) [58] . IgG1-Abvec for full-length IgG1 expression was kindly provided by Dr . P . Wilson ( University of Chicago ) . Biotinylated peptides were synthesized using a C-terminal biotin residue with a gly-ser-gly linker ( American Peptide , Sunnyvale , CA ) . Two 1a H77C E2 mutants were constructed containing either Y632A ( E2Y632A ) or D535A ( E2D535A ) substitution . H77C E2 ( GenBank accession no . AF009606 ) , aa384 to 661 , was cloned into the expression vector pSec in-frame with the Igκ signal peptide sequence and fused with a myc and six-histidine tag at the carboxyl terminus . An alanine substitution was introduced at residue 632 or 535 using a QuikChange II site-directed mutagenesis kit ( Agilent , La Jolla , CA ) in accordance with the manufacturer's instructions . Mutations were confirmed by DNA sequence analysis ( Sequetech , Mountain View , CA ) . The constructs were transfected into HEK293T cells via calcium-phosphate method and the supernatant was harvested after 5 days . E2 proteins were affinity purified over His-trap columns . The final products were more than 90% pure as judged by sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) analysis . The conservation of native E2 conformation of wild-type ( wt ) H77C E2 and the two mutants E2Y632A and E2D535A was confirmed using a panel of neutralizing and non-neutralizing HCV HMAbs to conformational epitopes on E2 by ELISA [27] , [44] . An immune library was constructed from peripheral blood B lymphocytes obtained from an asymptomatic individual infected with HCV genotype 2b infection . Total RNA , prepared from one million B cells , as previously described [18] , was converted into cDNA using random hexamers . The cDNA products were used in primary PCR reactions to amplify the gamma heavy chain , kappa light chain and lambda light chain using the primers described elsewhere with the following modifications: for VH primers , the sequences ( 5′-GT GGT GGTGGT TCT GCT AGC GGG GCC ATG GCC-3′ underlined is a NcoI site ) , ( 5′-ACC TCC GGA GCC ACC TCC GCC TGA ACC GCC TCC ACC TGT CGA CCC-3′ underlined is a SalI site ) were added to the 5′ end of the forward and reverse primers respectively . For VL primers , the sequences ( 5′-C GGT TCA GGC GGA GGT GGC TCC GGA GGT GGC GGA TCG -3′ underlined is a BspE1 site ) , and ( 5′-GG GAT AGG CTT ACC TTC GAA GGG CCC GCC TGC GGC CGC-3′ underlined is a NotI site ) were added respectively to all forward Vλ and Vκ primers , and all reverse Vλ and Vκ primers . In addition , a yeast display vector pYD2 . A2 displaying A2-scFv was created by modifying the pYD2 vector , which comprises a [ ( Gly4-Ser ) 3] linker region carrying SalI and BspEI restriction sites and NcoI and NotI restriction sites flanking the inserted scFv . The pYD2 construct also contains an in-frame SV5 epitope . PCR-amplified heavy chain genes were pooled and cloned into vector pYD2 . A2 using NcoI and SalI , yielding a heavy chain library of 5 . 0×106clones , and the library was further digested with BspE1 and NotI for gap repair with the light chain . In parallel , PCR-amplified light chain genes were pooled and ligated into vector pYD2 . A2 using BspE1 and NotI , yielding a light chain library of 5 . 0×106clones . The VL genes from the light chain library were re-amplified with primers HuJHF and Gap3 ( HuJHF: 5′-ACC GTC TCC TCA GGG TCG ACA-3′ , Gap3: 5′-GAG ACC GAG GAG AGG GTT AGG-3′ ) . The resulting repertoire ( 10 µg ) was then directly cloned into 50 µg BspEI- and NotI-pre-digested pYD2 . A2 . VH library through gap repair transformation into Saccharomyces cerevisiae strain EBY100 . Library size was determined by plating serial dilutions of the transformation mixture on SD-CAA plates . This resulted in a yeast surface display of immune yeast antibody library of approximately 2×107 clones . To validate the library , insert frequency and diversity were analyzed using colony PCR , DNA sequencing and SDS-PAGE/Western blot analyses . The yeast library was grown in SG-CAA for 48 hours at 18°C . Magnetic immunobead , MACS ( Miltenyi , Auburn , CA ) , sorting was performed in accordance with the manufacturer's instructions . For the first two MACS selection rounds ( R1 and R2 ) , 2×109 yeast cells were incubated with E2Y632A ( aa384–661 ) at 4°C for 20 min before being loaded onto a pre-treated column containing 25 µl of anti-myc microbeads . The column was then washed , followed by elution of bound yeast cells with 7 ml of SDCAA media using a plunger to push the cells out of the column , and then centrifuged at 2500× g for 5 minutes . The pellet was re-suspended and amplified in SD-CAA , followed by induction in SG-CAA . For the third round ( R3 ) by FACS selection , 1×107 MACS output cells ( designated as non- antigenic domain A cell population ) were incubated with the same E2 proteins ( E2Y632A ) at 4°C for 30 min in FACS wash buffer and then washed in cold wash buffer . The cells were then incubated with anti-V5 ( 1∶5000 , Invitrogen ) and HC-33 . 1 ( an anti-E2 HMAb to a defined epitope [45] ) , at 10 µg/ml for 1 hr at 4°C . The anti-V5 against the SV5 tag was employed to verify correctly displayed scFv on yeast surface . HC-33 . 1 was employed to detect bound E2 on yeast surface . This was followed by another incubation with FITC-anti-mouse ( 1∶200 ) and PE- or APC-anti-human IgG ( Fcγ specific ) for 30 minutes at 4°C in the dark . The labeled cells were washed and re-suspended in FACS wash buffer at 1×107 cells/ml for sorting by flow cytometry . Selection was performed using a BD Bioscience FACS Vantage Sorter and the sorting gates were set to collect the desired double positive cells . Collected cells were grown in SD-CAA medium and used for the next round of sorting after induction in SG-CAA , as described above . For the fourth round by FACS selection ( R4 ) , yeast cells ( 5×106 ) were incubated with E2D535A ( aa384–661 ) ( designated as non-antigenic domain B cell population ) . The cell sorting was performed as described above for the third round by FACS selection . After the final selection , collected cells were plated on SD-CAA plates and grown at 30°C for ∼2 days , after which individual clones were picked , induced , incubated with E2 proteins , and followed by detection with anti-E2 and anti-V5 antibodies . Double-positive clones were first analyzed by fingerprint . The scFv fragment amplified by PCR was digested with BstNI for 1 hour at 60°C . The reactions were separated on 2 . 5% agarose gel . The different banding patterns were analyzed and grouped . Selected clones representing each unique group were then sequenced to identify unique antibody sequences using primers PYFD and PYDR ( PYDFor: 5′-AGT AAC GTT TGT CAG TAA TTG C-3′; PYDRev: 5′-GTC GAT TTT GTT ACA TCT ACA C-3′ ) . The PCR product was then gel-purified and sequenced with primer GAP5 ( Gap 5: 5′-TTA AGC TTC TGC AGG CTA GTG-3′ ) . To produce soluble scFvs , genes encoding scFvs were cloned from pYD2 into pSYN1 ( a gift from Dr . J . D . Marks , UCSF ) , an expression vector imparting a c-myc and a hexahistidine tag at the COOH terminus . After isopropyl β-D-1-thiogalactopyranoside ( IPTG ) induction for overnight at 30°C , bacterial cells were harvested by centrifugation , resuspended in 200 mg/mL sucrose , 1 mM EDTA , 30 mM Tris-HCl ( pH 8 . 0 ) on ice for 30 min , and centrifuged again to collect the supernatant . The pellet was resuspended in 5 mM MgSO4 on ice for 30 min and centrifuged to collect the supernatant . Both supernatants were pooled and loaded on a Ni+-NTA column preequilibrated with 30 mM imidazole/PBS and washed with 30 mM imidazole/PBS . Bound scFvs were eluted with 250 mM imidazole/PBS , dialyzed against PBS , and analyzed by SDS-PAGE , spectrophotometry and Bio-Rad RC DC protein assay ( Bio-Rad , 500-0121 ) . Conversion of scFv to full-length IgG1 was performed essentially as described [59] . In brief , VH and VL genes were PCR-amplified using primers to restore the human framework and append restriction sites . The resulting fragments were cloned into Igγ , Igκ or Igλ mammalian expression vectors containing the signal peptide and constant region genes . IgG1 was expressed by co-transfection of 293 T cells and cultured in serum-free medium . The expression levels were measured by ELISA and the resulting IgG1 were purified using proteinA affinity chromatography [18] . Purity and integrity of the HMAbs were analyzed by reducing and non-reducing SDS-PAGE . Neutralization activities of HMAbs against different HCVcc genotypes were evaluated as previously described [43] , [60]–[62] . Briefly , for neutralization experiments performed with H77C and JFH1 HCVcc , a virus inoculum ( containing 50 FFU ) was incubated with serial dilutions of antibodies for 1 hr at 37°C before inoculation onto Huh-7 . 5 cells ( 3 . 2×104cells/well ) that were seeded 24 hrs previously into 8-well chamber slides ( Nalge Nunc , Rochester , NY ) . After 3 hrs of incubation at 37°C in the presence of 5% CO2 , the inoculum was replaced with 400 µl of fresh complete medium followed by incubation for an additional 72 hrs . Infected cells were fixed and examined for NS3 protein expression by immunofluorescence detection of foci . The entire well was visualized in approximately 16 non-overlapping fields to obtain the number of foci . Each experiment was performed in triplicate . The antibody concentrations ( µg/ml ) causing 50% reduction in FFU ( IC50 ) were determined by linear-regression analysis ( GraphPad Software ) . The percent neutralization was calculated as the percent reduction in FFU compared with virus incubated with an irrelevant control antibody . All assays were performed in triplicate . For neutralization experiments performed with a JFH1-based genotype 1–6 HCVcc panel , a virus inoculum ( ∼100 FFU ) were incubated for 1 h at 37°C with 50 µg/ml specific HMAbs prior to 3 h incubation with 6×103 Huh7 . 5 cells/well in poly-D-lysine-coated 96-wells plates ( Nunc ) . Cells were fixed and immunostained against NS5A 48 h post-infection [11] , [60] , [63] , [64] . For each test , neutralization was done in eight replicates with 12 control wells containing the virus only , performed in two separate experiments by two investigators . Percentage neutralization was calculated in relation to the mean of virus only controls . Titration studies to calculate IC50 against genotype 1–6 HCVcc were similarly performed with selected antibody but with four replicates and 6 control wells containing only the virus ( GraphPad Software ) . HCVpp were produced as described [20] , [65] by co-transfection of 293 T cells with pNL4-3 . Luc . R−E− plasmid containing the env-defective HIV proviral genome and an expression plasmid encoding the HCV glycoproteins or mutant E1E2 proteins . For the neutralization assay , the virus-containing medium was incubated with each HMAb at various concentrations , or phosphate-buffered saline instead of the antibodies as an infectivity control , plus 4 µg/ml polybrene at 37°C for 60 minutes [66] , [67] . The HCVpp-antibody mixture was transferred to Huh7 . 5 cells ( 8×103 cells/well ) pre-seeded in 96-well plates , and infections were centrifuged at 730×g for 2 hrs at room temperature . After incubation at 37°C in the presence of 5% CO2 for 15 hrs , the unbound virus was replaced with fresh complete medium , followed by additional incubation for a total of 72 hrs . The neutralizing activity of an antibody was calculated as the percent reduction of luciferase activity compared with an inoculum containing phosphate-buffered saline ( PBS ) . For HCVpp infectivity studies , the virus-containing extracellular medium was normalized for HIV p24 expression using a QuickTiter lentivirus titer kit ( Cell Biolabs , San Diego , CA ) . All assays were performed in triplicate . ScFv binding kinetics were measured using surface plasmon resonance in a BIAcore 3000 ( Pharmacia Biosensor ) and used to calculate the KD . Approximately 135 , 000 response units ( RU ) of CBH-4D , an anti-E2 HMAb to a conformational epitope [18]–[20] , were coupled to a CM5 sensor chip by using N-hydroxysuccinimide ( NHS ) and 1-ethyl-3- ( 3-dimethylaminopropyl ) carbodiimide ( EDC ) . Approximately 250 RU of purified secreted E2 ( sE2 ) in HBS-EP buffer ( 10 mM HEPES pH 7 . 4 , 150 mM NaCl , 3 mM EDTA , 0 . 005% v/v Surfactant P20 . ) ( GE Healthcare , BIAcore BR-1001-88 ) were captured by CBH-4D onto the surface of the chip . Another flow cell without sE2 capture was set as reference . The purified HC-84 . XX scFv at concentrations ranging 1000 nM-31 . 25 nM ( with two-fold serial dilution ) was injected for 2 minutes using a flow rate of 30 µl/min . Dissociation of bound HC-84 . XX scFv in HBS-EP buffer flow was followed for 3 min . The surfaces ( E2 and HC84 . XX scFv ) were regenerated after each cycle using regeneration solution ( 10 mM glycine-HCl , pH 2 . 5 ) . All sensorgrams were double-referenced before data analysis . First , the response from the reference flow cell ( without E2 ) was subtracted . Second , the response from an average of two-blank injections ( 0 nM E2 ) of HBS-EP buffer was subtracted . The sensorgrams ( duplicates for each concentration ) were globally fit with parameters Kon ( association rate constant ) and Koff ( dissociation rate constant ) using Scrubber 2 . 0 ( Center for Biomolecular Interaction Analysis , University of Utah , UT ) . KD was calculated as Koff/Kon . ELISA were performed as described [68] to measure antibody binding to the wt E1E2 from different genotypes or mutant E2 glycoproteins and to measure E2 binding to CD81 . Briefly , microtiter plates were prepared by coating each well with 500 ng of GNA and blocking with 2 . 5% non-fat dry milk and 2 . 5% normal goat serum . Lysates of cells expressing wt HCV E1E2 from different genotypes , mutant E1E2 , denatured E1E2 proteins or pelleted HCVpp were captured by GNA onto the plate and later bound by a range of 0 . 01–100 µg/ml of HMAb . E1E2 protein was denatured by incubation with 0 . 5% sodium dodecyl sulfate and 5 mM dithiothreitol for 15 min at 56°C . The bound HMAb was detected by incubation with alkaline phosphatase-conjugated goat anti-human IgG ( Promega; Madison , WI ) , followed by incubation with p-nitrophenyl phosphate for color development . Absorbance was measured at 405 nm and 570 nm . Data was analyzed for statistical significance by unpaired student's t-test , using Prism software ( GraphPad Software ) . In the case of the peptide ( “epitope II" [23] , [24] ) binding assay , biotinylated peptide at 2 µg/ml was captured in microtiter plates by streptavidin . The wells were then incubated with either antibodies at 10 µg/ml or human sera at 1∶100 dilution . Binding was detected after anti-human IgG-horseradish peroxidase incubation and TMB peroxidase substrate color development . For the peptide competition assay , E2 protein expressed in 293 T cells was captured in microtiter plates by GNA . The wells were then incubated with antibodies that were pre-incubated with peptide at concentrations 0 , 2 , 5 , 10 , 20 and 40 µg/ml . Antibody binding was detected as described above . Partially purified HCVpp , obtained by pelleting virus through a 20% sucrose cushion , were dissolved in TNE buffer ( 10 mM Tris-HCL , pH 7 . 4 , 150 mM NaCl and 1 mM EDTA ) . Equal amounts of HCVpp samples adjusted by the amount of HIV p24 incorporation were boiled for 3 min in Laemmli reducing sodium dodecyl sulfate ( SDS ) sample buffer and were electrophoresed in pre-cast 10% polyacrylamide gel ( Invitrogen , Carlsbad , CA ) . Following SDS-PAGE , separated proteins were transferred to nitrocellulose membranes and were immunoblotted with anti-E2 HMAb , HC-33 . 1 or MAb 6/82a , and anti-E1 HMAb , H-111 . HIV p24 was identified by an anti-HIV p24 antibody ( Invitrogen ) as a loading control . After washing , the blots were probed with secondary antibodies ( horseradish peroxidase-conjugated anti-human or anti-mouse IgG from Santa Cruz Biotech ) and visualized by enhanced chemilumminescence ( Amersham Pharmacia ) . Western images were captured using ChemiDoc imager system ( Bio-Rad , Richmond , CA ) . HMAbs at different concentrations were incubated for 20 min at 4°C with lysates of cells expressing wt H77c E1E2 . The mixture was added to microtiter plates pre-coated with anti-GST that captured recombinant fusion proteins containing the large extracellular loop of human CD81 fused to glutathione S-transferase . After 1 hr incubation at 4°C with gentle agitation , the wells were washed and 5 µg/ml of anti-cmyc was added to the wells , followed by incubation and addition of 100 µl/well of 1/10 , 000-diluted alkaline phosphatase-conjugated anti-mouse IgG ( Promega , Madison , WI ) . After color development , the plate was read at 405/570 nm using spectroMax 190 . The percentage of binding inhibition was calculated as reduction of E2 binding to CD81 compared to the value that was obtained in the absence of antibody . Background signal for binding of E2 to human CD81 was determined from wells coated with murine CD81-LEL . Signals obtained with biotinylated-CBH-4D and E2 in the presence of competing antibody were compared to signals obtained from biotinylated-CBH-4D and E2 in the absence of competing antibody . Epitope mapping was performed using alanine substitution mutants of three defined E2 regions ( region 1: aa418–446; region 2: aa526–536; region 3: aa611–617 ) by ELISA . Alanine substitution mutants were constructed in plasmids carrying the 1a H77C E1E2 coding sequence ( GenBank accession nos . AF009606 ) as previously described [43] . All the mutations were confirmed by DNA sequence analysis ( Sequetech , Mountain View , CA ) for the desired mutation and for exclusion of unexpected residue changes in the full-length E1E2 encoding sequence . The resulting plasmids were transfected into HEK293T cells for transient protein expression using the calcium-phosphate method . The mutated constructs were designated X#Y , where # is the residue location in H77C , X denotes the single-letter code for the H77C amino acid , and Y denotes the altered amino acid . JFH-1 2a HCVcc was employed in studies to isolate escape variants from HC-84 HMAbs and performed essentially as described [43] , [56] and diagramed in Figure S3 . Briefly , Huh7 . 5 cells ( 3 . 2×104/ml ) seeded 24 hrs previously in a 24-well plate were inoculated with a mixture of HCVcc ( 1×104 FFU ) and test antibody . The initial concentration of the neutralizing antibody employed to isolate escape HCVcc mutants was adjusted to the 50% inhibitory concentration ( IC50 ) of the antibody against the 2a HCVcc . Infectious virus was first incubated with the selection antibody for 1 hr at 37°C prior to inoculation onto naïve Huh7 . 5 cells . This was followed by a second incubation for 3 hrs at 37°C before the medium was replaced with fresh medium containing the same antibody concentration . The cultures were maintained for three days in the presence of individual HC-84 antibody or R04 ( as mock human IgG selection ) . The cells were collected for analysis by indirect immunofluorescent assay ( IFA ) and the extracellular virus was harvested for virus titration , the next passage of selection , and stored for future viral sequence analysis . The entire process constituted one passage of infectious virus under a specified antibody concentration . At each antibody concentration , the virus was repeatedly passaged until the virus titer reached 1×104 FFU/ml . The number of passages required for this purpose varied from antibody to antibody . If the virus titer was ≥104 FFU/ml , extracellular virus was subjected then to the next round of higher antibody concentration . Starting at IC50 , the antibody concentration was progressively increased ( 0 . 002 , 0 . 001 , 0 . 005 , 0 . 01 , 0 . 05 , 0 . 1 , 0 . 5 , 1 , 5 , 10 and 100 µg/ml ) . Viral growth was measured by FFU assay and the emergence of escape variants was monitored weekly by two-color confocal immunofluorescence microscopy staining with the respective neutralizing antibody and an anti-NS3 antibody . Confocal immunofluorescence microscopy , focus-forming unit ( FFU ) assay used in virus titers determination and viral yield assay were performed as previously described [43] , [50] . Selected viral supernatants were used for virus amplification followed by sequencing E2 genes to map the escape mutations . Viral supernatants were used for neutralization studies against escape mutants and as a source of virus stock . If and when virus under antibody selection reached an undetectable level , the selection antibody was withdrawn from the medium , and the culture was continued and monitored for an additional two passages . Total RNA or viral RNA from virus-infected cells or virus-containing culture supernatant was extracted using commercial kits ( Qiagen , Valenica , CA ) . cDNA of the E2 glycoprotein was synthesized with SuperScript III reverse transcriptase ( Invitrogen , Carlsbad , CA ) by using primer p7rev CCCGACCCCTGATGTGCCAAGC in a 20-µl reaction of the manufacturer's recommended buffer . Subsequent amplification was performed in a 50-µl reaction using the Expend High Fidelity PCR system ( Roche Applied Sciences , Indianapolis , IN ) and primers E1fwd GGTCATCATAGACATCGTTAGC and p7rev CCCGACCCCTGATGTGCCAAGC . The PCR consisted of 30 cycles at 94°C for 60 seconds , 45°C for 60 seconds , and 72°C for 90 seconds . A total of 2 µl of the resulting PCR product was used as template for a nested amplification , using primer pair E2F GGCACCACCACCGTTGGAGGC & E2R TGCTTCGGCCTGGCCCAACAAGAT . This second round of PCR comprised 25 cycles at 94°C for 60 seconds , 55°C for 60 seconds , and 72°C for 90 seconds . In some cases , when viral titer was low and failed to amplify the E2 gene , the number of PCR cycles in the nested round was increased . The PCR products were purified with the QIAquick gel extraction kit ( Qiagen , Valencia , CA ) , ligated into the TOPO cloning vector ( Invitrogen , Carlsbad , CA ) , and individual clones containing an insert of the expected size were sequenced in both sense and antisense strands ( ElimBiopharm , Hayward , CA ) . | Hepatitis C virus ( HCV ) is a highly diverse virus and a significant challenge for vaccine development is to identify protective epitopes conserved in the majority of viral genotypes and subtypes . This problem is compounded by the fact that the envelope E1E2 proteins , the targets for neutralizing antibody response , are two of the most variable proteins of the virus . Modified E2 antigens were constructed that are not bound by antibodies to previously recognized clusters of highly immunogenic epitopes on E2 . Their employment as screening antigens has led to the isolation of a novel panel of human monoclonal antibodies to HCV E2 . Functional and biochemical studies revealed that these antibodies bind and neutralize HCV of different genotypes and subtypes . Several of these antibodies neutralized cell culture infectious HCV with genotypes 1–6 envelope proteins . Furthermore , when virus was passaged in culture in the presence of each of these antibodies , virus escape was not observed . Thus , these epitopes are relevant in vaccine design for this virus . | [
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] | 2012 | Human Monoclonal Antibodies to a Novel Cluster of Conformational Epitopes on HCV E2 with Resistance to Neutralization Escape in a Genotype 2a Isolate |
Dark-grown seedlings exhibit skotomorphogenic development . Genetic and molecular evidence indicates that a quartet of Arabidopsis Phytochrome ( phy ) -Interacting bHLH Factors ( PIF1 , 3 , 4 , and 5 ) are critically necessary to maintaining this developmental state and that light activation of phy induces a switch to photomorphogenic development by inducing rapid degradation of the PIFs . Here , using integrated ChIP–seq and RNA–seq analyses , we have identified genes that are direct targets of PIF3 transcriptional regulation , exerted by sequence-specific binding to G-box ( CACGTG ) or PBE-box ( CACATG ) motifs in the target promoters genome-wide . In addition , expression analysis of selected genes in this set , in all triple pif-mutant combinations , provides evidence that the PIF quartet members collaborate to generate an expression pattern that is the product of a mosaic of differential transcriptional responsiveness of individual genes to the different PIFs and of differential regulatory activity of individual PIFs toward the different genes . Together with prior evidence that all four PIFs can bind to G-boxes , the data suggest that this collective activity may be exerted via shared occupancy of binding sites in target promoters .
A key component of the successful colonization of land by terrestrial flowering plants was the evolution of a developmental strategy termed skotomorphogenesis ( etiolated growth ) . This strategy enabled post-germinative seedlings emerging from buried seed to grow heterotrophically , on seed reserves , rapidly upwards through the subterranean darkness to the soil surface . Coupled with this was the evolution of a photosensory mechanism to trigger a switch to autotrophic , photomorphogenic ( deetiolated ) development upon emergence into sunlight . Genetic evidence indicates that a small subfamily of basic helix-loop-helix ( bHLH ) transcription factors , termed PIFs ( for Phytochrome ( phy ) -Interacting Factors ) are centrally critical to the promotion of such skotomorphogenic development in dark-grown seedlings [1] . A quadruple pif mutant ( pifq ) , lacking PIF-family members PIF1 , PIF3 , PIF4 and PIF5 ( termed the PIF quartet ) , displays morphogenic development in total darkness that strongly phenocopies that of normal light-grown seedlings [2] , [3] . This observation establishes that these factors act constitutively to promote skotomorphogenic development and that their absence induces the switch to photomorphogenic development . All four quartet members have been shown individually to bind preferentially to a core G-box DNA-sequence motif ( CACGTG ) ( a variant of the canonical E-box motif ( CANNTG ) ) [4]–[9] , and to function as transcriptional activators in transfection or heterologous systems [4]–[7] , [10] . Because monogenic mutants at each of these loci have no , or minimal , visible effects on skotomorphogenesis , and the various double and triple pif-mutant combinations progressively exhibit increasingly photomorphogenic phenotypes in darkness , it appears that the PIF quartet members act with partially additive or overlapping redundancy to drive the skotomorphogenic pathway [2] , [3] , [11]–[13] . The phy family of sensory photoreceptors ( especially phyA and phyB ) has a central role in inducing the switch from skotomorphogenic to photomorphogenic development ( deetiolation ) in response to initial exposure of dark-grown seedlings to light [1] , [14] , [15] . The existing evidence indicates that this is achieved in large part by rapid phy-triggered degradation of the PIF proteins . The mechanism underlying this process involves the rapid , light-induced translocation of the activated ( Pfr ) conformer of the phy molecule from the cytoplasm into the nucleus , where it physically interacts with PIF-quartet members . This interaction induces phosphorylation of the PIF proteins which in turn triggers ubiquitylation and proteolytic degradation of the transcription factors ( half-lives of 5–20 min ) via the proteasome system . The altered transcriptional landscape resulting from the consequent robust reduction in steady-state abundance of these factors is the major driving force in the switch from heterotrophic to autotrophic development inherent in the deetiolation process . A limited number of transcriptome analyses , using Affymetrix ATH1 microarrays , aimed at identifying genes regulated by the phy-PIF signaling pathway during deetiolation have been reported [3] , [12] , [16]–[19] . The data show that 80% of the genes that display altered expression in the pifq mutant in the dark are normally altered by prolonged light in fully deetiolated wild-type ( WT ) seedlings [17] , but that only a relatively small fraction of these are misexpressed in dark-grown pif1 [19] , pif3 [16]–[18] and pif4pif5 mutants [12] . These results affirm the central collective regulatory function of these four PIFs in regulating the overall transcriptional network that drives the developmental switch from skotomorphogenesis to photomorphogenesis , and provide initial indications of functional redundancy at the gene expression level . These genes could be either direct or indirect targets of PIF transcriptional regulatory activity [20] . Identification of those genes that respond rapidly ( within 1 h ) to initial light exposure has defined a subset of PIF-regulated genes that are likely to be enriched for loci that are directly transcriptionally regulated by the PIF-quartet proteins [17] . PIF-regulated genes that conversely respond rapidly to vegetative shade in fully-green , light-grown plants have also been identified by microarray-based expression profiling [11] , [21] . It is notable that these early-response genes are enriched for transcription-factor-encoding loci , suggesting a potential hierarchal network that drives a transcriptional cascade . However , rapid responsiveness alone obviously does not establish that transcriptional regulation is direct . The advent of ChIP-chip and ChIP-seq technology has provided the opportunity to identify genes that contain binding sites for transcription factors of interest , on a genome-wide scale [20] , [22] , [23] . When combined with full transcriptome analysis , the data provide identification of genes that are direct targets of transcriptional regulation by the factor ( s ) under study . A number of such studies have recently been reported for a diversity of factors in Arabidopsis , using either ChIP-chip or ChIP-seq analysis of factor binding sites , coupled predominantly with Affymetrix ATH1 microarrays ( representing about 80% of the protein-coding genes in the genome ) for expression analysis [21] , [23]–[29] . These data have begun to provide insight into the complexity of the transcriptional networks that coordinate a variety of the fundamental processes underlying plant growth and development . Despite these advances , the use of the ATH1 microarray for expression analysis in many of these studies means that important expression changes in genes not present on this array might have been missed . In addition , the question of whether , and to what extent , closely related transcription-factor family members , such as the PIF quartet , with apparently shared DNA-target-sequence specificity , contribute toward the transcriptional regulation of common target genes does not appear to have been addressed in many existing studies of eukaryotic systems [27] , [30]–[34] , although a recent report by Hornitschek et al shows differential binding of recombinant PIF4 and PIF5 to various E-box variants in vitro using protein-binding microarrays , as well as shared binding in vivo to four selected promoters using ChIP-PCR analysis [21] . Here , using ChIP-seq analysis , we have identified PIF3-binding sites , genome wide , and , in parallel , using RNA-seq analysis of selected pif-mutant lines , we have defined the genes regulated by PIF3 , genome-wide , in dark-grown seedlings . By merging these datasets , we have identified those genes whose expression is , at least partially , directly regulated by promoter-bound PIF3 . In addition , by profiling the expression of a selected subset of these direct PIF3-targets in multiple additional pif-mutant combinations , we have addressed the question of whether PIF1 , 3 , 4 and 5 display qualitative and/or quantitative functional divergence in regulating shared target genes .
Two-day-old dark-grown wild-type ( WT ) and MYC-epitope-tagged-PIF3 ( P3M ) -expressing , pif3-3 null-mutant seedlings were used for ChIP-seq analysis . DNA prepared from MYC-antibody-generated immunoprecipitates from four independent biological replicates of each genotype was subjected to high-throughput sequencing . Statistically-significant binding peaks were defined by comparing the parallel P3M and WT ChIP samples within each replicate using the MACS algorithm [35] . Replicate-specific peaks ( Table S1 ) were defined as reproducible if they were identified at the same genomic location in two or more biological replicates ( overlapping Venn sectors in Figure 1A; also Table S2 ) . For each reproducible peak , we assigned a common summit as the mean of the individual replicate-specific summits . This analysis identified 1064 reproducible peaks which form our “high-confidence” set of PIF3-binding sites ( Table S2 ) . These sites are evenly distributed on the five chromosomes and 89% are located in intergenic regions ( Figure 1B ) . In ChIP-qPCR validation assays , all but 1 of the 38 tested regions exhibited strong binding enrichment in the P3M samples compared to the WT controls ( Figure S1 ) , indicating a low false positive rate for our ChIP-seq procedure . Using the MEME program [36] , we performed de novo motif discovery on the +/−100 bp regions surrounding the 1064 “high-confidence” PIF3 binding-peak summits described above . Two E-box ( CANNTG ) variants were identified as statistically overrepresented motifs within these PIF3-binding regions ( Figure 1C ) . The CACGTG ( ‘G-box’ ) variant is well-established as a preferred PIF-binding motif [4]–[9] . By contrast , the CACATG variant is previously undescribed as a PIF3-binding motif , although PIF1 [37] and , recently , PIF4 [21] have been reported to bind . We conclude that this variant is a strong candidate for being a general alternative binding motif for PIF3 across the genome , and define it , therefore , as the PIF-binding E-box ( PBE-box ) . The relative distribution of these two motifs across the 1064 PIF3 binding-sites is summarized in Figure 1D . A majority ( 73% ) of the sites contain one or both motifs ( G-box 50% and PBE-box 36% , with 13% overlap ) within the 200-bp window . A broader analysis shows that 64% of the G-box and 30% of the PBE-box motifs present in the 2 kb windows surrounding the PIF3-binding summits cluster within the designated 200-bp binding sites ( Figure 1E ) . Similarly , both motifs are strongly enriched in these 200-bp windows compared to random 200-bp genome segments , and this enrichment increases toward the PIF3-binding summit ( Figure 1F ) . These data establish the highly significant coincidence between PIF3 and these two specific cis-elements . To examine the potential direct interaction of PIF3 with the newly-identified PBE-box compared to that of the G-box , we performed DNA-Protein-Interaction ( DPI ) -ELISA [38] . We tested the binding of PIF3 to several G-box- ( PIL1 , PHYB , and RGA1 ) or PBE-box- ( IAA2 , IBH1 , and AT4G30410 ) containing probes generated from various genomic PIF3-binding sites identified in the ChIP-seq analysis . Figure 1G shows that recombinant PIF3 binds sequence-specifically to all G-box- and PBE-box-containing probes , although the apparent affinity for the G-box seems overall to be higher than for the E-box . An EMSA analysis showed similar results ( Figure S2 ) . These in vitro binding-assay data indicate that the coincidence of PIF3-binding sites with the G-box or PBE-box motif in the ChIP-seq assay likely results from their direct interaction in vivo , and that the PBE-box is indeed another sequence-specific PIF-binding , E-box variant genome wide . Because all of the PIF3-binding sites tested by ChIP-qPCR in Figure S1 contain coincident G- or PBE-box motifs , these data validate the in vivo-binding of PIF3 to these motifs . The binding of PIF3 to the ATHB-2 probe , which contains one G-box and one PBE-box , provides an interesting insight . Neither the competitor mutated in both motifs ( Figure 1G; also Figure S2 ) , nor the competitor mutated only in the G-box motif ( Figure S3 ) displayed competitive activity , whereas the probe mutated only in the PBE-box motif showed competitive efficiency similar to that of the WT sequence ( Figure S2 ) . These findings suggest that PIF3 may have differential binding affinity toward these two motifs in specific genomic contexts . Although all ChIP-defined transcription factor binding sites may prove to be functionally significant , we have chosen here to focus on identifying those genes displaying motif-coincident PIF3-binding sites located in conventional promoter regions ( defined here as “PIF3-bound genes” ) . Initially , from the 1064 binding sites defined above , we identified 709 sites that are both intergenic and G- and/or PBE-box-coincident ( Table S2 ) . For these 709 sites , we defined PIF3-bound genes as having a binding site in the 5′ flanking DNA , within 5 kb of the transcription start site ( TSS ) , in the absence of intervening genes . This analysis identified 596 PIF3-binding sites , with 828 associated genes , where some sites are associated with two genes on opposite strands . Of these genes , 88% have PIF3-binding sites within 3 kb of TSS , whereas the remaining 12% have sites between 3 and 5 kb upstream ( Table S3 ) . These 828 genes thus constitute a set of PIF3-bound genes whose transcription is potentially directly regulated by PIF3 . To provide genome-wide visualization of the ChIP-seq analysis , we developed a platform using the Integrated Genome Browser [39] . Figure 2A shows the chromosomal regions around PIL1 and ATHB-2 , as examples . The chromosomal region surrounding the PIL1 gene shows a single PIF3-binding peak that is coincident with three G-box motifs located in the PIL1 promoter region . ATHB-2 is somewhat unusual in that it displays five specific PIF3-binding peaks in its extensive 5′-upstream region , each coincident with 1 to 3 G-box motifs ( Figure 2A ) . ChIP-qPCR analysis scanning across the PIL1 genomic region provides robust validation of the ChIP-seq data for this gene ( Figure 2B ) . To identify the genes regulated by PIF3 , genome-wide , in the promotion of skotomorphogenic development , we performed 3′-end-capture directional RNA-seq analysis , comparing the expression profiles of 2-d dark-grown WT , pif3 , pif1pif4pif5 ( pif145 ) and pif1pif3pif4pif5 ( pifq ) Arabidopsis seedlings . Genes displaying Statistically-Significant Two-Fold ( SSTF ) expression changes in the three mutant genotypes compared to the WT and each other were identified as being regulated by the relevant mutated PIF ( s ) ( Figure 3; listed in Tables S4 , S5 , S6 , S7 , S8 ) . The degree of overlap between SSTF genes identified in these comparisons is depicted in the Venn diagrams in Figure 3B , 3C and 3D . We defined a combined total of 345 genes in the pif3/WT and pifq/pif145 gene-sets as the composite PIF3-regulated gene-set ( Table S9 ) . Similarly , a combined total of 1454 genes in the pif145/WT and pifq/pif3 gene-sets were defined as the composite PIF1/4/5-trio regulated gene-set ( Table S10 ) . Comparison of these composite gene-sets is displayed in Figure 3D . The data indicate that 254 ( 74% ) PIF3-regulated genes are also redundantly transcriptionally regulated by one or more of the other three PIF-quartet proteins . Conversely , 1740 ( 86% ) PIF-quartet-regulated genes show no significant PIF3 dependence ( Figure 3B ) , whereas 918 ( 45% ) do display PIF1/4/5 regulation ( Figure 3D ) , indicating that one or more of the other PIF-quartet members function non-redundantly with PIF3 in regulating the expression of many target genes . The general robustness of our genome-wide RNA-seq expression profiling is demonstrated by the extensive RT-qPCR validation data presented in Figure S3 . To identify the genes that both physically bind PIF3 in their promoters , in a G-box- or PBE-box-coincident manner , and display PIF-regulated expression ( defined here as “direct-target genes” ) , we merged our ChIP-seq and RNA-seq data . This permitted gene-by-gene visualization of the PIF3-binding peaks and PIF-dependent transcription , genome-wide , as shown for PIL1 and ATHB2 in Figure 2A . The expression data for these two genes show a clear difference in transcript levels between the WT and pifq mutant , demonstrating the robust dependence of full expression on the presence of the PIF-quartet . Comparison of the expression peaks for the pif145 and pifq mutants also suggests that PIF3 acts in the absence of the other three quartet members to promote a moderate increase in transcript levels . Overall , the combined PIF-regulated expression-patterns and promoter-located PIF3-binding sites displayed by these two genes render them likely direct-targets of transcriptional regulation by PIF3 and one or more other quartet members in promoting skotomorphogenic development . The Venn diagrams in Figure 4 show the genome-wide overlap of the genes identified independently as displaying PIF-quartet- and/or PIF3-dependent expression in a SSTF manner , with those exhibiting promoter-located , motif-coincident PIF3-binding sites ( Figure 4A , Classes X , Y and Z; listed in Table S11 ) . By these criteria , a total of 22 genes ( Classes X and Z ) were identified as robustly-likely , direct-target genes of autonomous-PIF3 transcriptional regulation . Of these , 21 genes ( 19 PIF3-induced; 2 PIF3-repressed ) also display collective PIF-quartet regulation ( Class Z ) . The 19 PIF3-induced Z-Class genes are listed in Table 1 . The bar graphs in Figure 4B and 4C portray the mean expression level ( relative to WT ) of all the genes in each class , for each pif genotype . The quantitatively robust responsiveness of the PIF3-bound genes to the presence of PIF3 in the pif145 mutant background is evident from these data ( Class Z-associated bar graphs ) . This robust PIF3-responsiveness was validated using RT-qPCR for selected members of the 19 PIF3-induced , Z-Class gene-set , having a range of quantitative dependence on this bHLH factor ( Figure S3A ) . A striking feature of our data is the relatively large number of PIF-quartet-regulated genes ( 107 total genes; 88 PIF-induced and 19 PIF-repressed ) that display promoter-located , PIF3-binding sites , but lack evidence of SSTF-level PIF3 regulation in our RNA-seq analysis ( Figure 4 , Class Y; also Table S11 ) . Nevertheless , the bar graphs of the mean expression of these genes suggest a tendency toward a consistent difference in expression between the pif145 and pifq mutants , across the gene-set . In addition , combined analysis of the full set of PIF-induced , PIF3-bound genes ( Classes Y and Z together ) shows that there is a reciprocal continuum in the magnitude of the relative contributions of PIF3 and the PIF1/4/5-trio to the collective activity of the PIF quartet in transcriptionally activating these genes ( Figure S4 ) . To more closely examine the PIF3 contribution to the total PIF-quartet activity in the Y-Class genes , we therefore arrayed these genes by the pif145/pifq fold-change value and assayed the relative expression levels in 20 selected PIF-induced loci by RT-qPCR . Figure S3B shows that the 17 genes in this group with fold-changes >1 . 5 by the RNA-seq analysis , all exhibit statistically-significant ( Student's t-test , P<0 . 05 ) , PIF3-promoted expression increases in the pif145 mutant compared to the pifq mutant by RT-qPCR . This suggests that a subset of Y-Class genes may represent additional bona fide autonomously-PIF3-regulated genes that are below the resolution of the SSTF criteria we imposed on our RNA-seq analysis . We have therefore designated these 17 as Class YZ1 . 5 genes , having moderate ( >1 . 5-fold ) , but statistically significant , regulation by PIF3 ( Table 1 ) . The evidence indicates , therefore , that a combined total of at least 38 YZ1 . 5- and Z-Class genes are direct targets of moderate to robust transcriptional regulation by promoter-bound PIF3 . Because an additional 34 of the 88 Y-Class genes also display >1 . 5-fold PIF3-induced expression ( Figure S3B and Table S11 ) , it is possible that the number of direct targets of partial PIF3 transcriptional regulation is yet larger . The W-class genes are those that display promoter-localized , G- or PBE-box-coincident PIF3-binding peaks , but no differential expression between the pifq mutant and wild type ( Figure 4A ) . This observation is consistent with data from a variety of organisms that have shown that transcription factors vary greatly in their number of genomic binding sites , and that binding events can vastly exceed the number of known or possible direct gene targets [40] . The reasons for this phenomenon here are unclear but could include functional redundancy with other factors , including other PIF proteins . Consistent with this possibility , a subset of 41 of the total 699 W-class genes exhibit rapid light responsiveness [17] upon initial exposure ( Table S12 ) . The reciprocal continuum in relative PIF3 and PIF1/4/5-trio contributions to the collective PIF-quartet transcriptional activation of PIF-induced , Y- and Z-class genes referred to above ( Figure S4 ) , indicates that PIF1 , 4 and/or 5 contribute substantially to the regulation of these PIF3-bound genes . To identify the individual genes in this set displaying a significant PIF1/4/5 contribution , we compared the Y- and Z-class genes ( Table S11 ) with those defined above as PIF1/4/5-regulated ( Figure 3D; also Table S10 ) . Overall , 92 ( 72% ) of the 128 combined Y- and Z-class genes exhibit regulation by PIF1 , 4 and/or 5 ( Table S11 ) , as shown by significant differences in the pif145/WT and/or pifq/pif3 comparisons . More notably , all 38 PIF3-induced direct-target genes ( Class YZ1 . 5 and Z ) are also PIF1/4/5-induced ( Table 1 ) . Because all four PIFs have been shown to bind to the G-box motif in sequence-specific fashion [4]–[9] , it appears probable that these PIF-quartet-regulated genes , displaying promoter-located PIF3-binding sites ( Figure 4; also Table S11 ) , may be directly regulated by one or more of the other quartet members , in addition to , or instead of , PIF3 . Categorization of the YZ-class genes by the known or predicted functions of their encoded products reveals substantial enrichment in a diversity of transcription-factor-encoding genes ( Table 1; also Figure S5 and Table S11 ) , consistent with the concept that these multiple direct targets of the PIF quartet function at the apex of a primary transcriptional-cascade to regulate the downstream transcriptional network . It is also notable , however , that a considerable number of the YZ-class genes that have other cellular functions are also apparent direct targets of transcriptional regulation by the PIFs , including two non-protein-encoding genes of unknown function ( Table 1 ) . Previously , by microarray profiling , we identified a subset of genes ( designated Class 7 ) that , in dark-grown seedlings , exhibit a PIF-quartet-dependent expression pattern , that is rapidly reversed ( within 1 h ) upon initial exposure to phy-activating R light [17] . Of the 24 rapidly light-repressed Class 7 genes displaying promoter-localized , G- or PBE-box-coincident PIF3-binding peaks , 21 ( 88% ) are either Class YZ1 . 5 or Z genes here ( Table 1 ) . These genes are thus identified as a subset whose expression is directly promoted , at least partially , by PIF3 transcriptional activation in the dark , and is rapidly reduced in the light , at least in part , by photoactivated-phyB-induced PIF3 degradation . It is notable that 9 of these genes ( 43% ) encode transcription factors ( Table 1 ) , indicative of being master regulators at the apex of the downstream transcriptional cascade controlled by the phy signaling pathway . In striking contrast to the light-repressed Class 7 genes , only 7 of the 115 rapidly light-induced Class 7 genes ( 6% ) [17] display PIF3-binding peaks that are coincident with a G-box or PBE-box , and of these only 2 genes ( <2% ) ( PSY and KAI2 ) exhibit derepression here in the dark-grown pifq mutant . No individual PIF3 contribution to this repression was detectable here . Collectively , these data indicate that PIF3 acts predominantly , if not exclusively , to activate the expression of direct-target genes in dark-grown seedlings . Conversely , the 94% ( 108/115 ) of light-induced Class 7 genes that do not display G- or PBE-box coincident PIF3-binding peaks , might suggest that one or more of the proposed direct targets of the PIF quartet ( Table 1 ) can act as key repressor ( s ) that regulate a diverse set of light-induced genes . Recently , we defined a small core set of 14 Class 7 PIF-quartet-regulated genes ( called M-Class genes ) that display rapid , reciprocal , transcriptional responsiveness to light and vegetative shade in dark-grown and light-grown seedlings , respectively [11] . Our present analysis shows that 11 ( 79% ) of these M-Class genes are identical to those identified here as dual Class 7 and YZ1 . 5-/Z-Class genes ( Table 1 ) , indicating that they are likely direct targets of PIF3 regulation , not only during skotomorphogenesis and deetiolation , but also subsequently , on a continuing basis , through juvenile vegetative development . The correlated PIF3-binding and PIF-regulated transcriptional behavior of several of these M-Class genes , determined by merging the ChIP-seq and RNA-seq data , is depicted in Figure S6 . Because our data indicate that the contribution of PIF3 to the total level of expression collectively regulated by the PIF-quartet is quantitatively variable between genes ( Figure 2A; Figure 4B , 4C; Figures S3 and S4 ) , we wished to determine whether the other members of the PIF-quartet display a similarly variable pattern of regulation . For this purpose , we assayed the expression by RT-qPCR of a selected set of apparently PIF3 direct-target genes ( Classes Z and YZ1 . 5 ) , in the four different pif triple mutants compared to the pifq mutant and WT ( Figure 5A ) . The relative autonomous contribution of each individual PIF ( in the absence of the other three quartet members ) to the total , collective PIF-quartet-supported expression was calculated as a percentage of the total difference in expression between the WT and pifq mutant , for each separate gene . The data reveal a striking diversity of relative contributions , both between the individual PIFs , and between genes for any individual PIF , in two-dimensional-matrix fashion ( Figure 5B ) . Particularly notable is the dominant role played by PIF1 in promoting the expression of the majority of these genes . On the other hand , PIF3 contributes strongly to ARF18 , SNRK2 . 5 and BBX28 expression , PIF4 strongly activates ST2A and ATHB-2 , PIF5 contributes actively to AT5G02580 , ATHB-2 and XTR7 expression , while all four PIFs contribute substantially to IAA19 transcription . Because all these tested genes are prospective direct-target genes of multiple PIF-quartet members , our findings suggest that there is an intricate combinatorial network , in which the individual PIF-quartet factors collaborate to transcriptionally regulate an array of direct-target genes , through potentially common DNA binding sites , with quantitatively differential regulatory activity . Comparison of our data with recently published ChIP-seq-identified PIF4- and PIF5-binding sites [21] , [24] supports this conclusion ( Figure S7 ) . Although the three studies were performed under contrasting experimental conditions , our analysis shows that 82% of genes with promoter-located , G- or PBE-box-associated PIF3-binding peaks identified here , also display PIF4- and/or PIF5-binding peaks ( Figure S7A ) . Perhaps more striking , 89% of the 128 PIF3-binding , PIF-quartet-regulated genes identified here ( Y- and Z-class genes in Figure 4A ) , are also bound by PIF4 and or PIF5 , with 52% being bound by all three PIFs ( Figure S7B; Table S11 ) . One possible mechanistic basis for the differential control of shared target genes by the individual PIF-quartet members described above ( Figure 5 ) is that each PIF transcription factor has a different spatial expression pattern across the plant . To examine this possibility , we expressed pPIF:GUS fusions for each of the PIF genes transgenically in Arabidopsis seedlings , and assayed the distribution of GUS expression histochemically . The data show that all four PIF promoters support expression broadly throughout the seedling shoot tissue , with largely similar distribution patterns between the quartet members , within the resolution of this procedure ( Figure 6A–6D ) . In principle , differences in absolute expression levels among the PIF-quartet members could also be a fundamental determinant of differences in PIF-promoted expression of target genes ( Figure 5 ) . However , this does not appear to be the case here . Examination of the RNA-seq profiles , and independent RT-qPCR analyses , of PIF1 , PIF3 , PIF4 and PIF5 expression , shows that , while there are marked differences in expression between these genes in wild-type seedlings ( Figure 6F ) , these are not strongly correlated with the respective patterns of target-gene expression ( Figure 5 ) . In particular , PIF1 and PIF3 display expression levels that are robustly converse to their respective general levels of transcriptional activation . Similarly , and more importantly , although the expression levels of PIF4 and PIF5 are significantly elevated in the relevant triple mutant compared to wild-type ( Figure 6G ) , these differences also do not correlate with the overall differential expression patterns of the target genes . While these elevated levels could indicate that the computation in Figure 5B overestimates the normal , relative contributions of these two PIFs to the collective PIF-quartet activity , displayed when all four PIFs are present , they do not account for the apparent dominance of PIF1 or the diversity of response-patterns between the genes . Taken together , these results suggest that the sometimes strikingly different quantitative contributions of the individual PIFs to the expression of a given target gene appears unlikely to be primarily due to either differences in transcriptionally-driven PIF abundance or differences in spatially-determined abundance of the PIF-quartet-members . It appears more likely that these differences are due to intrinsic differential activities of the individual PIFs in the context of the individual target-gene promoters . In addition , because the GUS expression pattern driven by the CaMV 35S promoter ( Figure 6E ) overlaps substantially with that driven by the PIF promoters ( Figure 6A–6D ) , it seems reasonable to expect that the majority of PIF3-binding sites detected by ChIP-seq analysis here , using 35S-driven PIF3-Myc expression , will reflect sites that are normally available to PIF3 generated by endogenous PIF3 promoter activity . The robust binding of PIF3 to the G-box-containing region of the PIL1 promoter detected by ChIP-seq analysis ( Figure 2 ) and in vitro assay ( Figure 1G and Figure S2 ) , and the partial autonomous promotion of PIL1 expression by PIF3 observed by RNA-seq analysis ( Figure 2A , Figure 5 , and Figure S3A ) , provides strong evidence that PIL1 is a direct target of PIF3 transcriptional regulation via physical interaction of the bHLH factor with these cis-elements . Conversely , because the non-PIF3 quartet members contribute robustly to the collective PIF-quartet-dependent expression of PIL1 ( Figure 2A and Figure 5 ) , and given that these non-PIF3 members also bind selectively to G-box motifs [4]–[9] , it might be predicted that PIF1 , 4 and/or 5 transcriptional activation of PIL1 will , like PIF3 , be exerted through interaction with the G-boxes in the PIL1 promoter [5] , [6] . To examine this prediction , we tested the functional necessity of these G-box motifs to PIL1 expression using reporter constructs in transgenic seedlings . The data show that activation of the PIL1 promoter requires both the presence of one or more of the PIF quartet and one or more of the G-boxes ( Figure 5C ) , indicating that the G-box elements are the major , if not sole , targets of PIF-quartet transcriptional activation activity . By contrast , it is notable that , although a recent report shows that PIF7 also binds to the G-box region of the PIL1 promoter in a manner that is functionally important for shade-induced expression of this gene in light-grown seedlings [41] , the extremely low residual levels of PIL1 expression in dark-grown pifq seedlings compared to wild-type ( Figure 2A and Figure 5A ) indicate that PIF7 has minimal , if any , contribution under these conditions . Together , the evidence suggests that , to the extent that the PIF-quartet members share transcriptional activation of PIL1 ( Figure 5A and 5B ) , they do so by sharing the G-box motifs as interaction sites . This conclusion is consistent with the demonstration that PIF3 binds to all three G-box motifs in the PIL1-promoter cluster , both in vivo ( Figure 2B ) and in vitro ( Figure 1G and Figure S2 ) . By extrapolation , the other Y- and Z-Class , PIF-quartet-regulated genes , established here as being direct-targets of PIF3 transcriptional regulation through G- or PBE-box binding motifs , are strong candidates for likewise being targets of functionally active , direct binding-site sharing among the four PIF factors .
Previous genetic studies have established that the overarching biological function of the PIF quartet is to promote skotomorphogenic growth and development in post-germinative seedlings in darkness , and to promote shade-avoidance behavior in deetiolated seedlings in response to exposure to neighboring vegetation [2] , [3] , [11] , [42] . The evidence shows that the quadruple pifq mutant is strongly impaired in skotomorphogenic growth and development in dark-grown seedlings and has reduced shade-avoidance responsiveness to signals from neighboring vegetation in green seedlings [2] , [3] , [11] . In addition , there are indications that the contributions of individual PIF members to the collective activities of the quartet vary quantitatively , both between the PIFs for a given morphogenic-response feature , and between morphogenic-response features for a given PIF . For example , experiments comparing single , double , triple and quadruple pif-mutant combinations indicate that the individual PIFs appear to contribute additively or synergistically , in more or less equivalent fashion , to the promotion of hypocotyl-cell elongation growth in dark-grown seedlings [2] , [3] , [11] . By contrast , PIF1 appears to dominate the concomitant suppression of cotyledon separation that occurs in these same seedlings during dark-growth [2] , [11] . In green seedlings , on the other hand , PIF4 and/or PIF5 appear to have a major role in promoting the stem and petiole elongation intrinsic to shade-avoidance in response to vegetative shade [21] , [42] , whereas PIF3 [43] , together with PIFs 4 and 5 [44] , contribute strongly to growth during the night period under diurnal light/dark cycles . Consistent with this general pattern , another related bHLH factor , PIF7 , displays only moderate involvement in seedling deetiolation [45] , but has a prominent role in shade avoidance [41] . Although a limited number of previous studies have examined the transcriptome regulated by PIF-quartet members in seedlings in darkness [3] , [12] , [16]–[19] and vegetative shade [11] , [21] using the Affymetrix ATH1 array , these studies did not provide full genome coverage and did not permit dissection of potential quantitative differences in transcriptome profiles controlled by the individual PIFs . The RNA-seq analysis performed here defines , with full genome coverage , the transcriptome collectively regulated by the PIF quartet in promoting skotomorphogenesis , and provides initial definition of the extent , and quantitative partitioning , of shared transcriptional control of the genes within of this network between PIF3 and the PIF1/4/5-trio . Superimposed on these data , our ChIP-seq analysis has identified a subset of these genes that are likely direct targets of PIF3 transcriptional regulation , exerted by physical binding of this factor to promoter-localized G- or PBE-box recognition-motifs ( Class X , Y and Z genes , combined; Figure 4 ) . The predominant pattern of PIF-regulated expression of these PIF3-bound genes ( 108 ( 84% ) of 129 total ) is one of high levels in the presence of the wild-type PIF factors , and reduced levels in the genetically-imposed absence of these factors in dark-grown seedlings , indicative of transcriptional activation by PIF3 and/or one or more of the other three PIF-quartet members . This pattern is consistent with the existing reports that all four factors function intrinsically as transcriptional activators , at least in transfection or heterologous expression systems [4]–[9] , and with the demonstration here and elsewhere [21] that these PIFs function to activate PIL1-promoter-driven expression in transgenic seedlings ( Figure 5C ) . We have therefore focused here primarily on this predominant class of PIF-transcriptionally-activated genes . Our data indicate that there is a continuum , from robust to marginal , in the extent of the contribution of PIF3 to the combined transcriptional regulatory activity of the PIF quartet toward the PIF-induced , Class Y and Z genes ( Figure S4 ) . Conversely , by definition , there is a complimentary continuum in the share of this combined activity provided by the collective actions of PIFs 1 , 4 and 5 . These data imply at least some degree of shared , but quantitatively differential , transcriptional-regulatory activity among the PIF-quartet members toward individual genes that are apparent direct targets of PIF3-induced expression . Our RT-qPCR analysis of the expression patterns of selected genes from this subset , in all pif triple-mutant combinations , confirms that all four quartet members display such intra-subfamily differential activity toward individual genes in this set . Moreover , this analysis shows , conversely , that the individual PIF proteins induce differential levels of transcription in each different gene ( Figure 5A ) . The three-dimensional response surface generated by this comparison ( Figure 5B ) suggests that this pattern may be iterated across all PIF-regulated genes genome wide , and points to the potential for considerable signaling and regulatory complexity at the PIF-target-gene interface . Because it has been shown that all four PIF-quartet members bind robustly to the G-box motif [4]–[9] , it appears likely that many of the direct targets of PIF3 transcriptional regulation are also direct targets of these other PIFs [5] , [6] , and that the shared activation of genes by the individual quartet members observed here will involve some degree of shared occupancy of these binding sites by the different PIFs . This may also apply to the newly discovered PBE-box motif , as there is recent evidence that PIF4 also recognizes this motif [21] . However , there is also evidence of potential divergence in motif recognition , as PIF5 was shown in the same report not to bind to the PBE-box motif [21] . The prominent contribution of PIF1 to the transcriptional activation of many of the genes examined here ( Figure 5 ) , despite its apparent considerably lower expression level than PIF3 ( Figure 6F ) , is particularly intriguing in this respect , as this may imply that PIF1 may dominate promotion of target gene expression in dark-grown seedlings . Comparison of the genes identified here as direct targets of PIF3 transcriptional activation ( Class Z and YZ1 . 5 genes ) , with those previously identified as being rapidly ( within 1 h ) repressed by initial exposure of dark-grown seedlings to red light [17] , has defined an overlapping subset of 21 genes ( 22 including PIL1 ) ( Table 1 ) . The evidence is strong , therefore , that these 22 genes form a core set that are directly transcriptionally activated by PIF3 in darkness and repressed in light , at least in part , by direct , photoactivated-phy-induced PIF3 degradation . Moreover , because all of these genes are also transcriptionally activated , either collectively ( Table 1 and Table S11 ) , or individually ( Figure 5A and 5B ) by PIF1 , 4 and/or 5 in darkness , it appears likely that these PIFs share similarly directly in the light-reversible trans-activation of this core gene-set via photoactivated-phyB-induced degradation of the PIF-trio members . The predicted or established functional diversity of the PIF direct-target genes identified here ( Figure S5 ) suggests that PIF3 and/or one or more other PIF-quartet members act pleiotropically to directly regulate the transcription of a diversity of genes involved in a spectrum of cellular processes that sustain the skotomorphogenic developmental pathway . Consistent with previous analyses [3] , [11] , [17] , [21] , the PIF-induced genes are strikingly enriched for transcription-factor-encoding loci ( 40% of the annotated genes in this set ) . These data support the proposition , therefore , that the PIFs regulate an extensive transcriptional network via direct activation of a battery of primary target-genes in a hierarchal transcriptional cascade [20] . Because the encoded target-proteins represent multiple major classes of transcription factors ( including bHLH , homeobox , bZIP , ARF , AUX/IAA , AP2-EREBP , BBX and TCP ) , it appears likely that they act concomitantly to activate multiple , diverse downstream pathways in parallel . Interestingly , however , many apparent PIF direct-target genes are involved in other cellular processes ( including cytokinin metabolism , auxin-responsiveness , protein phosphorylation and cell-wall metabolism ) , suggesting a more immediate mode of PIF regulation of these processes . A central issue in understanding mechanisms of eukaryotic transcriptional regulation is how members of large transcription-factor families , with conserved DNA-binding domains ( such as the 162-member Arabidopsis bHLH family [46] ) , discriminate between target genes [22] , [30] , [47] . However , the specific question of whether , and to what extent , closely-related sub-family members , with potential overlapping functional redundancy ( like the PIF quartet ) , share regulation of target genes through shared binding to promoter-localized consensus motifs , does not appear to have been widely investigated [31]–[34] . Our data , together with those of others [21] , [24] , provide evidence suggesting that the PIF quartet members share directly in transcriptional activation of numerous target genes , potentially via redundant promoter occupancy , in a manner that varies quantitatively from gene to gene ( Figure 7 ) . This finding suggests that these PIFs function collectively as a signaling hub , selectively partitioning common upstream signals from light-activated phys at the transcriptional-network interface . Definition of the mechanistic basis and functional consequences of this apparent complexity will require further investigation .
The Colombia-0 ecotype of Arabidopsis thaliana was used for all experiments . The 35S:6×His-PIF3-5×MYC ( P3M ) transgenic line [48] , pif3 [18] , pif1pif4pif5 ( pif145 ) [11] , pif1pif3pif4 ( pif134 ) , pif1pif3pif5 ( pif135 ) , pif3pif4pif5 ( pif345 ) , and pif1pif3pif4pif5 ( pifq ) [2] were as described . Stratified seeds were irradiated with WL at 21°C for 3 h to induce germination , followed by a FR pulse for 15 min to suppress pseudo dark effects [2] , and grown in darkness at 21°C for 2 d before harvest . The ChIP assay was performed using about 2 g of Arabidopsis 2-d-old dark-grown whole seedlings as described [49] under green safelight . Polyclonal anti-MYC antibodies ( Abcam , ab9132 ) were used with BSA-blocked Protein G Agarose beads ( Millipore ) to immunoprecipitate the P3M-DNA complex . Wild-type Arabidopsis seedlings grown under the same conditions were used as the negative control following the same assay procedure . The ChIP-seq library was constructed according to Illumina's instructions ( www . illumina . com ) with some modifications . Four ChIP samples from technical replicates of each biological replicate were pooled together and concentrated to increase the starting amount of DNA . The end repair of DNA fragments was performed using End-It DNA End-Repair Kit ( Epicentre ) . The A-tailing was added to the end-repaired DNA fragments using Klenow Fragment ( NEB ) , and then Illumina's PE adapters were ligated by T4 DNA Ligase ( Promega ) at 16°C overnight . The adapter-ligated DNA fragments in the 200–300 bp size-range were selected by the gel purification , and then were amplified using Phusion High-Fidelity DNA Polymerase ( NEB ) with the Illumina PE PCR primer set . The library was purified using an Agencourt AMPure XP system ( Beckman Coulter Genomics ) , and then validated by Bioanalyzer 2000 ( Agilent ) . The parallel libraries from P3M and WT ChIP samples were assayed by single-end sequencing on an Illumina GAIIx platform . The 36-nt reads were aligned to the TAIR9 assembly of the Arabidopsis genome using Bowtie [50] with up to 2 mismatches allowed . Only reads mapped uniquely to the nuclear genome with the lowest number of mismatches were retained for binding-peak identification . To increase the uniformity of read-counts across biological replicates , two technical-replicate sequencing runs were performed on the 4 libraries from the 1st and 2nd ChIP experiments ( two of the four biological replicates ) . The aligned reads from the two technical sequencing replicates of each library were combined and processed as single biological replicate data . The statistical identification of PIF3-binding peaks was performed separately for each biological replicate using MACS [35] with the default 10−5 P-value cutoff . MACS analysis was customized to ensure a more uniform analysis across biological replicates , and to decrease the size of the window for detecting background enrichment ( due to the small size of the Arabidopsis genome ) by employing modified parameters ( gsize = 1 . 1e8 , bw = 100 , nomodel , shiftsize = 50 , slocal = 1000 , and llocal = 2000 ) . Four independent biological replicates of ChIP-seq data were collected , and replicate-specific binding peaks , identified in at least one other replicate , were defined as reproducible , if the distance between the summits of each replicate were less than 100 bp . For each reproducible peak , a mean summit position was assigned as the average position of the individual replicate-specific summits , and the PIF3-binding sites were defined as the 201 bp windows centered at each reproducible mean-summit position . De novo PIF3-binding motif discovery was performed on the 201-bp defined binding sites using MEME [36] , and the enrichment significance of identified G-box and PBE-box motifs beyond the genome background was quantified by 100 random simulations , where in each simulation 1064 randomly selected genomic regions of the same size were searched for the occurrence of each motif . The tight association of PIF3 binding with a specific motif was defined as the distance between the peak summit and the closest motif less than 100 bp . Definition of the closest neighboring genes to each binding peak was approached by scanning the regions within +/−5 kb centered at each peak summit , using CisGenome [51] , and the potential target genes downstream of each summit with no intervening genes were selected manually . Total RNA was extracted from 2-d-old dark-grown seedlings using QIAshredder column and RNeasy Plus Mini Kit ( Qiagen ) according to the manufacturer's instructions . The sequencing library construction was adapted from 3′-end RNA-seq protocol [52] . The mRNA was fractionated from 20 µg of total RNA using Dynabeads Oligo ( dT ) 25 ( Invitrogen ) , and fragmented using Fragmentation Reagents ( Ambion ) at 70°C for 2 . 5 min in 20 µl of reaction . The polyA-tailed 3′-end fragments were captured by another run of mRNA purification as described above , and then treated by Antarctic Phosphatase ( NEB ) and T4 Polynucleotide Kinase ( NEB ) at 37°C for 1 h and 2 h , respectively . The sample was purified using RNeasy MinElute Cleanup Kit ( Qiagen ) according to Illumina's protocol . The eluted mRNA fragments were ligated with 2 . 5 µM of Illumina's SRA 5′ adaptor by T4 RNA Ligase 1 ( NEB ) at 20°C for 4 h . The 3′ cDNA adapter derived from Illumina's v1 . 5 sRNA 3′ adapter was conjugated with the anchored oligo ( dT ) 20 primer , and introduced through reverse transcription using the SuperScript III First-Strand Synthesis System ( Invitrogen ) . The first-strand cDNA was purified using the Agencourt AMPure XP system , and then amplified by PCR reaction using Phusion High-Fidelity DNA Polymerase with Illumina's sRNA PCR primer set . The size of purified library DNA was validated by Bioanalyzer 2000 . Libraries from the 1st biological replicate were assayed by 36-cycle single-end sequencing on the Illumina GAIIx platform , while libraries from the 2nd and 3rd biological replicates were assayed by 50-cycle single-end sequencing on the HiSeq2000 platform . For consistency , only the 5′-end 36-nt trimmed reads from the 2nd and 3rd replicates , as well as the full-length 36-nt reads from the 1st replicate , were aligned to the TAIR9 representative transcriptome using Bowtie with zero mismatches allowed . Only reads mapping uniquely to the 3′-end 500-bp region of the coding strand were counted for gene expression . Differentially expressed genes were identified using the edgeR package [53] , and SSTF genes were defined as those that differ by ≥2-fold with an adjusted P value ≤0 . 05 as described [17] . The recombinant protein GST-PIF3-Flag and the GST control were purified from E . coli as described previously [9] . DNA probes were generated by annealing a 5′ biotinylated oligonucleotide ( IDT ) to a complementary unmodified oligonucleotide ( IDT ) . The complementary oligonucleotides were diluted in annealing buffer ( 10 mM Tris-HCl ( pH 7 . 5 ) , 50 mM NaCl , 1 mM EDTA ) to a final concentration of 40 µM , heated to 95°C for 5 min , and cooled down slowly ( 0 . 1°C/second ) to 12°C . The same procedure was followed to generate unmodified dsDNA fragments for competition assays . Probes are listed in Table S13 . The DPI-ELISA assays were performed as described [38] . Biotinylated probes were bound to Reacti-Bind Streptavidin High Binding Capacity Coated 96-Well Plates ( Thermo Scientific ) by applying 2 pmol/well of the probes in TBS-T buffer ( 20 mM Tris-HCl ( pH 7 . 5 ) , 180 mM NaCl , 0 . 1% ( v/v ) Tween 20 ) for 1 h at 37°C . The wells were blocked with 5% ( w/v ) non-fat dry milk in TBS-T buffer for 30 min , and then incubated with 100 ng of GST-PIF3-Flag or GST for 1 h . For competition assays , 2 , 10 or 50 pmol/well of the unlabeled probes were added at the same time with the proteins . After incubation , the wells were washed 3 times with TBS-T/PBS-T buffer , and then were incubated with 1∶2000 diluted THE GST Antibody [HRP] ( GenScript , A00866 ) in PBS-T buffer for 1 h . The wells were then washed twice with PBS-T and PBS buffers , respectively , after incubation . The protein binding was detected by adding the OPD solution ( Thermo Scientific ) , and the reaction was stopped by 2 . 5 M sulfuric acid . The color extinction was measured at 490 nm , using 650 nm as a reference wavelength in the ELISA reader . The EMSA assays were performed as described [9] . 100 ng of recombinant proteins and the biotinylated DNA probes were used in each assay . Gel electrophoresis using native 5% PAGE gel in ice cold 0 . 5× TBE buffer ( 280 V , 15 min ) was followed by wet-transfer electro blotting to Biodyne B Nylon membrane ( Pierce ) in 0 . 5× TBE buffer ( 80 V , 1 h ) . The Lightshift Chemiluminescent DNA EMSA kit ( Pierce ) was used for detection of the biotinylated probes according to the manufacturer's instructions . RT-qPCR was performed as described [17] . Each PCR reaction was repeated at least twice , and the mean value of technical replicates was recorded for each biological replicate . Data from biological triplicates were collected , and the mean value with standard error is represented in the bar graphs . Primers and gene accession numbers are listed in Table S13 . The 1 . 8 kb PIL1 promoter region ( pPIL1 ) upstream of the ATG was amplified by PCR using the pPIL1-F1/R1 primer set , and then the XhoI/EcoRI fragment was cloned into the pBluescript II SK ( pBSK ) vector ( Stratagene ) to produce pBSK-pPIL1 . The G-box mutations were introduced by two-step PCR amplification ( using pPIL1-F7/R7 , pPIL1-F8/R8 , pPIL1-F9/R9 , and pPIL1-F10/R10 primer sets ) , and the XhoI/MfeI fragment from the pPIL1-F1/R5 primer set was cloned to replace the unmutated fragment of pBSK-pPIL1 to produce pBSK-mpPIL1 . The HindIII/BamHI fragment containing the omega-LUC+-rbcS terminator from the pENTR/D-TOPO\arGIp::LUC+ construct was cloned into pBSK-pPIL1 and pBSK-mpPIL1 , respectively , to produce pBSK-pPIL1:LUC and pBSK-mpPIL1:LUC . The CDS of Renilla Luciferase ( RLUC ) was amplified by PCR using the Rluc-F1/R1 primer set , and then the NcoI/PmlI fragment was cloned into the pCAMBIA1302 binary vector to produce pC1302-35S:RLUC . The PstI/SacI fragments from pBSK-pPIL1:LUC and pBSK-mpPIL1:LUC were then sub-cloned into pC1302-35S:RLUC to produce pC1302-pPIL1:LUC-35S:RLUC ( pPIL1:LUC ) and pC1302-mpPIL1:LUC-35S:RLUC ( mpPIL1:LUC ) , respectively . The 2 . 5–3 . 0 kb promoter regions upstream of the ATG of PIF3 , PIF4 and PIF5 were amplified from Arabidopsis ( Col-0 ecotype ) genomic DNA by PCR using the TOPO-PIF3p-LP1/RP1 , TOPO-PIF4p-LP1/RP1 and TOPO-PIF5p-LP1/RP1 primer sets , respectively . The PCR products were cloned into the pENTR/D-TOPO vector ( Invitrogen ) to produce the pPIF3 , pPIF4 and pPIF5 entry clones . For the PIF1 promoter , the first 2 kb fragment upstream of the ATG was amplified by PCR using the TOPO-PIF1p-LP3/RP1 primer set , and then was cloned into the pENTR/D-TOPO vector to produce the intermediate entry clone . The second fragment of 2–4 kb upstream of ATG was amplified using the NotI-PIF1p-LP/XcmI-PIF1p-RP primer set , and then the NotI/XcmI fragment of the PCR product was subcloned into the intermediate entry clone to produce the pPIF1 entry clone . All four entry clones were subcloned into the gateway compatible pGWB3 binary vector [54] using Gateway LR Clonase II Enzyme Mix ( Invitrogen ) to produce pPIF:GUS constructs . The constructs were transformed into Arabidopsis plants as described [55] , and the individual transgenic lines were selected on MS medium containing 25 mg/L of Hygromycin B ( Invitrogen ) . The 2-d dark-grown seedlings of independent transgenic lines were ground in liquid nitrogen , and total protein was extracted in LUC extraction buffer ( 1× PBS , 4 mM EDTA , 2 mM DTT , 5% glycerol , 1 mg/ml BSA , 2 mM PMSF and 1× complete protease inhibitor cocktail ( Roche ) at 3× w/v ) as described [7] . 20 µl of the supernatant were used to measure the LUC and RLUC activity using a Dual-Luciferase Reporter Assay System ( Promega ) according to the manufacturer's instruction . The relative expression of LUC was represented by its enzyme activity compared to the RLUC internal control . Histochemical GUS staining assays were performed on 2-d-old dark-grown seedlings as described [56] using a modified substrate buffer ( 1× PBS ( pH 7 . 0 ) , 1 mM K3Fe ( III ) ( CN ) 6 , 0 . 5 mM K4Fe ( II ) ( CN ) 6 , 1 mM EDTA , 1% Triton X-100 , 1 mg/ml X-gluc ) . Data of biological triplicates were collected from two independent transgenic lines , and representative images are shown for each transgene . ChIP-seq and RNA-seq data reported in this study have been deposited in the Gene Expression Omnibus database under the accession number GSE39217 . | An important issue in understanding mechanisms of eukaryotic transcriptional regulation is how members of large transcription-factor families , with conserved DNA–binding domains ( such as the 162-member Arabidopsis bHLH family ) , discriminate between target genes . However , the specific question of whether , and to what extent , closely related sub-family members , with potential overlapping functional redundancy ( like the quartet of Phytochrome ( phy ) -Interacting bHLH transcription Factors ( PIF1 , 3 , 4 , and 5 ) studied here ) , share regulation of target genes through shared binding to promoter-localized consensus motifs does not appear to have been widely investigated . Here , using ChIP–seq analysis , we have identified genes that bind PIF3 to conserved , sequence-specific sites in their promoters; and , using RNA–seq , we have identified those genes displaying altered expression in various pif mutants . Integration of these data identifies those genes that are likely direct targets of transcriptional regulation by PIF3 . Our data suggest that the PIF quartet members share directly in transcriptional activation of numerous target genes , potentially via redundant promoter occupancy , in a manner that varies quantitatively from gene to gene . This finding suggests that these PIFs function collectively as a signaling hub , selectively partitioning common upstream signals from light-activated phys at the transcriptional-network interface . | [
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"... | 2013 | A Quartet of PIF bHLH Factors Provides a Transcriptionally Centered Signaling Hub That Regulates Seedling Morphogenesis through Differential Expression-Patterning of Shared Target Genes in Arabidopsis |
The increasingly complex and rapid transmission dynamics of many infectious diseases necessitates the use of new , more advanced methods for surveillance , early detection , and decision-making . Here , we demonstrate that a new method for optimizing surveillance networks can improve the quality of epidemiological information produced by typical provider-based networks . Using past surveillance and Internet search data , it determines the precise locations where providers should be enrolled . When applied to redesigning the provider-based , influenza-like-illness surveillance network ( ILINet ) for the state of Texas , the method identifies networks that are expected to significantly outperform the existing network with far fewer providers . This optimized network avoids informational redundancies and is thereby more effective than networks designed by conventional methods and a recently published algorithm based on maximizing population coverage . We show further that Google Flu Trends data , when incorporated into a network as a virtual provider , can enhance but not replace traditional surveillance methods .
Since the Spanish Flu Pandemic of , the global public health community has made great strides towards the effective surveillance of infectious diseases . However , modern travel patterns , heterogeneity in human population densities , proximity to wildlife populations , and variable immunity interact to drive increasingly complex patterns of disease transmission and emergence . As a result , there is an increasing need for effective , evidence-based surveillance , early detection , and decision-making methods [1]–[3] . This need was clearly articulated in by a directive from the Department of Homeland Security and the Centers for Disease Control and Prevention to develop a nationwide , real-time public health surveillance network [4] , [5] . The U . S . Outpatient Influenza-Like Illness Surveillance Network ( ILINet ) gathers data from thousands of healthcare providers across all fifty states . Throughout influenza season ( CDC mandating reporting during weeks , which is approximately October through mid-May ) , participating providers are asked to report weekly the number of cases of influenza-like illness treated and total number of patients seen , by age group . Cases qualify as ILI if they manifest fever in excess of F along with a cough and/or a sore throat , without another known cause . Although the CDC receives reports of approximately million patient visits per year , many of the reports may use a loose application of the ILI case definition and/or may simply be inaccurate . The data are used in conjunction with other sources of laboratory , hospitalization and mortality data to monitor regional and national influenza activity and associated mortality . Similar national surveillance networks are in place in EU countries and elsewhere around the globe [6]–[9] . Each US state is responsible for recruiting and managing ILINet providers . The CDC advises states to recruit one regularly reporting sentinel provider per residents , with a state-wide minimum of sentinel providers . Since , the Texas Department of State Health Services ( DSHS ) has enrolled a total of volunteer providers . Participating providers regularly drop out of the network; Texas DSHS aims to maintain approximately active participants through year-round recruitment of providers in heavily populated areas ( cities with populations of at least ) . DSHS also permits other ( non-targeted ) providers of family medicine , internal medicine , pediatrics , university student health services , emergency medicine , infectious disease , OB/GYN and urgent care to participate in the network . During the influenza season , the Texas ILINet included providers with approximately reporting most weeks of the influenza season . A number of statistical studies have demonstrated that ILI surveillance data is adequate for characterizing past influenza epidemics , monitoring populations for abnormal influenza activity , and forecasting the onsets and peaks of local influenza epidemics [10]–[16] . However , the surveillance networks are often limited by non-representative samples [17] , inaccurate and variable reporting [12]–[14] , and low reporting rates [6] . Some of these studies have yielded specific recommendations for improving the performance of the surveillance network , for example , inclusion of particular categories of hospitals in China [12] , preference for general practitioners over pediatricians in Paris , France [14] , and a general guideline to target practices with high reporting rates and high numbers of patient visits ( per capita ) [6] . Polgreen et al . recently described a computational method for selecting ILINet providers so as to maximize coverage , that is , the number of people living within a specified distance of a provider [17] . They applied the approach to optimizing the placement of the providers in the Iowa ILINet . While their algorithm ensures maximum coverage , it is not clear that maximum coverage is , in general , the most appropriate criterion for building a statistically informative ILINet . In , Google . org launched Google Flu Trends , a website that translates the daily number of Googles search terms associated with signs , symptoms , and treatment for acute respiratory infections into an estimate of the number of ILI patients per people . It was shown that Google Flu Trends reliably estimates national influenza activity in the US [18] , the state of Utah [18] , and in some European countries [19] , but it provided imperfect data regarding the H1N1 pandemic in New Zealand [20] . We assessed the correlation between Google Flu Trends for Texas and Texas' ILINet data and found a correlation of , similar to those presented in Ginsberg et al . 2009 [18] ( See Text S1 ) . The Google Flu Trends website includes ILI-related search activity down to the level of cities ( in beta version as of November ) . Thus , Google Flu Trends may serve as a valuable resource for influenza detection and forecasting if effectively integrated with public health data such as those coming from state ILINets . Here , we present an evaluation of the Texas Influenza-Like-Illness Surveillance Network ( ILINet ) , in terms of its ability to forecast statewide hospitalizations due to influenza ( ICD9 and ) and unspecified pneumonia ( ICD9 ) . Although we henceforth refer to this subset of hospitalizations as influenza-like hospitalizations , we emphasize that these data do not perfectly reflect influenza-related hospitalizations: some unrelated pneumonias may be classified under ICD9 , and some influenza cases may not be correctly diagnosed and/or recorded as influenza . Nonetheless , this subset of hospitalizations likely includes a large fraction of hospitalized influenza cases and exhibits strong seasonal dynamics that mirror ILINet trends . The inclusion of all three ICD9 codes was suggested by health officials at Texas DSHS who seek to use ILINet to ascertain seasonal influenza-related hospitalization rates throughout the state ( Texas DSHS contract numbers and ) . Hospitalizations associated with these three codes in Texas accounted for between and of all hospitalizations due to infections and roughly billion dollars of hospitalization payments in ( See Text S1 ) . Using almost a decade of state-level ILINet and hospitalization data , we find that the existing network performs reasonably well in its ability to predict influenza-like hospitalizations . However , smaller , more carefully chosen sets of providers should yield higher quality surveillance data , which can be further enhanced with the integration of state-level Google Flu Trends data . For this analysis , we adapted a new , computationally tractable , multilinear regression approach to solving complex subset selection problems . The details of this method are presented below and can be tailored to meet a broad range of surveillance objectives .
To construct new sentinel surveillance networks , we choose individual providers sequentially from a pool of approximately mock providers , one for each zip code in Texas , until we reach total providers . At each step , the provider that most improves the quality of the epidemiological information produced by the network is added to the network . We optimize and evaluate the networks in terms of the time-lagged statistical correlation between aggregated ILINet provider reports ( simulated by the model ) and actual statewide influenza-like hospitalizations . Specifically , for each candidate network , we perform a least squares multilinear regression from the simulated ILINet time series to the actual Texas hospitalization time series , and use the coefficient of determination , , as the indicator of ILINet performance . Henceforth , we will refer to these models as ILINet regression models . We compare the networks generated by this method to networks generated by two naive models and a published computational method [17] ( Figure 1 ) . Random selection models an open call for providers and entails selecting providers randomly with probabilities proportional to their zip code's population; Greedy selection prioritizes providers strictly by the population density of their zip code . Submodular optimization significantly outperforms these naive methods , particularly for small networks , with Random selection producing slightly more informative networks than Greedy selection . The Geographic optimization method of Polgreen et al . [17] selects providers to maximize the number of people that live within a specified “coverage distance” of a provider . Submodular optimization consistently produces more informative networks than this method at a mile coverage distance ( Figure 1 ) ( , , and mile coverage distances perform worse , not shown ) . To visualize the relative performance of several of these networks , we compared their estimates of influenza-like hospitalizations ( by applying each ILINet regression model to simulated ILINet report data ) to the true state-wide hospitalization data ( Figure 2 ) . The time series estimated by a network designed using submodular optimization more closely and smoothly matches true hospitalizations than both the actual Texas ILINet and a network designed using geographic optimization ( each with providers ) . The submodular optimization algorithm is not guaranteed to find the highest performing provider network , and an exhaustive search for the optimal provider network from the pool of providers is computationally intractable . However , the submodular property of the objective function allows us to compute an upper bound on the performance of the optimal network , without knowing its actual composition ( Figure 1 ) . The performance gap between the theoretical upper bound and the optimized networks may indicate that the upper bound is loose ( higher than the performance of the true optimal network ) and/or the existence of better networks that might be found using more powerful optimization methods . The networks selected by submodular optimization reveal some unexpected design principles . Most of the Texas population resides in Houston and the “I-35 corridor” – a North-South transportation corridor spanning San Antonio , Austin , and Dallas ( Figure 3a ) . The first ten provider locations selected by submodular optimization are spread throughout the eastern half of the state ( Figure 4a , pink circles ) . While most of the providers are concentrated closer to Texas' population belt , only two are actually located within Texas' major population centers ( in this case , College Station ) . The submodular networks are qualitatively different from the networks created by the other algorithms considered , which focus providers within the major population centers ( Figure 4b ) . The higher performance of the submodular ILINets suggest that over-concentration of providers in major population centers is unnecessary . Influenza levels in the major population centers are strongly correlated ( Figure 3b ) . Thus , ILINet information from San Antonio , for example , will also be indicative of influenza levels in Austin and Dallas . This synchrony probably arises , in part , from extensive travel between the major Texas population centers . Using submodular optimization , we augment the 2008 Texas ILINet by first subsampling from the enrolled providers and then adding up to new providers . When subsampling , performance does not reach a maximum until all providers are included in the network ( Figure 5 ) , indicating that each provider adds predictive value to the network . However , the theoretical upper bound plateaus around providers , suggesting that smaller ( more optimally chosen ) networks of equal predictive value may exist . During the second stage , additional providers improve the objective by . Most of these providers are located in relatively remote areas of the state . We also considered inclusion of Internet trend data sources as virtual providers , specifically , the freely available Google Flu Trends data for the state of Texas [21] . Google Flu Trends alone is able to explain about of the variation in state-wide hospitalizations; it outperforms the 2008 Texas ILINet and matches the performance of a network with traditional providers constructed from scratch using submodular optimization ( Figure 6 ) . However , the best networks include both traditional providers and Google Flu Trends . For example , by adding providers to Google Flu Trends using submodular optimization , we improve the objective by a third and halve the optimality gap ( from a trivial upper bound of one ) . The additional providers are located in non-urban areas ( Figure 4a , green circles ) distinct from those selected when Google Flu Trends is not allowed as a provider . To further validate our methodology , we simulated the real-world scenario in which historical data are used to design an ILINet and build forecasting models , and then current ILINet reports are used to make forecasts . Specifically , we used data to design ILINets and estimate multilinear regression models relating influenza-like hospitalizations to mock provider reports , and then used data to test the models' ability to forecast influenza-like hospitalizations . For networks with fewer than providers , the ILINets designed using submodular optimization consistently outperform ILINets designed using the other three strategies ( Figure 7 ) . Above providers , the predictive performance of the submodular optimization ILINet begins to decline with additional providers . As the number of providers approaches ( the number of weeks in the training period ) , the estimated prediction models become overfit to the period . Thus , the slightly increased performance of the Random method over the submodular optimization after providers is spurious . For the values presented in Figure 7 , the effect of noise and variable reporting are integrated out when calculating the expected provider reports . An alternative approach to out-of-sample validation is presented in Text S1; it yields the same rank-order of model performance .
Since the mid twentieth century , influenza surveillance has been recognized as an increasingly complex problem of global concern [22] . However , the majority of statistical research has focused on the analysis of surveillance data rather than the data collection itself , with a few notable exceptions [12] , [17] . High quality data is essential for effectively monitoring seasonal dynamics , detecting anomalies , such as emerging pandemic strains , and implementing effective time-sensitive control measures . Using a new method for optimizing provider-based surveillance systems , we have shown that the Texas state ILINet would benefit from the inclusion of a few strategically selected providers and the use of Internet data streams . Our method works by iteratively selecting providers that contribute the most information about influenza-like hospitalizations . We quantified the performance of various ILINets using the coefficient of determination resulting from a multi-linear regression between each provider's time series and state-wide influenza-like hospitalizations . Importantly , these simulated providers have reporting rates and error distributions estimated from actual ILINet providers in Texas ( see Text S1 ) . The result is a prioritized list of zip codes for inclusion in an ILINet that can be used for future ILINet recruiting . Although this analysis was specifically motivated by the Texas DSHS interest in predicting hospitalizations with ICD9 codes , , and , our method can be readily extended to design a network for any disease or influenza definition with the appropriate historical data . In general , the method requires both historical provider reports and historical time series of the prediction target . However , if one has reasonable estimates of provider reporting rates and informational noise from another source ( e . g . , estimates from a surveillance network in another region or for another disease ) , then historical provider reports are not necessary . ILINet provider reports do not necessarily reflect true influenza activity . Rather they are supposed to indicate the number of patients that meet the clinical ILI case definition , which results in a substantial number of false positives ( reported non-influenza cases ) and false negatives ( missed cases of influenza ) [23] . The case definition for ILI is often loosely applied , further confounding the relationship between these measures and true influenza . Similarly , the ICD9 codes used in our analysis do not correspond perfectly to influenza hospitalizations: some influenza cases will fail to be classified under those codes , and some non-influenza cases will be . Nonetheless , public health agencies are interested in monitoring and forecasting the large numbers of costly hospitalizations associated with these codes . We find that ILINet surveillance data correlates strongly with this set of influenza-like hospitalizations , and that the networks can be designed to be even more informative . Although we provide only a single example here , this optimization method can be readily applied to designing surveillance networks for a wide range of diseases on any geographic scale , provided historical data are available and the goals of the surveillance network can be quantified . For example , surveillance networks could be designed to detect emerging strains of influenza on a global scale , monitor influenza in countries without surveillance networks , or track other infectious diseases such as malaria , whooping cough , or tuberculosis or non-infectious diseases and chronic conditions such as asthma , diabetes , cancer or obesity that exhibit heterogeneity in space , time or by population subgroup . As we have shown with Google Flu Trends , our method can be leveraged to evaluate the potential utility of incorporating other Internet trend data mined from search , social media , and online commerce platforms into traditional surveillance systems . While optimized networks meet their specified goals , they may suffer from over optimization and be unable to provide valuable information for other diseases or even for the focal disease during atypical situations . For example , a surveillance network designed for detecting the early emergence of pandemic influenza may look very different from one optimized to monitor seasonal influenza . Furthermore , an ILINet optimized to predict influenza-like hospitalizations in a specific socio-economic group , geographic region , or race/ethnicity may look very different from an ILINet optimized to predict state-wide hospitalizations . When optimizing networks , it is thus important to carefully consider the full range of possible applications of the network and integrate diverse objectives into the optimization analysis . The optimized Texas ILINets described above exhibit much less redundancy ( geographic overlap in providers ) than the actual Texas ILINet . Whereas CDC guidelines have led Texas DSHS to focus the majority of recruitment on high population centers , the optimizer only sparsely covered the major urban areas because of their synchrony in influenza activity . This is an important distinction between submodular optimization and the other methods considered ( Geographic , Random and Greedy ) . The submodular method does not track population density and instead adds providers who contribute the most marginal information to the network . Consequently , it places far more providers in rural areas than the other methods ( Figure 4b ) . There can be substantial year-to-year variation in spatial synchrony for seasonal influenza , driven by the predominant influenza strains and commuter traffic between population centers [24] . As long as the historical data used during optimization reflect this stochasticity , the resulting networks will be robust . However , synchrony by geography and population density does not occur for all diseases including emerging pandemic influenza [24]; thus the relatively sparse networks designed for forecasting seasonal influenza hospitalizations may not be appropriate for other surveillance objectives , like detecting emerging pandemic strains or other rare events . For example , a recent study of influenza surveillance in Beijing , PRC suggested that large hospitals provided the best surveillance information for seasonal influenza , while smaller provincial hospitals were more useful for monitoring H5N1 [12] . Although our method outperforms the Maximal Coverage Method ( MCM ) , referred to as Geographic , proposed by Polgreen et al . ( 2009 ) , there are several caveats . First , population densities and travel patterns within Texas are highly non-uniform . The two methods might perform similarly for regions with greater spatial uniformity . Second , our method is data intensive , requiring historical surveillance data that may not be available , for example , in developing nations , whereas the population density data required for MCM is widely available . However , the type of data used in this study is readily available to most state public health agencies in the United States . For example , the CDC's Influenza Hospitalization Network ( FluSurv-NET ) collects weekly reports on laboratory confirmed influenza-related hospitalizations in fourteen states . In addition , alternative internet-based data sources like Google Flu Trends are becoming available . Third , as discussed above , our networks are optimized towards specific goals and may thus have no expected level of performance for alternate surveillance goals . Important future research should focus on designing networks able to perform well under a range of surveillance goals . Fourth , neither ILINet data nor influenza-like hospitalizations correspond perfectly to actual influenza activity . One could instead optimize ILINets using historical time series of laboratory-confirmed cases of influenza . Although some provider locations and the estimated regression models may change , we conjecture that the general geospatial distribution of providers will not change significantly . Fourth , we followed Polgreen et al . ( 2009 ) 's use of Euclidean distances . However , travel distance is known to correlate more strongly with influenza transmission than Euclidean distance [24] , and thus alternative distance metrics might improve the performance of the MCM method . Finally , while submodular optimization generally outperforms the other design methods in out-of-sample prediction of influenza-like hospitalizations , it suffers from overfitting when the number of providers in the network approaches the number of data points in the historical time series . The impressive performance of Google Flu Trends leads us to question the role of traditional methods , such as provider-based surveillance networks , in next generation disease surveillance systems . While Texas Google Flu Trends alone providers almost as much information about state-wide influenza hospital discharges as the entire 2008 Texas ILINet , an optimized ILINet of the same size contains more information than Google Flu Trends alone . Adding Google Flu Trends to this optimized network as a virtual provider increases its performance by an additional . Internet driven data streams , like Google Flu Trends , may have age and socio-economic biases that over-represent certain groups , a possible explanation for the difference in providers selected when Google Flu Trends is included , Figure 4a . Given the relatively low cost of voluntary provider surveillance networks , synergistic approaches that combine data from conventional and Internet sources offer a promising path forward for public health surveillance . This optimization method was conceived through a collaboration between The University of Texas at Austin and the Texas Department of State Health Services to evaluate and improve the Texas ILINet . The development and utility of quantitative methods to support public health decision making hinges on the continued partnership between researchers and public health agencies .
The Texas Department of State Health Services ( DSHS ) provided ( 1 ) ILINet data containing weekly records from reporting the number of patients with influenza-like-illness and the total number of patients seen by each provider in the network , and ( 2 ) individual discharge records for every hospital in Texas from ( excluding hospitals in counties with less than inhabitants , in counties with less than total hospital beds , or those hospitals that do not seek insurance payment or government reimbursement ) . We classified all hospital discharges containing ICD9 codes of , , or as influenza-related . Google Flu Trends data was downloaded from the Google Flu Trends site [21] and contains estimates of ILI cases per physician visits determined using Google searches [25] . Data on population size and density was obtained from the census [26] . The first step in the ILINet optimization is to build a data-driven model reflecting actual provider reporting rates and informational noise , that is , inconsistencies between provider reports and true local influenza prevalence . We model reporting as a Markov process , where each provider is in a “reporting” or “non-reporting” state . A provider in the reporting state enters weekly reports , while a provider in the non-reporting state does not enter reports . At the end of each week , providers independently transition between the reporting and non-reporting states . Such a Markov process model allows for streaks of reporting and streaks of non-reporting for each provider , which is typical for ILINet providers . We estimate transition probabilities between states from actual ILINet provider report data . For each provider , the transition probability from reporting to non-reporting is estimated by dividing the number of times the transition occurred by the number of times any transition out of reporting is observed . The probabilities of remaining in the current reporting state and transitioning from non-reporting to reporting are estimated similarly . We model noise in reports using a standard regression noise model of the form ( 1 ) where ( i ) denotes the number of ILI cases reported by the provider in week ; ( i ) denotes the estimated prevalence of ILI in the provider's zip code in week ; and are regression constants fixed for the provider; and is a normally distributed noise term with variance also fixed for the provider . For existing providers , we use empirical time series ( their past ILINet reporting data matched with local ILI prevalence , described below ) to estimate the constants and using least squares linear regression . This noise model has the intuitive interpretation that each provider's reports are a noisy reading of the percent of the population with ILI in the provider's zip code . We use the Texas hospital discharge data to estimate the local ILI prevalences ( ( i ) ) for each zip code . Given an estimate of the influenza hospitalization rate [27] and assuming that each individual with ILI is hospitalized independently , we can obtain a distribution for the number of influenza-related hospitalizations in a zip code , given the number of ILI cases in the zip code . Using Bayes rule , a uniform prior , and the real number of influenza-related hospitalizations ( from the hospital discharge data ) , we derive distributions for the number of ILI cases for each zip code and each week . We then set ( i ) for each zip code equal to the mean of the distribution of ILI cases in that zip code for week , divided by the population of the zip code . The second step in the ILINet optimization is to generate a pool of mock providers . For each actual provider in the Texas ILINet , we estimate a reporting profile specified by [1 ) ] transition probabilities between reporting and non-reporting ( Markov ) states , and the constants and , modeling noise in the weekly ILI reports . To generate a mock provider in a specified zip code , we select a uniformly random reporting profile out of all reporting profiles estimated from existing providers . The generated mock providers are thereby given reporting characteristics typical of existing providers . We can then generate an ILI report time series for a mock provider , by 1 ) generating reports only during reporting weeks , and calculating reports using equation ( 1 ) with the constants given in the provider's reporting profile and estimates of ( i ) for the mock provider's zip code . We select providers from pools consisting of a single mock provider from each zip code . Zip codes offer a convenient spatial resolution , because they have geographic specificity and are recorded in both the Texas ILINet and hospital discharge data . The optimization algorithm is not aware of a mock provider's reporting profile when the provider is selected ( discussed below ) . The final step in our ILINet design method is selecting an optimized subset of providers from the mock provider pool . We seek the subset that most effectively predicts a target time series ( henceforth , goal ) , as measured by the coefficient of determination ( ) from a least squares multilinear regression to the goal from the report time series for all providers in the subset . Specifically , the objective function is given bywhere is the goal random variable; is a subset of the mock provider pool; are provider reports for provider ; and the are the best multilinear regression coefficients ( values that minimize the second term in the numerator ) . There are several advantages to this objective function . First , it allows us to optimize an ILINet for predicting a particular random variable . Here , we set the goal to be state-wide influenza-related hospitalizations for Texas . This method can be applied similarly to design surveillance networks that predict , for example , morbidity and/or mortality within specific age groups or high risk groups . Second , the objective function is submodular in the set of providers , [28] , implying generally that adding a new provider to a small network will improve performance more than adding the provider to a larger network . The submodular property enables computationally efficient searches for near optimal networks and guarantees a good level of performance from the resulting network [29] . Without a submodular objective function , optimization of a provider ILINet may require an exhaustive search of all subsets of providers from the provider pool , which quickly becomes intractable . For example , an exhaustive search for the optimal provider Texas ILINet from our pool of approximately mock providers would require roughly regressions . Taking advantage of the submodular property , we rapidly build high performing networks ( with providers ) according to the following algorithm: This is guaranteed to produce a network that performs within a fraction of of the optimal network [28] . The submodularity property also allows us to compute a posterior bound on the distance from optimality , which is often much better than . Finally , even if implemented naively , the algorithm only requires approximately regressions to select providers from a pool of . When optimizing , it is important to consider potential noise ( underreporting and discrepancies between provider reports and actual ILI activity in the zip code ) . However , we assume that one cannot predict the performance of a particular provider before the provider is recruited into the network . To address this issue , the optimization's objective function is an expectation over the possible provider reporting profiles . Specifically , we define as a random variable describing the provider reporting profile for the entire pool of mock providers . If is a specific reporting profile , then the objective function can be written asTo design the ILINet , we solve the following optimization problemThe objective function is a convex combination of submodular functions , and thus is also submodular . This allows us to use the above algorithm along with its theoretical guarantees to design ILINets using a realistic model of reporting practices and informational stochasticity , without assuming that the designer knows the quality of specific providers a priori . We implemented the Maximal coverage model ( MCM ) following Polgreen et al . ( 2009 ) . Briefly , a greedy algorithm was used to minimize the number of people in Texas who live outside a pre-defined coverage distance , , of at least one provider in the selected set , . A general version of this algorithm was developed by Church and Re Velle ( 1974 ) to solve this class of MCM's [30] . As per Polgreen et al . ( 2009 ) , we assumed that the population density of each zip code exists entirely at the geographic center of the zip code and used Euclidean distance to measure the distance between zip codes . Using a matrix of inter-zip code distances we select providers iteratively , choosing zip codes that cover the greatest amount of population density within the pre-defined coverage distance , . We considered , 10 , 20 , and 25 miles , and found that miles yielded the most informative networks . We used two naive methods to model common design practices for state-level provider-based surveillance networks . To analyze similarities in ILI hospitalizations across different zip codes , we apply principal component analysis ( PCA ) [31] . Specifically , we perform PCA on the centered ( mean zero ) , standardized ( unit variance ) hospitalization time series of all zip codes in Texas . We first compute a time series for the first principal component , and then compute an for each zip code , based on a linear regression from the first principal component to the zip code's centered , standardized hospitalizations . Zip codes with high values have hospitalization patterns that exhibit high temporal synchronicity with the first principal component . To validate our method , we first use submodular optimization to create a provider network of providers , using only data from 2001 to 2007 , and then evaluate the performance of the network in predicting 2008 influenza-like hospitalizations . Specifically , after creating the -provider network ( ) , we use actual hospitalization data and mock provider reports for the 2001–2007 period to fit a multilinear regression model of the form where is time series of state-wide influenza-like hospitalizations at week for weeks in 2001 to 2007 , is the mock report time series of provider during week for weeks in 2001 to 2007 , and is the best multilinear regression coefficient associated with provider . We then use the estimated multilinear regression function to forecast state-wide influenza-like hospitalization during 2008 from mock provider reports of 2008 , and compare these forecasts to actual 2008 hospitalization data . This simulates a real-world prediction , where only historical data is available to create the provider network ( ) and estimate the prediction function ( 's ) , and then the most recent provider reports ( 's ) are used to make predictions . We evaluate the 2008 predictions using a variance reduction measure similar to , except that the multilinear prediction model uses coefficients estimated from prior data , as given bywhere is the hospitalization time series in 2008 , is the provider noise profile , and are the mock provider reports in 2008 . Importantly , we first calculate an expected value for the provider reports , , given the noise profiles , before calculating . We also considered an alternative validation method in which we first calculate an for each provider report and noise-profile combination , and then analyze the resulting distribution of values ( see Text S1 for results ) . | Public health agencies use surveillance systems to detect and monitor chronic and infectious diseases . These systems often rely on data sources that are chosen based on loose guidelines or out of convenience . In this paper , we introduce a new , data-driven method for designing and improving surveillance systems . Our approach is a geographic optimization of data sources designed to achieve specific surveillance goals . We tested our method by re-designing Texas' provider-based influenza surveillance system ( ILINet ) . The resulting networks better predicted influenza associated hospitalizations and contained fewer providers than the existing ILINet . Furthermore , our study demonstrates that the integration of Internet source data , like Google Flu Trends , into surveillance systems can enhance traditional , provider-based networks . | [
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] | 2012 | Optimizing Provider Recruitment for Influenza Surveillance Networks |
Ovules contain the female gametophytes which are fertilized during pollination to initiate seed development . Thus , the number of ovules that are produced during flower development is an important determinant of seed crop yield and plant fitness . Mutants with pleiotropic effects on development often alter the number of ovules , but specific regulators of ovule number have been difficult to identify in traditional mutant screens . We used natural variation in Arabidopsis accessions to identify new genes involved in the regulation of ovule number . The ovule numbers per flower of 189 Arabidopsis accessions were determined and found to have broad phenotypic variation that ranged from 39 ovules to 84 ovules per pistil . Genome-Wide Association tests revealed several genomic regions that are associated with ovule number . T-DNA insertion lines in candidate genes from the most significantly associated loci were screened for ovule number phenotypes . The NEW ENHANCER of ROOT DWARFISM ( NERD1 ) gene was found to have pleiotropic effects on plant fertility that include regulation of ovule number and both male and female gametophyte development . Overexpression of NERD1 increased ovule number per fruit in a background-dependent manner and more than doubled the total number of flowers produced in all backgrounds tested , indicating that manipulation of NERD1 levels can be used to increase plant productivity .
During plant reproduction , pollen tubes deliver two sperm cells to female gametophytes contained within ovules . This allows double fertilization to occur in order to produce the embryo and endosperm in the developing seed . Angiosperms with all kinds of pollination syndromes ( insect- , wind- , and self-pollinated ) produce much more pollen than ovules in order to ensure successful pollination . For example , most soybean varieties produce only 2 ovules per flower , but more than 3 , 000 pollen grains , a 1 , 500-fold difference[1] . Wind-pollinated plants such as maize have an even more extreme difference in pollen production vs . ovule production per plant , with more than 1 million pollen grains versus an average of 250 ovules per plant ( a 4000-fold difference[2] ) . Arabidopsis thaliana , which is a self-pollinating plant , also produces an excess of pollen , with at least 2000 pollen grains per flower compared to an average of 60 ovules per flower[3] . Since pollen is produced in excess , in self-pollinated plants the number of ovules ( i . e . female gametes ) sets the maximum seed number per flower . The ability to manipulate ovule number to increase the reproductive potential of plants requires an understanding of the molecular pathways that control ovule initiation . The model plant Arabidopsis thaliana produces flowers with four whorls of organs: sepals , petals , stamens , and carpels . The inner whorls ( 3 and 4 ) are responsible for sexual reproduction , with pollen ( the male gametophytes ) produced in the whorl 3 stamens and the female gametophytes ( also known as the embryo sacs ) , produced in ovules contained within the whorl 4 carpels . Specification of the 4 whorls is controlled by the “ABC” genes , with the C-class gene AGAMOUS ( AG ) a major regulator of carpel development[4] . In Arabidopsis , ovules are initiated from the carpel margin meristem ( CMM ) at stage 9 of floral development[5] . The Arabidopsis gynoecium comprises two carpels that are fused vertically at their margins [6] . The CMM develops on the adaxial face of the carpels ( inside the fused carpel cylinder ) and will give rise to the placenta , ovules , septum , transmitting tract , style , and stigma . Once the placenta is specified , all of the ovule primordia are initiated at the same time[7] . Subsequently , each primordium will be patterned into three different regions: the funiculus , which connects the ovule to the septum; the chalaza , which gives rise to the integuments; and the nucellus , which gives rise to the embryo sac . Ovule development concludes with the specification of the megaspore mother cell within the nucellus which undergoes meiosis followed by three rounds of mitosis to form the mature haploid embryo sac ( reviewed in[8] ) . In Arabidopsis , CMM development requires coordination of transcriptional regulators involved in meristem function with hormone signaling ( reviewed in[6] ) . Most mutants that have been reported to affect ovule number have pleiotropic effects related to the establishment of polarity and boundaries during gynoecial development ( reviewed in [9] ) . For example , the AINTEGUMENTA ( ANT ) transcription factor regulates organ initiation and cell divisions during flower development[10] . ANT acts redundantly with the related gene AINTEGUMENTA-LIKE6/PLETHORA3 to regulate carpel margin development and fusion which leads to a modest reduction in ovule number . This phenotype is exacerbated when ant is combined with mutations in other carpel development transcriptional regulators , such as SEUSS ( SEU ) , LEUNIG ( LUG ) , SHATTERPROOF1 and 2 ( SHP1 and SHP2 ) , CRABSCLAW ( CRC ) , FILAMENTOUS FLOWER ( FIL ) , and YABBY3 ( YAB3 ) . Mutant combinations between ant and these mutants leads to severe defects in carpel fusion coupled with severe reductions in the marginal tissues that give rise to the CMM ( summarized in[6] ) . An extreme example is the double mutant seu-3 ant-1 which results in a complete loss of ovule initiation due to defects in CMM development[11] . The organ boundary genes , CUP-SHAPED COTYLEDON1 and 2 ( CUC1 and CUC2 ) , are also required for CMM development and subsequent ovule initiation . ant cuc2 mutants with cuc1 levels decreased specifically in the CMM by an RNAi construct driven by the SEEDSTICK promoter show an 80% reduction in ovule number , indicating that ANT controls cell proliferation while CUC1/2 are necessary to set up the boundaries that allow ovule primordia to be initiated[12] . Plant hormones are also involved in gynoecium development and can have both indirect and direct effects on ovule number . Auxin biosynthesis , signaling , and transport mutants have varying effects on gynoecium development and patterning , many of which lead to pleiotropic effects on tissues and organs derived from the CMM[13] . Treatment of developing flowers with the auxin polar transport inhibitor NPA showed that an apical-basal auxin gradient in the developing gynoecium is necessary for patterning events that lead to ovule initiation[13] . Cytokinin has also been implicated in ovule initiation and development in Arabidopsis . Notably , triple mutants in the ARABIDOPSIS HISTIDINE KINASE ( AHK ) cytokinin receptors , AHK2 , AHK3 , and AHK4/CRE1 , displayed a 90% reduction in ovule number due to decreased cytokinin signaling[14] . Conversely , double mutants in the cytokinin degrading cytokinin oxidases/dehydrogenases ( CKXs ) displayed higher cytokinin levels in inflorescences and produced more than double the number of ovules as in wild-type controls[15] . Brassinosteroids ( BR ) may also be positive regulators of ovule initiation . Gain-of-function mutants in the BR-induced transcription factor BZR1 had increased ovule number per flower while BR-deficient and insensitive mutants had decreased ovule number compared to wild-type controls . Upregulation of the ovule regulators ANT and HUELLENLOS ( HLL ) was correlated with BZR1 activity , indicating the BR signaling positively regulates ovule development[16] . Recently , gibberellins were shown to negatively regulate ovule number independently of auxin transport and signaling . A gain-of-function mutation in the DELLA gene , GA-INSENSITVE ( GAI ) and loss-of-function mutations in GA receptors all led to increased ovule number in Arabidopsis [17] . To date , research on the factors controlling ovule number has been dominated by the analyses of mutants that have been identified based on pleiotropic effects on gynoecium development . In an attempt to identify loci that regulate ovule number without affecting other aspects of flower morphology , we took advantage of natural variation in ovule number in Arabidopsis accessions from diverse geographical locations . Over 7 , 000 natural accessions are now available with intraspecific variation , and next generation sequencing has been used to generate data on single nucleotide polymorphisms ( SNPs ) from over 1 , 000 of these accessions as part of the 1001 genomes project[18] . 100s of different phenotypes have been analyzed in this collection such as flowering time , leaf shape and size , the ability to resist to pathogens , etc . [19] . In this study , we identified variation in ovule number per flower in a screen of 189 Arabidopsis accessions and conducted a Genome Wide Association Study ( GWAS ) to identify loci associated with the ovule number trait . Further analysis of two loci identified in our GWAS revealed that the NEW ENHANCER of ROOT DWARFISM ( NERD1 ) and OVULE NUMBER ASSOCIATED 2 ( ONA2 ) genes participate in the determination of ovule number during Arabidopsis flower development . The discovery of new ovule number regulators in Arabidopsis has the potential to provide targets for the development of crop varieties with greater yield .
We set out to identify new regulators of ovule number in Arabidopsis by taking advantage of phenotypic variation in naturally occurring accessions . We obtained 189 Arabidopsis accessions from the ABRC and assayed them for variation in ovule number ( S1 Table ) . Since ovule number can vary throughout the life cycle of the plant[20] , we determined the average number of ovules from flowers 6–10 on the main stem of plants that were vernalized for 4 weeks and then grown in long days at 22°C . Under these growth conditions , accessions displayed a remarkable diversity in ovule number per flower , with a range of 39–82 ovules per flower ( Fig 1A and 1B ) . The commonly used reference accession , Col-0 , falls in the middle of the range with an average ovule number of 63±3 ovules . In contrast to flowering time variation which has been shown to correlate with latitude of origin in Arabidopsis accessions[21] , ovule number was not strongly correlated with location of origin in the accessions analyzed ( Figs 1C and S1 ) . Mapping ovule number data onto a cladogram of the accessions used in our study revealed a cluster of low ovule number accessions in one specific clade , indicating that these closely-related accessions may have similar genetic control of ovule number ( Fig 1D ) . Interestingly , several clades were made up of accessions with high , medium , and low ovule numbers . This suggests that the ovule number trait may be regulated by different loci that have been selected for in some lineages . In order to identify genomic regions linked to variation in ovule number , we assessed whether the average ovule number per flower from 148 accessions was predicted by Single Nucleotide Polymorphisms ( SNP ) available in the 1001 Full-sequence dataset; TAIR 9 . Logarithmic transformation was applied to the ovule number data to make the results more reliable for parametric tests . Associations were tested for each SNP using a linear regression model ( LM ) and the results were analyzed using GWAPP[22] ( Fig 2A ) . A significance cutoff value of–log10 ( p values ) ≥ 6 . 2 identified at least 9 genomic regions that are associated with variation in ovule number , while a higher cutoff of–log10 ( p values ) ≥ 7 . 5 identifies only four significant genomic regions . We next determined if known ovule number regulators [9 , 15 , 17 , 23] colocalized with our GWAS loci . Of the previously described loci , only BIN2 mapped close to a significant association ( S2 Fig ) . This indicates that our GWAS has identified novel functions for loci in the regulation of ovule number . For further analysis , we focused on the two loci with the lowest p-values which are both located on the long arm of chromosome 3 ( Fig 2B and 2C ) . Genes containing the most significantly associated SNPs as well as the 10 surrounding genes around the highest peak were considered as candidates for regulating ovule number . These genes were further prioritized based on whether they are expressed in developing pistils , by examining publicly available transcriptome data in ePlant[24] ( S3 Fig ) . Based on this prioritization , 35 candidate genes were selected , and of these 26 insertion mutants were available from the Arabidopsis Biological Resource Center . All 26 mutants were evaluated for changes in ovule number compared to the wild-type background , Col-0 ( S2 Table ) . Of these , two had significantly reduced ovule number compared to the Col-0 control ( Figs 3A and S4 ) . The strongest ovule number phenotype was found in insertion mutants in At3g51050 , a gene that was recently identified in a screen for enhancers of exocyst-mediated root phenotypes and named NEW ENHANCER OF ROOT DWARFISM 1 ( NERD1 ) [25] . The second locus with T-DNA insertions affecting ovule number identified in our screen was At3g60660 , which we call OVULE NUMBER ASSOCIATED 2 ( ONA2 ) . ONA2 encodes an unknown protein containing a DUF1395 domain ( TAIR ) ( Fig 2C ) . We focused our analysis on NERD1 since it had the strongest effect on ovule number . The fruits of homozygous mutants in two T-DNA insertion alleles , nerd1-2 ( described in [25] and nerd1-4 , had fewer ovules than wild-type plants ( Fig 3A ) . To confirm that this resulted from the disruption of NERD1 , we generated transgenic plants expressing a translational fusion of the NERD1 protein with the red-fluorescent protein tdTomato under the control of the native NERD1 promoter . In nerd1-2 and nerd1-4 mutants , this transgene fully rescued the reduced ovule phenotype and resulted in plants with ovule numbers indistinguishable from Col-0 ( Fig 3A ) . This demonstrates that NERD1 is a positive regulator of ovule number . In addition to reduced ovule number per flower , homozygous nerd1 mutants had fertility defects . Homozygous nerd1-2 mutants had 100% unfertilized ovules and homozygotes of the less severe nerd1-4 allele had 75% unfertilized ovules ( Figs 3B , 3C and S5A ) . nerd1 mutants displayed both male and female defects . Homozygous nerd1-2 mutants pollinated with Col-0 pollen displayed around 40% unfertilized ovules , indicating a female defect ( S5A Fig ) . A developmental analysis of embryo sac development in homozygous nerd1-2 mutants revealed that 38% embryo sacs were abnormal , with defects visible as early as the first mitosis ( stage FG2 ) and mature ovules having a range of defects from only having 2 nuclei to complete collapse of the embryo sac ( Figs 3D , 3E , S5A and S6A ) . Pollen tube attraction to ovules requires proper differentiation of the embryo sac since the synergid cells secrete the LURE pollen tube attractants [26 , 27] . The defective embryo sacs in nerd1-2 mutants do not appear to differentiate the synergid , egg , and central cells necessary for double fertilization . Consistent with the defects in embryo sac development , only 41% of ovules attracted pollen tubes in nerd1-2 pistils pollinated with Col-0 pollen ( S5B and S5C Fig ) . Homozygous nerd1-4 mutants displayed similar defects in embryo sac development , but a higher percentage of embryo sacs differentiated normally ( S7A and S7B Fig ) . Pollen development is also defective in nerd1 mutants . During pollen development , the microspore mother cell undergoes meiosis to form a tetrad of four microspores . Tetrads were produced in nerd1-2/nerd1-2 anthers , but 3 out of the four microspores collapsed and appeared to be aborted in mutant tetrads ( Fig 3F ) . In the nerd1-2 allele , later stages of pollen development were also defective and no viable pollen grains could be detected in mature anthers ( Figs 3G , 3H and S6B ) . The less severe nerd1-4 allele displayed similar defects in pollen development , but , similar to embryo sac development in both alleles , some normal pollen grains were produced ( S7C and S7D Fig ) . Both nerd1-2 and nerd1-4 segregate as recessive mutations in a 1:2:1 ratio in F2 populations ( Table 1 ) . This suggests that the male and female reproductive defects are sporophytic rather than gametophytic . To test this , we performed reciprocal crosses between heterozygous nerd1-2 mutants and Col-0 wild-type plants ( Table 2 ) . When heterozygous nerd1-2/NERD1 was used as the female , there was no transmission defect , demonstrating that the reduced female fertility in nerd1-2 mutants was not female gametophytic . When nerd1-2/NERD1 was used as the pollen donor , the transmission efficiency of the mutant allele was reduced to 45% , indicating a partial male gametophytic transmission defect ( Table 2 ) . Even though the nerd1 F2 segregation ratios fit a 1:2:1 segregation by a Chi-squared test , it should be noted that the number of homozygous nerd1 individuals is lower than wild-type individuals in both alleles ( Table 1 ) , consistent with a mild male gametophytic transmission defect . Phylogenetic analyses indicate that NERD1 is a member of a low-copy number , highly conserved gene family that is found throughout the plant kingdom and in cyanobacteria ( Fig 4A and 4B and [25] ) . The NERD1 protein is predicted to be an integral membrane protein with a signal peptide and one transmembrane domain ( Fig 5A ) . The majority of the protein is predicted to be extracellular , with the transmembrane domain located near the C-terminus and a 17 amino acid cytoplasmic extension . Transient expression of a NERD1-GFP fusion in Nicotiana benthamiana with subcellular markers confirmed that NERD1 puncta colocalize with the Golgi marker and partially overlap with a plasma membrane marker ( Fig 5B and 5D ) . NERD1 does not co-localize with ER , peroxisome , and plastid markers ( Figs 5C and S8 ) . Consistent with NERD1-GFP localization reported in Cole , et al . , 2018 , our Arabidopsis plants complemented with the NERD1-tdTomato fusion protein driven by the native NERD1 promoter had NERD1-tdTomato signal in a punctate pattern consistent with Golgi in roots and early stages of ovule development , but it was not detected in plasma membrane ( S9 Fig ) . The nerd1 ovule number and fertility phenotypes suggest that NERD1 should be expressed in developing flowers . We used a NERD1pro::gNERD1-GUS fusion to examine NERD1 expression throughout Arabidopsis development . In NERD1pro::gNERD1-GUS inflorescences , GUS activity was detected throughout flower development , including inflorescences , developing and mature anthers , and in the stigma , ovules , and carpel walls of mature pistils ( Fig 6 ) . NERD1-GUS activity was present in the carpel margin meristem ( CMM ) in stage 9 flowers , where ovule initiation occurs ( Fig 6C and 6E ) . NERD1 reporter expression in the CMM during pistil development is consistent with a role for NERD1 during ovule initiation . During seedling development , the NERD1-GUS reporter was detected in shoot and root apical meristems ( SAM and RAM ) and in the vasculature ( Fig 6I ) . Our GUS reporter results are consistent with tissue-specific transcriptome data from ePlant ( S10 Fig ) , suggesting that NERD1 is ubiquitously expressed throughout the plant and that the NERD1 promoter used in our experiment accurately reflects endogenous transcription . We examined NERD1 transcript levels in the low ovule number accession Altai-5 compared to Col-0 . NERD1 transcript accumulation was reduced in Altai-5 and Kas-2 buds as compared to Col-0 but similar in Altai-5 and Col-0 leaves ( Fig 7A and 7B ) . We hypothesized that the low ovule number in Altai-5 may be linked to reduced NERD1 expression in developing flowers . In order to determine whether increasing NERD1 expression is sufficient to increase ovule number , we transformed Col-0 and the low ovule number accession Altai-5 , with a NERD1-GFP fusion construct driven by the constitutively expressed Cauliflower Mosaic Virus 35S promoter ( 35S::NERD1-GFP ) . Overexpression of NERD1 had no effect on ovule number in the Col-0 background , but significantly increased ovule number in the Altai-5 background ( Fig 8A–8C ) , indicating that the NERD1 effect on ovule number is background-dependent . The 35S::NERD1-GFP plants displayed an even more striking phenotype when overall plant architecture was examined ( Fig 8A ) . In both the Altai-5 and Col-0 backgrounds , NERD1 overexpression led to increased branching ( Fig 8D ) and shortened internode lengths between flowers , leading to an overall increase in flower number in the overexpression plants compared to untransformed controls ( Fig 8E–8H ) . Thus , NERD1 overexpression leads to increased biomass and reproductive capacity , with up to a 2 . 5-fold increase in total flower number over the lifespan of the plant . While all independent transformants displayed increased branching and flower number , some of the 35S::NERD1 plants were male sterile ( S11 Fig ) . This male sterility correlated with NERD1 expression levels and plants with higher NERD1 transcript levels had more severe male sterility ( S11 Fig ) . The sterility effect was more severe in Col-0 than in Altai-5 ( S11 Fig ) . The lower endogenous NERD1 expression in Altai-5 inflorescences might explain the lower sensitivity of Altai-5 to NERD1 overexpression with respect to male fertility , demonstrating background-dependent sensitivity to NERD1 levels for both ovule number and male sterility . NERD1 was recently identified in enhancer screen performed on exocyst mutants with weakly dwarfed roots . nerd1 mutants have reduced root growth as a result of impaired cell expansion , indicating that NERD1 may be a positive regulator of exocyst-dependent root growth[25] . Consistently , the 35S::NERD1-GFP plants had longer roots compared to the Col-0 control , indicating that root development is also sensitive to NERD1 expression levels ( S12 Fig ) . Four SNPs in NERD1 exons showed significant correlation with ovule number in our GWAS ( Fig 9A and 9B ) . Three of them are synonymous SNPs that are not predicted to change the amino acid sequence , but the fourth is a non-synonymous SNP ( C to A change in comparison to Col-0 reference ) causing a Serine to Tyrosine change at amino acid 230 of NERD1 ( Fig 9A and 9B ) . This non-synonymous SNP was present in 10 out of the 16 lowest ovule number accessions and not present in the 16 highest ovule number accessions ( Fig 9A ) . Across all of the accessions in our GWAS panel , the “A” allele at this position was significantly associated with lower ovule numbers ( Fig 9C ) . However , some accessions with the “C” allele of NERD1 have low ovule numbers ( Fig 9A ) , including the Altai-5 accession described above . We examined NERD1 transcript levels in additional low ovule number accessions to determine if NERD1 expression correlated with ovule number in other accessions . Like Altai-5 , the “C” allele accession Kas-1 had reduced NERD1 transcript levels in flowers when compared to Col-0 ( Fig 7A and 7B ) . However , four “A”-containing accessions with the S230Y amino acid change had similar or higher expression levels of NERD1 in developing flowers ( Fig 7C and 7D ) , indicating that reduced NERD1 expression in flowers is not the explanation for reduced ovule number in these accessions . A second possibility is that the S230Y amino acid change affects the protein function of NERD1 in these accessions . To test this hypothesis , we transformed nerd1-2 mutants with NERD1 from both Gre-0 and Hh-0 expressed as td-Tomato fusions driven by their native promoters . Both of these NERD1 genes could complement the nerd1-2 ovule number phenotype ( S13 Fig ) , indicating that some other mechanism influences ovule number in these accessions .
A quantitative trait locus ( QTL ) mapping study utilizing variation between Ler and Cvi identified QTL residing on chromosomes 1 , 2 ( near the ERECTA gene ) , and two QTL on chromosome 5[23] . No follow-up study has identified the genes underlying these QTL . Our population displayed a much larger range of ovule numbers per flower ( 39–84 ) than that seen in Cvi vs Ler ( 66 and 56 , respectively ) . We identified 9 significant associations in our GWAS , but only one potentially overlaps with a known ovule number QTL ( see chromosome 5 in S2 Fig ) . Of the genes with an identified effect on ovule number , only BIN2 overlapped with a GWAS peak , indicating that our study has revealed at least seven novel regulators of ovule number in Arabidopsis . We molecularly identified two new genes that control ovule number that were linked to two GWAS peaks on chromosome 3 . This expands that number of known ovule number determinants and confirms the value of GWAS as a forward genetics tool . NERD1 is a plant-specific , low copy number gene that is found throughout the plant kingdom . NERD1 was recently identified as an enhancer of root development phenotypes in weak mutants of the exocyst subunits SEC8 and EXO70A1[25] . While a direct interaction with the exocyst could not be identified , the authors identified Golgi localization of NERD1 and hypothesized that NERD1 could be involved in synthesizing or modifying pectins or other polysaccharide components of the cell wall that are synthesized in the Golgi and transported to the cell wall [25] . Changes in cell wall elasticity mediated by pectin modifications have been correlated with lateral organ initiation at the shoot apical meristem [28 , 29] . The decreased ovule number in nerd1 mutants and increased lateral branching and flower initiation in 35S::NERD1 transformants could be consistent with alterations in Golgi-synthesized cell wall components affecting cell wall mechanics and organ initiation . Homozygous nerd1 mutants are completely male sterile and partially female sterile . This sterility is due to a lack of pollen production and problems in female gametophyte development leading to aborted embryo sacs . Transmission efficiency tests using heterozygous loss-of-function mutants revealed that nerd1 could be transmitted through the egg at near 100% efficiency , indicating a sporophytic effect on female gametophyte development . Ovule development mutants that have defective integument development such as short integuments 1 ( sin1 ) , bell 1 ( bel1 ) and ant fail to produce functional female gametophytes , indicating that female gametophyte development is dependent on properly differentiated sporophytic cells in the ovule[30–34] . Sporophytic development in ovules seem to be normal in nerd1 mutants , yet embryo sacs abort . As a membrane protein , NERD1 could transmit some unknown signal between sporophytic and gametophytic cells during embryo sac development . The male fertility defect in nerd1 plants is more severe than the female defect . Homozygous nerd1 anthers have severe reductions in pollen production resulting from defects in early stages of pollen development . Transmission efficiency tests using pollen from heterozygous nerd1 plants crossed to wild-type females revealed that nerd1 also has gametophytic effects on pollen function . The sporophytic effects could be related to early stages of anther development . In particular , specification of the tapetum is critical for pollen development ( reviewed in[35] ) . In our 35S::NERD1 experiment , transformants that accumulated the most NERD1 transcripts were male sterile . Together these results indicate that pollen development is sensitive to NERD1 levels , i . e . either too much or too little NERD1 is detrimental to pollen development . Future experiments should focus on determining the stage of anther and/or pollen development that is affected in nerd1 mutants and the specific cell types that express NERD1 in developing anthers . Even though NERD1 is expressed broadly throughout the plant , above ground vegetative development appears to be normal in nerd1 loss-of-function mutants . However , nerd1 roots are shorter than normal and have root hair defects that include bulging and rupture[25] . NERD1 could have distinct or related developmental functions in roots and flowers , as is seen for many of the genes involved in hormonal regulation of development[36] . NERD1 overexpression under control of the constitutive 35S promoter dramatically changed plant architecture in both the Col-0 and Altai-5 backgrounds . Overexpression phenotypes can be difficult to interpret since the 35S promoter could be active in cells where NERD1 is not normally expressed , causing NERD1 to interfere with pathways specific to those cell types . Alternatively , the increase in lateral organ formation seen in 35S::NERD1 plants could be related to increased root capacity that changes source/sink relationships within the plant and leads to higher plant productivity . This hypothesis is consistent with the smaller roots reported in nerd1 mutants and could be related to the secretory machinery and trafficking [25] . A third possibility is that NERD1 is a positive regulator of meristem activity . NERD1 overexpression plants produced significantly more branches than wild-type controls and produced significantly more flowers that were produced at closer intervals along the stem . Like branches and flowers , ovules are lateral organs produced from meristematic cells . Lateral meristem activation and subsequent branching have been shown to be affected by several different hormones ( reviewed in[37] ) . Classic experiments in Vicia faba showed that auxin produced in the shoot apex inhibits axillary meristems[38] , and more recently strigolactones were identified as graft-transmissible suppressors of branching ( reviewed in [39] ) . Gibberellins also repress shoot branching in Arabidopsis , maize , and rice , with GA-deficient mutants displaying increased branching compared to wild-type controls[40–43] . In contrast , cytokinins and brassinosteroids are both positive regulators of branching[42–46] . The mechanism through which these plant hormones work together to regulate branching is unknown , but auxin has been proposed to control cytokinin and strigolactone biosynthesis[47 , 48] , while the brassinosteroid signaling regulator BES1 may inhibit strigolactone signaling to promote branching[49] . The promotion of branching in 35S::NERD1 plants could be related to regulation of one or more of these hormonal pathways . Like NERD1 , upregulation of both cytokinin and brassinosteroid signaling pathways have been shown to positively regulate branching and ovule number[15 , 46 , 49 , 50] , suggesting that NERD1 may be intimately connected to these pathways . Future research is needed to explore the intersection between NERD1 and hormonal pathways . Overexpression of NERD1 in the Col-0 and Altai-5 backgrounds led to increased branching and flower number , but ovule number was only increased in the Altai-5 background , suggesting a background-dependence on the ovule number trait . In Arabidopsis , natural accessions were shown to respond differently in their ability to buffer GA perturbations caused by overexpressing GA20 oxidase 1 , which encodes a rate-limiting enzyme for GA biosynthesis[51] . Genetic background dependence has been shown to be a wide-spread phenomenon in C . elegans , with approximately 20% of RNAi-induced mutations ( out of 1400 genes tested ) displaying different phenotypic severity in two different genetic backgrounds[52] . Similar to our results with NERD1 , where Altai-5 has lower endogenous levels of the NERD1 transcript in developing flowers compared to Col-0 , the severity of the C . elegans phenotypes could be linked to gene expression levels of either the target gene itself or other genes in the same pathway[52] . Our GWAS revealed multiple novel loci that control ovule number in Arabidopsis . It remains to be seen if exocyst function is linked to NERD1’s role in ovule number determination . Identification of NERD1 interacting proteins will provide candidates for other players in the NERD1 pathway . Some of the other novel loci identified in our GWAS may well participate in the same signaling pathway as NERD1 . While NERD1 is a promising candidate for engineering plants with increased ovule number , the differential responses to NERD1 overexpression seen in Altai-5 and Col-0 suggest that specific alleles at other loci may be necessary for achieving maximum effect of NERD1 overexpression .
Arabidopsis thaliana accessions and insertion mutants were ordered from the Arabidopsis Biological Resource Center at Ohio State University ( ABRC ) . ABRC stock numbers for the accessions and insertion mutants used in our study are listed in S1 and S2 Tables . Seeds were sterilized and plated on ½ Murashige and Skoog ( MS ) plates . All plates were sealed and stratified at 4°C for two days , and then transferred to the growth chamber ( long day conditions , 16h of light and 8 h of dark at 22°C ) for germination and growth . After one-week , seedlings were transplanted to soil . Many of the Arabidopsis accessions require vernalization for flowering[53] , we therefore chose to vernalize all of the accessions in our study for 4 weeks at 4°C . After vernalization , the plants were returned to the growth chamber and grown under the long day conditions described above . Seeds from transformed lines were sterilized and plated on ½ MS plate with 20mg/L hygromycin for selection of transgenic seedlings , which were then transplanted to soil and grown in long days . For determination of ovule number per flower , we counted four to five siliques per plant and five plants per accession . Carpel walls were removed with a dissecting needle and total ovule number ( including unfertilized and aborted ovules ) was counted with the aid of a Leica dissecting microscope . To minimize age-related variation in ovule number , we counted siliques from flowers 6–10 on the primary shoot for all accessions . GWAS was performed using GWAPP , which is a GWAS web application for Genome-Wide Association Mapping in Arabidopsis ( http://gwas . gmi . oeaw . ac . at ) [22] . In our study , 148 accessions had single nucleotide polymorphisms ( SNPs ) data available on the 1001 Full-sequence dataset; TAIR 9 . Logarithmic transformation was applied to make the results more reliable for parametric tests . A simple linear regression ( LM ) was used to generate the Manhattan plot by using GWAPP[22] . SNPs with P values ≤ 1 × 10−6 were further considered as candidate loci linked to alleles that regulate ovule number ( a horizontal dashed line in Fig 2 shows the 5% FDR threshold -log10p value = 6 . 2 , which was computed by the Benjamini-Hochberg-Yekutieli method ) . SNPs with < 15 minor allele count ( MAC ) were not considered to help control false positive rates . 10 genes flanking the highest SNP for each locus were tabulated as candidate genes for each significant association . For complementation and overexpression experiments , Gateway Technology was used to make all the constructs . Genomic DNA fragments corresponding to the coding regions of candidate genes were amplified from either beginning of the promoter ( defined by the end of the upstream gene ) or the start codon to the end of the CDS ( without stop codon ) by PCR with primers that had attB1 and attB2 sites from Col-0 genomic DNA ( see S3 Table for primer sequences ) . For amplifying At3g60660 and At3g51050 , PHUSION High-Fidelity Polymerase ( NEB , M0535S ) was used . All the PCR products were put into entry vector pDONR207 by BP reactions and then were recombined into destination vector pMDC83 ( GFP ) by LR reaction[54] . Native promoter constructs were amplified from ~2KB promoter region to the end ( without stop codon ) by PCR with primers that had attB1 and attB2 sites from Col-0 genomic DNA . The PCR products were put into entry vector pDONR207 by BP reactions and then were recombined into destination vector pMDC32 ( tdTomato ) and pMDC163 ( GUS ) by LR reaction . All constructs were transformed into Agrobacterium tumefaciens strain GV3101 and then used for plant transformation by the floral-dip method[55] . GUS staining was performed as previously described[56] . Samples were imaged with Differential Interference Contrast ( DIC ) on a Nikon Eclipse Ti2-E microscope . 12 independent T1 NERD-GUS transformants were analyzed and showed similar GUS patterns . Leaves from 3–4 week old N . benthamiana were co-infiltrated with 35S::NERD1-GFP and Golgi-mCherry , ER-mCherry , PM-mCherry , Plastid- mCherry and Peroxisome-mCherry markers from[57] as previously described[58] . Leaves were imaged 2–3 days after infiltration with a Nikon A1Rsi inverted confocal microscope under 20x dry and 40x water objectives with GFP excited by a 488nm laser and mCherry excited by a 561nm laser in normal mode . Analysis of embryo sac development was conducted using confocal microscopy based on [59] . Pistils were dissected from FG2 , FG3 , FG4 and mature stages of flowers from Col-0 , nerd1-2 and nerd1-4 and fixed in 4% glutaraldehyde and 12 . 5mM cocadylate , PH = 6 . 9 for two hours at room temperature . Pistils were dehydrated in 20% , 40% , 60% , 80% and 100% ethanol for 10 min each . Samples were then cleared in a 2:1 mixture of benzyl benzonate: benzyl alcohol for 2–4 hours and mounted in immersion oil for imaging . Images were captures using a Nikon A1Rsi inverted confocal microscope under 60x oil objectives and excitation with a 561nm laser . Pollen was released from tetrad , microspore , bi-cellular , tri-cellular and mature stage anthers from Col-0 , nerd1-2 and nerd1-4 . Samples were incubated in 1ug/ml DAPI ( 4′ , 6-diamidino-2-phenylindole ) staining solution for 2hrs and imaged using a Nikon A1Rsi inverted confocal microscope under 60x oil objectives with DAPI excited by a 405nm laser . Mature anthers from Col-0 and nerd1-2/nerd1-2 were dissected under the Leica dissecting microscope and placed into a drop ( 20 μl ) of Alexander staining solution on a microscope slide[60] . After several minutes of staining , samples were imaged with a Nikon Eclipse Ti2-E microscope . Flowers that two days after emasculation from nerd1-2 plants were cross-pollinated with wild-type Col-0 . The pollinated pistils were then collected at 36 h after hand pollination and fixed immediately in ethanol-acetic acid ( 3:1 v/v ) overnight . Pistils were rehydrated in 70% , 50% , 30% ethanol for 5 min each at room temperature . After clearing in 5 N NaOH at 60°C for 5 min and washing 2 × in 0 . 1M phosphate buffer ( pH-8 ) , they were stained using aniline blue ( 0 . 1% aniline blue in K3PO4 ) for 15mins . Five pistils per sample were analyzed and images were captured with a Nikon Eclipse Ti2-E microscope ( Nikon Instruments Inc , Melville , NY ) . The cladogram tree was generated in MEGA7 , which nucleotide distance and neighbor-join tree file were calculated by PHYlogeny Inference Package ( PHYLIP , version 3 . 696 ) . The phylogenetic tree of NERD1 was inferred using neighbor-joining method in MEGA7[61] . The associated taxa clustered together with the bootstrap test ( 1000 replicates ) [62] . All the branch lengths are in the same units as those of the evolutionary distances used to generate the phylogenetic tree . For qRT-PCR , leaves and young flowers were collected from mature plants of Col-0 and Altai-5 . Samples were immediately frozen in liquid nitrogen , ground , and total RNA was extracted using the E . Z . N . A Plant RNA kit ( OMEGA , USA ) . Oligo-dT primers and Superscript II reverse transcriptase ( Invitrogen ) was used for cDNA synthesis . qRT-PCR reactions were prepared using SYBR Green PCR Master Mix and PCR was conducted with a StepOnePlus RT-PCR system . Relative quantifications were performed for all genes with the Actin11 used as an internal reference . The primers used for qRT-PCR shown in S3 Table . | Ovules are the precursors of seeds in flowering plants . Each ovule contains an egg cell and a central cell that fuse with two sperm cells during double fertilization to generate seeds containing an embryo and endosperm . The number of ovules produced during flower development determines the maximum number of seeds that can be produced by a flower . In this paper , we used natural variation in Arabidopsis thaliana accessions to identify regions of the genome that are associated with ovule number . Polymorphisms in the plant-specific NERD1 gene on chromosome 3 were significantly associated with ovule number . Mutant and overexpression analyses revealed that NERD1 is a positive regulator of ovule number , lateral branching , and flower number in Arabidopsis . Manipulation of NERD1 expression levels could potentially be used to increase yield in crop plants . | [
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"experimental"... | 2019 | A genome-wide association study reveals a novel regulator of ovule number and fertility in Arabidopsis thaliana |
Many animals , including humans , rely on active tactile sensing to explore the environment and negotiate obstacles , especially in the dark . Here , we model a descending neural pathway that mediates short-latency proprioceptive information from a tactile sensor on the head to thoracic neural networks . We studied the nocturnal stick insect Carausius morosus , a model organism for the study of adaptive locomotion , including tactually mediated reaching movements . Like mammals , insects need to move their tactile sensors for probing the environment . Cues about sensor position and motion are therefore crucial for the spatial localization of tactile contacts and the coordination of fast , adaptive motor responses . Our model explains how proprioceptive information about motion and position of the antennae , the main tactile sensors in insects , can be encoded by a single type of mechanosensory afferents . Moreover , it explains how this information is integrated and mediated to thoracic neural networks by a diverse population of descending interneurons ( DINs ) . First , we quantified responses of a DIN population to changes in antennal position , motion and direction of movement . Using principal component ( PC ) analysis , we find that only two PCs account for a large fraction of the variance in the DIN response properties . We call the two-dimensional space spanned by these PCs ‘coding-space’ because it captures essential features of the entire DIN population . Second , we model the mechanoreceptive input elements of this descending pathway , a population of proprioceptive mechanosensory hairs monitoring deflection of the antennal joints . Finally , we propose a computational framework that can model the response properties of all important DIN types , using the hair field model as its only input . This DIN model is validated by comparison of tuning characteristics , and by mapping the modelled neurons into the two-dimensional coding-space of the real DIN population . This reveals the versatility of the framework for modelling a complete descending neural pathway .
Active tactile sensing is a widespread strategy for near-range orientation in the animal kingdom . The whiskers of mammals [1] or antennae of insects and crustacea [2] are moved actively to acquire information about the near-range environment . Tactually elicited changes in behaviour often involve fast , adaptive motor reactions . In insects , these adaptive movements include rapid body axis inclination [3] or turning [4] , but also aimed reaching movements of the front legs [5] . These behaviours have in common that tactile information must be mediated to thoracic locomotor networks with short latency . To date , several descending interneurons ( DINs ) likely to be involved in tactually induced adaptive behaviours have been characterised ( e . g . , [6 , 7] ) . As yet , very little is known about how the population of descending neurons encodes and mediates the sensory essentials to lower motor control centres that drive leg movement . Current modelling approaches to descending sensory-motor control concern detailed models of individual descending neurons ( e . g . , [8] ) , or conceptual models of how a population of neurons with similar response properties may encode complex information such as object motion [9] . Yet other models follow a control-theoretic approach devoid of any neuronal components ( e . g . , [10] ) or use analogue signals to simulate the bulk activity in a descending pathway [11] , thus not taking into account the diversity of descending interneurons involved . Missing until today is a computational framework that allows both the simulation of individual known neurons , and the combined modelling of an entire population of descending neurons , while accounting for diverse coding properties . Here , we propose such a computational framework for modelling a population of DINs and their afferent sensory input . The DIN models simulate the multivariate information transfer from an active touch system to a motor control system . For three reasons , we choose the antennal mechanosensitive pathway of the stick insect Carausius morosus as the paragon for our model: i ) A behavioural reach-to-grasp paradigm in stick insects [5] requires fast information transfer of antennal posture and movement; ii ) both the antennal tactile system [12 , 13] and the thoracic leg motor control networks [14 , 15] are well studied; and iii ) a population of spiking DINs has been described that conveys diverse , multivariate information about antennal posture and movement to thoracic neural networks [16]; [17] . Our objective was to devise models of several different DIN types , including position- and motion-sensitive DINs , drawing from a small set of computational modules such as linear filters and a noisy spike generator . We include a model of the afferent input that drives these DINs . This "afferent model" is upstream from the DIN models and simulates the afferent activity generated by antennal hair fields . Hair fields are insect-specific patches of external mechanoreceptors which , because of their location , function as proprioceptors . Our model simulates known properties of antennal hair field afferents , which can encode antennal joint angle and angular velocity owing to their phasic-tonic response characteristics [18–20] . The objective of the afferent model is to test how much of the DIN response properties can be explained by a single type of mechanoreceptor afferents . Our model explains spike response patterns of four distinct DIN classes as described by [16] , making it an exceptionally complete and versatile model of a descending neural pathway . We show that input from hair fields alone is sufficient to explain a large fraction of the response variants found in a real DIN population , including position-and motion-sensitive , ON-type , and OFF-type interneurons . For validation , we present a data-driven analysis of the multivariate DIN coding properties measured in electrophysiological experiments . By doing so , we derive a two-dimensional coding-space and show that real and modelled neurons are co-localised in this coding-space . Thus , we present a model that can explain the activity of an entire population of descending interneurons involved in the control of adaptive locomotion .
Experimental data for model validation and derivation of the coding-space ( see below ) were taken from a recent electrophysiological study on descending interneurons ( DINs ) in the stick insect ( Carausius morosus ) [16] . We used a sub-sample of 59 intracellular DIN recordings . All of these DINs were sensitive to changes in antennal position or motion . Stimulation of the antenna was delivered by a custom-built stimulator that moved only one of the two antennal joints , the scape-pedicel joint ( Sc-Pd joint ) . The stimulator deflected the pedicel and the base of the flagellum in a contact free manner , by moving a magnet that guided a metal minutin pin that was inserted into the cut flagellum . The proximal antennal joint , the head-scape joint , was kept immobile , making sure that only the Sc-Pd joint was deflected . Thus , we predominantly stimulated proprioceptors of the Sc-Pd joint . Additional slight bending of the flagellar base and associated stimulation of cuticular strain sensors ( campaniform sensilla ) could not be excluded ( see discussion , and [16] ) . For further details about the stick insect antennal sensory and motor system , see [13] . The stimulus time course was a staircase of ramp-and-hold deflections . Each trial comprised two upward and two downward ramps ( i . e . , antennal movement ) separated by hold phases without antennal movement ( Fig 1A ) . In each trial , the Sc-Pd joint was initially held at the ventral extreme position ( -50° ) , then moved to the resting position ( 0° ) , and further to the dorsal extreme position ( 50° ) . After the hold phase at the dorsal position , the antenna was moved back in the reverse sequence . Therefore , the stimulus time course per trial was symmetrical in space and time . Between trials , the deflection velocity was varied between 1 and 800°/s . Trials were repeated two to eight times , then the ramp velocity was changed pseudorandomly . For each DIN , stimulus trials covered a wide range of naturally occurring movement velocities . For further analysis of the spike response of each DIN , membrane potential traces were resampled at 5000 Hz and imported to Matlab ( Version 7 . 9 , Mathworks , Natick , USA ) . All data analysis and modelling was done in Matlab , using custom-written scripts . For a detailed description of the electrophysiological methods , see [16] . For validation of the "afferent model" , we used a graphical comparison with published data on antennal hair field responses in the cockroach [20] . In order to allow a quantitative comparison of the physiological coding properties of the real DINs with the output of our DIN model , we needed to define an appropriate frame of reference . This was done in two steps . In the first step , we used three major descriptors of the stimulus-induced spike responses of a recorded DIN , and determined the value of each descriptor at four different stimulus velocities . The descriptors were scores of the direction , position , and motion selectivity of the DIN response . As selectivity scores , we calculated three signed contrast measures relating the mean spike rates of the DIN during certain parts of the ramp-and-hold stimulus , using the equation: s=A−BA+B where s is the seletivity score , and A and B are the mean spike rates of a given DIN during the stimulus parts depicted in the schematics in Fig 1B ( part A: black solid lines; part B: grey solid lines ) . Thus , s varied continuously between 1 ( A > B ) and -1 ( B > A ) . Rather than using the absolute value of s , we deliberately kept the sign at this point of the analysis , because distinct groups of DINs have ON- or OFF-properties [16] and , therefore , differ in the sign of s but not necessarily in its absolute magnitude . Larger absolute values of s indicated stronger selectivity . Direction selectivity was scored by relating the mean spike rate during the upward stimulus ramps ( levation of the antennal Sc-Pd joint ) to the mean spike rate during the downward stimulus ramps ( depression of the antennal Sc-Pd joint , top schematic in Fig 1B ) . As a result , direction selectivity was 1 if the DIN spiked only during levation , and -1 if the DIN spiked only during depression of the Sc-Pd joint . For scoring the position selectivity , the mean spike rate during the lower ( ventral ) stimulus ramps and hold phase was contrasted against the mean spike rate during the upper ( dorsal ) stimulus ramps and hold phase ( middle schematic in Fig 1B ) . Finally , motion selectivity was scored by relating the mean spike rate during antennal movement ( stimulus ramps ) against the mean spike rate during rest ( hold phases , lower schematic in Fig 1B ) . In case of several trials with repeated identical stimuli , spike rates were averaged across trials . Generally , the variability in the response to such repeated stimulus presentations was low , which can be seen by similar scores for any given velocity in Fig 1C . This indicated that stimulus history was of little importance . For example , the DIN in Fig 1 responded strongly to Sc-Pd joint movement towards the dorsal and ventral extreme positions ( Fig 1Ai-iv ) . Moreover , the number of spikes and the mean spike rate depended strongly on the stimulus velocity . Increasingly faster upward movement resulted in fewer spikes at lower rates , while increasingly faster downward movement resulted in higher spike rates . ( Fig 1Ai-iv ) . This resulted in velocity-dependent direction and position selectivities ( Fig 1Ci and 1Cii ) but nearly constant motion selectivity ( Fig 1Ciii ) . The motion selectivity was always approximately 1 because the DIN almost never spiked during hold phases . Since the coding properties of each individual DIN were quantified by only three types of descriptors , the analysis was not sensitive to more particular properties of some DINs . For example , the selectivity scores did not include a measure of relative direction , so that a DIN like the one shown in Fig 1 got pooled with other motion-selective neurons , despite the fact that it spiked only during movement away from the resting position . In the case of the relative direction selectivity , this didn't matter much , because only two DINs of the type shown in Fig 1 were included in the analysis . Nevertheless , this example illustrates that the versatility of our analysis framework comes at the cost of losing detail in the description of DIN types with rare and particular properties . Despite this trade-off , the selectivity scores gave a reasonably detailed account of the most common DIN response properties , and allowed us to characterize the overall stimulus encoding properties of each DIN by a small set of selectivity scores ( compare Fig 1A and 1C ) . We used the selectivities at four intermediate velocities for further analyses ( Fig 1C , black symbols ) , which reduced the number of variables that described each DIN to 12 . In the second step of the coding-space analysis , we applied multivariate statistics analysis to further reduce the dimensionality of the mapping . More specifically , we applied a principal component analysis ( PCA ) in order to transform the twelve-dimensional space spanned by the selectivity scores into an orthogonal 12-D space in which the axes pointed into the directions that explained most of the data variance , i . e . , the principal components ( PC ) . Each PC must be viewed as a linear combination of twelve selectivity scores . Because in PCA the eigenvalue of each PC reports the fraction of the total data variance explained , we could determine those PCs which explained more variance than each selectivity score alone ( Kaiser-Guttmann criterion ) . In our case , this was the case for two PCs only ( see Fig 2 and Results section ) . Throughout this study , we will call the space spanned by the first two PCs the "coding-space" of the DIN population . Since PC1 loaded mostly on motion selectivity , and PC2 loaded mostly on position selectivity , we chose to label the axes of the coding space ‘movement sensitivity’ ( PC1 ) and ‘posture sensitivity’ ( PC2 , see Results ) . The same analysis as described for the real neurons above was applied to the modelled neurons , allowing us to validate the model against experimental data , and to conduct a sensitivity analysis of model parameters . The computational framework devised in this study comprises two types of models: an "afferent model" capturing the essential properties of antennal hair field afferents , and a "DIN model" with four variants , capturing the essential response properties of different DIN types . The afferent model is upstream to the DIN model variants . For both models , we used the same computational modules , such as gain factors , thresholds , linear filters , and a noisy spike generator . These modules were deliberately kept at a simple , conceptual level , to not make more assumptions than justified through experimental data . The response properties of hair field afferents [20] and DINs [16] , which we chose for model validation , were described by means of their spike response patterns . Therefore , it was essential to ground our model validation on spike rate comparisons . Time-varying data was filtered using linear first-order high- and low-pass filters with the time constant ( tau ) as the only variable . Series of filters were used to convert stimuli into appropriate activation functions that may be considered an approximation of a neuron’s membrane potential . Using these activation functions , a noisy spike generator yielded the spike patterns of the modelled neurons , both in hair field afferents and DINs . A spike was generated at a given time step , t , if the scaled activation function , act ( t ) exceeded a random number , rand , drawn from a uniform distribution between zero and one: spike ( t ) =1ifact ( t ) *dS*Rmax≥rand;otherwisespike ( t ) =0 , where spike ( t ) is a discrete-valued time series indicating whether or not a spike is being fired at time t , dS is the time step , and Rmax determines the spike rate . Appropriate choices of dS and Rmax thus allowed scaling the mean spike rate of the modelled neuron to that observed in a real neuron . As dS was kept fixed at 1 ms , only Rmax was adjusted . The spike generator is probabilistic , and the likelihood of the modelled neuron to spike is determined by the activation function and Rmax . Spikes cannot be elicited at activation levels below zero . For activation levels between zero and one , the likelihood of a spike to be elicited at a given Rmax rises linearly . The noisy spike generator was thus appropriate for turning continuous activation functions into discrete spike times , as it generated relatively broad spike rate distributions around the firing rate Rmax . However , the spike generator was not designed to generate exact spike times for a particular type of neuron . The simplicity of the spike-generating mechanism permitted us to use it for the mechanosensory afferents as well as for all DIN types . Further details and model parameters are explained in the Results section .
Our first objective was to quantify the activity of a population of recorded antennal mechanosensory DINs as the basis for our model . We chose three main descriptors to distinguish between episodes of antennal stimulation . These were: i ) movement versus rest , ii ) upper versus lower working-range , and iii ) upward versus downward direction of movement ( Fig 1B ) . Accordingly , we used three selectivity scores ( motion , position and direction ) to map the spike response pattern of each DIN into a three-dimesional coordinate system . Since antennal movement velocity clearly modulated the responses of several DIN types ( [16] , see example shown in Fig 1 ) , we included selectivity scores determined at four different movement velocities . Thus , each DIN was characterized by a vector of twelve values comprising three types of selectivity scores for each one of four joint angle velocities . Joint angle velocities ranged between 12 and 400°/s , covering a large part of the natural velocity range during tactile searching and sampling . A total of 59 DINs were included in the analysis . To reduce the dimensionality of the original dataset , we used principal component analysis ( PCA ) . This served two major purposes: first it allowed us to validate our earlier , inspection-based classification scheme [16] by a data-driven method; second , it allowed us to explain a substantial part of the variance in the coding-space of the DINs , using a minimal set of orthogonal axes . The number of meaningful axes was determined by use of the Kaiser-Guttman criterion . This criterion includes only principal components ( PC ) , i . e . , axes of the coding-space , which explain a larger fraction of the total variance than any of the twelve original variables would ( 1/12 = 8 . 3% ) . According to this criterion , only the first two principal components ( PCs ) were meaningful . Together , they explained about 73% of the variance in the selectivity scores ( Fig 2D , black dotted line ) . PC1 loaded most strongly on motion selectivity ( Fig 2A , PC1 ) and explained 43% of the total variance in the selectivity scores ( Fig 2D ) . The loading of motion selectivity scores gradually increased with increasing velocity , indicating that there was a tendency for stronger motion selectivity during faster displacement ( Fig 2A , PC1 ) . The ‘loading’ is a scaling factor that indicates the weight of a given score for a PC . Note that the sign of the loading is not meaningful , because the direction of the axis is arbitrary . PC2 loaded predominantly on position selectivity ( Fig 2A , PC2 ) , with a weak but graded influence of both direction and motion . Three of the four position selectivity scores had about equal weight , indicating similar position selectivity at all but the fastest velocities . PC2 explained about 30% of the total variance ( Fig 2D , PC2 ) . The third PC loaded most strongly on direction selectivity , with hardly any influence of position and a weak , graded , and clearly bidirectional influence of motion ( Fig 2A , PC3 ) . The latter indicates opposite effects of very slow and very fast velocities . Since PC3 explained less than eight percent of the total variance ( Fig 2D ) , we did not include it in the further analysis . In summary , the population of DINs was most sensitive to Sc-Pd joint movement and the Sc-Pd joint angle , i . e . , pointing direction , of the antenna . In contrast , direction selectivity explained only little variance in the data , suggesting that it is not a key variable of the coding-space of these neurons . To make sure that these results were not influenced by a sampling bias due to different numbers of recordings obtained from different DIN types , the PCA was repeated on random samples ( Fig 2B ) and on selected subsamples containing equal numbers of DINs from the different , empirically defined groups ( Fig 2C ) . Grouping was based on the criteria presented in [16] . The PCAs on both random and selected DINs yielded very similar results as the original PCA on the full dataset ( Fig 2A , 2B and 2C ) . Whereas the absolute loadings of the first three PCs were always similar , the order of PCs one and two was swapped in some subsamples ( compare , e . g . , Fig 2A PC2 and 2C PC1 ) , and the direction of the PCs was inverted in some cases ( compare , e . g . , Fig 2A PC1 and 2B PC1 ) . Both the order and the direction of the PCs are not important to our analysis , because they only affect the arrangement and direction of the axes of an orthogonal subspace of the original data . Since the loadings were essentially the same , the orientations of the axes that span this subspace varied only little . Moreover , the variance explained by the first three PCs was similar for all three PCAs , with slightly more variance explained in case of the random samples than in case of the original data . This result varied little when using different random subsamples ( Fig 2D , error bars ) . We conclude that our data-driven construction of a two-dimensional ( 2-D ) coding-space of DIN responses was not influenced by a sampling bias . The two significant axes of this coding-space essentially correspond to antennal posture and movement sensitivity , with very little effect of movement direction . Accordingly , we conclude that our set of DINs mainly mediates position and motion information to thoracic neural networks . Within the 2-D coding-space ( PC space ) derived above , we plotted the 59 DINs according to the projection of their 12 selectivity scores onto PCs 1 and 2 ( Fig 2E ) . With regard to the type of selectivity score that loaded most strongly on PC1 and PC2 , we chose to label the axes of the coding-space ‘movement sensitivity’ ( PC1 ) and ‘posture sensitivity’ ( PC2 ) . As a result , each DIN’s sensitivity to antennal movement ( PC1 ) and posture ( PC2 ) is intuitively understandable by its location in coding-space . Note , however , that PC1 and PC2 are strongly correlated but not equal to the selectivity scores for ‘motion’ and ‘position’ , and that a DIN's location in coding-space does not provide a direct measure of its sensitivity . To further simplify the interpretation of the data , the negative part of PC2 , i . e . , the part corresponding to ventral joint angles , was flipped over , such that only the absolute PC2 score , or posture sensitivity , is shown . As a consequence , DINs at the top of the graph responded most strongly to strong excursion of the Sc-Pd joint ( either dorsal or ventral ) , whereas the response of DINs at the bottom of the graph exhibited little influence of the joint angle . DINs in the far positive range of PC1 fired more during rest ( stimulus hold phases ) than during movement ( stimulus ramps ) , whereas DINs in the middle and negative range fired more during movement than during rest ( Fig 2E ) . The distribution of DINs within the coding-space was widely spread ( Fig 2E ) , showing that recorded DINs had very different selectivity profiles . Although not all DINs fell into clear clusters , it was possible to assign them all to distinct groups ( Fig 2E ) . This was done in two ways . First , we manually assigned each DIN to one of six groups after inspection of the original recording traces , according to empirically defined criteria [16] . The criteria were the baseline spike rate , spike patterns during antennal stimulation , sensitivity to antennal movement velocity , position-sensitivity , peak spike rates , and response latencies . In a second step , we attempted to define linear separators that divide the coding-space into distinct sub-regions in a functionally meaningful manner . In the first approach , the data point of each DIN was coloured according to the group it was assigned to ( Fig 2E ) . The major DIN groups were simple position-sensitive DINs ( dark blue , SP ) , which spiked whenever the Sc-Pd joint angle was within a certain range; dynamic position-sensitive DINs ( cyan , DP ) , which spiked during antennal movement within a given joint angle range; ON-type velocity-sensitive DINs ( red , ON ) , which spiked during antennal movement; and OFF-type velocity-sensitive DINs ( green , OFF ) , which had a high baseline spike rate and spiked less during antennal movement ( see [16] , for a more detailed description of the DIN response properties ) . The data-set also contained DINs of the few-fast type , which fired one to two spikes during fast Sc-Pd joint movement only ( dark grey ) , and unspecific movement-sensitive DINs , which had a high background spike rate and fired few additional spikes during fast antennal movement ( light grey ) . As can be seen in Fig 2E , each symbol colour is confined to a sub-region of the coding-space . Indeed , it was possible to separate all but one DINs of the four major groups into disjunct regions of the coding-space using simple linear thresholds ( Fig 2E , grey dotted lines ) . The right vertical line separates DINs that fired more during movement from those that fired more during rest , and the horizontal line separates position-sensitive DINs from the other groups . Only one DIN of the ON group ( red ) had a posture sensitivity that fell into the range of the position-sensitive groups DP and SP ( light and dark blue , respectively ) . This corresponds to 98% of correct class assignments . Therefore , the data-driven analysis allows automatic , thus unbiased classification of DINs with high precision . We conclude that , despite the fact that the 2-D coding-space was constructed in a data-driven way , linearly separated sub-regions of this coding-space coincided very well with the empirical DIN groups proposed earlier . For example , ON-type velocity-sensitive DINs formed a tight cluster in the lower left corner of the coding-space , i . e . , at high movement- and low posture sensitivity ( red dots , ON , Fig 2E ) . The relatively tight clustering indicates low variability in the ON-type velocity-sensitive DIN population , and thus might suggest that relatively few individual DINs belong to that cluster . At the opposite end of the movement sensitivity range , OFF-type velocity-sensitive DINs formed a tight , stripe-shaped cluster that spanned regions from high movement sensitivity to low movement sensitivity , close to the centre of the antennal coding-space ( green dots , OFF , Fig 2E ) . As expected , all OFF-type velocity-sensitive DINs fired more during hold phases than during movement , but their sensitivity to antennal movement varied strongly . Position-sensitive DINs did not form very tight clusters , but occupied distinct regions in coding-space ( blue and cyan dots , Fig 2E ) . Simple position-sensitive DINs ( SP ) occupied the upper right half of the coding-space , indicating that they fired less during movement than an average DIN . They were still located to the left of OFF-type velocity-sensitive DINs , indicating that their spike rate was unaffected by antennal movement . Dynamic position-sensitive DINs ( DP ) spiked almost exclusively during antennal movement , and accordingly covered the same region as ON-type velocity-sensitive DINs on PC1 ( cyan dots , Fig 2E ) , but in the upper half of the coding-space . The two empirical groups for which the linear separators did not allow unambiguous assignment were the unspecific ( light grey ) and few-fast type DINs ( dark grey , Fig 2E ) . Both of them overlapped with ON-type velocity-sensitive DINs . Since few-fast type DINs fired more during movement than during hold-phases , they were all positioned in the left half of the coding-space . Accordingly , our data-driven analysis suggests that this group may be considered a sub-group of ON-type velocity-sensitive DINs . In case of the Unspecific DINs , their predominant location near the origin of the coding-space underscores the appropriateness of the group’s name . As yet , it cannot be assigned automatically . The good overall correspondence of the empirical grouping and distinct sub-regions of the coding-space suggests that the coding-space could also be used for quantitative comparison of real DINs with modelled DINs ( see below ) . Since the analysis above showed that DINs were mostly sensitive to antennal movement and posture , their presynaptic sensory afferents must encode at least these two variables . Out of the different mechanoreceptors on the insect antenna [2] and of those of stick insects in particular [13] , antennal hair fields seemed to be particularly well-suited for mediating the information required . Antennal hair fields have phasic-tonic response properties and an intrinsic velocity sensitivity [20] . Ablation of antennal hair fields affects active antennal movements in stick insects [21] , and impairs tactually-induced turning in cockroaches [22] . Therefore , antennal hair fields are involved in the local coordination of antennal joints and the descending control of locomotion . Moreover , afferents from antennal hair fields overlap with DIN dendrites in the brain and gnathal ganglion [23] , where they are likely to provide direct input to at least some DINs [17] . Therefore , a major objective of our study was to test how much of the DIN response properties could be explained with antennal hair fields providing their only afferent input . As a first step towards this goal , we developed a computational model of antennal hair field afferents to estimate the synaptic input to DINs during Sc-Pd joint movement ( Fig 3 ) . The model parameters are listed in the caption to Fig 3 , their values are given in Table 1 . Hair fields contain patches or rows of several mechanoreceptive hairs [2] . In stick insects , Sc-Pd joint movement is monitored by two hair fields on the dorsal , and a further two on the ventral surface of the pedicel [21] . As described in detail for the cockroach antenna [20] , our model assumes that antennal movement causes the deflection of individual hairs ( or sensilla ) of antennal hair fields . The further an antennal joint moves away from its resting posture , the more hairs of a hair field are being deflected ( Fig 3 , schematic on top ) . Each hair contributes to encoding a fraction of the joint angle range . This range is defined by a minimum and a maximum joint angle , equivalent to the onset of deflection and its saturation . In our model , we collapsed these hair fields into two hair rows , one on each side of the joint ( Fig 3 , schematic on top ) . Each hair row consisted of 20 hairs ( Table 1 ) , which is a conservative estimate of the average number of hairs in the stick insect [21] . The spacing of hairs was assumed such that the number of deflected hairs varied linearly with antennal joint angle . Dorsal hairs were only deflected in the dorsal joint angle range ( Sc-Pd joint angle > 0° ) , and ventral hairs only in the ventral joint angle range ( Sc-Pd joint angle < 0° , Table 1 ) . For simplicity , the deflection angle of each hair ( hair deflection in Fig 3 ) was assumed to vary linearly with the Sc-Pd joint angle within the hair’s sensitivity range . The activation of sensory afferents was then calculated by low- ( LPF ) and highpass-filtering ( HPF ) the hair angle . The filtered traces are shown in Fig 3 ( grey boxes ) . Subsequently , the two filtered traces were added ( Fig 3 , + ) . The resulting activation function can be viewed as the hypothetical membrane potential of the sensory afferent . After normalization and subtraction of a constant Offset , the activation function was fed into a simple Noisy spike generator , as described in the section Material and Methods . The spike generator turned the activation function into a series of discrete spike times . Each model afferent had an absolute refractory period of 3 ms . The same computations were carried out for all hairs of the two hair fields , i . e . , for 2x20 mechanosensory afferents ( Table 1 and Fig 3 ) . Note that the assumption that the properties of all sensilla were the same is a simplification [20] . The output of the hair field model was a set of Spike trains , one for each afferent ( Fig 3 , Hair field ) . It was possible to tune the parameters of our hair field model such that the properties of output spike trains reflected those measured in hair field afferents of the cockroach antenna ( Fig 4 , compare to Fig 7 of [20] ) . Note that the stimulus time courses in Fig 4 ( Hair deflection ) were chosen such that they mimicked those used by [20] . Most importantly , the transient activity of the sensory afferents during ramp-and-hold stimuli was proportional to the deflection velocity ( Fig 4A ) , while the sustained activity was proportional to the deflection amplitude ( Fig 4B ) . Hence , each individual afferent had the same overall response properties as afferents on the cockroach antenna [20] . While it is possible to record from individual hair field afferents during deflection of an individual hair field sensillum , it is difficult to estimate the activity of the population of sensory hairs during antennal movement . Therefore , we arranged the modelled afferents in a way that reflected the situation on the stick insect antenna ( schematic in Fig 3 ) , and stimulated the hair field model using the same stimulus time course as was used for the experimental characterization of DIN responses ( Fig 5 , Joint angle ) . As a result , we obtained an estimate of the bulk activity of all hair field afferents during antennal movement ( Fig 5 , Spike freq . ) . Owing to the different location of individual hairs , movement of the Sc-Pd joint led to distinct time courses of hair deflection ( Fig 5 , Hair deflect . ) , activation of the corresponding mechanoreceptive afferent ( Activation functions ) , and the ensuing spike train elicited in the afferent ( Spike trains ) . Hairs of the ventral hair field were deflected in ventral joint angle ranges ( Fig 5 , light grey ) , and hairs of the dorsal hair field in dorsal joint angle ranges ( Fig 5 , dark grey ) . No hairs were deflected during the stimulus hold phases at the resting posture , i . e . , at an Sc-Pd joint angle of 0° . Accordingly , no afferent activity was generated during these hold phases . As the Sc-Pd joint was moved away from this resting posture ( in either direction ) , the sequential deflection of additional hairs caused sequentially delayed activation of the corresponding afferents . Afferents that were activated only at large joint angles did not reach the maximal activation level . This was because the corresponding distal hairs never reached full deflection and , therefore , their low-pass filter contributed less positive activation than in afferents of more proximal hairs . Accordingly , the complete set of afferent spike trains revealed two main differences among afferents: the cascaded onset of spike trains during movement , and the different maximum transient and persistent spike rates for hairs that did not reach full deflection . The aim of the DIN model was to capture the most important properties of the DIN population and different DIN types as described by [16] . We validated the model by quantifying the differences between real and modelled DINs in the multivariate coding-space . In particular , we tested how much of the diverse DIN properties could be explained by assuming exclusive input from antennal hair field afferents and no other mechanoreceptors . As in the hair field model , we used only first-order linear filters and simple mathematical operations to integrate the afferent hair field activity . Four types of DINs were modelled , using slightly differing variants of the same computational framework ( Fig 6 , different colours represent different DIN types ) . The first step was common to all model variants ( Fig 6 , grey ) . Here , the spike trains of all hair field afferents , i . e . , the output of the hair field model ( Fig 3 ) , were low-pass filtered separately ( LPF , Fig 6 ) . This spread individual spikes in time , and thus generated what may be interpreted as postsynaptic potentials in DIN dendrites . The time constant Tau L1 of this first filtering step was the same for all DIN types ( Table 2 ) . The resulting afferent input functions were scaled by separate , adjustable gains for dorsal and ventral afferents ( Wd and Wv ) , and summed ( + ) , yielding the lumped afferent synaptic input to a DIN . This input function was normalized ( Table 2 ) in order to provide a similar activation range to all variants of the DIN model . The resulting input activation level can be considered an abstraction of the membrane potential at the spike-initiating zone . The main DIN model was split into two computational branches ( Fig 6 ) . The simplest model variant was that of simple position-sensitive DINs ( Fig 6 , blue , SP ) . This DIN group is characterized by firing in dorsal or ventral joint angle ranges , or both , and by a spike rate that is independent of antennal movement per se [16] . Dorsal or ventral simple position-sensitive DINs could be modelled by summing the activity of all dorsal or ventral hair field afferents , respectively . The input activation level was then directly fed into the random spike generator . The resulting spike time series exhibited similar position dependence as that of real DINs ( compare blue spike trains in Fig 7A and 7B ) . Varying the normalization of the activation function and the frequency of the spike generator ( Table 2 ) could be used to improve this match . Recorded simple position-sensitive DINs fired more regularly than modelled DINs , because the spike generator used in the model was relatively noisy . We refrained from adjusting the spike-generator in different model variants in order to use as few model parameters as possible , and to thus keep the DIN model as simple as possible . For the same reason , we did not weigh each afferent input separately but used only one gain for all afferents from the same hair field . As a consequence , the tuning of the range boundaries of the position dependence was limited . As yet , note that there is no need for a spatial map or somatotopy within the hair field sensory system for the derivation of an accurate joint angle signal . The activity of the population of afferents simply increased with an increasing number of deflected hairs , and with stronger deflection of individual hairs ( Fig 5 ) . All known ON- and OFF-type velocity-sensitive DINs in the stick insect respond to antennal motion throughout the entire joint angle work range [16] . Note that the two DIN classes termed velocity-sensitive DINs in [16] are not exclusively velocity-sensitive: they are characterised by very strong movement sensitivity and relatively weak posture sensitivity ( Fig 2E ) . Therefore , modelled velocity-sensitive DINs received input from both dorsal and ventral hair fields . Accordingly , the gains associated with these two input streams , Wd and Wv , were set to one ( Fig 6 and Table 2 ) . The activity of all afferents was then bandpass-filtered and normalized ( Fig 6 , green and red branches ) , to generate transient DIN activity whenever the activity of hair field afferents changed rapidly . As a result , DIN activation was high during Sc-Pd joint movement . However , upon returning to the resting position , the transients in the DIN activation level turned negative . This occurred when ventral hairs ( first stimulus ramp ) or dorsal hairs ( third stimulus ramp ) stopped spiking ( see Fig 5 ) . In contrast , real velocity-sensitive DINs showed similar responses during all four stimulus ramps , i . e . , whenever the antenna moved ( Fig 7A , red and green spike trains , see also [16] ) . To avoid these negative transients in the DIN activation level , we used a full-wave rectification ( Fig 6 , abs ) . Consequently , antennal movement led to depolarization during any antennal movement for ON-type velocity-sensitive DINs , yielding a spike time series similar to that observed in real DINs ( Fig 7A and 7B , red ) . Note that full-wave rectification is not simulating a physiological mechanism per se , but may be considered a simple mathematical operation that could be achieved by more complex physiological processes that may involve interneuron stages ( see also Discussion ) . Two further details were special about the model variant for ON-type velocity-sensitive DINs: First , an offset was subtracted to keep the activation of the DIN below threshold during hold-phases of the stimulus , and thus to account for their low baseline activity [16] . Second , the spike rate of the spike generator was higher than in other model variants , and set to 120 Hz ( Table 2 ) . OFF-type velocity-sensitive DINs are characterized by a high baseline spike activity that gets increasingly reduced with increasing movement velocity of the antenna . To account for this property , the activation function as obtained in the ON-type velocity-sensitive DINs was inverted , scaled by a factor of 0 . 5 , and subtracted from the constant offset of 1 ( Table 2 ) . The offset accounted for the baseline activity , which was increasingly reduced with increasing afferent activity , i . e . , with increasing movement velocity . As a consequence , the models of OFF- and ON-type velocity-sensitive DINs received the same afferent input , but the gain was half as strong in OFF-type DINs and led to either excitatory ( ON-type DINs ) or inhibitory drive ( OFF-type DINs , -abs in Fig 6 , green ) . This model could account for the velocity sensitivity of ON- and OFF-type DINs without tuning the afferent input in a velocity-sensitive manner ( Fig 8 ) . Modelled DINs had essentially the same velocity sensitivity as their real counterparts , even when considering a wide range of movement velocities ( Fig 8 , compare solid and dotted lines ) . The velocity sensitivity emerged because individual hairs of the hair fields were deflected at higher rates and , more importantly , with decreasing delays between adjacent hairs . This led to higher synchronicity of afferent spikes at higher Sc-Pd joint angle velocities , which in turn resulted in stronger activation of ON-type and stronger inhibition of OFF-type DINs during faster antennal movement . Therefore , the model did not only account for the distribution of spikes during individual stimuli , but also for DIN properties across different stimuli . As yet , velocity sensitivity broke down for velocities beyond 400/s . This occurred mainly because the impact of filter-induced phase shifts and the resulting latencies of our DIN model were larger during faster movement velocities ( Fig 8 , grey ) . Response latencies also affected the analysis of the recorded DIN activity , in that spike rates were underestimated for very fast movements . In order to prevent this effect from confounding data-driven clustering of modelled DIN types and the comparison with real DINs ( see below ) , the responses to the fastest movement stimuli were excluded from the quantitative analysis . The fourth model variant concerned dynamic position-sensitive DINs ( Fig 6 , cyan ) , and was based upon the structure of the variant for ON-type velocity sensitive DINs . Dynamic position-sensitive DINs are characterized by a combination of position- and motion-sensitivity , in that they spike only during motion within extremely ventral or dorsal joint angle ranges [16] . By setting the gains Wd ( or Wv ) to zero ( Table 2 ) , the modelled DINs received afferent input from the ventral ( or dorsal ) hair field only . As a result , motion sensitivity was restricted to a limited range of positions leading to tuning characteristics that are typical for ventral or dorsal specimen of this DIN type ( see cyan spike trains in Fig 7A ) . To account for the fact that spike rates of dynamic position-sensitive DINs were generally lower than those of ON-type velocity-sensitive DINs , the spike rate of the spike generator was reduced to 100 Hz ( Table 2 ) . Dynamic position-sensitive DINs that fire during movement towards both dorsal and ventral extreme positions ( see lowest cyan trace in Fig 1 ) could be modelled by removing the full-wave rectification from the ON-type velocity-sensitive DIN model . As a consequence , the modelled neuron spiked during movement towards the dorsal and towards the ventral extreme position ( Fig 7A and 7B , cyan ) . In summary , all four important antennal mechanoreceptive DIN groups found in stick insects could be modelled by the same computational framework , with minor differences among the four model variants ( Table 2 ) . All response types could be derived from the integration of afferent input from two antennal hair fields only . We did not attempt to model neurons of the few-fast and unspecific movement-sensitive types , because their spike patterns can be explained by co-stimulation of mechanoreceptors which are not located at the Sc-Pd joint , such as campaniform sensilla . To validate our computational modelling framework by means of a quantitative comparison of modelled and recorded DINs , we used the coding-space described above ( Fig 2 ) to compare the model variants to their physiological counterparts . Moreover , we systematically varied model parameters to illustrate their effect on the DINs’ coding properties in a graphical kind of sensitivity analysis . For this , modelled DINs were analyzed exactly like recorded DINs ( see Fig 1 ) , and plotted alongside the recorded DINs in the 2D coding-space ( Fig 9; compare with Fig 2 ) . Thus , the whole set of model variants , including systematic parameter variation , could be mapped into the same space as the population of recorded antennal mechanosensory DINs . First , to better define the properties of the coding-space , we modelled a DIN without any antennal input , which fired spikes at random during antennal stimulation ( filled grey circle , Fig 9 ) . This DIN ended up close to zero on the y-axis , indicating no posture sensitivity , as expected ( Fig 9 ) . However , on the x-axis , which indicates the movement sensitivity , the DIN ended up in the right half of the coding-space . This occured because most DINs in the population fired more during antennal movement than during hold phases . Therefore , DINs exhibiting a spike rate that is independent of antennal movement , i . e . , with zero motion-selectivity , end up in the right half of the coding-space , with x = 0 . 8 indicating no sensitivity to antennal movement in the coding-space . Modelled and recorded ON-type velocity-sensitive DINs occupied the same region in coding-space ( Fig 9 , compare red stars and red polygon ) . Increasing the background activity by increasing the offset of modelled DINs resulted in less movement sensitivity , as illustrated by increasingly light red stars in Fig 9 . The offsets used were -0 . 2 ( original model , black star , 1 ) , -0 . 1 , 0 , 0 . 05 , and 0 . 01 ( dark to light red , 5 ) . Thus , variation of this single parameter created a trajectory of movement sensitivity in coding-space . This trajectory traversed the range covered by ON-type velocity sensitive DINs ( red line , Fig 9 ) in a curved manner . In general , the modelled DINs of this variant were not posture sensitive because they received symmetric input from dorsal and ventral hair fields ( Fig 6 ) . However , there was a tendency for more movement- sensitive neurons to score higher on PC2 , too . This was due to the small position component in PC2 ( see Fig 2A ) . Notably , most recorded ON-type velocity-sensitive DINs scored higher on PC2 than modelled DINs , and their position selectivities were often larger than zero , suggesting that they received slightly asymmetric input from dorsal and ventral antennal hair fields . In the case of OFF-type velocity-sensitive DINs , modelled DINs were almost perfectly co-localized with recorded DINs in coding-space ( compare green stars and green polygon , Fig 9 ) . OFF-type DINs scored highest on PC1 , because their spike patterns were most different from all the other DINs . OFF-type DINs were the only neurons that fired less during movement than hold-phases . In the OFF-type DIN model variant , increasing the weight of the input , i . e . , the strength of inhibition during antennal movement , led to a trajectory ( green stars ) that neatly traversed the narrow range covered by real DINs ( green polygon ) . In the modelled OFF-type DIN in Figs 7 and 8 , the normalization was twice as strong as for ON-type DINs . Therefore , the effect of afferent activity on the DIN activity was half as strong as in ON-type DINs . To traverse the complete area covered by recorded OFF-type DINs in the coding-space ( Fig 9 , green polygon ) , the normalization value for modelled neurons ( Table 2 ) was varied . We divided the original value ( 40 , black star , original model , see Table 2 ) by 0 . 1 , 0 . 4 , 0 . 7 , and 1 . 3 . This yielded increasingly sensitive modelled OFF-type DINs because the modelled DINs were increasingly affected by the afferent activity ( light to dark green stars , Fig 9 ) . The resultant normalization value of the most-sensitive modelled OFF-type DINs was 40/1 . 3 = 30 . 1 . This was still 1 . 5 times the normalization value used for modelling ON-type DINs ( 20 , Table 2 ) . Therefore , even the most sensitive OFF-type DINs were less sensitive to antennal movement than an average ON-type DIN . Similar to ON-type DINs , OFF-type DINs that were more sensitive to antennal motion scored higher on PC2 , albeit with different sign ( Fig 9 ) . The least sensitive recorded OFF-type DINs overlapped with a modelled DIN that was not sensitive to antennal movement at all ( filled grey circle ) . However , these OFF-type DINs were inhibited at very high movement velocities outside the range used for the quantitative analysis . To cover the range of posture sensitivity found in real DINs , modelled position-sensitive DINs either received input from only one hair field ( black stars at the top , 1 ) , or increasingly strong input from the second hair field as well ( light blue/cyan stars , 5 ) . For both dynamic and simple position-sensitive DINs , the Wd:Wv proportion ( Table 2 ) was varied ( 1:0 , 1:0 . 2 , 1:0 . 4 , 1:0 . 6 , and 1:0 . 8 ) to account for their distribution along the position axis in coding-space ( PC 2 ) . Modelled dynamic position-sensitive DINs covered the same region as their recorded counterparts . In terms of the motion-sensitivity , the range was similar to that covered by ON-type velocity-sensitive DINs ( Compare red and light blue stars , Fig 9 ) . It was possible to increase the spread of the position-sensitive DINs on the x-axis by changing the offset , similar to ON-type position-sensitive DINs . Since the selectivity scores were not well suited to distinguish between dynamic extreme position-sensitive DINs and other dynamic position-sensitive DINs ( see description of Fig 1 ) , modelled dynamic extreme position-sensitive DINs were excluded from the PCA comparison . Modelled simple position-sensitive DINs covered the same region in the coding space as recorded simple position-sensitive DINs ( dark blue , Fig 9 ) . They were slightly shifted towards higher movement sensitivity compared to the modelled DIN with no motion- or position-selectivity ( filled grey circle , Fig 9 ) . Because of the low spike rates of simple position-sensitive DINs , the movement sensitivity varied somewhat due to trial-by-trial variability ( dark blue trajectory , Fig 9 ) . This again reflected the relatively large variability of the movement-sensitivity in the population of recorded simple position-sensitive DINs ( dark blue polygon , Fig 9 ) . Some of these DINs fired only five to ten spikes per second while the antenna was held in the dorsal or ventral extreme position . Therefore , the presence or absence of even a single spike during antennal movement had a relatively large effect on the calculated motion sensitivity . Simple position-sensitive DINs were nonetheless clearly separated from all other DIN types in the coding-space , because they were clearly less movement-sensitive than dynamic position-sensitive DINs , and more posture-sensitive than OFF-type velocity-sensitive DINs . In summary , we modelled different DIN types that matched the properties of recorded DINs in terms of the spike patterns and the sensitivity to antennal posture and movement . Notably , the posture sensitivity and the movement sensitivity of DINs could be accounted for when considering only antennal hair fields as the input mechanoreceptors . Therefore , the largest fraction of the coding properties of the DIN population recorded by [16] could be explained without including any of the other known mechanoreceptors of the antenna .
Our data-driven analysis used a small set of contrast measures to quantify the selectivity scores of 59 DINs . The scores concerned movement versus rest , movement direction , and position . These simple measures were chosen to account equally well for a large variety of DIN tuning curves instead of reflecting the properties of a subgroup of DINs with high fidelity . For example , the analysis was sensitive to the difference between dynamic and simple position-sensitive DINs , but was not suited to differentiate between dynamic extreme position-sensitive DINs and other dynamic position-sensitive DINs . For that , it would have been necessary to include further selectivity scores , such as relative direction . As the selectivity scores we used are intuitively understandable , the PCA results are interpretable in physiological terms . Another advantage of our selectivity scores is that they can also be applied to active movements . This is possible because the mean spike rates used to calculate the scores are largely independent of the duration of movement or hold phases . Hence , the selectivity scores are also suitable for application to more erratic , asymmetric movements and random hold periods , as expected during active antennal movement [12 , 21] . Thus , it will be possible to compare DIN responses during active and passive movements of the antennal joints , using the same measures , in the coding-space . Moreover , the same data-driven analysis could be used to characterize and compare antennal mechanosensory DINs across species [7] , or across different regions of the central nervous system [24] . In the coding-space , it was possible to use simple , linear thresholds to assign DINs to different subgroups , which supported our initial empirical grouping [16] . This opens the door to easily sort DINs recorded in different contexts . For example , spike trains obtained from extracellular multi-electrode array recordings ( e . g . , see [24] ) could thus be linked to intracellular recordings . The data-driven analysis and the coding-space presented here will therefore be useful for a deeper analysis of different mechanosensory DIN populations . Despite the fact that insect antennae carry different types of mechanoreceptors [2] , our model included only antennal hair field afferents . By focussing on hair fields , we tested whether their input would be sufficient to drive different DIN types . The modelled sensory hairs were tuned to reflect the properties of antennal hair field afferents recorded in the cockroach [20] . Using a lead-lag system with parallel first-order high- and low-pass filters , it was possible to model spike trains that greatly resembled the recorded afferent spike trains . Our model is therefore grounded on physiological data . An earlier model of hair fields at a stick insect leg joint focused on the timing of hair deflection and the role of spike rate adaptation for the faithful encoding of the joint angle in presumptive follower neurons [25] . In contrast to this study , we had the advantage of knowing the properties of follower neurons , so that we could directly test whether our hair field model can explain the activity of postsynaptic descending interneurons in the CNS . It was sufficient to use only two hair rows , one dorsal and one ventral , to drive the activity of all important characterized DIN groups . Thus , we proved that hair field input is sufficient for explaining the activity of downstream neurons . Moreover , we showed that hair fields cannot only account for positional responses of downstream neurons , but also for their motion selectivity and velocity tuning . Of course , this is no proof that no other mechanoreceptors are involved in shaping DIN responses , too . Indeed , [7] presented evidence suggesting that antennal campaniform sensilla are involved in encoding antennal movement in crickets . Nevertheless , the high descriptive power of our model suggests that no essential mechanoreceptive input components have been omitted . This is even more important to note when considering two further simplifications of our model: First , we reduced the complexity of the hair field arrays at the stick insect Sc-Pd joint from two hair fields and two hair rows [21] to two hair rows , only . Second , we assumed that all afferents have the same properties . In general , afferents can have different response properties within a single hair field , but most examples presented in the literature are phasic-tonic , and thus have similar properties as our model afferents . Our hair field model could therefore be generalized to other insect limb joints , too . In summary , we presented the first computational model of an insect hair field with relation to its role in driving the activity of downstream interneurons . We showed that hair field activity can be used to derive an appropriate position signal , but also to derive information about joint movement and the velocity thereof . The proposed computational framework contains relatively few parameters in four variants to explain the response properties of a population of DINs . Modelled DINs reflected the population of recorded DINs well . In particular , the spike patterns of different simple and dynamic position-sensitive DINs , as well as ON- and OFF-type velocity-sensitive DINs could be modelled by simple integration of hair field spike trains ( Fig 7 ) . More complex properties like the velocity sensitivity of ON- and OFF-type DINs were captured by second order integration and band-pass filtering of the hair field activity ( Fig 8 ) . Finally , our data-driven analysis revealed a large degree of overlap between the two populations in the coding-space ( Fig 9 ) , further validating our DIN model . Moreover , by systematic variation of particular model parameters , we were able to account for the spread of recorded DINs in coding-space and , hence , for the properties of individual DINs belonging to the different groups ( Fig 9 ) . Our DIN model thus accurately represents the properties of the different DIN types . To generate response patterns of position-sensitive DINs , the afferent spike trains were low-pass filtered and summed up , which can be interpreted as an abstraction of spatially summated EPSPs . It is conceivable that real DINs receive input from different sensory hairs . Subsequently , the input could be summed up at the spike-initiating zone to elicit spike trains . In such a model , DINs would get direct input from hair field afferents , without the need for an additional interneuronal integration step . Notably , it was not necessary to distinguish between individual afferents or to weigh their contribution according to their position in the hair field . Hence , our model shows that there is no need for labelled lines or somatotopy in this sensory system . Instead , simple summation of hair field activity is sufficient to explain the coding properties of position-sensitive DINs . In contrast , the position of antennal contacts along the flagellum was proposed to be encoded by a labelled-line principle [26] . Extracting dynamic information from the hair field activity was more complicated . First , it was necessary to add a band-pass filter to extract transients in the afferent activity during Sc-Pd joint movement ( Fig 6 ) . A neural correlate of this could be fast adaptation of the DINs , so that only the initial afferent spikes lead to DIN spikes . After band-pass filtering , the onset of afferent spiking during movement away from the resting position leads to an increase in DIN activation . However , the offset of afferent spiking during movement towards the resting position leads to a decrease . Hence , it was necessary to rectify the DIN activation in order to generate similar DIN responses during movement in both directions at the same joint angles . The implementation of such a full-wave rectification is not straight-forward to explain in a real neuron . Several mechanisms are possible . First , DINs could have post-inhibitory rebound properties that might lead to the initiation of spikes after hyperpolarisation ( Fig 10A; [27 , 28] ) . In that case , one should expect that spikes generated by excitatory input would occur faster than rebound spikes , which usually have relatively long delays [29] . Hence , DIN spikes during stimulus ramps one and three would be expected to occur later than spikes during stimulus ramps two and four ( Fig 10A , vertical dotted lines ) . Such systematic differences were not present in velocity-sensitive DINs [16] . Therefore , a rebound mechanism is an unlikely explanation . Second , the DIN response could be explained by a polysynaptic pathway ( Fig 10B ) . Afferents could drive an interneuron that inhibits a fast-adapting DIN . In such a network , stop of afferent activity would lead to a rapid decrease in the spike rate of the interneuron which , in turn would release the DIN from inhibition . This would have to happen only during movement towards the resting position , when dorsal or ventral hairs return to their initial position ( Fig 10B ) . The appeal of this model is that ON-type velocity-sensitive DINs have a very low baseline activity , which could be due to strong inhibition . However , hair field afferents and ON-type velocity-sensitive DINs overlap morphologically in the gnathal ganglion ( or suboesophageal ganglion ) , suggesting they could be directly connected . This is also supported by the short delays of the velocity-sensitive DIN responses , which forward spikes to the thorax within 11 ms after antennal stimulation [16 , 17] . Nonetheless , it is possible that an interneuron might be interspersed between antennal mechanosensory afferents and DINs . Third , the DIN could receive input from antennal mechanoreceptors with bidirectional phasic properties ( Fig 10C ) . These would be well-suited to make direct synaptic contacts with DINs . For example , the activity of dynamic position-sensitive , ON-type , and OFF-type velocity-sensitive DINs could be derived by simple summation of the activity of bidirectional , phasic sensory hairs that fire both during deflection away from and towards their resting position . Type 1 afferents in the cockroach trochanteral hair field have exactly these properties [19] . It is either possible that the stick insect antenna carries hairs with similar response properties , or that other mechanoreceptors , like campaniform sensilla [30] or chordotonal organs [31] , provide this kind of input . Which one of the above options ( Fig 10 ) may be implemented can be tested in future experiments , for example by measuring the effect of hair field ablation on the DIN spike patterns . The quantitative analysis shown in Fig 9 allows us to draw conclusions about the DIN population that otherwise would be difficult to justify . For example , the distribution of DINs in the coding-space and the number of parameters needed to explain the spread of modelled DINs within the coding-space permits the derivation of hypotheses concerning the number of individual DINs belonging to the different clusters . All recorded OFF-type DINs formed a continuous cluster and could be modelled by the gradual variation of a single parameter , the input strength or normalization . This suggests that that there are only few individual OFF-type DINs per antenna . This is also supported by the DIN morphology because , so far , only a single morphological type of OFF-type DIN was identified [17] . Assuming only a single OFF-type DIN on each side of the CNS , the sensitivity of the DIN could either differ between animals , or with dependence on the animal’s state during the recording . It was not possible to model the different sensitivities of OFF-type DINs by changing other parameters , like the baseline spike rate , which differed across animals in the recordings [16] . A similar argument can be made for ON-type velocity-sensitive DINs . The dense distribution of these DINs in the coding-space and the possibility to model them well by shifting a single parameter suggests that only few individual DINs belong to this DIN cluster . In fact , physiological data suggest that only two individual DINs with very similar properties belong to this cluster . The two DINs mainly differ in that one responds to the ipsi- , and the other to the contralateral antenna [17] . The widely varying sensitivity values of both simple and dynamic position-sensitive DINs , on the other hand , and the fact that both posture sensitivity and movement sensitivity needed to be varied to capture the variance in the distributions of these DIN types in the coding-space suggest the presence of a larger number of individual neurons . This is also supported by strongly varying response patterns when comparing different individual recordings [16] . In summary , the quantitative analysis and the DIN model support our findings that few individual DINs belong to the velocity-sensitive DIN clusters . Moreover , the modelling approach suggests that many individual DINs belong to the position-sensitive DIN clusters . Hence , we assume that antennal movement velocity is encoded by a small number of broadly-tuned DINs , while antennal position is encoded by a large number of DINs with variable tuning characteristics . The latter was also suggested by [7] for antennal mechanosensory DINs in the cricket . While the neural substrate of the connection between DINs and thoracic inter- and motorneurons has not yet been unravelled , the modelled descending pathway is likely to contribute to the intersegmental spatial coordination of antennae and legs in a reach-to-grasp paradigm [5] . As such , it might well represent coding principles in sensory-guided locomotion in general , and inter-limb coordination in particular . Given the detailed knowledge about antenna-driven behaviour and thoracic motor networks in the stick insect , our model of the pathway comprising antennal mechanoreceptors and descending interneurons may be linked to models of thoracic neural networks ( e . g . , [32 , 33] ) or more abstract , behaviour-based single-leg controllers ( e . g . , [34 , 35] ) . This will pave the way for a better understanding of descending influences on thoracic motor networks , as our model bridges the gap between two well-characterized systems . | Many nocturnal and burrowing animals rely on their tactile sense to explore the surrounding space , and tactile cues are often used to adapt locomotion to a structurally complex environment . Most mammals use facial whiskers for active tactile exploration , while most insects use their antennae . Since whiskers and antennae are long , thin , cylindrical structures , they must be moved to probe the surrounding space . The nervous system therefore has to keep track of tactile sensor movement by encoding sensor position and motion in order to locate tactile contacts . Here , we model a descending neural pathway of the stick insect , which transfers information about tactile sensor movement to thoracic neural networks with short latency . We show that information about sensor position and motion can be derived from a single class of proprioceptors at the antennal joints , and present a computational model that explains the activity of four previously described groups of descending interneurons during antennal stimulation . Our model is validated against electrophysiological data on antennal mechanoreceptors and descending interneurons . | [
"Abstract",
"Introduction",
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] | [] | 2015 | A Computational Model of a Descending Mechanosensory Pathway Involved in Active Tactile Sensing |
American Tegumentary Leishmaniasis ( ATL ) caused by Leishmania braziliensis is endemic in Corte de Pedra , Northeast Brazil . Most L . braziliensis infections manifest as localized cutaneous leishmaniasis ( CL ) . Disseminated manifestations include mucosal leishmaniasis ( ML ) , present at a low constant level for several decades , and newly emerging disseminated leishmaniasis ( DL ) . Surprisingly , DL has recently surpassed ML in its spatial distribution . This led us to hypothesize that distinct forms of ATL might spread in different patterns through affected regions . We explored the incidence and geographic dispersion of the three clinical types of ATL over a span of nearly two decades in Corte de Pedra . We obtained the geographic coordinates of the homes of patients with ATL during 1992–1996 , 1999–2003 and 2008–2011 . The progressive dispersion of ML or DL in each time period was compared to that of CL in 2008–2011 with the Cusick and Edward’s geostatistical test . To evaluate whether ATL occurred as clusters , we compared each new case in 2008–2011 with the frequency of and distance from cases in the previous 3 to 12 months . The study revealed that DL , ML and CL actively spread within that region , but in distinct patterns . Whereas CL and DL propagated in clusters , ML occurred as sporadic cases . DL had a wider distribution than ML until 2003 , but by 2011 both forms were distributed equally in Corte de Pedra . The incidence of ML fluctuated over time at a rate that was distinct from those of CL and DL . These findings suggest that CL and DL maintain endemic levels through successive outbreaks of cases . The sporadic pattern of ML cases may reflect the long and variable latency before infected patients develop clinically detectable mucosal involvement . Intimate knowledge of the geographic distribution of leishmaniasis and how it propagates within foci of active transmission may guide approaches to disease control .
Leishmaniasis is a vector borne disease whose clinical presentations can be categorized in two broad groups: visceral and tegumentary leishmaniasis . The latter causes lesions in skin , and mucosal surfaces of the upper airway and digestive tracts [1] . Approximately 0 . 7 to 1 . 2 million new cases of tegumentary leishmaniasis occur every year worldwide [2] . American tegumentary leishmaniasis ( ATL ) extends from Mexico to Argentina [3] , and is caused by various species of the Leishmania braziliensis and Leishmania mexicana complexes of parasites [4] . Leishmania ( subgenus Viannia ) braziliensis is responsible for the majority of ATL cases in South America , occurring at times in the form of localized cutaneous ( CL ) , and mucosal leishmaniasis ( ML ) [5] . This species has also been implicated in the emergence of the new syndrome disseminated leishmaniasis ( DL ) , in which patients most commonly present with approximately fifty skin lesions spread throughout different body parts , often with involvement of the oropharyngeal mucosa [6–9] . ML and DL are severe , hard to treat variants of ATL , which may result in disfiguring outcomes . These deserve particular attention during implementation of control measures . Some epidemiologic reports have called attention to differences in the spatial organization of distinct forms of ATL . As examples , it has been shown that in Peru and Ecuador CL has a countrywide distribution , whereas ML is limited to the areas covered by the Amazon rain forest [10 , 11] . We previously reported that ML and DL segregate differently within one of the foci with the highest endemic rates of L . braziliensis in northeast Brazil , in the region of Corte de Pedra [12] . We also observed significant clustering between new and recently diagnosed DL cases , indicating that proximity to a patient with DL is a risk factor for developing this form of leishmaniasis [12] . DL has emerged as the prominent form of disseminated disease caused by L . braziliensis in this region of Brazil . Given our findings that distinct L . braziliensis clades are associated with specific forms of leishmaniasis [13] , we reasoned that their patterns of spread over time may provide clues as to the mode of propagation of those parasite strains within the endemic population . In this study , we explored incidence and geographic localization of the three main clinical forms of ATL in patients presenting to a clinic in Corte de Pedra , over a period of nearly two decades . This data allowed us to evaluate the dynamics of ML and DL during the time intervals of 1992–1996 , 1999–2003 , and 2008–2011 . These were compared to the baseline current data for CL , the most common form of the disease , which assumes a wide distribution within the region . We report that the three forms of ATL actively spread within the affected focus , but in different patterns . CL and DL occur in clusters of cases , whereas ML occurs in a sporadic manner . A deeper understanding of the patterns of spatial spread may suggest the most efficacious venues to target in efforts to control the disease .
The Corte de Pedra region is composed of 20 municipalities in a rural area previously dominated by the Atlantic rain forest . The sand fly species Lutzomyia ( Nyssomyia ) whitmani and Lu . ( N . ) intermedia , which transmit L . braziliensis , are part of the local fauna . Residents in this area work mostly in agriculture , often close to primary or secondary forests . Among the population , there is little migration in or out of the region . The mean time of study participant residence at their addresses at the time of diagnosis and parasite sampling was 17 years ( more precisely: mean = 17 . 6 years; median = 18 . 0 years; minimum time of residence = 1 . 0 year; maximum time of residence = 70 . 0 years ) . More than 90% of the study participants lived on farms . Three distinct time-periods were considered in the study . Two were historical derived from subjects’ records , and the most recent included subjects actively enrolled according to the criteria of approximately two CL patients for each ML or DL subject , matched for month of diagnosis . Almost all ML and DL patients diagnosed in the region during this last period were included in the study . We then included roughly double the number of CL subjects relative to ML and DL to partially account for the fact that CL is much more frequent in Corte de Pedra , and thus avoid underrepresenting its actual geographic distribution in the region . The historic samples consisted of 21 DL patients enrolled between 1992 and 1996 , and 30 ML , 30 DL and 30 CL cases enrolled between 1999 and 2003 . Geographic information was not available on any subjects other than DL individuals before 1999 . The third , most recent sample consisted of 35 patients with ML , 76 with DL , and 225 with CL enrolled between 2008 and 2011 . All cases in this study were self-referred to and diagnosed at the health post of Corte de Pedra . The health post services approximately 70% of the ATL patients in the region . All subjects resided in the L . braziliensis endemic region . Clinical criteria for CL included fewer than 10 ulcerative skin lesions without evidence of mucosal involvement . DL was defined as a disease with more than 10 , acneiform , inflammatory papular or ulcerative skin lesions spread over 2 or more body areas , with or without mucosal involvement . ML was defined by metastatic mucosal lesions affecting the nose , palate , pharynx , or larynx but not contiguous with primary cutaneous lesions , with or without the skin lesions of CL . Additionally , all patients had their diagnosis confirmed by at least two of the following criteria: ( 1 ) live parasites isolated in culture from lesion aspirates; ( 2 ) parasites visualized on lesion histopathology; and ( 3 ) delayed type hypersensitivity skin test to leishmania antigen ( LST , Leishmania skin test ) . All subjects enrolled between 2008 and 2011 also had infection confirmed by parasite DNA detection in lesion biopsy specimens by PCR [14] . Leishmania cultures were prepared from aspirates of the borders of skin or mucosal lesions . Aspirate material was immediately suspended in biphasic LIT/NNN medium and incubated at 26°C for one to three weeks . The species of Leishmania promastigotes from positive cultures was confirmed by PCR as follows . Suspensions were transferred to Schneider’s medium with 10% heat-inactivated foetal calf serum and 2 mM L-glutamine , and incubated at 26°C until they reached a density of 107 cells/mL . 1 . 7 x 107 promastigotes were incubated in 150 μL of TELT buffer ( Tris HCl 50 mM , EDTA 62 . 5 mM , LiCl 2 . 5 mM , Triton 100x 4% ) for 5 min at room temperature . 150 μL of phenol-chloroform was added , and cells were vortexed and pelleted by microcentrifugation at 13 , 000 rpm for 5 min . DNA was ethanol precipitated , washed with 100% ethanol , air dried , re-suspended in 100 μL of Tris-EDTA ( Tris-HCl 10 mM , EDTA 1 mM ) , and stored at -70°C . Aliquots of stored DNA were adjusted to 20 ng/μL before confirmation of Leishmania species was performed by PCR [14] . For detecting parasite DNA in patients’ skin and nasal mucosa , biopsy specimens from the lesions borders were stored in RNA Later solution ( Ambion , Life Technologies , Thermo Fisher Scientific , USA ) immediately after the procedure in the field , and kept at room temperature for approximately six hours until they were stored at 4°C in the laboratory . Two to three days later , nucleic acids were extracted from the skin and mucosal fragments using the DNA Purification kit ( Promega Co . , USA ) , according the manufacturer’s recommendations . The leishmania skin test was performed with antigen prepared from a stock of L . braziliensis isolated from a localized cutaneous leishmaniasis patient of Corte de Pedra , maintained in our collection of frozen stocks of parasites . It has been used as our standard antigen for LST during diagnosis of ATL in the region . LST test was considered positive if a region of induration 5 mm or greater formed at the antigen injection site 48 to 72 hours post-test administration . Histopathology was considered positive when Leishmania spp . amastigotes were observed upon thorough examination of H&E stained biopsy fragments . The above described diagnostic procedures were performed in all patients that participated the study , except for the detection of parasite DNA by PCR in biopsy specimens . PCR was only performed on samples of patients enrolled between 2008 and 2011 . High-resolution distribution of ATL cases was determined by acquisition of geographic coordinates of likely places of disease transmission by global positioning system . Geographic coordinates were obtained using a Brunton Multi-Navigator GPS apparatus ( Brunton Company , Riverton , WY , USA ) , which has a precision range of 15 m . Because leishmaniasis is believed to be transmitted mostly within plantations where most residents of the region live and work , patients’ residences were used as reference points for standardization purposes . The data were statistically evaluated as described below , and plotted for visual inspection onto a high-definition satellite photograph of Corte de Pedra region ( ENGESAT , Curitiba , Brazil ) using ArcGis version 10 software ( Environmental Systems Research Institute Inc . , Redlands , CA , USA ) . Comparisons between global distributions of different forms of ATL in Corte de Pedra employed the Cuzick and Edward's test ( Clusterseer version 2 . 3 , Terraseer Inc . , Ann Arbor , MI , USA ) . To analyse whether proximity to a previous ATL case was accompanied by an increased frequency of ATL diagnosis among residents of the region , we used the ruler tool found in the ArcGIS software to measure the distances between the residence of each new case occurring between 2008 and 2011 ( novel cases ) , and the homes of all cases occurring in the preceding 3 , 6 or 12 months relative to the novel case ( recent cases ) . The resulting data were stratified into discrete distance intervals of 0–2500 , 2501–5000 , 5001–7500 , 7501–10000 and 10001–12500 meters from the novel cases . Then Spearman correlation between the number of closest recent cases and the distances from the novel cases was tested ( Graphpad Prism version 5 , Graphpad Software Inc . La Jolla , CA , USA ) . In these correlations , for each novel ( i . e . newly diagnosed ) case of leishmaniasis , the closest recent case of the same clinical type was determined . Then , the number of newly diagnosed cases that presented a closest recent case at a discrete 2 . 5 km distance interval was plotted against the distance interval units . The analysis was carried out for the entire set of ATL cases , and separately for each of the three forms of ATL ( i . e . CL , DL and ML ) . For each novel case , only the closest recent case of the same form of disease was employed in the analysis , except when overall ATL was under consideration . Correlation between the incidence of ATL overall , and the incidence of CL , ML and DL separately , employed Pearson’s test ( Graphpad Prism version 5 , Graphpad Software Inc . La Jolla , CA , USA ) . Values of p<0 . 05 were considered significant . The study was approved by the local Institutional Review Board ( IRB ) of the Federal University of Bahia ( CAAE– 3041 . 0 . 000 . 054 . 07 ) . Written consent was obtained from all subjects that participated in the study , including prospectively enrolled and historical subjects . The study was explained to and the written consent were obtained from all patients by the research team at the moments of their homes' geographic coordinates acquisition by GPS . Confidentiality was maintained by removing identification of study participants from their data records used in the research .
The cumulative distribution of subjects with CL during the 2008–2011 period was the most widespread and stable collection of ATL patients in the dataset . Therefore , in order to track the spatial progression of DL and ML , we compared the distribution of DL and ML over time to the distribution of CL occurring in Corte de Pedra between 2008 and 2011 . In these comparisons , the significances of the Cusick and Edward’s test were used as a metric of spread for each of the less common forms of ATL . The less significant the comparison to the cumulative distribution of 2008–2011 CL cases , the wider the spread of DL or ML . The satellite views in Fig 1 depict Corte de Pedra divided by a line into inner and coastal halves of roughly similar areas to facilitate visual inspection . Fig 1A shows a progressive spread of DL in that region , with cases of the disease more often occurring within or very close to the inner half during the 1992–1996 period . The disease spreads toward the coastal half during the second ( 1999–2003 ) and third ( 2008–2011 ) study periods , during which DL cases assumed a more even distribution over Corte de Pedra . This visual impression was confirmed by Cusick and Edward's comparisons documenting significant difference between distributions of DL in 1992–1996 and CL in 2008–2012 , but non-significant differences in 1996–2003 or 2008–2011 ( Table 1 ) . The increasing values of the comparisons significances through the three periods indicate the progressive nature of the spread of this disease in the region . Likewise , ML progressively acquired an even spatial spread throughout Corte de Pedra between 1999–2003 and 2008–2011 ( Fig 1B ) . This was confirmed by a significantly different distribution when ML is compared to CL ( 2008–2011 ) in the earlier time , but a non-significant difference from CL in the latter period , according to Cusick and Edward`s test ( Table 1 ) . Fig 1C shows the home locations of CL subjects enrolled between 2008 and 2011 that was used in the Cusick and Edward’s comparisons with DL and ML above . The figure demonstrates that CL was also evenly spread in Corte de Pedra during the 1999–2003 time period . Our prior reports demonstrated that distinct strains of L . braziliensis are associated with the different clinical types ( DL , ML , CL ) [13 , 15] . Clustering of cases of similar clinical type would suggest that vector borne transmission of parasite strains between individuals contributes to the distribution and frequency of disease types . Lack of clustering would suggest that alternate risk factors predominate . Thus we evaluated whether ATL , CL , ML and DL occur as clusters of cases in time and space . Our method was to evaluate whether there was a correlation between the frequency of close recent cases of the same clinical disease type and the distances from a newly diagnosed case of CL , DL or ML . Recent cases were defined as ATL patients diagnosed in the three , six or twelve months preceding the diagnosis of the new case . In these analyses the closest cases varied according to the disease form under consideration . When the entire spectrum of ATL was being considered , the newly diagnosed case and the closest recent case could present any form of the disease ( i . e . CL , ML or DL ) . For example , the new case could present CL and the closest recent case of ATL could present CL , ML or DL . When a specific form of ATL was being considered , then both the newly diagnosed case and the closest recent case should present that same form of disease . For example , if the disease in consideration was DL , then the new and closest recent cases should present DL . So , we started by assessing if leishmaniasis cases have a general tendency to cluster in the affected region , analysing the correlation between number of closest recent cases of ATL in general ( i . e . CL , ML or DL ) and distance to a new case of leishmaniasis ( i . e . again , CL , ML or DL ) . Then , we investigated if any form of the disease would depart from this general trend , testing the same correlation stratified by clinical presentation of ATL , that is: CL , ML and DL . In such analyses , the recent cases and the newly diagnosed cases would need to be of either CL or ML or DL . Considering all forms of ATL together , the number of closest recently diagnosed cases diminished proportionally with the distance from a new case of leishmaniasis ( Fig 2A ) . Statistical correlation analyses confirmed a significant aggregation of ATL patients in all 3 time frames analyzed ( Table 2 ) . As an example , within the six months preceding a newly diagnosed case of ATL , approximately 90% of closest recent cases occurred within a maximum 7 . 5 Km distance from a newly diagnosed patient ( ATL within 7 . 5 Km radius / total ATL: 201/224 ) . Considering the different forms of ATL associated with different strains of L . braziliensis , we evaluated the aggregation of CL , DL or ML individually within the endemic region . Both CL and DL followed the same pattern of the majority of ATL cases , with approximately 94% ( CL within 7 . 5 Km radius / total CL: 153/162 ) or 82% ( DL within 7 . 5 Km radius / total DL: 40/49 ) of closest recent cases occurring within 7 . 5 km of a new CL or DL case , respectively ( Fig 2B and 2C , Table 2 ) . Surprisingly however , there was no correlation between the frequency of recent ML cases and distance to a patient newly diagnosed with ML ( Fig 2D , Table 2 ) . Together these data are consistent with clustering of CL and of DL within the 10 , 000 Km2 area of Corte de Pedra . Because CL makes up the majority of all cases , the clustering of all ATL cases reflected the pattern seen in CL . However , ML occurred in a sporadic pattern , suggesting that factors other than the mere spread of index L . braziliensis strains were influential . Besides the distinct pattern of spread over Corte de Pedra , ML also exhibited fluctuation in annual incidence that was distinct from those of CL and DL in the region ( Table 3 ) . During the last two study periods , the annual incidence of CL and DL were positively correlated with the total ATL incidence . CL presented correlation coefficients ( Pearson r ) to ATL of 0 . 99 ( p<0 . 001 ) in both study periods , while DL presented correlations of 0 . 71 ( p = 0 . 037 ) in 1999–2003 , and of 0 . 90 ( p = 0 . 025 ) in 2008–2011 . No correlation was observed between ML and ATL in either period for which there were data , with the coefficients consisting of -0 . 15 ( p = 0 . 40 ) in 1999–2003 , and 0 . 16 ( p = 0 . 42 ) in 2008–2011 .
Human disease by Leishmania spp . of the sub-genus L . Viannia differs from that due to the L . Leishmania sub-genus , in that the former more often leads to disseminated and severe forms of tegumentary leishmaniasis . Our prior studies revealed that the parasites causing the different clinical forms of L . braziliensis disease are genetically distinct , and can be distinguished with a limited number of anonymous markers [13 , 15 , 16] . During the current study we performed a comparison of the distribution of CL , ML and DL secondary to L . braziliensis infection at intervals since 1992 . The data revealed preferential spread of both CL and DL to geographically closer individuals , but spread of ML occurred independent of proximity to a recent case . This indicates that CL and DL occur in clusters , whereas ML appears in a sporadic pattern within the endemic focus . We had previously reported the incidence of different forms of ATL in Corte de Pedra during early 2000's [12] . Although in the past ML was the most common disseminated form of leishmaniasis due to L . braziliensis , DL has emerged and is now more common than ML in that region [6 , 7 , 17] . Our analyses revealed that DL was distributed over a significantly wider area than ML in 1999–2003 [12] . In the current study , it became apparent that both disseminated forms of ATL were indeed spreading , but in different patterns . One plausible explanation for the clustering patterns of DL and CL propagation would be the occurrence of localized outbreaks of cases due to transmission of DL-prone or CL-prone parasite strains [13 , 15] . Reservoir investigation was out of the scope of the current study , but it needs to be carried out to elucidate the mechanism underlying such clustering of CL and DL cases . Possible reasons for the observed clustering might involve the existence of peri-domestic reservoirs in the region , or even a human-vector-human amplification of transmission cycles initiated by peri-domestic or sylvatic hosts and reservoirs . The observation that ML does not occur in clusters of ill individuals may relate to the fact that it tends to be a late outcome among individuals with past history of CL , and chronically infected with the parasite [5 , 18] . For individuals being inoculated with a ML-prone parasite strain , ML might develop only as a complication of CL when certain host or environmental conditions are present [19 , 20] . Furthermore , variability in manifestations of this chronic infection might result in different times of mucosal lesions onset among patients , what would interfere with the definition of a cluster , implying temporal and spatial aggregation of cases . For example , some individuals that acquired infection at about the same time might develop symptoms months to years apart , precluding time aggregation . Alternatively , patients might have moved from their original locations , thus masking spatial aggregation . Finally , there may be variability in the time patients seek medical attention upon appearance of symptoms . Thus some individuals might seek treatment before a diagnosis of ML becomes apparent . Although early diagnosis would decrease the incidence of ML , it would not affect CL or DL . We raise two possible explanations for the observation that ML presented a slower pace of propagation in Corte de Pedra than CL and DL . One plausible explanation would be the longer incubation time of ML . This would cause diagnosis of infections leading to ML to be performed months to years later than diagnosis of those infections that occurred in the population during the same L . braziliensis transmission season but leaded to CL or DL . The other possibility would be a more frequent and early treatment of all CL cases in this area , where there is naturally more intensive surveillance for and people’s knowledge about ATL . Treatment delay and failure are risk factors for ML development [19 , 20] . The early treatment and frequent retreatments might not only prevent part of the ML burden , but also delay its onset and therefore cause the geographic distribution of such cases in this region to be more stationary . The approach employed in the current study is adequate for the description we provide based on the cross-sectional definitions of cases we used: ATL , CL , DL and ML . However , this approach is inadequate to assess whether individuals that will develop ML in the long run of infection geographically cluster during the initial moments of disease . For addressing this question , the current approach would need to be coupled to a very large cohort of newly diagnosed CL cases . This CL patients cohort would also have to be followed up for several years , in order to detect those few individuals that would develop ML . Only then , the original residence sites of these subjects , where they lived when initially diagnosed with CL , would be retrospectively identified and georeferenced . With these retrospective geographic coordinates , the correlation between frequency of closest recent cases and distances to novel cases of CL patients who would develop ML could be appropriately performed . Leishmaniases are disorders of the poverty [2] , ranking among the world’s most neglected diseases [21] . Control of ATL has been based on treatment of ill individuals [1]; self-protection among dwellers of endemic foci by the use of repellents , proper clothing and bed nets [22–24]; and vector control with insecticides [25] . There is no available safe vaccine or other safe form of prophylaxis for ATL [26 , 27] . Likely reasons for the inadequate control of the leishmaniases are: ( 1 ) case finding is usually based on self-referral to health care units , and as a result only a fraction of cases are treated [28]; ( 2 ) protective measures are not uniformly practiced by the people at risk [29 , 30]; and ( 3 ) vector control is not systematically employed by the local health agencies [30 , 31] . Active surveillance for non-diagnosed cases of ATL could help control disease . However , endemic regions usually span large areas with precarious road systems , precluding comprehensive surveillance measures . As an example , Corte de Pedra spans approximately 8 to 10 thousand square kilometers served by sparse non-paved roads , and hosts a population of about 240 , 000 inhabitants [17] . The observation that ATL cases cluster in time and space might provide a basis for a targeted approach to active surveillance . This would reduce the area to be covered . Furthermore , even though ML did not show time-space aggregation , its control might benefit from active surveillance to decrease the incidence of CL . This is because ML is often a consequence of late or incompletely treated localized cutaneous leishmaniasis [5 , 19] . ATL has been mostly uncontrolled and continues to increase in incidence in large parts of South America [3 , 32 , 33] , despite extensive studies of its epidemiology , geographic features , vector populations [2 , 34] , and risk factors [19 , 30 , 35–41] . In addition to existing proposals to prevent human infection with the Leishmania spp . [42 , 43] , a detailed comprehension of the dynamics and propagation of ATL within affected foci will shed fresh light on the factors maintaining leishmaniasis in endemic regions . This could be used to tailor control measures toward early detection of sentinel patients coupled with focused active surveillance for new cases in areas of likely spread . Such surveillance strategy would be aimed at capturing undiagnosed cases , hence decreasing the overall incidence of leishmaniasis in endemic regions . | American tegumentary leishmaniasis ( ATL ) caused by Leishmania braziliensis is characterized by lesions to the skin and/or mucosal surfaces of the oropharynx . It is widely distributed in endemic regions of northeast Brazil and has been difficult to control . Three common clinical forms of L . braziliensis infections are localized skin ulcers called cutaneous leishmaniasis ( CL ) , mucosal leishmaniasis ( ML ) affecting mucosal surfaces , and disseminated leishmaniasis ( DL ) , a recently described form with widespread skin lesions . Using GPS and epidemiologic data we explored the incidence and pattern of spread of ATL in the highly endemic region of Corte de Pedra , Brazil between 1992 and 2011 . Geographic clusters of CL and DL cases were observed . In contrast , there was a sporadic non-clustered pattern of ML cases in the study area . The numbers of new cases of CL and DL presented similar fluctuation during the study period , but ML incidences were never correlated to those of CL and DL . We conclude that all forms of ATL actively spread within affected foci , but in different patterns . CL and DL cases occur in clusters suggesting active spread of causative parasite strains , whereas ML cases occurred in a sporadic pattern suggesting it may emerge due to factors such as host immunity or environmental conditions . | [
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"leishmania"... | 2017 | Dynamics of American tegumentary leishmaniasis in a highly endemic region for Leishmania (Viannia) braziliensis infection in northeast Brazil |
To reduce expression of gene products not required under stress conditions , eukaryotic cells form large and complex cytoplasmic aggregates of RNA and proteins ( stress granules; SGs ) , where transcripts are kept translationally inert . The overall composition of SGs , as well as their assembly requirements and regulation through stress-activated signaling pathways remain largely unknown . We have performed a genome-wide screen of S . cerevisiae gene deletion mutants for defects in SG formation upon glucose starvation stress . The screen revealed numerous genes not previously implicated in SG formation . Most mutants with strong phenotypes are equally SG defective when challenged with other stresses , but a considerable fraction is stress-specific . Proteins associated with SG defects are enriched in low-complexity regions , indicating that multiple weak macromolecule interactions are responsible for the structural integrity of SGs . Certain SG-defective mutants , but not all , display an enhanced heat-induced mutation rate . We found several mutations affecting the Ran GTPase , regulating nucleocytoplasmic transport of RNA and proteins , to confer SG defects . Unexpectedly , we found stress-regulated transcripts to reach more extreme levels in mutants unable to form SGs: stress-induced mRNAs accumulate to higher levels than in the wild-type , whereas stress-repressed mRNAs are reduced further in such mutants . Our findings are consistent with the view that , not only are SGs being regulated by stress signaling pathways , but SGs also modulate the extent of stress responses . We speculate that nucleocytoplasmic shuttling of RNA-binding proteins is required for gene expression regulation during stress , and that SGs modulate this traffic . The absence of SGs thus leads the cell to excessive , and potentially deleterious , reactions to stress .
Eukaryotic cells exposed to physical , chemical , or nutritional stress , activate a variety of responses . For short-term survival , rapid events mainly regulated at the post-translational level , such as phosphorylation , protein binding and relocalization , dominate . For optimization of medium-term survival and recovery , the expression program needs to change rapidly towards proteins needed for survival during the stress phase , at the expense of products linked to proliferation . Although transcriptional initiation is a major regulatory level , in particular during chronic stress , a substantial fraction of the expression change occurs on the post-transcriptional level . This is because protein synthesis consumes a major fraction of the cell's total energy production . Hence , translation needs to be tightly regulated under stress conditions , which invariably confer an energy drain on the cell . Consequently , following an increase in stress level , S . cerevisiae cells decrease the stability [1]–[3] and translation rate [4]–[6] of most mRNAs . Under such conditions , transcripts encoding proteins needed for stress survival are instead stabilized and their translation enhanced . This transition can be mediated through a shift in translation initiation factors [7] . As part of the post-transcriptional stress response program , eukaryotic cells form two types of large cytoplasmic granules containing RNA and proteins . Processing bodies ( PBs ) are present constitutively , but increase in number upon stress . They contain components of the mRNA degradation and silencing machineries . Stress granules ( SGs ) only form under conditions of severe stress . They are considered to consist of stalled translation initiation complexes , and contain initiation factors and 40S subunit components , as well as other mRNAs binding proteins; however 40S components are absent from budding yeast SGs formed after glucose deprivation [8] . Although certain proteins are found in PBs but not in SGs and vice versa , there is also a number of proteins common to PBs and SGs [9] , [10] , indicating the possibility of dynamic exchange of material between the two types of granules . Mutants deficient in PB formation most often also have SG deficiencies , whereas the opposite does not hold: a mutant deficient in SG formation does not necessarily have reduced PB numbers [11] . Upon stress , the number of PBs increases before the appearance of SGs; the latter are frequently observed physically close to or overlapping with PBs . These observations have led to a model where the formation of SGs is contingent on the prior formation of PBs in their immediate vicinity [11] . However , this model has recently been challenged . In yeast , SGs have been observed to be regulated by different signaling pathways than PBs [12] , and individual SGs can be observed microscopically to arise independently of pre-existing PBs [13] . In mammalian cells , knockdown of specific mRNAs suppressed formation of PBs but not SGs [14] . More recently , several indications have appeared that SGs not only sequester transcripts and translation components , but also signaling and catalytic proteins . Members of the yeast TORC1 signaling complex , Tor1 ( mTOR in mammalian cells ) and Kog1 ( Raptor ) , are found in SGs after heat stress [15] . In mammalian cells , SGs sequester RACK , a MAPKKK modulator under certain stress conditions , suppressing activation of p38 signaling that would otherwise promote apoptosis [16] . The G3BP1 ( S . cerevisiae Bre5 ) and USP10 ( S . cerevisiae Ubp3 ) proteins are both SG components . Under normal conditions , G3BP1 binds to and inhibits USP10 , but in stress , when both proteins become incorporated into SGs , their interaction changes and this unleashes the activity of USP10 [17] . The homologous proteins in fission yeast , Nxt3 and Ubp3 , are also SG components [18] , providing an example of evolutionary conservation of function . A possible connection between SGs and neurological disorders has attracted interest , as several neuronal proteins with roles in RNA binding and/or protein aggregation have been localized to SGs [19] . For instance , FRMP , a translational regulator implicated in fragile X syndrome , localizes to SGs under stress [20] . These findings have extended the view of SGs from being sites for mRNA storage , sorting , silencing , and reactivation , into centers for modulation of a variety of cellular stress responses . There are several notable species-specific differences in how signaling pathways affect SG formation . Phosphorylation of the translation initiation factor eIF2α is both necessary and sufficient in mammalian cells [21] , whereas eIF2α phosphorylation is not required for SG formation after heat shock in budding yeast [22] , nor under any condition tested in fission yeast [23] , [24] . Also , mounting evidence indicates that the signaling pathways needed for SG formation are stress-specific . In fission yeast , protein kinase A ( PKA ) is required for SG formation after glucose starvation , but not after hyperosmotic shock [24] . In S . cerevisiae , different stress conditions lead to formation of SGs of different composition , and with different assembly requirements . Thus , SGs can form after azide treatment independently of PB formation ability or of the mRNA-binding proteins Pbp1 ( Ataxin-2 homolog ) and Pub1 ( TIA-1 homolog ) , but not after glucose deprivation [25] . Further evidence for a determined assembly order of SGs comes from the observation that in budding yeast cells upon mild heat shock , translation factors form foci at sites where SGs will later form when the temperature is raised further [26] . Previously , only few studies of the global requirements for formation and disassembly of SGs in mammalian cells and in yeast have been performed . An RNAi screen for genes necessary for normal SG formation in human cultured cells yielded an overrepresentation of functions related to O-linked glycosylation [14] . Recently , a global screen in S . cerevisiae for mutants defective in SG disassembly identified autophagy involving the yeast homolog of valosin-containing protein ( VCP ) as the pathway for clearance of SGs from the cell [27] . In this work , we have screened a genomic library of haploid deletion mutants , representing all non-essential genes of S . cerevisiae , for their ability to form SGs under nutrient stress induced by 2-deoxyglucose ( 2-DG ) . Mutants identified in the primary screen as deficient or hyperproficient in SG formation were also tested for SG formation under other stress agents . Most of these mutants display a general defect in SG formation , whereas the defect in others is specific for certain stress types . The protein products of the genes that we identify as required for SG formation are enriched for low-complexity regions ( LCRs ) . SG defects are associated with enhanced mutability upon heat stress . We find functions related to the RanGTP/RanGDP system , driving nucleocytoplasmic transport , to be necessary for regulated formation of SG under stress . Importantly , we observe here that mutants unable to form SGs are also deficient in their ability to modulate changes gene expression associated with the stress response; stress-activated transcripts are hyperinduced , whereas those down-regulated by stress are repressed even further than in the wild-type . This strongly suggests that not only is SG formation regulated by stress-activated signaling pathways , but SGs actively moderate the stress response at the transcript level .
To be able to perform a genome-wide screen , we defined the stress conditions under which to perform it , applying specific criteria . First , we wanted a robust induction of SGs . Second , for the purposes of a high-throughput genome-wide screen , with robotic handling in microtiter plate format , stress induction needed to be quick and synchronized between many samples . This consideration ruled out any scheme that included centrifugation and aspiration of medium during the induction procedure . Third , in order to minimize a potential influence from proteins and pathways regulating formation of cytoplasmic aggregates of damaged proteins , we wanted conditions where there was a clear physical separation between SGs and such other protein foci . To this end , we used RFP-labeled Pab1 ( poly A-binding protein ) as a marker for SGs , and GFP-labeled Hsp104 as a marker for aggregates of damaged proteins ( Fig . 1 A ) . We evaluated treatments established to induce SG formation in S , cerevisiae or other organisms , including glucose deprivation , arsenite , hyperosmotic shock by elevated Na+ or K+ concentration , heat , oxidative stress through H2O2 , ethanol , stationary phase , and combinations of these ( Fig . S1 ) . The most reproducible and stable appearance of SGs was found to occur after glucose deprivation , or after a combination of 8% ethanol and heat shock . However , only after glucose deprivation was a clear physical separation observed between Pab1-RFP and Hsp104-GFP foci ( Fig . 1 A and Fig . S1 ) . We selected glucose starvation as the stress agent , but needed to modify the procedure further to eliminate washing and centrifugation steps . Therefore , we substituted removal of glucose for addition of the glucose competitor , 2-DG , to a final concentration of 400 mM , into the medium . This treatment was found to induce SGs in about 35% of wt cells , and this number was relatively stable over a period of 60 min . ( Fig . 1 E ) . For the screening purposes , we constructed a genome-wide set of S . cerevisiae haploid genomic deletion mutant strains expressing Pab1 carboxy-terminally tagged with RFP from the PAB1 chromosomal locus in the BY4741 background as described in Materials and Methods ( Fig . 1 B ) . All resultant strains were screened , as described in Materials and Methods , for the fraction of cells foci positive for Pab1-RFP after 2-DG exposure , using automated image analysis ( Fig . 1 D , E ) . The result was quantitated as the deviation from the level in wt cells , which was set to ±0% ( Fig . 1 C ) . We selected 194 mutants ( Table S1 ) which fulfilled the following two criteria: a ) the absolute value of the SG phenotype in 2-DG , decreased or increased , exceeded 20%; b ) the difference to the level in wt cells under the same conditions was statistically significant ( P<0 . 05 ) . Out of this set , a total of 73 null mutants ( Table S2 A ) with strongly decreased SG phenotypes were verified by manual microscopy . The confirmation rate ( a statistically decreased SG phenotype was also observed by visual inspection ) was high , with increasing rates up to 100% for the most extreme phenotypes ( Table 1 ) . This verifies that the image analysis algorithm efficiently and selectively identifies SGs . Our screening approach could potentially score mutants expressing lower levels of Pab1 as SG negative . To test how much this was a concern , we measured Pab1 levels in nine mutants identified as SG-defective by western blotting . All these mutants ( except one , arc1Δ ) have normal Pab1 levels , showing that deficient Pab1 expression was not the explanation why these mutants scored as SG-defective ( Fig . S2 ) . We further used another established SG marker , Pbp1 , to verify that the phenotypes scored for Pab1-positive SGs also applied to other SG components . For the tested mutants , the relative changes in Pbp1-positive SGs upon glucose starvation largely paralleled those for Pab1-positive SGs ( Fig . S3 ) , with the exception of top3Δ . It is known that defects in a particular stress signaling pathway can affect SG formation ability specifically for that stress condition [24] whereas other mutations cause a general SG deficiency . We investigated the SG phenotype of the 194 mutants using other stress conditions: heat shock ( 44°C for 45 min ) or hyperosmosis ( 1 . 5 M KCl or 1 . 5 M NaCl for 90 min ) . As seen in Fig . 2 and Fig . S4 , some of the mutants display a strong phenotype uniformly across all the investigated stress conditions ( e . g . tif4632Δ , arc1Δ , set3Δ , top3Δ , mft1Δ , ypr172wΔ , and ski3Δ ) . For tif4632Δ ( lacking the gene encoding eIF4G2 , one of two isoforms of the translation initiation factor eIF4G in yeast ) , a defect has previously been observed in glucose starvation [11] . However , a substantial fraction were specific to either 2-DG or to various combinations of the stress conditions ( e . g . gtr2Δ , ncs6Δ , and hgh1Δ ) ( Fig . 2 , Fig . S4 ) . Among the set of 73 mutants with verified phenotypes in 2-DG , 10 showed defects under all four stress conditions , and a further 10 under at least three of the four conditions . Mutants with a strong phenotype in 2-DG were more likely to display phenotypes also in other stress conditions ( Table S1 ) . Over half of the mutants , 40 , were unique to 2-DG . The greatest degree of overlap with the 2-DG SG phenotype was found with NaCl stress ( 24 mutants ) ; all the mutants that displayed a defect in KCl were also defective in NaCl ( Fig . S4 ) . A survey of this group of mutants with a verified decrease in SG number reveals a heterogeneous composition of biochemical and cellular functions , a reflection of the complexity of the functions required to assemble SGs , and to regulate their formation under different stress conditions . The network overview of functional clusters in this group ( Fig . 3 A ) displays large groups related to protein translation and endosomal vesicles , and the interrelated groups of the EGO/GSE complex and the Ran GTPase ( Gsp1 ) with regulators . GO Term analysis showed that there was a considerable enrichment for gene products involved in cytoplasmic protein translation ( 12/73 , 16 . 4% vs . 2 . 4% in the background; P = 2 . 8×10−5 ) . Among these , there is a moderate enrichment of mutants lacking cytoplasmic ribosomal proteins ( cRPs ) . The distribution of relative SG scores of all cRP mutants ( Fig . 3 C ) contains both increased and negative values; the average value being slightly negative . A few cRP mutants are outliers with strong SG defects , notably rpl20bΔ , rps10aΔ , and rps28bΔ . Mapping the physical position of the corresponding proteins in the ribosome , we observe that these three are scattered in the 40S and 60S subunits ( Fig . S5 ) . The location thus does not immediately explain why they are so strongly required for SG formation . Further , the tif4632Δ mutant is almost completely devoid of SGs , in line with previous observations [11] . Another strongly SG-defective mutant implicated in translation is arc1Δ ( human AIMP1 ) , lacking a tRNA synthetase-binding protein . Mutants representing members of the overlapping EGO/GSE complexes were also notable in this group ( gtr1Δ , gtr2Δ , slm4Δ , ltv1Δ; 4/73 , 5 . 5% vs . 0 . 1% in the background; P = 5 . 4×10−6 ) . The members of the EGO complex were found in a genetic screen for failure to resume growth after rapamycin treatment [28] . Finally , we noted that gene products involved in endosomal transport were enriched in this mutant set ( 8/73 , 11 . 0% vs . 1 . 2% in the background; P = 6 . 6×10−4 ) . In the screen , we also observed a large number of mutants with an increased fraction of SG-positive cells ( ≥20% higher than wt ) , indicating either a deregulation in SG formation or a defect in SG breakdown . To verify that the increased number of Pab1-positive aggregates seen in these mutants really represent bona fide SGs , we pretreated six of them with cycloheximide ( Chx ) before , as well as during , exposure to 2-DG , a scheme previously shown to prevent SG induction by stress [29] . As expected if they are actually SGs , the Chx pretreatment did completely block formation of aggregates in these mutants ( Fig . S6 ) . GO term analysis of the 56 mutants in this group that were confirmed by manual analysis ( Table S2 B ) revealed that several of them are linked to autophagy/microautophagy ( Atg15 , Vam6 , Vam7 , Vtc4 , Meh1 , Ypt7 , Pep4; 7/56 , 12 . 5% vs . 0 . 6% in the background; P = 1 . 4×10−5 ) . Many of the mutations are in genes encoding proteins located in internal membranes; the vacuolar membrane ( 8/56 , 14 . 3% vs . 2 . 4% in the background; P = 4 . 9×10−3 ) or the ER ( 5/56 , 8 . 9% vs . 0 . 8% in the background; P = 5 . 2×10−3 ) . The ontology of this mutant group is thus largely compatible with defects in breakdown of SGs using vesicular transport and autophagy/microautophagy . We did not consider this mutant group further in this report . The functional interplay between SGs and PBs is not fully characterized; one model is that formation of SGs is secondary to formation of PBs [11] . If so , we should not necessarily expect all SG-defective mutants to have defective PB formation as well . To examine whether this holds true for the mutants with impaired SG formation found in our genome-wide screen , we transformed selected mutant strains with a plasmid [30] expressing the PB marker Dcp2-GFP . After glucose deprivation , the localization of Dcp2-GFP was in one to five spots per cell , in some cases in the immediate vicinity of Pab1-RFP , as expected ( Fig . 3 D ) . For gtr1Δ , gtr2Δ , and top3Δ mutants , PB formation was essentially normal , except for a slight depression in gtr1Δ mutants ( Fig . 3 E ) . Thus , our observations do not contradict the view that SG formation is contingent on PB formation , but not vice versa . The question whether there are common sequence motifs and other structural features of proteins contained in SGs or essential for formation of SGs has attracted interest since the realization that TIA-1 and TIA-R , both containing prion-like aggregation-driving domains , are critical for assembly of mammalian SGs [21] . Low-complexity regions ( LCRs ) in RNA-binding proteins ( RBPs ) make them prone to aggregation in vitro [31] . We wanted to test these suppositions in our large set of SG defective null mutants obtained from an unbiased genome-wide screen . Analysis of the number of LCRs [32] in the proteins deleted in the 60 mutants with the strongest SG-defective phenotype shows that 100% of those contain at least one LCR , vs . 70% of all yeast proteins . This protein group contains on average 4 . 1 LCRs per molecule , vs . 2 . 1 for all yeast proteins , and LCRs are thus strongly enriched among these proteins ( Table 2 ) . It has also been proposed that intrinsically disordered domains ( IDRs ) are essential to drive the aggregation process in SG formation [33] . We examined the S . cerevisiae genes predicted by Malinovska et al . [33] to encode proteins disordered in their entire length , but did not find them to be enriched among our SG-defective mutants ( Table 2 ) . However , a moderate overrepresentation of proteins containing IDRs longer than 30 amino acid residues [34] was observed ( Table 2 ) . Only one of the SG-defective proteins ( Bem2 ) was found to carry a prion-like domain [35] . Thus , our data gives support for a possible role of LCRs in SG formation , but not for the other putative aggregation-driving protein domains . Heat stress increases mutation frequency in budding yeast , which has been suggested to result from increased ROS production in mitochondria [36] . This mutation peak can be suppressed by overexpression of Pbp1 or Dhh1 , both of which cause increased SG formation [15] . The authors concluded that SGs formed under stress serve as a dampening factor of some aspects of the heat-induced stress response , which attenuate the mutagenic effects of the heat shock . We were interested to see if the converse was true , that the absence of SGs under stress per se would enhance the mutation rate . We also wanted to investigate the generality of this phenomenon , and so recorded the frequency of inactivating mutations in the CAN1 gene , after heat shock ( 60 min at 44°C , as applied by Takahara , 2012 [15] ) for 12 mutants with strongly negative SG phenotypes upon heat shock ( Fig . 2 , Fig . S4 ) but otherwise without any obvious functional bias . As seen in Fig . 4 A , indeed a marked increase in canavanine resistant ( canR ) clones ( 3–15 fold ) was observed for four of the mutants; top3Δ , set3Δ , ski3Δ and gtr1Δ . However , for the remaining 8 mutants , no significant increased mutation frequency was found; thus , an increased heat-induced mutation rate does not occur in all SG-defective mutants . We wanted to see if other stress types normally causing SG formation would similarly lead to an elevated mutation frequency in SG defective mutants . Thus , we examined top3Δ and vta1Δ mutants for the frequency of canR clones after hyperosmotic shock ( 1 . 5 M NaCl ) . Both mutants are unable to form SGs under these conditions ( Fig . 2 ) . However , no increased mutation frequency was observed neither in the wt nor in either mutant ( Fig . 4 B ) , indicating that the stress-induced mutability may be specific to heat shock . Gtr1 and Gtr2 have been implicated both in activation of TORC1 as members of the GSE complex [37] , and as regulators of the budding yeast Ran homolog , Gsp1 , required for a large fraction of all nucleocytoplasmic transport of protein and RNA in the cell . We wanted to examine if the SG defects of gtr1Δ and gtr2Δ mutants were due to interactions with the TOR pathway , with Ran functions , or both . As many of the Ran function genes are essential ( GSP1 , RNA1 , SRM1 ) , we resorted to temperature-sensitive ( ts ) alleles of those [38] . Cells carrying the gsp1-P162I allele are highly defective in SG formation in glucose starvation at the restrictive temperature ( 36°C ) ( Fig . 5 B ) . RNA1 encodes the GTPase-activating protein for Gsp1 ( RanGAP ) . The rna1-1 allele likewise confers decreased SG numbers; the rna1-S116P only moderately so ( Fig . 5 B ) . Srm1 ( Prp20 ) encodes the guanine nucleotide exchange factor ( GEF ) of Gsp1 ( RCC1 in human cells ) . In glucose starvation , the srm1-G282S allele instead causes a marked increase of the SG-positive fraction , to 90% of all cells , whereas the srm1-ts allele gives lower SG numbers ( Fig . 5 D ) . These results show that those mutants with a functional connection both with Gtr1 and Gtr2 and with the Ran nucleocytoplasmic transport system , are also affected in their SG forming ability . The other EGO/GSE mutants , slm4Δ and ltv1Δ , also did display reduced SG numbers , though with weaker phenotypes ( Fig . 5 H , I ) . It was previously shown that gtr2 mutants recover slowly from rapamycin-induced arrest , mimicking a state of amino acid starvation [28] . More recently , the mammalian Gtr1 homolog RagA was also implicated in glucose homeostasis [39] . Since SGs are presumed to play a regulatory role in the transitions between stress and favorable conditions , and given the defects in glucose starvation-induced SG formation that we observed in the gtr1 and gtr2 mutants , we wanted to see if this defect would affect growth and survival after glucose starvation in these single mutants and also in the double mutant , gtr1Δ gtr2Δ . First , we examined the gtr1Δ gtr2Δ mutant SG phenotype in glucose starvation ( Fig . 2 A , B ) which was found to be more exacerbated than in the single mutants . To analyze the growth behavior , cells in log phase ( wt , gtr1Δ , gtr2Δ , or gtr1Δ gtr2Δ ) were glucose-starved for 48 h by addition of 2-DG , and the rate of recovery of growth in medium without 2-DG was recorded . We found that both the gtr2Δ and the gtr1Δ gtr2Δ mutants recover appreciably slower than the wt ( Fig . 6 E ) . We also investigated loss of viability of the same strains in medium containing 2-DG . As seen in Fig . 6 F , loss of viability occurs fastest in the gtr2Δ and the gtr1Δ gtr2Δ mutants . We conclude that Gtr1 and Gtr2 affect growth recovery and maintenance of viability in glucose starvation , with Gtr2 having the bigger effect . It has also been shown that SGs physically incorporate , and potentially dampen the activation of , signaling proteins [15] , [16] , [40] . Now having access to mutants deficient in SG formation from an unbiased genome-wide screen , we wanted to test the possibility that the SG-forming ability under stress is mechanistically linked to the ability to raise and regulate various aspects of the cellular stress response . To do this , we investigated the expression pattern of stress-responsive genes in mutant backgrounds which we had shown to be unable to form SGs under different stresses ( heat shock , hyperosmotic shock , and glucose deprivation ) ( Fig . 2 , Fig . S4 ) . Hsp12 changes its conformation and associates with the plasma membrane to stabilize it under stress . Hsp12 is strongly upregulated under several stresses , including heat shock , hyperosmotic shock and glucose deprivation , and is regulated through the RAS-PKA pathway [41] . We measured induction of HSP12 expression in mft1Δ , arc1Δ , rpl20Δ , rps28Δ , and top3Δ mutants after two different stress conditions: 2-DG ( 400 mM ) and heat stress ( 44°C ) . In four out of these five mutants ( mft1Δ , arc1Δ , rpl20Δ , and rps28Δ ) , we observed a dramatic hyperinduction of HSP12 . At 60 min . after addition of 2-DG , the level of HSP12 mRNA was between 10 and 40 times higher than in the wt ( Fig . 7 A ) . Similar results were obtained by heat shock: after 45 min at 44°C , the HSP12 mRNA was upregulated in the same four SG-defective mutants to levels 10 to 16 times higher than in the wt ( Fig . 7 B ) . The HSP104 transcript in the same mutant set followed a related pattern in glucose starvation and heat shock as HSP12 ( Fig . 7 A , B ) . PWP1 , UTP13 , and DIP2 all encode proteins involved in rRNA processing , and form part of the RiBi regulon [42] . They are under positive control by the TOR pathway via protein kinase Sch9 [43]; under stress conditions their expression decreases concomitant with diminished TOR signaling . When challenged by heat stress , the expression response in these mutants ( same set as in Fig . 7 A ) is similar for all three reporter genes ( Fig . 7 C ) . Thus , for PWP1 , UTP13 , and DIP2 alike , the mRNA levels decreased to 65–80% of their pre-stress levels at 45 min . at 44°C in the wt . In contrast , the expression of PWP1 and UTP13 in mft1Δ , arc1Δ , rps28Δ , and top3Δ mutants in heat stress decreased to between 10 and 60% of the level in undisturbed wt cells . The expression of DIP2 was more reduced than in wt in all the mutants , including rpl20Δ . In summary , gene expression response to heat stress resulting from reduced TOR signaling in these SG-defective mutants ( mft1Δ , arc1Δ , rps28Δ , top3Δ ) is hyperactivated , with the change in expression level between different mutants highly correlated between the three reporter genes . Having observed deregulation of the stress response in heat shock and glucose starvation , we wanted to also address hyperosmotic shock . STL1 encodes a plasma membrane-bound glycerol transporter , is strongly induced after hyperosmotic shock , and is controlled by the HOG pathway [44] . After severe hyperosmotic shock ( 1 . 5 M NaCl ) , where SGs are clearly visible , the expression level of STL1 in wt cells was quite low for an extended time after application of stress ( Fig . S7 D ) . This made it difficult to assess to what extent an induction had taken place , and so we therefore chose to investigate STL1 mRNA levels after moderate hyperosmotic shock ( 0 . 4 M NaCl ) . Under these conditions , mft1Δ and arc1Δ mutants demonstrated somewhat elevated levels after stress ( Fig . S7 A ) . We then wanted to see if maybe differences similar to what we observed after heat or glucose starvation stress could be seen at the protein level after hyperosmotic shock . As the role of SGs in the stress response is generally perceived to be in cytoplasmic silencing and storage of mRNA , it appeared reasonable to also consider the impact on the post-transcriptional level . We tested the induction of β–galactosidase protein from a STL1-LacZ promoter fusion construct after moderate ( 0 . 4 M NaCl ) hyperosmotic shock in the same SG-defective mutant backgrounds as above . As seen in Fig . S7 C , the induction of STL1-lacZ upon hyperosmosis is further ( 1 . 5–4 fold ) elevated in all mutants compared to the wt in the time interval 30–180 min after hyperosmotic shock . The exception is mft1Δ , where instead STL1-lacZ fails to induce upon hyperosmosis ( Fig . S7 B ) . The levels of STL1-driven lacZ transcript , however , were not appreciably altered in the mutants ( except again in mft1Δ ) . We infer that in the case of STL1-lacZ , the deranged regulation is likely to be on the translational level . As negative controls , we examined in the same fashion mutants with no observed SG phenotype , ybl059wΔ and ybl060wΔ , as well as mutants with increased numbers of SG , ccw12Δ and ynl198cΔ . No abnormal STL1-lacZ induction was seen in ybl059wΔ and ybl060wΔ , whereas slightly elevated induction was observed in ccw12Δ and ynl198cΔ ( Fig . S7 C ) . We had thus demonstrated that most mutants which show a strongly reduced ability to form SGs upon stress , either induce stress response genes excessively , or in exceptional cases ( STL1-lacZ induction in mft1Δ ) are unable to mount a stress-activated gene expression response at all . It also appears that this deregulation occurs both at the transcriptional and post-transcriptional levels . We then wanted to determine if these defects were caused by altered activity in the stress-activated signaling pathway upstream of the gene expression response . We examined activation of the HOG pathway , measured as Hog1 phosphorylation , under moderate ( 0 . 4 NaCl ) or severe ( 1 . 5 M NaCl ) hyperosmotic shock , in wt and SG-defective mutant cells . We also measured regulation of the TOR pathway through phosphorylation of eIF2α ( Sui2 ) after heat shock in SG-defective mutants . Reduced Tor1 activity leads to increased phosphorylation of Gcn2 , the protein kinase targeting eIF2α [45] . No differences in phosphorylation level after stress of either Hog1 or eIF2α between wt and mutants were observed , however ( Fig . S8 ) . We conclude that the cause of the deregulated stress-induced gene expression in SG-defective mutants is not alterations in signal transduction per se , but must instead lie at the levels of transcription or stability of target mRNAs .
Proteins with an ability to drive SG formation based on their content of aggregation-prone domains could be suspected to act as assembly factors . An examination of the requirements for formation of RNA granules in cell-free systems has identified proteins with low-complexity regions as capable of holding such large structures together [31] . Our finding that LCRs are highly enriched in the proteins affected in SG-defective mutants lends support to this notion that multiple weak interactions between LCRs are a driving force behind aggregation of SGs [31] , [49] . On the other hand , only a minority of these SG-defective mutants encode proteins that have been shown to actually reside in SGs . Thus , many could be indirectly acting ( class “c” ) , making the interpretation of this finding less clear . Mammalian TIA-1 and TIA-R , carrying prion-like domains , have been shown to have a key role in SG formation [21] . Recently , the presence of intrinsically disordered regions ( IDRs ) in proteins from genetic model organisms has been mapped and proposed as a determining factor for assembly of SGs [33] . We did find a moderate enrichment of IDRs , but not of prion-like domains among the SG-deficient mutants identified here ( Table 2 ) . For instance , the translation initiation factor eIF4G has been found to be an SG component in all species studied . We discovered the tif4632Δ mutant , lacking the eIF4G2 isoform , to have a strong SG defect . The Tif4632 protein contains IDRs , and it is conceivable that they serve as drivers for nucleation of SGs [8] , given this strong requirement of eIF4G2 for SG formation . We have found gene products common to all stresses applied , as well as others that are specific to one or few stress agents . Earlier work investigating individual SG components has yielded results in line with this: thus , eIF3 components are found in budding and fission yeast SGs after hyperosmotic shock or heat stress , but not after glucose deprivation [11] , [22] , [24] . On the other hand , in a directed study in budding yeast of preselected mutant alleles , alterations in the TOR or PKA pathways were reported not to affect formation of SGs under several different stress conditions [12] , demonstrating that not every pathway component is necessarily required for SG induction . SGs formed in budding yeast after NaN3 treatment contain translation initiation factor components not found after glucose starvation , including components of eIF3 ( Rpg1 and Prt1 ) , eIF4 ( eIF1A , eIF4B , eIF5B ) , and eIF5B [25] . The sets of proteins required for SG formation ( assembly factors ) under diverse stresses are likewise non-identical . After glucose deprivation , pbp1Δ and pub1Δ mutants are defective in SG formation , but not after NaN3 treatment [25] . In line with this , the majority of the mutants identified in this screen are SG defective in response to some , but not all , stress conditions . However , SG defects common to all stress conditions tested was most frequent among the mutants with the most severe SG defect in 2-DG , suggesting that such mutants lack components fundamental to all SG assembly . In four of the tested SG-defective mutants ( top3Δ , set3Δ , ski3Δ , and gtr1Δ ) , the induced mutability upon heat stress was substantially increased compared to the wt ( Fig . 4 ) . In some cases the change was even higher than what was earlier observed when the SG amount was artificially manipulated by other means ( Chx , overexpression of PBP1 or DHH1 ) [15] . However , we observed no altered induced mutation frequency for the remaining SG-defective mutants . Desequestration of TOR pathway signaling components by the lack of SGs under heat stress does lead to more rapid reactivation of TOR signaling in the recovery phase [15] . Mutants lacking SGs are predicted by the model proposed by Takahara and Maeda [15] to have more TOR signaling components active , and to display an increased mutation rate upon heat stress . However , in view of our present study which extends to more SG-defective mutants , most of which are affected in functions unrelated to the TOR pathway , this does not necessarily confer an elevated heat-induced mutation rate . While our results therefore give some support to the notion that SGs moderate heat-induced hypermutability , it is clear that the mechanism , remains ambiguous , and additional yet unidentified factors are required for this to occur . The gtr1 and gtr2 mutants both appeared in our screen . The corresponding proteins are closely related unconventional G proteins with metazoan homologs; RagA and RagB ( Gtr1-like ) , RagC and RagD ( Gtr2-like ) [50] . Gtr1 and Gtr2 are established in at least two plausible roles to affect SG formation . On one hand , they regulate the S . cerevisiae Ran homolog Gsp1 , and its paralog Gsp2 , which are required for the bulk of RNA processing and nucleocytoplasmic transport . GTR1 was originally identified as a suppressor of a mutant allele of SRM1 [51] . On the other hand , Gtr1 and Gtr2 are part of the EGO/GSE complex , which is required for microautophagy [28] , [37] , [52] . EGO/GSE members are Gtr1 , Gtr2 , Meh1 , Slm4 , and Ltv1 [28] , [37] , [52] . EGO/GSE is implicated as a TOR signaling activator . Thus , the mammalian Gtr1/Gtr2 homologs RagA – D bind Raptor ( S . cerevisiae homolog Kog1 ) and so mediate signaling from growth-promoting amino acid levels to mTOR [53] . In yeast , Gtr1 in its GTP-loaded state binds Tco89 and this interaction is required for activation of TORC1 [37] . The explanation for the observed SG defect in gtr1 and gtr2 mutants could thus lie in their involvement in nucleocytoplasmic RNA transport , activation of the TOR pathway and microautophagy , or both . Mutations along the RanGDP/GTP axis ( gsp1-P162I , rna1-1 , rna1-S116P , srm1-ts , srm1-G282S ) invariably affected SG formation ( Fig . 5 ) . As haploid null alleles are not available for these genes , it is difficult to judge from the phenotype strength of the different point mutants with various expressivities the exact degree of involvement in SG formation of the corresponding gene product . However , it does appear to be significant , as at least one allele of each of these genes gives a strong SG phenotype , gsp1 itself strongest among them . On the other hand , mutations in remaining EGO/GSE components ( slm4Δ , ltv1Δ ) also affected SG levels , albeit moderately . Recently , the mammalian Gtr1/Gtr2 homologs , RagA – D , were shown to play a role in glucose sensing through the mTOR pathway , as newborn mice expressing the constitutively active form RagAGTP become hypoglycemic [39] . It therefore appears plausible that the effects we observed under glucose starvation in gtr1 and gtr2 mutations on SG formation , as well as recovery rate and survival ( Fig . 6 ) are mediated through defective TOR signaling . There is some support for a nuclear role of EGO/GSE . Thus , Gtr1 , Gtr2 , Prp20 and Gsp1 co-fractionate with chromatin . The gtr1 and gtr2 mutants display derepression at telomeres . Gtr2 physically interacts with Rvb1 and Rvb2 , members of the Ino80 chromatin remodeling complex , and gtr1 is synthetically lethal with ino80 [54] . Further , Ltv1 has been implicated in nucleocytoplasmic export of the ribosomal 40S subunit [55] . Gtr1 also physically associates with Rpc19 , a subunit of RNApolI and RNApolIII , and with Nop8 , an rRNA processing enzyme [56] . Gtr1 could thus link ribosome biogenesis with signals for cell growth . In keeping with this interpretation , rRNA synthesis , as well as RNApolIII activity , are reduced in gtr1Δ mutants [56] . Gtr1 and Gtr2 could be the focal point of both the TOR signaling and nuclear functions . In line with this , the gtr1 gtr2 double mutant displayed quite a strong SG defect ( Fig . 6 A ) . Intriguingly , a majority , 30 out of the 54 genes deleted in the verified SG-defective mutants encode nuclear proteins , whereas only 8 are reported as predominantly cytoplasmic . This points at a role for nucleocytoplasmic communication in regulation of SGs . Previous observations also hint at an essential role for nucleocytoplasmic transport in regulated SG formation . Importins α1 , α4 , α5 , and β1 are located in mammalian SGs [57] . The ubiquitin-like protein Mdy2 ( homologous to human Rad23 ) localizes to heat-induced budding yeast SGs , and is required for the heat-induced stress response . Interestingly , removing the nuclear localization signal from Mdy2 prevents its association with Pab1 in SGs [58] , indicating that nucleocytoplasmic transport of Mdy2 is essential for its role in the heat-induced SGs and stress response . Recent data indicate that the fate of a transcript is determined by RBPs associating with the nascent mRNA molecule , a process dubbed co-transcriptional mRNA imprinting . Cytoplasmic mRNA degradation factors shuttle into the nucleus to bind to chromatin , thereby enhancing the synthesis rate of novel transcripts [59] . In this view , nucleocytoplasmic transport of RNA and proteins is central to control of gene expression . As stated above , we have implicated the Ran-based system for nucleocytoplasmic transport in SG formation ( SG phenotypes found in gtr1 , gtr2 , gsp1 , rna1 , and srm1 mutants ) . It likely that formation of SGs , which sequester many RBPs including mRNA degradation factors , will dampen their cycling back into the nucleus . Thus , in the absence of SGs , more degradation factors and other RBPs will be free to move into the nucleus . If this cycling of RBPs into the nucleus is required to mount a transcriptional response upon stress , then this may be the explanation why we observe a hyperinduction of stress-activated mRNAs in SG-defective mutants ( Fig . 7 A , B ) . Conversely , in the case of stress-repressed mRNAs that are hyperrepressed in SG-defective mutants ( Fig . 7 C ) , RBPs with a role in repression may accompany the transcript . This model then predicts that every SG-defective mutant should display deregulation of stress-regulated genes . The number of mutants investigated here is insufficient to establish this as a general principle but the model appears viable for a large fraction of SG-defective mutants . The top3Δ mutant did not conform fully to this pattern however; while it did display hyperrepression ( Fig . 7 C ) , as well as hyperinduction of STL1-lacZ in severe hyperosmosis ( Fig . S7 D ) , it induced the HSP12 and HSP104 genes similar to wt ( Fig . 7 A , B ) . It remains a possibility that certain SG-defective mutants , where the fundamental role of the cognate protein is in other cellular functions than RNA-related processes ( topoisomerase III is primarily implicated in DNA recombination ) , are not affected in stress-related transcription . We therefore propose a view where , not only do stress-responsive signaling pathways affect SG formation , but SGs themselves can also influence the outcome of the stress response . In support of this , it was recently shown that in mammalian cells , formation of large SGs by overexpression of G3BP takes place before eIF2α phosphorylation through protein kinase R . In this setting , formation of SGs can be the cause , but not the effect , of eIF2α phosphorylation [60] . Sequestration of signaling components in SGs has previously been proposed to affect cytoplasmic signaling events from the TOR pathway in budding yeast [15] , or MTK1-SAPK in mammalian cells [16] . Our data indicate that such effects also can influence the levels of stress-related transcripts . This could occur through increased transcription rates or increased mRNA half-lives; both mechanisms are compatible with our data . Given the requirement for nucleocytoplasmic traffic for SG formation evident from previous and our present data as discussed in the above section , it is natural to speculate that nuclear events are also involved , through the action of RBPs shuttling into the nucleus . Our global genetic screen has unraveled several unforeseen components required for regulated SG accumulation . We found arc1Δ mutants to be almost completely unable to form SGs under any stress condition tested ( Fig . 2 ) . ARC1 encodes a tRNA and aminoacyl-tRNA synthetase binding protein [61] , and is the only gene with a specific role in tRNA metabolism to appear in our screen with a strong SG-defective phenotype . This finding is intriguing , as the mammalian stress-activated ribonuclease angiogenin cuts tRNAs , generating small RNA fragments that inhibit initiation of translation , and are essential for angiogenin-dependent SG accumulation [62] . Arc1 has no sequence similarity to angiogenin and has no recognizable RNase domains . However , it is conceivable that it performs another function in an analogous process in yeast . Elucidation of the role of Arc1 and the other ostensibly diverse proteins identified here by genetics as required for stress-induced SG formation , will lead to a broader appreciation of the function in stress response modulation of these large cellular aggregates , which have so long defied exhaustive biochemical characterization .
Strains were from the BY4741 background and were grown at 30°C or at the indicated temperatures . Cells were grown in synthetic defined ( SD ) : 0 . 14% yeast nitrogen base ( YNB; Difco ) without amino acids; 0 . 5% ammonium sulphate; 1% succinic acid; 2% D-glucose; 20 mg/l each of histidine , methionine , lysine , and uracil; 100 mg/l of leucine ( pH 5 . 8 ) , or in synthetic complete ( SC ) ; SD plus 0 . 079% synthetic complete drop out mix ( Difco ) . To efficiently incorporate the SG marker ( Pab1-RFP ) into the yeast single deletion collection ( SGA-V2 ) , we applied the yeast synthetic genetic array ( SGA ) methodology , which allows the Pab1-RFP marker to be combined into a single haploid cell through standard mating and meiotic recombination via a robotic SGA procedure [63] , [64] using the Singer RoToR HDA system ( Singer Instruments ) . Wt cells were transformed with the centromeric vector p5586 [65] carrying the gene of interest including its native promoter and terminator sequences , and compared with wt cells carrying empty p5586 . Gene deletion strains expressing Pab1-RFP were transferred to 96-well microtiter plates and frozen at −80°C . Prior to screening for SG formation , one 10 µl sample per strain was allowed to grow in 200 µl of SD at 30°C for 2–3 days without shaking . Then , each preculture was diluted 75-fold into a final volume of 215 µl of SD medium and grown with shaking at 30°C . After 16 h , when the OD600 of most wells had reached 0 . 5 , 2-deoxyglucose ( 2-DG ) was added to a final concentration of 400 mM . After 2 h of incubation with continued shaking , cells were fixed for 30 min . at room temperature by adding formaldehyde to a final concentration of 3 . 7% . Cells were then washed twice with PBS and stored at room temperature in 200 µl PBS . For quantification of SGs , fixed cells were imaged in ImageXpress MICRO ( MDC ) , automated cellular imaging and analysis system . After image acquisition , images were quantified by MetaXpress ( Version 3 . 1 ) software . A MetaXpress sub-program was designed for the quantification of SG phenotypes , which has the ability to automatically query images from our database , extract key SG morphology features and export the measurements as a separate output . For manual quantification , cells were grown in logarithmic phase in 15 ml Falcon tubes to an OD600 nm of 0 . 5 before application of stress , formaldehyde-fixed and washed as described above before microscopy . The functional enrichment analysis was based on the result from Gene Ontology Term Finder [66] , and P-values were calculated using a hypergeometric distribution with multiple hypothesis correction . Manually confirmed hits were analyzed for enrichment of Gene Ontology ( GO ) biological processes , molecular function , and cellular component categories by comparing with a background set list representing SGA-V2 plus an array of slow-growing mutants ( 4691 genes ) . A cut-off of P<0 . 05 was used . The interaction network diagram of SG-defective mutants was extracted from the interaction analysis using Osprey 1 . 2 . 0 [67] and the physical interactions between confirmed hits were added according to the BioGRID interaction database [68] . To identify domains implicated in protein aggregation several bioinformatics algorithms was used: GBA [32] for low-complexity regions ( LCRs ) , IUPRED and ANCHOR [33] , [34] for intrinsically disordered domains ( IDRs ) and PAPA [35] for prion-like domains . Visualization of the position of cytoplasmic ribosomal proteins in the ribosome was done in UCSF Chimera [69] . Ribosome structures ( 40S subunit; PDB ID: 3U5G; 60S subunit PDB ID: 3U5I ) [70] were downloaded from the RCSB Protein Data Bank ( http://www . rcsb . org/pdb/home/home . do ) . Determination of growth parameters was performed in a Bioscreen Analyzer V as described [71] after recovery from treatment with 400 mM 2-DG . Strains were run in 5 duplicates on separate plates , with five wild-type ( wt ) strains on each plate as controls . Induced mutation frequencies were estimated as the forward mutation frequency of the CAN1 gene [72] . Wt or mutant cells were grown in SC until late log phase ( OD600 = 1 . 0 ) and exposed to stress as indicated . Cells were washed with sterile water and plated on SC lacking arginine ( SC-Arg ) with or without L-canavanine sulfate ( 60 µg/ml ) . Canavanine-resistant colonies were counted after 7 days at 30°C and normalized for total viable cells on SC without canavanine . Wt or mutant cells were treated as specified for different time periods , and subsequently flash-frozen on dry ice/ethanol and harvested . mRNA levels of the indicated stress-responsive genes were determined using real-time quantitative PCR ( qPCR ) . Total RNA was obtained from cell pellets using phenol-chloroform extraction and ethanol precipitation . RNA was treated for 15 min at 25°C with DNase I RNase-free ( Roche ) according to the manufacturer's protocol . Then cDNA was synthesized in 20 µl reactions containing 25 ng/µl of DNase I treated RNA , 5 µM of Oligo d ( T ) ( Thermo Scientific ) , 10 units/µl of SuperScript II Reverse Transcriptase ( Invitrogen ) , 1× First Strand Buffer , 10 mM DTT , and 0 . 8 mM dNTPs . Afterwards , qPCR was performed in a reaction final volume of 10 µl using the Eva Green ( Solis ) for fluorescent labeling , 2 . 5 µl cDNA and 0 . 2 µM of the corresponding oligonucleotides . Real-time PCR reactions were performed under the following conditions: 95°C for 15 min to activate the polymerase , followed by 40 cycles of 10 s at 95°C , 20 s at 60°C and 10 s at 72°C . At the end of the amplification cycles , a melting curve analysis was conducted to verify the specificity of the reaction . For each analyzed primer pair , a negative control was included and a standard curve was made with serial dilutions of cDNA samples pool ( 1/2 , 1/5 , 1/10 , 1/25 , 1/50 , 1/100 and 1/500 ) . Alternatively , the protein product level was determined using a lacZ assay , where β-galactosidase activity was determined and normalized for cell number as described [73] . Hog1 Tyr176 phosphorylation was assayed by western blot using α-phospho-p38 antibodies ( Cell Signaling Technologies ) . eIF2α ( Sui2 ) Ser51 phosphorylation was determined using α-phospho-eIF2α antibodies ( Biosource ) . | When cells encounter harsh conditions , they face an energy crisis since the stress will reduce their energy production , and at the same time cause extra demands on energy expenditure . To tackle this dilemma , cells under stress form giant agglomerates of RNA and protein , called stress granules . In these , mRNA molecules are kept silent , preventing waste of energy on producing proteins not needed under these conditions . A few mRNAs , encoding proteins required for the cell to survive , stay outside of stress granules and escape this silencing . This mechanism can protect plants and microbes against cold spells or heat shocks , and human cells exposed to oxidative damage or toxic drugs . We have investigated which genes are necessary to form stress granules , and their impact on the stress response . We discovered that mutant cells unable to form stress granules overreacted to stress , in that they produced much higher levels of the induced mRNAs . We think this means that gene regulatory proteins are sequestered inside stress granules , inhibiting their action . Stress granules may thus function as moderators that dampen the stress response , safeguarding the cell against excessive reactions . | [
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] | 2014 | Stress Granule-Defective Mutants Deregulate Stress Responsive Transcripts |
There is a pressing need for drug discovery against visceral leishmaniasis , a life-threatening protozoal infection , as the available chemotherapy is antiquated and not bereft of side effects . Plants as alternate drug resources has rewarded mankind in the past and aimed in this direction , we investigated the antileishmanial potential of Cinnamomum cassia . Dichloromethane , ethanolic and aqueous fractions of C . cassia bark , prepared by sequential extraction , were appraised for their anti-promastigote activity along with apoptosis-inducing potential . The most potent , C . cassia dichloromethane fraction ( CBD ) was evaluated for anti-amastigote efficacy in infected macrophages and nitric oxide ( NO ) production studied . The in vivo antileishmanial efficacy was assessed in L . donovani infected BALB/c mice and hamsters and various correlates of host protective immunity ascertained . Toxicity profile of CBD was investigated in vitro against peritoneal macrophages and in vivo via alterations in liver and kidney functions . The plant secondary metabolites present in CBD were identified by gas chromatography-mass spectroscopy ( GC-MS ) . CBD displayed significant anti-promastigote activity with 50% inhibitory concentration ( IC50 ) of 33 . 6 μg ml-1 that was mediated via apoptosis . This was evidenced by mitochondrial membrane depolarization , increased proportion of cells in sub-G0-G1 phase , ROS production , PS externalization and DNA fragmentation ( TUNEL assay ) . CBD also inhibited intracellular amastigote proliferation ( IC50 14 . 06 μg ml-1 ) independent of NO production . The in vivo protection achieved was 80 . 91% ( liver ) and 82 . 92% ( spleen ) in mice and 75 . 61% ( liver ) and 78 . 93% ( spleen ) in hamsters indicating its profound therapeutic efficacy . CBD exhibited direct antileishmanial activity , as it did not specifically induce a T helper type ( Th ) -1-polarized mileu in cured hosts . This was evidenced by insignificant modulation of NO production , lymphoproliferation , DTH ( delayed type hypersensitivity ) , serum IgG2a and IgG1 levels and production of Th2 cytokines ( IL-4 and IL-10 ) along with restoration of pro-inflammatory Th1 cytokines ( INF-γ , IL-12p70 ) to the normal range . CBD was devoid of any toxicity in vitro as well as in vivo . The chemical constituents , cinnamaldehyde and its derivatives present in CBD may have imparted the observed antileishmanial effect . Our study highlights the profound antileishmanial efficacy of C . cassia bark DCM fraction and merits its further exploration as a source of safe and effective antieishmanial compounds .
Visceral leishmaniasis ( VL ) or kala-azar is the fatal form of leishmaniasis caused by Leishmania donovani , a digenetic parasite that preferentially invades the host liver , spleen and bone marrow macrophages . VL has colonized tropical and sub-tropical countries most of which are either under developed or developing where it clouts economically ailing population , causing an immense death toll of 20 , 000–40 , 000 each year [1] . The implications associated with VL are not only restricted to mortality; in fact , VL negatively impacts whole socio-economic structure of the affected society by producing physical and reproductive disabilities [2] . VL also occurs as a relapse infection in the form of post kala-azar dermal leishmaniasis ( PKDL ) in 5–10% of clinically recovered patients in India after 2–3 years of treatment and , in 50% of clinically cured patients in Sudan within 6 months of treatment duration . PKDL patients being highly infectious serve as putative reservoirs in between epidemic outbreaks posing a grave challenge against effective VL control [3] . Despite being a neglected tropical disease associated with poverty , VL has gathered significant attention recently due to its association with acquired immunodeficiency syndrome ( AIDS ) . In its non-endemic foci , VL is a common co-infection with AIDS where up to 70% of adult leishmaniasis cases have been reported to be related with human immunodeficiency virus ( HIV ) [4] . More than 90% of the new VL cases arise in its endemic foci comprised by India , Bangladesh , Brazil , Sudan , South Sudan and Ethiopia where both death and drug resistance are at escalation [5] . About half of the global burden of VL is borne by India as per GBD statistics [6] , in states of Bihar , and neighbouring districts of West Bengal , Uttar Pradesh and Jharkhand , with Bihar alone accounting for 90% of the cases [7] . Despite decades of research , no commercial vaccine is available against VL and chemotherapy is failing owing to emerging resistance , adverse side effects and cost [8] . Pentavalent antimonials , the 1st line of drugs against leishmaniasis , are at back-foot with more than 60% of failure cases in Bihar , India , making Amphotericin B ( AmB ) , as a drug of choice [9] . AmB exhibits high clinical efficiency but long treatment duration , parenteral administration and associated renal toxicity are hard to bear . Miltefosine , the first oral drug against the disease is teratogenic and its efficacy has been reduced by escalating drug resistance; and combination therapy of paromomycin and miltefosine is also not bereft of limitations [10] . The repercussions associated with VL are dreadful , chemotherapy is unsatisfactory and expansion of VL to its non-endemic areas has gathered sufficient attention for its immediate control . Unavailability of licensed antileishmanial vaccine despite few being in clinical or pre-clinical trial , and limited chemotherapy [8] has paved the way for drug discovery from alternate sources such as plants . Plant kingdom is endowed with unparallel molecular diversity that has aided mankind in discovery of many potent drugs including anti-protozoals [11] . Quinine derived from Cinchona succirubra till date remains an important antimalarial drug after forty decades of its discovery [12] and artemisinin derived from Artemisia annua has led to a paradigm shift in antimalarial research [13] . Exploration of plants for candidate antileishmanial compounds has been exhaustive and many plant extracts , fractions , and isolated plant secondary metabolites have shown significant leishmanicidal activities , some with potent immunomodulation [14] . Encouraged by these studies , and utilizing the benefit of leishmaniasis from drug repurposing or piggy-back chemotherapy [15] , and further to open a possibility of VL elimination drive from the Indian sub-continent [16] , we evaluated antileishmanial potential of Cinnamomum cassia ( family , Lauraceae ) which is commonly used in traditional Chinese medicine [17] . C . cassia has been reported to possess many pharmacological properties such as antibacterial [18] , anticancer [19] , antidiabetic [20] , antifungal [21] , neuroprotective [22] and prevents oxidative stress-related diseases [23] . To the best of our knowledge , this is the first report on in vitro and in vivo leishmanicidal potential of C . cassia against L . donovani .
For in vivo studies , the experimental protocols were approved by the Jamia Hamdard Animal Ethics Committee ( JHAEC ) ( Ethical approval judgment number 499 ) . JHAEC is registered under the Committee for the purpose of control and supervision of experiments on animals ( CPCSEA ) . Female BALB/c mice aged 6–8 weeks ( 20–25 ) g and male Syrian golden hamsters 4–6 weeks of age were used for in vivo antileishmanial studies . All the animals were individually housed in standard size polycarbonate cages under standard conditions in the Central Animal House of Jamia Hamdard according to the internationally accepted principles . BALB/c mice were infected with L . donovani promastigotes ( 2 . 5×107/animal ) via tail vein while hamsters were infected intra-cardially . Mice were bled from retro-orbital plexus and hamsters by intra-cardiac puncture . The animals were anesthetized with isoflurane prior to carbon dioxide ( CO2 ) euthanasia . L . donovani promastigotes were cultured in complete medium 199 ( M199 ) ( with 10% heat-inactivated FBS ) , pH 7 . 4 , supplemented with penicillin G sodium ( 100 U ml-1 ) , streptomycin sulfate ( 100 μg ml-1 ) , HEPES ( 25 mM ) . The WHO reference strain of L . donovani ( MHOM/IN/83/AG83 ) was obtained as a kind gift from Dr . Nahid Ali , Scientist , IICB , Kolkata . The L . donovani strain was maintained as amastigotes in BALB/c mice and promastigotes in culture as described previously [24] . To culture and perform studies with peritoneal macrophages , Roswell park memorial institute ( RPMI ) -1640 medium devoid of phenol red was used [24] . The media was supplemented with 100 μg ml-1 streptomycin sulfate , 100 U ml-1 penicillin G-sodium , 0 . 2% sodium bicarbonate , 25 mM HEPES . FBS ( 10% ) was used as and when required according to experimental conditions . Also , for all in vivo experiments , splenic lymphocytes were isolated and cultured in phenol red free RPMI-1640 medium . All the cell cultures were maintained in a humidified atmosphere at 37 °C with 5% CO2 . C . cassia bark was purchased locally , and authenticated at NISCAIR , CSIR , New Delhi , by Dr . H . B . Singh ( voucher no . NISCAIR/RHMD/Consult/-2010-11/1440/38 ) . The extraction was performed as described previously by us [25] with few modifications . In brief , 100 g of powdered bark was immersed in 500 ml of dichloromethane ( DCM ) for 24 h , followed by four consecutive washes with half the volume of DCM at 24 h interval . This was followed by 5 washes each with ethanol and water in a sequential manner . The filtrate obtained at each step was passed through Whatman filter paper ( No . 1 ) and pooled following concentration in a rotary evaporator at 35°C . The aqueous fraction was lyophilized . The dried fractions were kept at -20°C until used for bioassay . Bioactivity of C . cassia fractions was appraised by growth kinetics assay wherein stationary phase promastigotes ( 2×106 cells ml-1 ) were cultured in the absence or presence of various test fractions ( 500 μg ml-1 ) at 22°C . Pentamidine ( a known antileishmanial drug ) served as positive control ( 500 μg ml-1 ) and dimethyl sulphoxide ( DMSO , 0 . 25% ) was taken as solvent control ( maximum concentration used to solubilize the extracts ) to ascertain any unspecific parasite death . Following treatment , culture aliquots from all the groups were taken at every 24 h for 7 days , erythrosin B ( 0 . 2% , 1:1 ) stained , and counted in a haemocytometer under phase contrast microscope ( 40X ) to ascertain the cell density [25] . To obtain photomicrographs , treated and untreated cells were erythrosin B stained and observed under oil immersion at 100 X using a phase contrast microscope . C . cassia fractions were examined for their cytocidal or cytostatic mode of action in a growth reversibility assay . Briefly , the treated or untreated parasites cultured as above were harvested after 7 days . The drug was withdrawn by washing the promastigotes twice ( 3000×g , 10 min , 4°C ) with incomplete M199 ( without FBS ) and all the samples were re-incubated in fresh M199 ( complete media , with 10% FBS ) at 22°C . Post 96 h of incubation , culture aliquots were taken and cell density determined [25] . For determination of 50% promastigote growth inhibitory concentration ( IC50 ) , stationary phase parasites at a cell density of ( 2×106 ml-1 ) were incubated either in the absence or presence of C . cassia bark DCM fraction ( CBD ) and pentamidine ( 500 μg ml-1 ) at serial two fold dilutions ( 500 to 3 . 90 μg ml-1 ) for 96 h at 22°C . The parasite survival was assessed by enumerating the live cells in a haemocytometer after erythrosin B staining . The cell density was determined as per the standard formula: [Cell count ( in 16 squares ) × dilution factor ×104 = cell density ( 106 ml-1 ) ] and percent ( % ) viability was calculated as per the formula: %Viability=Averageviablecellcountperml ( traatedsamples ) Averageviablecellcountperml ( parasitecontrol ) ×100 IC50 , the concentration that inhibited the parasite growth by 50% was determined by graphical extrapolation [24] . The anti-amastigote potential of CBD was evaluated in L . donovani parasitized peritoneal macrophages isolated from BALB/c mice as described elsewhere [24] . The mouse peritoneal macrophages ( 5× 106 cells ml-1 ) were seeded in 24–well plates ( Corning , USA ) and left for adherence for 24 h . After removal of non-adherent macrophages , the adherent cells were infected with L . donovani promastigotes ( Leishmania: macrophage; 10:1 ) for a further 24 h in a CO2 incubator . Non-internalized promastigotes were removed by gentle washing and the infected macrophages were either left untreated or subjected to treatment with CBD or pentamidine ( 0–200 μg ml-1 ) at serial four fold dilutions . The drug treatment was carried out for 48 h following which the culture media was aspirated and the coverslips washed with PBS followed by fixation and giemsa staining . At least 200 macrophage nuclei per coverslip were counted and the number of resident amastigotes enumerated . Percent amastigote infectivity was determined using the formula: %Infectivity=Numberofamastigotesper200macrophages ( treatedsamples ) Numberofamastigotesper200macrophages ( infectedcontrol ) ×100 IC50 was determined by graphical extrapolation . To assay the effect of CBD on NO production , culture supernatants were collected after 48 h from CBD treated macrophages and from infected and uninfected controls , in parallel . The NO production was measured by assaying the nitrite ( NO2- ) content using Griess reaction as described elsewhere [28] . The cytotoxicity of CBD against murine peritoneal macrophages was determined by MTT [3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyl tetrazolium bromide] assay as described previously [24] . In brief , peritoneal macrophages ( 2×106 ml-1 ) were seeded in 96 well tissue culture plates and left for adherence for 24 h in CO2 incubator . The non-adherent cells were removed by gentle washing with serum free media and the adherent macrophages were cultured in the absence or presence of serial two fold dilutions of CBD ( 0 to 500 μg ml-1 ) in triplicates . Post 48h , MTT reagent ( 5 mg ml-1 ) was added and formazan crystals were solubilized in isopropanol: dimethylsulfoxide ( 1:1 ) . The plate was read at 570 nm in an ELISA plate reader . Macrophages without treatment served as control and their absorbance was considered as 100% . Percent viability for different experimental groups was calculated according to the formula: %Viability=Meanspecificabsorbance ( treatedsamples ) Meanspecificabsorbance ( controlsamples ) ×100 GC-MS analysis was carried out to identify the plant secondary metabolites present in C . cassia bioactive fraction . Shimadzu QP2010 instrument quipped with DB-5 column ( 30m , film 0 . 25 μm , ID 0 . 25 mm ) was used to carry out GC-MS . The column was heated gradually from 60°C to 310°C at 5°C min-1 and the injector and detector temperatures were kept at 260°C . Rest of the conditions were as elaborated previously [24] and the identification of plant secondary metabolites was performed by correlating the recorded mass spectra with those present in WILEY8 . LIB and NIST08 . LIB library provided along with the software of the GC-MS system . Briefly , 4–6 weeks old male hamsters were infected intra-cardially by L . donovani promastigotes ( 2 . 5× 107/animal ) . To ascertain the infection , post six weeks of infection , three hamsters were arbitrarily selected , euthanized and impression smears of liver and spleen prepared . Microscopic investigation of giemsa stained slides as well as transformation of splenic amastigotes into promastigotes under in vitro culture conditions confirmed the infection . The animals ( n = 5 ) were then randomly assorted into different groups as mentioned for mice studies . Hamster dosing was performed daily for ten days according to dose and route of administration . After , one week of treatment , animals were bled by intra-cardiac puncture , sacrificed and parasite burden was determined as described previously [32] . Adverse effects of CBD treatment on host liver and kidney functions was evaluated in normal as well as L . donovani infected mice ( n = 5 ) . The mice were administered CBD at 100 mg/kg bw , or AmB ( 5 mg/kg bw ) or DMSO ( 0 . 25% in PBS ) as described above , daily for ten days . Post one-week , mice were bled and the sera analysed for the presence of serum glutamate pyruvate transaminase ( SGPT ) , serum oxaloacetate transaminase ( SGOT ) and alkaline phosphatase ( ALP ) . The serum enzyme concentrations were estimated as a marker for liver toxicity according to commercially available kits ( Span Diagnostics Ltd . , Surat , India ) . To assess any adverse effects on renal function , serum concentrations of creatinine and urea were estimated as per the manufacturer’s instructions ( Span Diagnostics Ltd . , Surat , India ) . Toxicity profile of CBD was assessed in normal as well as infected hamsters ( n = 3 to 5 ) . The hamsters were fed CBD 100 mg/kg bw orally and the other control groups were treated as mentioned above . After one week , levels of SGOT , SGPT , and ALP along with urea and creatinine were estimated in the sera using commercially available kits ( Span Diagnostics Ltd . , Surat , India ) as mentioned above . All the in vitro experiments were carried out thrice in triplicates . The results shown are from one of the three independent experiments performed and are expressed as mean ± standard error of mean of the samples in triplicate . The in vivo study was performed twice; five mice and hamsters per group were used in respective studies unless indicated . The data shown are from one of the two independent experiments and expressed as mean ± SEM . Graph-Pad Prism 5 software was employed for doing statistical analysis . Differences were considered statistically significant at P<0 . 05 and statistical significance was indicated with the help of appropriate symbols in graphs or explained in respective result section .
Anti-promastigote potential of C . cassia fractions were assessed against L . donovani promastigotes . CBD profoundly declined the growth of L . donovani promastigotes ( P<0 . 001 ) in vitro ( Fig 1a ) . In case of ethanolic ( CBE ) and aqueous ( CBA ) fractions , no significant suppression in parasite growth ( P>0 . 05 ) was visible . Solvent control ( 0 . 25% DMSO ) was also inert in arresting the parasite growth while pentamidine completely inhibited the growth of parasites at three days of culture ( P<0 . 001 ) . CBD treated parasites lost their typical shape and became reduced in size with absent or shortened flagella post 96h of CBD treatment . In contrast , untreated parasites retained their flagellated and elongated shape . Pentamidine treated parasites were clumped with each other , reduced in size , and had no flagella ( Fig 1b ) . To assess whether the mode of induced cell death by the bioactive fractions was cytostatic or cytocidal , the treated and untreated L . donovani promastigotes were washed to remove the drug and re-incubated in fresh M199 media for 96 h . Analysis of cell count post-drug withdrawal , revealed that there was no reversion of growth in CBD treated sample ( P<0 . 001 ) indicating its leishmanicidal effect . Substantial reversion in parasite growth was evident in all the other ( Fig 1c ) groups ( P>0 . 05 ) except in pentamidine treated samples wherein no reversal in parasite growth was observed ( P<0 . 001 ) . IC50 of CBD was determined by incubating the parasites with serial two-fold dilution of respective fractions or compounds . CBD induced a dose dependent reduction in parasite viability with IC50 of 33 . 66±3 . 25 μg ml-1 . IC50 achieved with pentamidine was 1 . 09±0 . 055 ( Fig 1d ) . The anti-amastigote efficacy of C . cassia bioactive fraction was evaluated in L . donovani infected peritoneal macrophages derived from BALB/c mice . CBD was found to be effectual in reducing L . donovani amastigote infection ex vivo with IC50 of 14 . 06±2 . 46 μg ml-1 while that of pentamidine was observed to be 0 . 85±0 . 02 μg ml-1 ( Fig 5a ) . The reduction in parasite burden post CBD treatment was also evident in giemsa stained photomicrographs taken at 100X under oil immersion in a phase contrast microscope ( Fig 5b ) . Effect of CBD treatment on NO production was assessed in culture supernatants of uninfected , infected and variously treated macrophages . CBD treatment ( 200 μg ml-1 ) enhanced the NO production in an inconspicuous manner by ~1 . 69 folds ( P>0 . 05 ) in comparison to infected controls ( 4 . 79 μM ) . Basal level of NO production was observed in normal uninfected macrophages ( 4 μM ) while none of the lower concentrations of CBD significantly impacted the NO production ( Fig 5c ) . In vitro cytotoxicity of C . cassia bioactive fraction was evaluated against peritoneal macrophages isolated from normal BALB/c mice . MTT assay was performed to assess the cell viability , and the data obtained indicated no apparent decline in cell viability at any of the concentrations tested ( Fig 6 ) . This indicated that C . cassia lacked any cytotoxicity against peritoneal macrophages even at the highest concentration of 500 μg ml-1 . GC-MS was performed to identify the chemical constituents present in CBD . A total of 118 compounds were detected which are listed in S1 Table . Cinnanmaldehyde ( 36 . 27% ) , cinnamaldehyde dimethyl acetal ( 21 . 52% ) , 1 , 2-Benzenedicarboxylic acid ( 10 . 92% ) and o-methoxycinnamaldehyde ( 5 . 58% ) were detected in major proportions along with other compounds listed in Table 1 . The in vivo antileishmanial effectualness of CBD was assessed at 50 and 100 mg/kg bw dose in L . donovani infected BALB/c mice . Heavy parasitic load was detected in host liver and spleen ( Fig 7 ) . Marginal levels of protection were witnessed in VC control group with percent reduction in parasite load being as low as 7 . 43% and 6 . 54% in liver and spleen , ( P>0 . 05 ) respectively . CBD treatment at 100 mg/kg bw rendered significant protection corresponding to 80 . 91% ( liver , P<0 . 001 ) and 82 . 92% ( spleen , P<0 . 001 ) . Treatment at 50 mg/kg bw CBD rendered partial ( liver = 46 . 34%; spleen = 47 . 95% ) but significant ( P<0 . 05 ) protection whereas AmB treatment ( liver = 92 . 88% , spleen = 93 . 09% ) resulted in substantial protection ( P<0 . 001 ) to L . donovani infected mice . In vivo antileishmanial efficacy of CBD was also examined in hamster model of the disease . 4–6 week old male hamsters infected with L . donovani parasites were treated with CBD at 50 and 100 mg/kg bw . High parasitic load was detected in untreated hamsters with LDU in liver and spleen corresponding to 4147 . 75±171 . 11 and 1433 . 69±197 . 59 , respectively . Vehicle control group conferred least protection to L . donovani infected hamsters ( Fig 10 ) . CBD treatment at 100 mg/kg bw significantly declined the parasite burden ( P<0 . 001 ) with 75 . 61% and 78 . 93% protection in liver and spleen , respectively . At 50 mg/kg bw , the reduction in parasite burden was partial but significant ( P<0 . 05 ) in comparison to infection control group and 42 . 92% protection was achieved in case of liver whereas 44 . 64% was observed in spleen . AmB treatment substantially decreased the parasite burden ( P<0 . 001 ) and conferred 88 . 51% ( liver ) and 90 . 71% ( spleen ) protection .
As a step towards VL elimination drive , the quest for better antileishmanials has become a necessity in the face of resurgence of apparently cured VL infections as PKDL , and its co-infection in AIDS patients . Also , in the absence of any effectual vaccine , the present chemotherapeutics are ineffective due to rapidly increasing drug resistance and toxicity [3 , 4 , 9] . Thus , switching to alternate therapies , especially plant-derived drugs may provide a breakthrough in the search for better , safe and cost-effective antileishmanials [33] . Use of plant extracts in case of leishmaniasis treatment has also been supported by WHO [34] and several studies in past have demonstrated potent leishmanicidal activities of plant extracts [33 , 35] . Encouraged from the previous reports , and also harnessing the benefit of leishmaniasis from pre-purposed drugs [36 , 37] , we investigated antileishmanial potential of Cinnamomum cassia bark dichloromethane fraction against L . donovani parasites in vitro and in vivo . CBD exhibited discernable anti-promastigote efficacy and inhibited the growth of L . donovani promastigotes in time- and dose-dependent manner . Anti-promastigote effect of CBD was leishmanicidal in nature , which is preferred over leishmanistatic effect as it aids rapid parasite clearance thereby rendering lesser risk of relapse . Many plant fractions have been previously reported to be leishmanicidal in nature [33] . In fact , moderate antileishmanial activity of C . cassia essential oil has been reported by Le et al . , where in the authors screened essential oils from different Vietnamese plants for activity against L . mexicana promastigotes [38] . The anti-promastigote activity of CBD was mediated via apoptosis as evidenced by PS externalization , mitochondrial membrane depolarization , ROS generation , DNA fragmentation and increased percentage of cells in sub G0/G1 population . Loss of mitochondrial function is critical to apoptosis induction and mitochondria are thought to be both source and target of ROS [39 , 40] . Upon loss of Ψm , cells become committed to apoptosis and ROS has also been reported to participate in cellular DNA damage [41] which was characterized by TUNEL assay and cell cycle analysis . Aqueous extract of C . cassia bark has been divulged to induce apoptosis in human cancer cell line through loss of Ψm [42] indicating that C . cassia constituents are capable of initiating programmed cell death . Pentamidine , the known antileishmanial drug , used as positive control in this study has been regularly employed by us [24 , 25] and others [43 , 44] to compare the antileishmanial efficacy of different plant extracts or compounds . It was also employed as a positive control for all apoptotic studies as it is known to induce apoptosis in Leishmania promastigotes by inducing DNA fragmentation and by modulating mitochondrial function including Ψm depolarization [45 , 46] . Since , amastigote form is the clinically relevant stage of Leishmania parasites , it is imperative for a drug to be able to inhibit the growth of Leishmania amastigotes . CBD also exhibited profound anti-amastigote efficacy , which was found to be independent of NO generation . Such NO independent leishmanicidal activity has also been previously observed by us [47] and others . We have earlier found that P . nigrum hexane and ethanolic fractions exhibited strong antileishmanial activity without significant NO production [47] . Some other leishmanicidal compounds such as piperine [48] , trans-β-caryophyllene [49] , nimodipine [50] and coronaridine [51] have also been demonstrated to exert their antileishmanial activity without inducing NO generation . The plant secondary metabolites present in C . cassia DCM fraction that may have contributed to the observed leishmanicidal effect were identified by GC-MS analysis . Cinnamaldehyde detected in maximum proportion , is known to inhibit various cancer or tumor cell lines [52] , and its antibacterial [53] and antifungal [54] properties are also reported . Cinnamaldehyde has also been observed to induce apoptosis in cancer cell lines [55] and may have contributed to apoptosis-inducing potential of CBD in our study . Another compound , o-methoxycinnamaldehyde , detected in CBD is reported to be antifungal [56] . Coumarin also detected in significant proportion is widely present in numerous plants and is endowed with multiple pharmacological properties . Coumarin , its various analogues and derivatives are reported to be antileishmanial [57 , 58] , antitrypanosomal [59] , antifungal [60] , anticancer [61] and antimicrobial [62] . Viridiflorol , another plant secondary metabolite present in CBD , has also been detected in other medicinal plant fractions or extracts that were reported to be antileishmanial [63] , antifungal [64] and antibacterial [65] . The in vivo therapeutic potential of C . cassia bioactive fraction was evaluated both in BALB/c mice and hamster model of the disease . BALB/c mice are the most commonly used disease model as they are highly susceptible to Leishmania infection and also have been proven to be extremely useful for evaluation of mechanisms related to disease pathogenesis and cure [29 , 66] . Hamsters are an optimal model to study the in vivo efficacy of potential antileishmanial formulations or compounds as hamsters closely mimic the clinical and immunopathological aspects of human disease [67] . Moderate levels of protection were achieved with CBD at 50 mg/ kg bw treatment whereas 100 mg/kg bw dose rendered significant protection in case of both L . donovani infected mice and hamsters . A small percentage of parasites still persisted in host liver and spleen , which has also been evident after successful treatment with other drugs [68 , 69] . Also , it is very well established in VL that even after apparent cure , complete elimination of parasites is not achieved and despite this , recovered immune-competent hosts show all signs of clinical cure even after long periods of recuperation [70] . Since , recovery from Leishmania infection includes both direct parasite killing and an elevation of CMI , immunomodulatory nature of CBD treatment was investigated to determine its Th1 stimulatory potential . The positive control used for in vivo studies i . e . , AmB in addition to being directly antileishmanial is also known to be Th1 inducing [71] . Therefore , AmB was also employed as a reference drug for all immunomodulation experiments . Cell-mediated immune responses were either only partially or marginally affected by CBD ( 100 mg/kg bw ) treatment . DTH and lymphoproliferative responses were partially strengthened whereas NO production was negligibly stimulated in both mice and hamster models . This may be attributed to the presence of cinnamaldehyde ( detected in notable amounts in CBD ) which is known to favour the elicitation of anti-inflammatory immune response by decreasing the production of tumor necrosis factor-alpha ( TNF-α ) , prostaglandin E2 ( PGE2 ) , Cyclooxygenase-2 ( COX-2 ) [72] . Cinnamaldehyde has also been reported to suppress NO production by reducing the expression of nitric oxide synthase II ( NOSII ) , an enzyme activated by Th1 cells that converts L-arginine into citrulline and NO . Another component , o-methoxycinnamaldehyde ( or 2-methoxycinnamaldehyde ) has been shown to inhibit IL-1β , a pro-inflammatory cytokine along with COX-2 activity [72 , 73] . Both cinnamaldehyde and o-methoxycinnamaldehyde have been demonstrated to be anti-inflammatory as they inhibited NO generation and TNF-α production in RAW 264 . 7 and J774A . 1 macrophages [74] . In fact , coumarin has also been reported to be associated with the induction of anti-inflammatory immune response [61] . Presence of these anti-inflammatory compounds may have led to restoration of only pro-inflammatory cytokines ( INF-γ and IL-12 ) whereas IL-4 and IL-10 ( anti-inflammatory cytokines ) levels remained more or less unchanged in CBD treated BALB/c mice . Changes in serum levels of IgG2a and IgG1 are induced by INF-γ and IL-4 , respectively , which in turn demonstrate the generation of Th1 or Th2 immune responses , respectively . There was neither significant decline in IgG1 nor appreciable elevation in IgG2a levels , which can also be corroborated with marginal changes in IL-4 and no potent stimulation of INF-γ levels with CBD . Similar observations have been made with other plant extracts or plant-derived molecules . Chloroformic extracts of Portulaca werdermannii and Portulaca hirsutissima despite being antileishmanial had an inhibitory effect on lymphocyte proliferation [75] . Another compound Cyclosporin-A which is a known immunosuppressant with wide range of antiprotozoal activities , has been shown to inhibit L . major infection in NO and TNF-α independent manner . Further , its antileishmanial efficacy was not compromised in the presence of Th1 antagonizing cytokines such as IL-10 and IL-13 [76] . Sen et al . [77] reported in vivo efficacy of artemisinin , where artemisinin was found to be highly effective without inducing a Th1-biased immune response in L . donovani infected BALB/c mice . Artemisinin treatment was incapable of inducing NO production; it only restored the levels of pro-inflammatory cytokines to normal range and did not specifically alter IL-4 and IL-10 levels . All these studies suggest that even in the presence of mixed Th1/Th2 response , drugs can exhibit profound parasiticidal activity . Same has been advocated by Sen et al . [77] , that therapeutic agents with direct parasiticidal action may even prove more beneficial when treating immunocompromised hosts . The toxicity profile of C . cassia bioactive fraction was studied both in vitro and in vivo . CBD did not induce any cytotoxicity against peritoneal macrophages even at 500 μg ml-1 indicating its inertness . Cinnamaldehyde , the major constituent of CBD has been reported to be non-toxic against purified T cells and macrophages [52] . C . cassia is known to be hepatoprotective [78 , 79] and in our studies also , CBD did not specifically alter serum levels of SGOT , SGPT and ALP from normal range in both normal mice as well as hamsters . Creatinine and urea levels also remained close to normal values in normal uninfected mice and hamsters . In L . donovani infected mice , SGOT and SGPT levels were found to be considerably enhanced whereas in infected hamsters , ALP levels were observed to be particularly elevated . All other parameters did not exhibit any significant deviation from normal range . CBD treatment brought down escalated SGOT and SGPT as well as ALP levels in infected mice and hamsters indicating its non-toxic nature . Despite a plethora of knowledge about parasite biology and immunology , fight against leishmaniasis has been less fruitful than expected . Due to complex disease pathogenesis and interlinked immune-mechanisms , many encouraging leads are not being translated into clinical success . Thus , it is imperative to search for better antileishmanial drugs from alternate sources . In our present study , we investigated antileishmanial efficacy of C . cassia bark DCM fraction ( CBD ) and observed that the bioactive fraction bore a remarkable direct parasiticidal activity with partial modulation of Th1 immune response . We suggest , further exploration of the major plant secondary metabolites present in CBD for their antileishmanial efficacy and immunomodulatory potential that may shed insight into the mechanisms underlying their direct leishmanicidal capacity . Alternatively , the bioactive fraction may be standardized and used in combination with the known or pipeline drugs in synergy for effective maintenance of VL and as a stepping-stone in regional drive towards VL elimination . | Leishmaniasis encompasses a broad spectrum of vector-borne neglected tropical diseases with significant worldwide health impact , ranging from the self-healing cutaneous lesions to stigmatizing and disfiguring skin ulcers ( mucocutaneous ) , and the systemic visceral manisfestations ( kala azar or visceral leishmaniasis , VL ) . A poverty-stricken disease , VL is fatal , if left untreated and resurfaces as post-kala azar dermal leishmaniasis after several years of apparent cure . Moreover , there is an upward trend in development of resistance to most of the currently available chemotherapeutic arsenal . Absence of vaccines , progressive emergence of HIV-Leishmania co-infection delineate the gravity of VL affliction . Natural products from medicinal plants have shown leishmanicidal effect , which in some cases , is potentiated by immunomodulation . Here , we elucidate the antileishmanial efficacy of Cinnamomum cassia bark fraction ( CBD ) with no adverse side effects . The parasites were eliminated by apoptosis in vitro while the protection in vivo was complemented by partial immunomodulation . CBD may be used in synergy with known or pipeline drugs for effective maintenance of VL . Our study represents an important step in the regional drive towards VL elimination . | [
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"cy... | 2019 | Cinnamomum cassia exhibits antileishmanial activity against Leishmania donovani infection in vitro and in vivo |
The availability of highly susceptible HIV target cells that can rapidly reach the mucosal lymphoid tissues may increase the chances of an otherwise rare transmission event to occur . Expression of α4β7 is required for trafficking of immune cells to gut inductive sites where HIV can expand and it is expressed at high level on cells particularly susceptible to HIV infection . We hypothesized that HSV-2 modulates the expression of α4β7 and other homing receptors in the vaginal tissue and that this correlates with the increased risk of HIV acquisition in HSV-2 positive individuals . To test this hypothesis we used an in vivo rhesus macaque ( RM ) model of HSV-2 vaginal infection and a new ex vivo model of macaque vaginal explants . In vivo we found that HSV-2 latently infected RMs appeared to be more susceptible to vaginal SHIVSF162P3 infection , had higher frequency of α4β7high CD4+ T cells in the vaginal tissue and higher expression of α4β7 and CD11c on vaginal DCs . Similarly , ex vivo HSV-2 infection increased the susceptibility of the vaginal tissue to SHIVSF162P3 . HSV-2 infection increased the frequencies of α4β7high CD4+ T cells and this directly correlated with HSV-2 replication . A higher amount of inflammatory cytokines in vaginal fluids of the HSV-2 infected animals was similar to those found in the supernatants of the infected explants . Remarkably , the HSV-2-driven increase in the frequency of α4β7high CD4+ T cells directly correlated with SHIV replication in the HSV-2 infected tissues . Our results suggest that the HSV-2-driven increase in availability of CD4+ T cells and DCs that express high levels of α4β7 is associated with the increase in susceptibility to SHIV due to HSV-2 . This may persists in absence of HSV-2 shedding . Hence , higher availability of α4β7 positive HIV target cells in the vaginal tissue may constitute a risk factor for HIV transmission .
Vaginal HIV transmission is a relatively rare event [1] and virions and host characteristics influence the probability of this rare event to occur . Host-related factors include the epithelial and mucus thickness , hormonal environment , presence of inflammation and infection with other sexually transmitted pathogens [2]–[7] . In particular Herpes Simplex Virus Type 2 ( HSV-2 ) infection is associated with a three-fold increased risk of HIV acquisition even in the absence of HSV-2 replication [8] , [9] . Clarifying the mechanisms involved in the increased susceptibility of HSV-2 positive individuals to HIV infection may help understanding the characteristics of mucosal microenvironment that facilitate HIV transmission . It was reported that the vaginal mucosa of HSV-2 infected women retains an increased number of CCR5+ CD4+ T cells long after HSV-2 replication abates . Likewise , plasmacytoid and myeloid dendritic cells ( DCs ) , which infiltrate areas of skin infected with HSV-2 , persist after lesion healing even in the context of acyclovir therapy [10] , [11] . More recently , an increased total number of CD4+ T cells expressing CCR5 and chronic activation markers CD38 and HLA-DR was found in the cytobrush samples of HSV-2 positive asymptomatic women [12] . These factors may partially explain the enhanced risk of HIV acquisition in HSV-2 positive individuals . However , a direct association between the HSV-2-driven increased frequency of these cell subsets and the HSV-2-driven increase in the risk of HIV acquisition has never been demonstrated . Immune cell trafficking can affect the susceptibility of the genital mucosa to HIV infection by influencing the availability of HIV cell targets at the site of exposure , the immune response to the virus and the ability of infected cells to reach sites of viral expansion and dissemination , such as draining lymph nodes , gut and the gut inductive sites . Thus changes in the expression of integrins or other adhesion molecules influence the susceptibility to vaginal HIV infection . Integrin α4β7 ( α4β7 ) is an adhesion molecule specifically involved in trafficking of immune cells in the gut and gut inductive sites [13] , [14] . However , α4β7+ cells are also involved in immune response in the vaginal tissue [15]–[17] . CD4+ T cells that express high levels of α4β7 , α4β7high CD4+ T cells , are highly susceptible to HIV infection [18]–[20] , they are preferentially depleted during acute SIV infection [21] and we recently reported that their frequency correlates with susceptibility to rectal SIV infection [22] . Moreover , administration of a monoclonal antibody ( mAb ) against α4β7 prior to intravenous challenge with SIVmac251 resulted in lower plasma and tissue viral load and lower proviral DNA compared to the control animals [23] . Notably the animals treated with the anti-α4β7 mAb showed no signs of progression to AIDS . Finally , pre-treatment with the mAb significantly reduced vaginal SIV infection of rhesus macaques ( RM ) ( Byrareddy et al . , Nature , in press ) . We previously showed that rectal HSV-2 infection increases the frequency of α4β7high CD4+ T cells at the site of exposure , in the rectal draining LNs and in blood [22] . In this report we explored the possibility that HSV-2 infection increases the frequencies of α4β7+ cell subsets in the vaginal tissue . We further asked if these increases persist in absence of HSV-2 replication in vivo and whether these and other changes in the expression of adhesion molecules correlate with the HSV-2-driven increase in SIV acquisition . Using a model of HSV-2 vaginally infected macaques , we found that , as in humans , HSV-2 positive animals appeared to be more susceptible to SHIVSF162P3 vaginal infection even when challenged with SHIVSF162P3 over a year after HSV-2 infection . Notably , HSV-2 positive animals had a trend toward higher frequency of α4β7high CD4+ T cells and higher expression of α4β7 and CD11c on DCs in the vaginal tissue . Moreover , we found that HSV-2 infection of vaginal tissue ex vivo increased the susceptibility of the tissue to SHIVSF162P3 and that specific HSV-2-driven changes in the expression of α4β7 and other adhesion molecules correlated with the HSV-2-driven increase in susceptibility to SHIV .
In humans the risk of HIV acquisition is higher in HSV-2 infected individuals , including asymptomatic subjects [8] . To establish if our HSV-2 macaque model recapitulated the HSV-2/HIV interplay in humans , we compared the susceptibility to SHIV vaginal infection of 5 HSV-2 positive ( HSV-2+ ) and 5 negative ( HSV-2− ) RMs . The 5 HSV-2+ animals were challenged with HSV-2 one year prior to the start of our study and were confirmed infected by detection of the virus in vaginal swabs at one or more time points after infection ( Fig . 1 ) . However , HSV-2 was undetectable at the time when baseline biopsies were taken ( 5 weeks before SHIV challenge ) and 2 , 3 and 4 weeks before SHIV challenge ( 6 out of 6 replicates of HSV-2 nested PCR on vaginal swabs resulted negative ) . All the animals were challenged vaginally with 250 TCID50 of SHIVSF162P3 . 4 out of 5 HSV-2+ RMs ( 80% ) acquired SIV , while only 1 out of 5 HSV-2− became SIV+ ( 20% ) . The animals were followed for 3 weeks . The 1 HSV-2− SIV+ RM exhibited acute plasma SIV VL similar to the HSV-2+ SIV+ RMs ( Fig . 1 ) . We hypothesized that the increased susceptibility to SHIVSF162P3 of the HSV-2 latently infected animals correlated with HSV-2-driven changes in the expression of molecules that are HIV receptors and important for immune cell trafficking in the mucosa . We compared the expression of CCR5 , α4β7 and CD103 on CD4+ T cells and CD103 , α4β7 , CD11c , CD141 and CD80 on Lin− HLA-DR+ DCs in the vaginal tissue and blood of the HSV-2+ RMs with that of the HSV-2− RMs 5 weeks prior to the SHIV challenge . We found that the HSV-2+ RMs had a trend toward higher frequency of α4β7high memory CD4+ T cells ( within the CD95+; gating strategy depicted in S1 Fig . ; p = 0 . 05 ) in the vaginal tissue than the HSV-2− , while there was a trend toward a lower frequency in blood ( Fig . 2 ) . Notably , the two RMs in the HSV-2+ group that had the highest frequency of α4β7high CD4+ T cells in the vaginal tissue at BL had also the highest peak VL ( respectively 46×106 at week 2 and 16×106 at week 3 ) . In contrast , there was no difference in the frequency of blood and vaginal memory CCR5+ CD4+ T cells and CD103+ CD4+ T cells ( Fig . 2 ) . We also compared the frequencies of total ( not within CD95+ ) α4β7high , CCR5+ and CD103+ CD4+ T cells and again we found a higher frequency of vaginal α4β7high CD4+ T cells in the HSV-2+ RMs ( S2 Fig . ) . No differences were noted for other α4β7+ subsets and in the expression of CD95 . Moreover , there was no difference between HSV-2+ and HSV-2− RMs in the total frequency of CD3+ and CD3+ CD4+ T cells in the vaginal tissue ( p∼1 ) . Similarly to CD4+ T cells , the expression of α4β7 on vaginal Lin− HLA-DR+ DCs was significantly higher in the HSV-2+ RMs compared with the HSV-2− , while in blood there was a tendency toward lower expression ( Fig . 3 ) . HSV-2+ vaginal DCs had a trend toward higher expression of CD103 ( integrin αE; p = 0 . 055 ) and significantly higher expression of CD11c ( integrin αX ) both in blood and in the vaginal tissues ( Fig . 3 ) . Moreover , we found that the HSV-2+ RMs had a trend toward higher frequency of CD141+ DCs both in blood ( p = 0 . 055 ) and in the vaginal tissue ( p = 0 . 093 ) . There was no difference in the expression of HLA-DR and CD80 . Finally , we examined blood and vaginal pDCs , but there was no difference in the expression of α4β7 , CD103 , CD141 and CD80 . Previously , we described a strong positive correlation between the frequencies of α4β7high CD4+ T cells in blood and rectal mucosa [22] . However , the finding of this study suggested no correlation between blood and vaginal tissue . To verify this , we analyzed data from 25 SIV uninfected animals when blood and vaginal biopsies were collected on the same day . Indeed , we found a trend toward a negative correlation in the frequency of α4β7high CD4+ T cells between blood and vaginal mucosa and this was independent of the HSV-2 status of the RMs ( S3 Fig . ) . To further explore factors potentially associated with the increased susceptibility of the HSV-2 latently infected animals , we examined the levels of 29 different soluble proteins in the vaginal fluids of the HSV-2+ and HSV-2− RMs 2 weeks before SHIV challenge . We found that the HSV-2+ animals had significantly higher levels of IL-6 , TNF-α , GM-CSF and IFNγ compared to the HSV-2− animals ( Fig . 4 ) . There was also a trend for a higher concentration of CXCL8 and the macrophage-derived chemokine ( MDC ) ( Fig . 4 ) and for IL-1β , CCL3 and IL-12p70 ( S4 Fig . ) . To further evaluate the impact of HSV-2 infection on the vaginal mucosa and how this modulates the susceptibility to SHIVSF162P3 , we developed a novel ex vivo model of HSV-2 and SHIV infection of RM vaginal tissue polarized cultures . This model , a modified version of the model described in Cummins et al . [24] , [25] , allowed us to investigate the early events after HSV-2 infection and HSV-2/SHIV co-infection and to distinguish the effect of HSV-2 on cells migrating outside the mucosa from cells retained in the mucosa . In this system , RM vaginal biopsies , equalized in size with 3 mm skin biopsies punches , were placed on a transwell insert inside a hole with the mucosa facing up and matrigel was applied to seal the hole [25] . This resulted in a neat separation of the mucosal tissue from the cells migrating out of the tissue . The vaginal tissue was infected with HSV-2 in presence ( uninfected control ) or absence of acyclovir ( ACV ) for 3 hours . Then the tissues were washed and infected with SHIVSF162P3 for 18 hours . The infected tissues were washed and cultured for additional 3 days . HSV-2 infected and uninfected tissues were cultured in parallel with tissues infected with SHIV alone or infected with the two viruses . In agreement with our in vivo results , the HSV-2 infected vaginal explants were significantly more susceptible to SHIV infection than the HSV-2 uninfected ( Fig . 5 ) . Consistent with the in vivo data , the HSV-2 infected vaginal tissues had higher frequency of α4β7high memory CD4+ T cells than the control tissues 3 days after infection . A tendency toward a higher frequency of α4β7high CD4+ T cells was found also after 18 hours ( p = 0 . 181; Fig . 6A ) . Notably , the frequency of α4β7high CD4+ T cells in the vaginal mucosa 3 days after HSV-2 infection correlated with HSV-2 replication ( Fig . 6B ) . Moreover , we found that CD4+ T cells in the mucosa of HSV-2 infected explants had a significantly higher expression of CD103 3 days after HSV-2 infection than the uninfected tissues ( Fig . 6C ) and a non-significantly higher expression after 18 hours ( p = 0 . 185; Fig . 6C ) . The frequency of CD103+ CD4+ T cells was also increased after 18 hours ( p = 0 . 112 ) and 3 days ( p = 0 . 055 ) . However , the expression of CD103 inversely correlated with HSV-2 replication . This may be explained by a decrease in the expression of CD103 with an increase in cell death due to HSV-2 replication ( S5 Fig . ) . We found no difference in the frequencies of CCR5+ , CCR6+ , CD62L+ and CCR7+ CD4+ T cells or in the expression of these markers between HSV-2 infected and control tissues . We then investigated if ex vivo HSV-2 infection would also modulate the expression of integrins on CD3− HLA-DR+ antigen presenting cells ( APCs ) in the vaginal mucosa . Although , there was no HSV-2-driven increase in the expression of α4β7 on all the APCs in the tissue , we found that the frequency of α4β7high CD80+ APCs 3 days after infection was higher in the HSV-2 infected tissues than in the controls ( Fig . 7A ) . We examined this population because in an earlier study we found that a small population of α4β7high CD80+ DCs correlated with SIV rectal infection [22] . The frequency of CD80+ APCs was also significantly higher and the frequencies of both subsets correlated with HSV-2 replication ( Fig . 7B ) . A non-significant increase in the α4β7high cells within the CD80+ APCs ( p = 0 . 125 ) suggests that the changes in α4β7 are independent of the increase in CD80 . Interestingly , although there was no difference in the frequencies of CD103+ , CD62L+ , CCR7+ APCs in the HSV-2 infected vaginal mucosa ( Fig . 7A ) , the frequencies of these subsets directly correlated with HSV-2 replication ( Fig . 7B and 7C ) . Since the rate of HIV systemic dissemination could be greatly influenced by the ability of infected cells to leave the tissue and traffic to the GALT , we explored the effect of HSV-2 on the first cells that migrated out of the vaginal tissue ex vivo . We found that the migratory CD4+ T cells in the HSV-2 infected explants had higher α4β7 expression in the α4β7high subset 18 hours after HSV-2 infection compared to the control tissues ( Fig . 8A ) . The frequency of migratory CD62L+ CD4+ T cells was also significantly higher in the HSV-2 infected conditions than in the uninfected . In contrast , there was no difference in the frequency of migratory CCR6+ CD4+ T cells ( Fig . 8A; p = 0 . 232 ) or in the migratory CCR5+ , CD62L+ , CCR7+ CD4+ T cells ( p∼1 ) . Finally , there was a significant increase in the migratory α4β7high CD80+ APCs in the HSV-2 infected tissues compared to the uninfected controls and a non-significant decrease in migratory CCR6+ APCs ( Fig . 8B ) 3 days after HSV-2 infection . No significant change in migratory CD103+ or CCR7+ APCs was noted ( Fig . 8B ) . Exploring the effect of HSV-2 infection on the release of soluble factors , we found that , after 18 hours , HSV-2 infection of vaginal tissue induced the release of the inflammatory macrophage migration inhibitory factor ( MIF ) , CXCL10 ( IP-10 ) and a trend toward an increased release of IFNγ ( Fig . 9A ) and of the epidermal grow factor ( EGF; p = 0 . 125 ) . After 3 days the concentration of MIF was still higher in the HSV-2 infected tissues compared to the controls , while there was also a significant increase in IL-6 and a almost significant increase in the granulocyte-colony stimulating factor ( G-CSF ) ( Fig . 9B ) . In a set of experiments performed using a simple immersion model of SHIVSF162P3 infection of vaginal explants , we found that , in the absence of HSV-2 , the expression of α4β7 and CCR5 on CD4+ T cells and the frequency of α4β7+ HLA-DR+ Lin− DCs at base line ( prior to infection ) correlated with SHIVSF162P3 replication 3 days post-infection ( Fig . 10A ) . Thus , we investigated in our new model of vaginal explants , whether the HSV-2-driven increase in the frequency of α4β7high CD4+ T cells could be correlated with the HSV-2-driven increase in SHIV infection . Confirming our hypothesis , we found that the higher was the HSV-2-driven increase in the frequency of α4β7high CD4+ T cells ( in the HSV-2 infected tissues compared with the control HSV-2− tissues ) the higher was the SHIV replication in the HSV-2/SHIV co-infected tissues . Specifically , the fold increase in the frequency of α4β7high CD4+ T cells in the HSV-2+ SHIV+ tissues compared with the HSV-2− controls directly correlated with the SHIV replication in the HSV-2+SHIV+ tissues , evaluating matched cultures from the same animal ( Fig . 10B ) . A similar trend , although non-significant , was found for the increase in the CD62L+ CD4+ T cells ( Fig . 10B ) . Moreover , we found that the HSV-2-driven increase in the frequency of α4β7+ APCs ( HSV-2 only vs ACV control ) trended toward a direct correlation with the HSV-2-driven increase in SHIV infection ( HSV-2/SHIV co-infection compared to the SHIV only condition in matched cultures from the same animal; Fig . 10C ) . No other HSV-2-induced change or frequency of CD4+ T cells subsets ( CCR5+ , CCR6+ , CD103+ and CCR7+ ) and APCs subsets ( CD103+ , CD62L+ , CCR7+ and CD141+ ) examined could be correlated with SHIV replication or with an increase in SHIV replication ( in HSV-2/SHIV co-infected vs SHIV alone; p values >0 . 2 ) . Interestingly , within the same SHIV infected tissue , independent of the presence of HSV-2 , the frequencies of migratory α4β7high CD4+ T cells ( Fig . 10D ) and α4β7+ HLA-DR+ cells within the mucosa ( Fig . 10E , left ) strongly correlated with SHIV replication 3 days after infection . A similar tendency was found for mucosal CD62L+ APCs ( Fig . 10E , right ) . As expected , the vaginal tissues infected with SHIV alone released significantly more pro-inflammatory factors compared to the uninfected ( ACV control; S1 Table ) . However , interestingly , when HSV-2 was present ( SHIV+HSV-2 condition ) some of the cytokines that were released in a significantly higher amount by the explants infected with SHIV alone , were no longer significant ( S1 Table ) . This suggests that HSV-2 may dampen the inflammatory response to SHIV . In fact , comparing the cytokine profile of SHIV with that of HSV-2 , there were numerous significant differences ( Table S2 ) .
Several groups have shown that HSV-2 increases the frequency of HIV target cells at the site of HSV-2 infection and that these cells persist in the mucosa in absence of HSV-2 shedding [10] , [17] , [26] . However , a direct correlation between the HSV-2-driven increase in the frequency of these cell subsets and the increased risk of HIV and SIV acquisition has never been shown . We used RMs that were infected vaginally with HSV-2 1 year before the start of this study to explore the differences in the microenvironment of the vaginal mucosa and the vaginal fluids between HSV-2 infected , asymptomatic animals and uninfected . We found that , as in humans , even in absence of visible lesions or demonstrable active HSV-2 replication , the HSV-2+ animals appeared to be more susceptible than the HSV-2− to SHIV vaginal infection ( although the study is underpowered to detect a significant difference ) . The presence of very low levels of HSV-2 replication in the tissues , below the sensitivity of our assay , cannot be ruled out . Moreover , since HSV-2 shedding is sporadic , it is possible that we missed it because of the infrequency of sampling . The same uncertainty is reported for human subjects [27] . Susceptibility to HIV and SIV infection may vary with the stage of the menstrual cycle and we have shown that the levels of progesterone and estrogens may impact the frequency of α4β7+ cells in the endocervix [28] , [29] . The RMs in the present study were not treated with depot medroxyprogesterone acetate ( DMPA ) and it is possible that sex hormones played a role in the differential susceptibility to SHIV infection of the HSV-2+ and HSV-2− RMs . However , it is unlikely that the HSV-2+ animals had a synchronized menstrual cycle and that at the time of challenge they were all in the follicular phase , which has been suggested to constitute a “window of vulnerability” to HIV infection [29] . In addition to our in vivo experiments , to thoroughly dissect the effect of HSV-2 on the vaginal mucosa , we used a novel ex vivo model of HSV-2 infection of macaque vaginal tissue . Importantly , this model allowed us to determine which changes in the mucosal microenvironment directly correlated with higher susceptibility to SHIV . As with our in vivo studies , we found that the HSV-2 infected vaginal explants were more susceptible to SHIV infection . However , the ex vivo system modeled the acute phase of HSV-2 infection , and , as such , differed from the in vivo study which modeled the asymptomatic stage . Although , the in vivo study was underpowered and we could not have drawn any conclusion from the in vivo data alone , the comparison of the results obtained in vivo and ex vivo suggests that at least some of the HSV-2-driven changes that we have determined ex vivo persist in vivo after the HSV-2 replication abates . Notably , both in vivo and ex vivo , we found a higher frequency of α4β7high CD4+ T cells in HSV-2 infected vaginal tissue after HSV-2 infection . Although in vivo this did not quite reach significance likely because of the very small number of animals per group , together with the ex vivo findings , our results suggest HSV-2 infection directly increases the availability of α4β7high CD4+ T cells in the vaginal mucosa and this effect appears to persist long after the resolution of the acute phase of HSV-2 infection . This finding is particularly relevant in HIV infection because after exposure to SIV/HIV the α4β7high CD4+ T cells migrate more rapidly to the GALT than α4β7low or α4β7− . A higher rate of infection of the gut is likely associated with a more rapid viral dissemination and a more profound damage to the intestinal mucosa . While we focused this work on the impact of HSV-2 on SHIV acquisition and the factors associated with it , further analysis of the tissues of the HSV-2/SHIV co-infected animals will help to determine if HSV-2 truly influences the distribution of SHIV in different anatomical sites . This is a planned follow up to the present study . In contrast to a previous report [10] , we found no difference in the vaginal mucosa in the frequency of CCR5+ CD4+ T cells . The apparent contradiction can be explained by the fact that the frequency of CCR5+ CD4+ T cells in that report was compared between areas of the genital mucosa where lesions were present and unaffected areas within the same subject . In our macaque model , since we did not detect lesions , the vaginal mucosa was sampled randomly . Thus , we report that the overall frequency of CCR5+ CD4+ T cells within the entire vaginal tissue of HSV-2 infected macaques is not different than in HSV-2 uninfected animals . CD103 ( integrin αE ) is expressed on lymphocytes that traffic to the peripheral mucosa and its receptor , E-Cadherin , is expressed mainly on epithelial cells [14] , [30] . CD103 pairs with β7 to form αEβ7 , which mediates T cells adhesion to epithelial cells and can influence the epithelial cells function [31] . While we found no difference in vivo , ex vivo HSV-2 infection of the vaginal mucosa increased the frequency of CD103+ CD4+ T cells . This may be explained by the active HSV-2 replication ex vivo compared to the latent infection in vivo . However , the increase in the CD103+ T cells during HSV-2 acute infection may not persist once the infection abates . In contrast to the findings in mucosa , there was no significant difference in the frequency of blood α4β7high CD4+ T cells . In blood , the frequency of this population tended to be lower in the HSV-2+ animals than in the negative . This agrees with a trend towards an inverse correlation in the frequency of α4β7high memory CD4+ T cells between blood and vaginal tissue . It is important to note that these data suggest that the correlation of α4β7high memory CD4+ T cells between blood and rectal tissue that we describe previously [22] does not apply to the vaginal tissue . Therefore the association between susceptibility to rectal infection and the frequency of α4β7high memory CD4+ T cells in blood does not occur in the context of vaginal SIV infection . Our results also differ from those described by another report [12] . Shannon et al . found that women with asymptomatic HSV-2 infection had a higher frequency of blood α4β7+ CD4+ T cells . We found no difference in the frequency of the entire population of α4β7+ CD4+ T cells in blood . The discrepancy may be due to a difference in the method used to detect the α4β7 population . While in our work we used a clone against the dimeric form of the integrin , in Shannon et al . the population was identified by the double staining of α4 and β7 . Both molecules can form dimers with other α and β chains and be present on the same cells as α4β1 and αEβ7 . Thus , the frequency of cells that co-express α4 and β7 may differ from that of cells that express the α4β7 dimer ( our population of interest ) . DCs that reside in submucosal tissues have been proposed as among the first cells that encounter HIV following sexual transmission [32] , [33] . Herein we report that vaginal DCs in HSV-2+ animals express higher level of α4β7 than in HSV-2− RMs . Thus , after mucosal HIV exposure , the HIV loaded DCs in the vaginal tissue of HSV-2+ individuals may travel more efficiently to the GALT than vaginal DCs in HSV-2− , causing a more rapid viral spread . On the other hand , ex vivo acute HSV-2 replication significantly increased the frequency of α4β7high CD80+ APCs , while no increase in α4β7 expression was noted . Virtually all tissue APCs have the potential to transfer the virus to the T cells [34] . However , HSV-2 may affect the expression of α4β7 only on specific DCs subset ( e . g . migratory DCs ) . Vaginal DCs of HSV-2+ RMs expressed also higher levels of CD103 and CD11c . Mucosal CD103+ DCs are migratory DCs that populate the lamina propria ( LP ) especially of the intestinal tract . An increase in CD103 expression by DCs may contribute to HIV transport to the gut LP . CD11c is also an important adhesion molecule . In its dimeric form with CD18 ( β2 chain ) , CD11c can bind ICAM-1 [35] and this interaction may increase the formation of cell-cell synapses enhancing HIV replication . Mucosal chemokines and cytokines released in the vagina influence the local immune environment and modulate cell migration . We found that the vaginal fluids of HSV-2+ RMs contained more pro-inflammatory molecules and Th1 skewing cytokines than fluids from HSV-2− animals . Since the soluble factors were measured at a time when no HSV-2 and no lesions could be detected , we can assume that a pro-inflammatory vaginal microenvironment persisted in HSV-2+ RMs in absence of HSV-2 symptomatic infection . This is particularly relevant since a pro-inflammatory environment could increase the activation status of HIV target cells rendering them more permissible to HIV replication . Interestingly , HSV-2 and SHIV have very different cytokine profiles ex vivo and the presence of HSV-2 seems to decrease the inflammatory response to SHIV . We also found that HSV-2 infection ex vivo modulates the phenotype of the cells migrating out of the tissue early during the culture . Although after few days the fact that cells leave the tissue ex vivo may be the result of its disintegration , at earlier time points changes in the expression of adhesion molecules in specific subsets of cells may influence their ability to adhere to the extracellular matrix , to epithelial cells and fibroblasts . HSV-2 may influence these cell-cell contacts . We found that , while the increase in the expression of α4β7 was relatively small , there were twice as many CD62L+ CD4+ T cells in the migratory population of the HSV-2 infected tissues than in the uninfected . Considering the importance of CD62L in tethering and migration of lymphocytes to peripheral LNs [36] , this finding is particularly relevant to HIV dissemination after exposure . It is possible that HSV-2 , especially during the acute phase , facilitates the migration of HIV infected cells to the peripheral LNs , where it can expand . The correlation of CCR7+ and CD62L+ DCs with HSV-2 replication can be similarly interpreted . Another interesting finding of our ex vivo studies was the correlation of the base-line expression of α4β7 and CCR5 with SHIV replication 3 days post-infection . In line with this finding , blockage of the α4 , β7 and β1 integrins was reported to significantly inhibit HIV-1 infection of both DCs and CD4+ T cells in human cervical explant [37] . Notably , supporting our initial hypothesis , we found that the increase in the frequency of α4β7high memory CD4+ T cells in the HSV-2 infected tissues correlated with SHIV replication in the HSV-2/SHIV co-infected tissue . This , for the first time , directly associates a specific impact of HSV-2 on the mucosa microenvironment with SHIV infection . Moreover , the HSV-2-driven increase in the frequency of α4β7+ DCs trended toward a direct correlation with the HSV-2-driven increase in SHIV replication . Taken together , our data suggest that an increased availability of α4β7+ CD4+ T and DCs may facilitate HIV and SIV infection locally , because of an increased availability of susceptible targets and systemically , because of a more rapid viral spread . In order to develop an effective HIV vaccine or microbicide , it is critical to understand the mucosal microenvironment where they must act and identify the most relevant factors that can influence susceptibility to infection . A way of doing so is to use known epidemiological correlates of increased risk , explore their impact on the mucosa microenvironment and identify the changes that can be associated with enhanced HIV infection . Herein we describe several changes in the vaginal tissue due to HSV-2 infection , we identify which changes likely persist in absence of HSV-2 replication and how they relate to susceptibility to SHIV . We found that the differential expression of specific adhesion molecules , primarily integrin α4β7 , but also CD103 , CD11c and CD62L , is associated with HIV susceptibility in the context of HSV-2 infection and probably in similar settings , in presence of inflammation . However , they may constitute determinants of susceptibilities in also absence of those external factors .
A total of 16 female Indian rhesus macaques ( Macaca mulatta , RM; mean age: 11 years range: 7–14 years; mean weight: 8 . 24 kg range: 5 . 0–12 . 1 kg ) were housed in compliance with the regulations under the Animal Welfare Act , the Guide for the Care and Use of Laboratory Animals , at Tulane National Primate Research Center ( TNPRC; Covington , LA ) . Animals were socially housed , indoors in climate controlled conditions with a 12/12-light/dark cycle . The RMs were monitored continuously by veterinarians to ensure their welfare and were fed commercially prepared monkey chow twice daily . Supplemental foods were provided in the form of fruit , vegetables , and foraging treats as part of the TNPRC environmental enrichment program . Water was available at all times through an automatic watering system . The TNPRC environmental enrichment program is reviewed and approved by the IACUC semiannually . Veterinarians at the TNPRC Division of Veterinary Medicine have established procedures to minimize pain and distress through several means . Monkeys were anesthetized with ketamine-HCl ( 10 mg/kg ) or tiletamine/zolazepam ( 6 mg/kg ) prior to all procedures . Preemptive and post procedural analgesia ( buprenorphine 0 . 01 mg/kg ) was required for procedures that would likely cause more than momentary pain or distress in humans undergoing the same procedures . The above listed anesthetics and analgesics were used to minimize pain or distress associated with this study in accordance with the recommendations of the Weatherall Report . The animals were euthanized at the end of the study using methods consistent with recommendations of the American Veterinary Medical Association ( AVMA ) Panel on Euthanasia and per the recommendations of the IACUC . Specifically , the animals were anesthetized with tiletamine/zolazepam ( 8 mg/kg IM ) and given buprenorphine ( . 01 mg/kg IM ) followed by an overdose of pentobarbital sodium . Death was confirmed by auscultation of the heart and pupillary dilation . All studies were approved by the Animal Care and Use Committee of the TNPRC ( OLAW assurance #A4499-01 ) and in compliance with animal care procedures . TNPRC is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC#000594 ) . 6 RMs were used to collect vaginal tissue biopsies for the ex vivo studies . 5 HSV-2+ and 5 HSV-2− RMs were used for the in vivo study . The 5 HSV-2+ RMs were infected with HSV-2 ∼12 months before the base line vaginal biopsies for this study were collected . They all resulted positive at one or more time points out of 11 time points tested after HSV-2 infection ( tested every week for the first 3 month post- HSV-2 infection ) . HSV-2 shedding was detected by nested-PCR on DNA extracted from vaginal swabs as previously described [38] . A swab sample was considered positive if at least 1 out of 6 PCR replicates was positive . The veterinarians monitored the animals for signs of vaginal lesions at each time point in which swabs were collected . Baseline blood , vaginal biopsies , and vaginal swabs were collected 5 weeks prior to SHIV treatment . HSV-2+ and HSV-2− RMs were challenged vaginally with 250 TCID50 of HSV-2 SHIVSF162P3 in 1 mL of PBS . They were not treated with depot medroxyprogesterone acetate ( DMPA ) prior to infection . Virus was propagated and titrated in RMs PBMCs . Three weeks after vaginal SHIVSF162P3 challenge , the animals were euthanized and blood and vaginal tissues were collected . Plasma VLs were measured by quantitative reverse transcriptase-polymerase chain reaction ( PCR ) as described [39] . Infection was confirmed by nested SIV-PCR on MLNs on day 21 as previously described [40] . HSV-2 stocks were propagated in Vero cells ( American Type Culture Collection [ATCC] Manassas , VA ) , titered by plaque formation on Vero cells , and aliquots stored at −80°C [41] . RMs vaginal biopsies were equalized in size using 3 mm skin biopsy punches ( Fisher Scientific , Waltham , MA ) and infected in duplicates or triplicates with 2×107 pfu/mL of HSV-2 in absence or presence ( uninfected control ) of 125 µg/mL acyclovir ( Calbiochem , Billerica , MA ) for 3 hours in a 96 well plate . Tissues were extensively washed with PBS and placed into a hole in a 24-well 3 µm pore size and 6 . 5 mm diameter trans-well insert ( Corning , NY ) using 2 mm skin biopsy punches ( Fisher Scientific ) , with the mucosa facing the upper chamber . After either 18 hours or 3 days the tissue was digested and the cellular phenotype was determined by multi-color flow cytometry ( LSRII ) . After 18 hours ( T cells ) and 3 days ( DCs ) , the cells that migrated to the bottom chamber were collected and the cellular phenotype was determined by multi-color flow cytometry . In the experiments with SHIV , each vaginal punch biopsy was infected with 3000 TCID50 of SHIV162p3 in the presence of 1 µg/mL PHA ( Sigma-Aldrich , St . Louis , MO ) , and 50 U/mL recombinant IL-2 ( Roche , South San Francisco , CA ) after the 3 hour HSV-2 infection . After 18 hours , the tissues were extensively washed with PBS and cultured for an additional 48 hours . PBMCs were isolated using Ficoll-Hypaque density gradient centrifugation . Vaginal biopsies were cut into small pieces and incubated for 45 mins in HBSS in 1 mg/mL hyaluronidase and 0 . 5 mg/mL collagenase II ( Sigma-Aldrich , St . Louis , MO ) . The cell suspension was passed through a 40-µm nylon cell strainer . For the in vivo study , cells were stained with the LIVE/DEAD Aqua dye ( Invitrogen ) and , for the T panel , anti-: CD4-QDot605 and α4β7-PE ( non-human primate repository; Beth Israel Medical Center , Boston , MA ) , CD3-AF700 , CD103-APC , CCR5-PeCy7 , CD95-V450 ( all BD Bioscience ) and for the DCs panel , anti-: CD3-CD14-CD20-V450 ( Lin ) , HLA-DR-QDot605 ( Invitrogen , Grand Island , NY ) , α4β7-PE , CD11c-AF700 , CD103-APC , CD80-APC-H7 , CD123-PCP-Cy5 . 5 , CD141-AF488 ( BD-Bioscience ) . CCR5 and CD141 were directly conjugated using Innova Bioscience Lightning-Link kits . For the ex-vivo experiments 1 combined panel was used alternating some of the mAbs . In addition to the mAbs mentioned , the panel included anti-: CCR7-PCP-Cy5 . 5 ( BioLegend ) , CD62L-APC-H7 ( AbD Serotec ) and CCR6-PCP-Cy5 . 5 ( Biolegend ) , all conjugated with the Innova Bioscience kits . For the ICP-8 intracellular detection , cells were fixed and permeabilized with the Fix/Perm kit ( BDBioscience ) and incubated with the anti-HSV-2 ICP-8 mAb ( IgG2a isotype; Virusys , North Berwick , ME ) diluted in Perm/Wash buffer 20 mins at room temperature , washed and analyzed within 24 hours with BD LSRII . The ICP-8 mAb was directly conjugated with Alexa647 ( Zenon Antibody labeling kit , Invitrogen , Life Technologies ) . Greater than 200 , 000 events were acquired in the lymphocyte live-cells gate using the BD LSRII Flow Cytometer . Data were analyzed with FlowJo 9 . 6 . 1 . HSV-2 infection in the explants was determined by HSV-2 qPCR directly on the supernatants . Briefly primers that amplify the UL30 region of the polymerase gene were used: FW 5′ GCTCGAGTGCGAAAAAACGTT 3′ and REV 5′ TGCGGTTGATAAACGCGCAGT 3′ . qPCR was performed using the KAPA SYBR FAST qPCR ( Kapa Biosystems , Wilmington , MA ) . 5 µl of supernatant was used in each reaction . The ViiA™ 7 Real-Time PCR System ( Applied Biosystems , Carlsbad , CA ) was used for carrying out the reaction . Cycling conditions: 95°C 3 mins , 40× ( 95°C 3″ , 60°C 20″ ) . Dissociation curves were generated to verify absence of unspecific amplification . Results were analyzed using the ViiA™ 7 Software ( Applied Biosytems ) . The standard curve was generated using 10 fold dilutions of plasmid TOPO UL30 . ( Calenda et al . , manuscript in preparation ) SHIV infection was measured directly in 5 µl of tissue culture supernatants by a one-step SIV gag RT-qPCR using a One-step RT-qPCR Kit ( KAPA Biosystems , Wilmington , MA ) , using the ViiA™ 7 Real-Time PCR System ( Applied Biosystems , Carlsbad , CA ) ( Calenda et al , manuscript in preparation ) . Primers ( Integrated DNA Technologies , Coralville , IA ) were SIV667gag ( 5′GGTTGCACCCCCTATGACAT3′ ) and SIV731 gag ( 5′TGCATAGCCGCTTGATGGT 3′ ) . Results were analyzed using the standard curve method , using SIVmac1A11 DNA obtained from Dr . Paul Luciw through the NIH AIDS Reagent Program , Division of AIDS , NIAID , NIH . Cycling conditions: Step 1: 1× 42°C 5 mins , Step 2: 1× 95°C 5 min , Step 3:40× ( 95°C 3 sec , 60°C 20 sec ) . Dissociation curves were generated to verify absence of unspecific amplification . Data were analyzed using the ViiA™ 7 software ( Applied Biosystems ) ) . Soluble factors in clarified vaginal swabs two weeks prior to SHIV challenge and in cultures supernatants were measured using the monkey Novex multiplex Luminex assay ( Cytokine Monkey Magnetic 29-Plex Panel; Invitrogen ) on a Luminex 200 instrument ( Luminex Corporation , Austin , TX ) . Complete list of factors measured: IL1RA , I-TAC , MIF , FGF-Basic , MCP-1 , G-CSF , IFNγ , MDC , IL15 , CXCL8 , EGF , HGF , VEGF , CXCL9 , CCL5 , Eotaxin , CCL4 , CXCL10 , GM-CSF , TNFα , IL1β , IL2 , IL4 , IL5 , IL6 , IL10 , IL12 , CCL3 , IL17 . Unpaired Mann-Whitney test was used to compare variables between groups of animals ( HSV-2-POS vs HSV-2-NEG ) . Linear regression analysis and Spearman rank correlation test were performed to determine the correlation between cell subsets in blood and vaginal tissue and in the ex vivo experiments . Ex vivo , each condition ( ACV , HSV-2 , SHIV and SHIV/HSV-2 ) was performed in duplicates or triplicates per animal ( with 2 biopsies per well ) . In order to take into account possible small variations in the amount of mucosa in each tissue , for each experiment/animal , the value of a parameter ( MFI , % and CC/CK ) measured in each replicate of the 4 conditions was normalized on the value of the lowest replicate in the ACV control before: ( i ) performing the Wilcoxon signed-rank test to determine significant differences between conditions , ( ii ) correlation analysis between parameters and ( iii ) before averaging the values for the fold increases shown in the graphs and used for the correlations in Fig . 10B–C . For the correlations of ICP-8 and HSV-2 and SHIV copies ( Fig . 6B–D , 7B–C and 10C ) a variable was plotted against another variable from the same replicate ( same well ) . A two-tailed p = α<0 . 05 was considered significant . The analysis was performed using Prism5a ( GraphPad Software , Inc ) . | Understanding the factors that correlate with an increased risk of acquiring HIV infection is key to identify new means of preventing HIV transmission . HSV-2 infection increases the risk of HIV transmission even in absence of visible lesions and inflammation . In order to explore HSV-2− associated factors that could explain this phenomenon , we used a model of asymptomatic HSV-2 infection in macaques and ex vivo cultures of biopsied vaginal tissue . We determined that HSV-2 infection is associated with an increase in subsets of immune cells that express high levels of α4β7 , a molecule needed by the cells to reach the gut and the gut lymphoid tissues . The gut is an important site for HIV infection and pathogenesis and CD4+ T cells expressing high levels of α4β7 ( α4β7high ) are highly susceptible to the virus . We determined that the HSV-2-driven increase in these cells correlates with an increased susceptibility of the vaginal mucosa to SIV infection . Thus , our results suggest that an increased availability of α4β7high cells at the mucosal site of HIV exposure may constitute a risk factor for HIV acquisition in HSV-2 positive and , possibly , negative individuals . | [
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] | 2014 | HSV-2-Driven Increase in the Expression of α4β7 Correlates with Increased Susceptibility to Vaginal SHIVSF162P3 Infection |
The biological actions of steroid hormones are mediated primarily by their cognate nuclear receptors , which serve as steroid-dependent transcription factors . However , steroids can also execute their functions by modulating intracellular signaling cascades rapidly and independently of transcriptional regulation . Despite the potential significance of such “non-genomic” steroid actions , their biological roles and the underlying molecular mechanisms are not well understood , particularly with regard to their effects on behavioral regulation . The major steroid hormone in the fruit fly Drosophila is 20-hydroxy-ecdysone ( 20E ) , which plays a variety of pivotal roles during development via the nuclear ecdysone receptors . Here we report that DopEcR , a G-protein coupled receptor for ecdysteroids , is involved in activity- and experience-dependent plasticity of the adult central nervous system . Remarkably , a courtship memory defect in rutabaga ( Ca2+/calmodulin-responsive adenylate cyclase ) mutants was rescued by DopEcR overexpression or acute 20E feeding , whereas a memory defect in dunce ( cAMP-specific phosphodiestrase ) mutants was counteracted when a loss-of-function DopEcR mutation was introduced . A memory defect caused by suppressing dopamine synthesis was also restored through enhanced DopEcR-mediated ecdysone signaling , and rescue and phenocopy experiments revealed that the mushroom body ( MB ) —a brain region central to learning and memory in Drosophila—is critical for the DopEcR-dependent processing of courtship memory . Consistent with this finding , acute 20E feeding induced a rapid , DopEcR-dependent increase in cAMP levels in the MB . Our multidisciplinary approach demonstrates that DopEcR mediates the non-canonical actions of 20E and rapidly modulates adult conditioned behavior through cAMP signaling , which is universally important for neural plasticity . This study provides novel insights into non-genomic actions of steroids , and opens a new avenue for genetic investigation into an underappreciated mechanism critical to behavioral control by steroids .
Steroid hormones are essential modulators of a broad range of biological processes in a diversity of organisms across phyla . In the adult nervous system , the functions of steroids such as estrogens and glucocorticoids are of particular interest because they have significant effects on the resilience and adaptability of the brain , playing essential roles in endocrine regulation of behavior . Reflecting their importance in neural functions , steroid hormones are implicated in the etiology and pathophysiology of various neurological and psychiatric disorders , and are thus often targeted in therapies [1]–[7] . The biological actions of steroids are mediated mainly by nuclear hormone receptors—a unique class of transcription factors that activate or repress target genes in a steroid-dependent manner [8] . Substantial evidence suggests , however , that steroid hormones can also exert biological effects quickly and independently of transcriptional regulation , by modulating intracellular signaling pathways [9] . Such “non-genomic” effects might be induced by direct allosteric regulation of ion channels , including receptors for GABA [10] and NMDA [11] . Alternatively , in certain contexts , non-genomic steroid signaling could be mediated by classical nuclear hormone receptors acting as effector molecules in the cytosol [12] , [13] . G-protein coupled receptors ( GPCRs ) that directly interact with steroids have the potential to play an important role in non-genomic steroid signaling . So far , however , only few GPCRs have been identified as bona fide steroid receptors in vertebrates [14] , [15] . The G-protein coupled estrogen receptor 1 ( GPER , formally known as GPR30 ) is the best studied GPCR that is responsive to steroids . Pharmacological and gene knockout approaches suggest that this protein has widespread roles in the reproductive , nervous , endocrine , immune and cardiovascular systems [15] . Although other G-protein coupled receptors were predicted to be responsive to steroids ( e . g . , the Gq-coupled membrane estrogen receptor and estrogen receptor-X ) , their molecular identity is not known [16] , [17] . Overall , the physiological roles of the GPCR-mediated actions of steroids and the underlying molecular mechanisms remain poorly understood , and sometimes controversial , in spite of their importance [18] , [19] . In particular , it is unknown how this non-canonical steroid mechanism influences neural functions and complex behaviors . Drosophila genetics has been extensively used to study the roles and mechanisms of action of steroid hormones in vivo . The major steroid hormone in Drosophila is the molting hormone 20-hydroxy-ecdysone ( 20E ) , which orchestrates a wide array of developmental events , including embryogenesis , larval molting and metamorphosis [20]–[22] . Recent studies revealed that 20E also plays important roles in adult flies , regulating: the innate immune response [23] , stress resistance , longevity [24] , the formation of long-term courtship memory [25] and the active/resting state [26] . In general , the functions of 20E during development and adulthood are thought to be executed by ecdysone receptors ( EcRs ) , members of the evolutionarily conserved nuclear hormone receptor family [21] , [27] , [28] . In addition to canonical ecdysone signaling via EcRs , Srivastava et al . identified a novel GPCR called DopEcR , and showed that it propagates non-genomic ecdysone signaling in vitro [29] . DopEcR shares a high level of amino-acid sequence similarity with vertebrate β-adrenergic receptors . In situ hybridization [29] and microarray data ( FlyAtlas , http://flyatlas . org/ ) revealed that DopEcR transcripts are preferentially expressed in the nervous system . In heterologous cell culture systems , DopEcR is localized to the plasma membrane and responds to dopamine as well as ecdysteroids ( ecdysone and 20E ) , modulating multiple , intracellular signaling cascades [29] . Furthermore , Inagaki et al . recently detected DopEcR expression in the sugar-sensing gustatory neurons of adult flies , and showed that DopEcR-mediated dopaminergic signaling enhances the proboscis extension reflex during starvation [30] . Nonetheless , little is known about whether DopEcR functions as a steroid receptor in vivo , and about how it drives responses in the central nervous system ( CNS ) to modulate complex behaviors . Here , we report for the first time that DopEcR mediates non-genomic ecdysone signaling in the adult brain , and that it is critical for memory processing . We also show that , during memory processing , DopEcR transmits information via novel steroid signals that interact with the cAMP pathway , a signaling cascade that is universally important for neuronal and behavioral plasticity . Our genetic study thus uncovers underappreciated GPCR-mediated functions and mechanisms of action that employ non-canonical steroid signaling to regulate the adult nervous system and , thereby , behavior .
PBac ( PB ) c02142 is a piggyBac transposon insertion in the second intron of the DopEcR gene ( Figure 1A ) . Adult flies homozygous for PBac ( PB ) c02142 displayed a significant reduction in DopEcR transcript levels ( <20% of levels in control ) , in both the head ( Figure 1B ) and the body ( data not shown ) . Df ( 3L ) ED4341 is a chromosomal deficiency that removes multiple genes on 3L , including DopEcR ( Flybase: http://flybase . org/ ) . Flies trans-heterozygous for PBac ( PB ) c02142 and Df ( 3L ) ED4341 showed levels of DopEcR transcript comparable to those in PBac ( PB ) c02142 homozygotes ( Figure 1B ) . PBac ( PB ) c02142 is therefore a hypomorphic allele of DopEcR , and it was mainly used in this study to investigate the functions of DopEcR in behavioral plasticity . PBac ( PB ) c02142 is referred to as DopEcRPB1 hereafter . DopEcRPB1 homozygotes reached adulthood and exhibited no gross morphological defects . General motor activity was not significantly impaired , as judged by analysis of reactive climbing behavior ( Figure S1 ) . In order to obtain some insight into the endogenous expression pattern of DopEcR , we generated DopEcR-Gal4 , a Gal4 driver that contains the putative enhancer/promoter sequence of DopEcR ( a 588-bp DNA fragment upstream of the DopEcR transcription start site ) . DopEcR-Gal4 was found to induce GFP reporter gene expression preferentially in the nervous system . In the adult brain , DopEcR-Gal4-regulated reporter gene expression was particularly prominent in the mushroom body ( MB ) ( Figure 1C and 1D ) . It is not likely that the endogenous DopEcR expression is accurately recapitulated by the 588-bp DNA fragment used for DopEcR-Gal4 . Nevertheless , the reporter gene expression shown in Figure 1C and 1D implies the presence of the endogenous DopEcR in the MBs of the adult brain ( see Discussion ) . Reporter gene expression driven by DopEcR-Gal4 was also observed in neuronal soma and fibers localized in each segment of the thoracicoabdominal ganglion ( Figure 1F and 1G ) . In addition , a number of fibers connecting the ganglion to the brain , abdomen and appendages were found to be GFP-positive ( Figure 1F and 1G ) . To investigate the role of DopEcR in the CNS , we tested DopEcR mutations for effects on the electrophysiological properties of the adult giant-fiber ( GF ) pathway [31] , [32] . Visual or mechanical stimulation activates the descending GF neurons ( Figure 2A ) , triggering the stereotypical jump-and-flight response . This behavioral response is associated with a consistent pattern of spiking in both the dorsal longitudinal flight muscle ( DLM ) and the tergotrochanteral jump muscle ( TTM ) ( Figure 2A ) . Strong electrical stimulation of the brain can bypass sensory receptors and directly trigger the neuronal circuit at the GF neurons ( short-latency response ) [32] , [33] . Alternatively , with stimulation of the brain at lower intensity , the circuit is activated at GF afferents in the brain ( long-latency response ) [34] . As shown in Figure 2B ( left and middle panels ) , both the short- and long-latency thresholds ( SLT and LLT; the lowest intensities required to trigger short- and long-latency responses in the DLM ) were indistinguishable between DopEcR mutants ( DopEcRPB1/DopEcRPB1 , DopEcRPB1/Df ( 3L ) ED4341 and DopEcRPB1/+ ) and wild-type flies . This indicates that reducing DopEcR expression does not significantly affect the overall neuronal sensitivity of the GF pathway . In contrast , the refractory period ( RP; the minimum time required for the GF system to recover from the 1st stimulus and fire a response to the 2nd stimulus ) was significantly reduced in DopEcRPB1/DopEcRPB1 compared to control flies ( Figure 2B , right panel ) . The RP in DopEcRPB1/Df ( 3L ) ED4341 and DopEcRPB1/+ flies also showed a similar tendency , although the differences between these mutants and control flies did not reach statistical significance , possibly due to the weak nature of this DopEcRPB1 phenotype and the small sample numbers . Nonetheless , the shorter RP implies that circuits in DopEcR mutants are less vulnerable or more resistant to activity-dependent modifications than the relevant circuits in controls are . Diminished neuronal plasticity in DopEcR mutants was unequivocally demonstrated when habituation of the GF pathway was analyzed . Habituation is a simple form of non-associative learning , in which the reaction to a particular stimulus becomes diminished when the stimulus is applied repeatedly . Habituation does not lessen behavioral responses due to sensory adaptation or motor fatigue [35] . When electrical stimulation is repeatedly delivered across the brain , the GF pathway undergoes habituation and the probability of a motor output significantly decreases [36] . Previous studies by us and others revealed that the loci responsible for this neuronal plasticity are localized to the brain , namely neuronal circuits afferent to the GF neurons ( aff; Figure 2A ) [36]–[40] . Other elements in the GF pathway—including the GF neuron , the peripherally synapsing interneuron ( PSI ) , and the motor neurons that innervate the flight and jump muscles ( DLMs and TTMs; Figure 2A ) —are robust enough to reliably respond to sustained high-frequency stimuli ( up to ∼100-Hz ) [32] , [33] , [36] . In our experiments , control flies became rapidly habituated to 5-Hz stimulation of the brain , as evidenced by a failure of their DLM to respond ( Figure 2C , control ) . The reduced behavioral response was not a consequence of sensory adaptation or motor fatigue because the response was readily recovered by a novel stimulus , such as an air puff ( dishabituation; Figure 2C , control ) . In contrast to controls , DopEcRPB1 homozygotes and DopEcRPB1/Df ( 3L ) ED4341 trans-heterozygotes consistently showed a delay in habituation ( Figure 2C ) , and thus their cumulative response was greater than that of controls ( Figure 2D ) . DopEcRPB1 heterozygotes ( DopEcRPB1/+ ) showed a similar tendency , although the effect was less extreme ( Figure 2C and 2D ) . When habituation was arbitrarily defined as five or more consecutive failures , DopEcRPB1 mutants needed more repetitive stimulations than control flies to reach habituation status ( Figure 2E ) . The average numbers of 5-Hz stimuli required for habituation were 46±31 and 637±236 in control flies and DopEcRPB1 homozygotes , respectively ( Figure 2E ) . DopEcRPB1 heterozygotes also showed a slow habituation phenotype ( Figure 2C–E ) . These results demonstrated that DopEcR is an essential modulatory component of the GF pathway , and that its endogenous role is to positively regulate activity-dependent modification of the relevant CNS neuronal circuits . In light of the abnormalities in GF habituation , we next tested DopEcR mutants for experience-dependent courtship suppression , an ethologically relevant associative-learning paradigm [41] , [42] . In wild-type control males ( +/+ ) and DopEcRPB1 heterozygous males ( DopEcRPB1/+ ) , 1 hour of conditioning with a mated female induced “courtship memory” , which was readily detectable 30 minutes after conditioning as a statistically significant , experience-dependent reduction in courtship activity ( P = 0 . 0004 for control and 0 . 0046 for DopEcRPB1/+; Figure 3A ) . In contrast , DopEcRPB1 homozygotes and hemizygotes ( DopEcRPB1/Df ( 3L ) ED4341 ) did not display courtship memory ( P>0 . 05; Figure 3A ) . These results strongly suggested that DopEcR is essential to the processing of courtship memory . The performance indices ( PIs; % decrease in courtship index in response to courtship conditioning , see Materials and Methods for details ) of these DopEcRPB1 mutants at 30 minutes post conditioning were significantly lower than that of wild-type flies ( P<0 . 05; Figure 3A ) . Notably , although DopEcRPB1 homozygotes did not display courtship memory at both 15 and 30 minutes after conditioning ( P>0 . 05 ) , they exhibited memory immediately after courtship conditioning ( P = 0 . 00026 ) . The PIs of DopEcRPB1 homozygotes for 0 and 30 minutes after conditioning were significantly different from each other ( Krustal-Wallis One-Way ANOVA; P<0 . 05; Figure 3B ) . These results indicated that DopEcR mutants retain the ability to acquire courtship memory , but that the memory is labile and severely disrupted within 30 minutes . To confirm that the memory phenotype in DopEcR mutants is due to the defect in DopEcR function , we examined the effects of DopEcR RNAi on courtship memory . When the DopEcR RNAi was conditionally and globally expressed in adult flies using the RU486-inducible driver tubulin5-GeneSwitch-Gal4 ( tub5-GS-Gal4; a gift from Dr . Pletcher , University of Michigan ) [43] , the level of DopEcR transcripts was significantly reduced in an RU486-dependent manner ( Figure S2 ) . When DopEcR expression was conditionally knocked down by RNAi in this context , the courtship memory phenotype of the DopEcR mutants was mimicked ( Figure 3C ) . These results support our conclusion that adult male flies require functional DopEcR for normal courtship memory . Next we sought to identify the sites within the nervous system in which DopEcR is required for the processing of courtship memory . We found that DopEcRPB1 males displayed courtship memory ( P = 9 . 6×10−6 ) when the wild-type DopEcR transgene was expressed using DopEcR-Gal4 ( Figure 4A ) . In contrast , control DopEcRPB1 males carrying only DopEcR-Gal4 or UAS-DopEcR were defective for courtship memory ( Figure 4A ) . The PIs of these control males were significantly lower than that of DopEcRPB1 males carrying both the Gal4 and UAS constructs ( P<0 . 001 and P<0 . 05 , respectively; Figure 4A ) . DopEcR-Gal4 directed gene expression in the adult brain , particularly in the neurons of the MB ( Figure 1C ) . These observations , together with the importance of the MBs in processing courtship memory [25] , [44] , [45] , led us to suspect that the rescue of the DopEcR memory phenotype by DopEcR-Gal4 was a consequence of the expression of wild-type DopEcR in the MB . This possibility was tested by performing rescue experiments for DopEcRPB1 mutants in which UAS-DopEcR expression was driven using three MB-positive Gal4 lines: c772 , c739 and 201Y . Courtship memory was restored in DopEcRPB1 males when the wild-type DopEcR cDNA was expressed using either c772 or c739 ( P = 2 . 9×10−5 or 0 . 0063; Figure 4B ) . In contrast , the 201 y driver failed to rescue the memory defect of DopEcRPB1 mutants ( Figure 4B ) . c772 and c739 drive gene expression in all three types of MB neurons ( α/β , α′/β′ and γ ) and primarily in the α/β neurons , respectively , whereas 201 y drives gene expression mainly in the γ neurons [46] . These results suggested that the MBs , in particular the α/β neurons , are the key anatomical site in which DopEcR regulates courtship memory . In support of this idea , expression of the DopEcR RNAi in wild-type MB neurons using c772 or c739 led to a lack of 30-minute courtship memory in males ( Figure 4C ) . The PIs of males carrying both the Gal4 and UAS-RNAi constructs were significantly lower than that of control males ( Figure 4C ) . Dominant temperature-sensitive 3 ( DTS-3 ) is a dominant mutant allele of molting defective ( mld; personal communication , P . Maroy , University of Szeged , Szeged , Hungary ) , a gene that encodes a putative transcription factor required for ecdysone biosynthesis [47] , [48] . We previously reported that , unlike wild-type males , DTS-3/+ males did not exhibit an increase in 20E levels in response to 7-hour courtship conditioning , and that they were defective in long-term courtship memory ( courtship LTM ) [25] . As shown in Figure 5A , DTS-3/+ males did not show courtship suppression 30 minutes after 1-hour conditioning . Intriguingly , when DTS-3/+ males were fed 20E ( 0 . 1 mM ) for 10 minutes immediately before courtship conditioning , the courtship-memory defect was rescued and courtship suppression was observed ( P = 0 . 0043 ) ( Figure 5A ) . disembodied ( dib ) is one of the Halloween-family genes encoding the cytochrome P450 enzymes that are essential for ecdysone biosynthesis [49] . When dib expression was conditionally suppressed by treating mature adult males carrying the UAS-dib RNAi ( gift from Dr . O'Connor , University of Minnesota ) and tub5-GS-Gal4 with RU486 , they exhibited a defect in courtship memory ( Figure 5B ) . As with DTS-3/+ males , when dib-knockdown flies were fed 20E ( 0 . 1 mM ) before courtship conditioning , they displayed experience-dependent courtship suppression ( P = 0 . 006 ) . In the dib-knockdown flies , this rescue effect of 20E was not observed when DopEcR expression was suppressed using the DopEcR RNAi ( Figure 5B ) . Functional DopEcR is thus required for 20E-dependent courtship memory . These findings , together with the phenotypes of the DopEcR-mutant and DTS-3/+ males ( Figure 5A ) , strongly suggest that ecdysone signaling plays a critical role in 30-minute courtship memory , and this signaling is mediated by DopEcR . In addition to ecdysteroids , dopamine has been shown to be a direct ligand for DopEcR [29] . We fed flies 3-Iodotyrosine ( 3-IY ) to block dopamine synthesis and examined the effect on courtship memory . As reported previously , courtship memory was defective in these flies [50] , [51] ( Figure 5C ) . We found that when the flies were additionally fed 20E ( 0 . 1 mM ) 10 minutes before courtship conditioning , courtship memory was restored in spite of the block in dopamine synthesis ( P = 0 . 00017; Figure 5C ) and the PI was significantly increased ( P<0 . 05; Figure 5C ) . The compensatory effect of 20E was also observed in 3-IY-treated flies of a different genetic background ( Figure 5D ) . In contrast , when DopEcR RNAi was conditionally expressed in dopamine-depleted adults , 20E was not able to rescue courtship memory ( Figure 5D ) . These results show that 20E compensates for the adverse effect of dopamine deficiency on courtship memory through the actions of DopEcR . We next examined which intracellular signaling events are involved in the regulation of courtship memory by DopEcR . Here we focused our attention on the cAMP signaling pathway , because it plays a central role in learning and memory processes in diverse animal species [52] . We investigated whether 20E and DopEcR exert their effects on courtship memory via this signaling . The functional significance of cAMP for DopEcR-mediated signaling was indicated by a previous study in heterologous cell-culture systems , showing that DopEcR modulates intracellular cAMP levels in response to ligand binding [29] . One Drosophila gene that is crucial for regulating cAMP signaling is rutabaga ( rut ) , which encodes a type I Ca2+/CaM-dependent adenylyl cyclase ( AC ) [53] , [54] . Loss-of-function rut mutations result in lower cAMP-synthesizing activities and affect various forms of neural plasticity , including habituation of the GF pathway [36] and experience-dependent courtship suppression [55] . Habituation in the GF pathway was suppressed in both DopEcR and rut mutants ( Figure 2C–E ) [36] , implying that the encoded proteins may have related functions in regulating neural plasticity . Consistent with a previous report [42] , males carrying a hypomorphic rut mutant allele ( rut2 or rut1084 ) were defective for courtship memory and showed no experience-dependent courtship suppression 30 minutes after 1-hour courtship conditioning ( P>0 . 05; Figure 6A ) . Remarkably , the memory defect in rut mutants was restored when they were fed 20E ( 0 . 1 mM ) for 10 minutes immediately before courtship conditioning ( P = 0 . 0043 and 5 . 5×10−5 for rut2 and rut1084 , respectively; Figure 6A ) . The PIs for rut2 and rut1084 increased significantly following treatment with 20E ( P<0 . 05 and P<0 . 01 for rut2 and rut1084 , respectively; Figure 6A ) . This pharmacological rescue of the rut memory phenotype was not observed in rut and DopEcRPB1 double mutants ( Figure 6B ) . These results strongly indicated that DopEcR mediates the compensatory effect of 20E on defective memory in rut mutants . Considering the significance of the MB and rut for DopEcR-mediated memory processing , we examined their relationship . Courtship memory was analyzed in adult rut2 mutants overexpressing DopEcR in the MB neurons . Courtship memory was restored by conditional overexpression of DopEcR using RU486-inducible MB-GS-GAL4 [56] ( P = 1 . 4×10−9 ) , and the PI increased significantly ( P<0 . 001; Figure 6C ) . Although the memory defect in rut1084-mutant males was not rescued by solely overexpressing DopEcR in the MB ( Figure 6D ) , feeding them a low concentration of 20E ( 0 . 01 mM ) led to significant courtship suppression ( P = 1 . 2×10−6; Figure 6D ) . Notably , administering 20E at this concentration was not sufficient to rescue the rut1084 memory phenotype in the absence of DopEcR overexpression ( Figure 6D , middle ) . The different requirements for rescuing courtship memory in rut2 and rut1084 may reflect differences in the severity of the mutations . Indeed , an olfactory-associated memory defect in rut1084 mutants is similar to that in mutants of a presumptive rut null allele ( rut1 ) [53] , [57] , whereas the rut2 memory defect is milder [58] , [59] . Overall , these findings demonstrate that the memory defect in rut mutants can be compensated by strengthening DopEcR-mediated ecdysone signaling in MB neurons . Another “memory gene” involved in cAMP signaling is dunce ( dnc ) , which encodes a cAMP-specific phosphodiesterase ( PDE ) that is required for cAMP degradation [60] , [61] . Like rut mutants , dnc loss-of-function mutants are defective for various types of neuronal and behavioral plasticity [36] , [61] , [62] . In contrast to rut mutants , dnc mutants display an increased rate of GF habituation [36] , possibly reflecting the fact that rut and dnc mutations have opposite effects on cAMP levels . As shown previously [42] , hypomorphic dnc mutants ( dnc1 and dnc2 ) did not exhibit experience-dependent courtship suppression 30 minutes after 1-hour courtship conditioning , and were therefore defective for courtship memory ( Figure 6E ) . Notably , double mutants carrying both dnc and DopEcR loss-of-function mutations displayed courtship suppression ( P = 0 . 0016 for dnc1/Y; DopEcRPB1 and 0 . 002 for dnc2/Y; DopEcRPB1; Figure 6E ) . In addition , dnc1 males displayed courtship memory , which manifests as significant experience-dependent courtship suppression ( P = 0 . 0274 ) when DopEcR was conditionally down-regulated using tub5-GS-Gal4 in conjunction with the DopEcR RNAi ( Figure 6F ) . dnc2 males showed a similar tendency when DopEcR was down-regulated , although the difference in CIs between naïve and conditioned flies was not statistically significant . Overall , these experimental results with rut and dnc mutants strongly suggest that DopEcR exerts its critical function in courtship memory by regulating cAMP signaling pathway . The courtship-memory defect in rut mutants can be attributed to their inability to appropriately increase intracellular cAMP levels during courtship conditioning . Because 20E feeding and DopEcR overexpression resulted in restoration of courtship memory in rut mutants , we hypothesized that strengthening DopEcR-mediated ecdysone signaling would increase in cAMP levels in brain regions critical for memory processing , such as the MBs . To investigate this possibility , we examined the effects of 20E feeding on cAMP levels in the MBs of live adult flies using UAS-Epac1-camps , a Förster ( fluorescence ) resonance energy transfer ( FRET ) -based cAMP reporter [63] . The reporter was expressed in MBs using c772 , one of the MB drivers that were effective in the rescue and phenocopy experiments described above ( Figure 4 ) . Immediately after the flies were fed 20E , the cAMP levels in the MBs were assessed by cAMP-induced changes in FRET , as the ratio between YFP and CFP signals . In wild-type males , feeding either 3 mM or 1 mM 20E caused a time- and dose-dependent decrease in the average FRET ( Figure 7A ) . The effects of 20E on FRET are statistically significant ( P<0 . 001; Figure 7B ) . Because a decrease in FRET corresponds to an increase in cAMP levels , our results indicated that 20E increases cAMP levels in the MB . This effect was eliminated by simultaneously expressing the DopEcR RNAi in the MB ( Figure 7C and 7D ) . These data indicate that the activation of ecdysone signaling rapidly increases cAMP levels in the MBs , and that it does so through DopEcR .
Our electrophysiological analyses revealed that the GF pathway of DopEcR mutant flies is more resistant to habituation than that of control flies ( Figure 2 ) . Direct excitation of GF or its downstream elements would lead to a short-latency response of the DLM , which could follow high-frequency stimuli up to several hundred Hz [32] , [33] , [36] . In contrast , the afferent input to the GF leads to a long-latency response that is labile and fails to follow repetitive stimulation well below 100 Hz and displays habituation even at 2–5 Hz [36]–[40] . Although there is the possibility that DopEcR-positive thoracic neurons may modulate thoracic motor outputs and contribute to certain parameters of the habituation process not characterized in this study , the more effective modulation would occur in the more labile element afferent to the GF circuit rather than the robust GF-PSI-DLMn downstream pathway , which is responsible for the reliability of the escape reflex . Thus , the mutant phenotype in habituation indicates that DopEcR positively controls activity-dependent suppression of neuronal circuits afferent to the GF neurons in the brain . Moreover , our finding that DopEcR and rut mutants have a similar GF habituation phenotype raises the possibility that DopEcR positively regulates cAMP levels in the relevant neurons following repetitive brain stimulation . Besides GF habituation , Drosophila displays olfactory habituation , which is mediated by the neural circuit in the antennal lobe [38] . Interestingly , Das et al . found that olfactory habituation is induced by enhancement of inhibitory GABAergic transmission , and that rut function is required for this neuronal modulation [64] . Similar modulation of GABAergic transmission may also be responsible for habituation of the GF pathway . It will be interesting to examine whether and how DopEcR contributes to the regulation of rut and enhanced GABAergic transmission in GF habituation . Several studies already suggested that 20E has rapid , EcR-independent effects in Drosophila and other invertebrate species . For example , 20E was shown to reduce the amplitude of excitatory junction potentials at the dissected Drosophila larval neuromuscular junction ( NMJ ) , and to do so within minutes of direct application [65] . Whereas treatment with 20E did not change the size and shape of the synaptic currents generated by spontaneous release , it led to a reduction in the number of synaptic vesicles released by the motor nerve terminals following electrical stimulation [65] . A similar effect of 20E was observed in crayfish , and it was suggested that the suppression of synaptic transmission by 20E may account for the quiescent behavior of molting insects and crustaceans [66] . These observations suggested that 20E suppresses synaptic efficacy under certain conditions by modulating presynaptic physiology through a non-genomic mechanism . It is possible that such actions of 20E are mediated by DopEcR . To detail the mechanisms underlying DopEcR-dependent neural plasticity , it will be worthwhile to determine if and how DopEcR contributes to 20E-induced , rapid synaptic suppression at the physiologically accessible larval NMJ , and to determine the extent to which non-genomic mechanisms of steroid actions are shared between the larval NMJ and the adult brain . One surprising finding made in this study is that ecdysone signaling can modify the phenotypes associated with mutations in the classic “memory genes” , namely rut and dnc , through the actions of DopEcR . rut and dnc encode central components of the cAMP pathway , which is required for memory processing in vertebrates as well as invertebrates . Our demonstration that genetically and/or pharmacologically enhancing DopEcR-mediated ecdysone signaling restores the courtship memory phenotype of loss-of-function rut mutants ( Figure 6A–D ) suggests that 20E-mediated DopEcR activation triggers an outcome similar to rut activation , i . e . , increased cAMP levels . This assumption is supported by our finding that loss-of-function dnc mutants restore courtship memory when DopEcR activity is suppressed ( Figure 6E and 6F ) . A similar restoration of the dnc memory phenotype was previously reported in a dnc and rut double mutant [58] , again supporting the idea that DopEcR positively regulates cAMP production . The results of rescue and phenocopy experiments ( Figure 4 ) indicate that the MB is critical for the DopEcR-dependent processing of courtship memory . Although the endogenous expression pattern of DopEcR is not known , DopEcR is thus likely to modulate cAMP levels in the MB in response to 20E during courtship conditioning . We have recently generated a new Gal4 line , in which a portion of the first coding exon of DopEcR is replaced with a DNA element that contains the Gal4 cDNA whose translation initiation codon is positioned exactly at the DopEcR translation start site ( Q . Li and Y . Rao are preparing a paper describing the details of this Gal4 line ) . When this line was used to drive UAS-GFP , the reporter gene was widely expressed in the adult brain with prominent signals in the MB ( unpublished observation ) . This preliminary result strongly indicates the endogenous expression of DopEcR in the MB . We have also directly shown that cAMP levels in the MB increase rapidly in flies fed 20E ( Figure 7A ) , and that this increase does not occur when DopEcR expression is down-regulated specifically in the MB ( Figure 7B ) . Taken together , these findings suggest that DopEcR expressed in the MB responds to 20E and acts upstream of cAMP signaling in a cell-autonomous manner . Surprisingly , enhancement of DopEcR-mediated ecdysone signaling restored courtship memory in flies harboring a strong hypomorphic allele of rut ( rut1084 ) ( Figure 6A and 6D ) . A similar result was obtained even in mutants harboring a presumptive rut null allele rut1 ( data not shown ) . These results suggest that , upon stimulation by 20E , DopEcR may be able to signal via another adenylyl cyclase that can compensate for the lack of Rut . This interesting possibility requires further investigation . In this study , we have focused on the roles and mechanisms of action of DopEcR-mediated , non-genomic ecdysone signaling . As we previously found that 20E levels rise in the head during courtship conditioning [25] , the data presented here suggest that DopEcR is activated by 20E during conditioning , triggers a rise in cAMP levels and induces physiological changes that subsequently suppress courtship behavior . This interpretation assumes that 20E directly activates DopEcR to increase cAMP levels . Previous cell-culture studies suggested that DopEcR also responds to dopamine to modulate intracellular signaling [29] . Furthermore , Inagaki et al . have demonstrated that flies respond to starvation by sensitizing gustatory receptor neurons to sugar via dopamine/DopEcR signaling [30] . We thus need to consider whether dopamine is directly involved in the processing of courtship memory through DopEcR . There is a possibility that 20E initially stimulates the production and/or release of dopamine , and that it in turn activates DopEcR and elevates cAMP levels to induce courtship memory . We think that this possibility is unlikely because even when courtship memory is disrupted by pharmacological suppression of dopamine synthesis , 20E feeding can compensate for decreased dopamine and allow restoration of memory ( Figure 5C and 5D ) . Although dopamine plays a significant role in courtship memory [50] , our results suggest that DopEcR does not act as the major dopamine receptor in this particular learning paradigm . We thus favor the possibility that dopamine contributes to courtship memory in parallel with , or upstream of , DopEcR-mediated ecdysone signaling . Consistent with this view , Keleman et al . reported that the formation of courtship memory depends on the MB γ neurons , which express DopR1 dopamine receptors , receiving dopaminergic inputs [51] . Notably , our results indicate that the processing of courtship memory requires DopEcR expression in the αβ , but not γ , neurons of the MB ( Figure 4 ) , which makes it unlikely that DopEcR is directly influenced by the dopaminergic neurons innervating γ neurons . Ecdysone signaling through nuclear EcRs is necessary for forming long-term courtship memory that lasts at least 5 days , but appears not to have a significant effect on short-term courtship memory [25] . In contrast , we found that DopEcR-mediated ecdysone signaling is critical for habituation and 30-minute courtship memory . These findings suggest that DopEcR and EcRs control distinct physiological responses to courtship conditioning , and that the former regulates short-term memory , while the latter regulates long-term memory . Although non-genomic actions of steroid hormones have been implicated in vertebrate learning and memory [67] , [68] , such actions have been attributed mainly to the classical nuclear hormone receptors that function outside of the nucleus and exert roles distinct from those of steroid-activated transcription factors [12] . Although recent evidence has shown that membrane-bound receptors independent of the classical estrogen receptors are involved in estradiol-induced consolidation of hippocampal memory [69] , the molecular identities of these proteins have not been established . Our findings here provide a novel framework for dissecting GPCR-mediated steroid signaling at the molecular and cellular levels . Furthermore , future analysis of the functional interplay between genomic and non-genomic steroid signaling pathways is expected to reveal novel mechanisms through which steroid hormones regulate plasticity of the nervous system and other biological phenomena .
Flies were reared at 25°C and 64% humidity , in a 12-hour light/dark cycle and on a conventional glucose-yeast-cornmeal agar medium . The DopEcRPB1 strain used in this study was produced by outcrossing with Cantonized w mutant flies . The DopEcR-Gal4 and UAS-DopEcR strains were generated in this study . For DopEcR-Gal4 , the putative promoter region of DopEcR ( a 588-bp upstream sequence ) was fused to the yeast Gal4 gene . For UAS-DopEcR , the DopEcR coding sequence was inserted downstream of the UAS ( upstream activating sequence ) in the pUAST vector . Other fly strains used in this study were obtained from the following sources: c772 , c739 , 201 y , UAS-CD4-tdGFP and UAS-Epac1-camps ( 55A ) ( Bloomington Drosophila Stock Center ) ; tub5-GS-Gal4 ( Scott D . Pletcher , Baylor College of Medicine , Houston , TX , USA ) ; MB-GS-Gal4 ( Ronald L . Davis , The Scripps Research Institute , Jupiter , FL , USA ) ; UAS-DopEcR RNAi ( VDRC ) ; UAS-disembodied RNAi ( Michael B . O'Connor University of Minnesota , Minneapolis , MN , USA ) ; DTS-3 and Samarkand ( Anne F . Simon , Western Ontario University , Ontario , Canada ) . The Canton-S ( 2202u ) strain was used as the wild-type control . Adult brains were dissected from 3 to 5-day-old male flies in PBS and fixed for 1 hour with 3 . 7% formaldehyde at 25°C , in PBS containing 0 . 05% Triton X-100 ( PBST ) . The brains were blocked with PBST containing 0 . 1% normal goat serum for 1 hour . Rabbit anti-GFP antibody ( 1∶1000; A11122 , Invitrogen ) was used for the primary antibody . The brains were counter-stained with nc82 , the mouse anti-Bruchpilot antibody ( 1∶20; Developmental Studies Hybridoma Bank , University of Iowa ) . Alexa Fluor 555-conjugated anti-rabbit IgG ( 1∶300; Invitrogen ) and Alexa 647-conjugated anti-mouse IgG ( 1∶300; Invitrogen ) were used as secondary antibodies for detection of anti-GFP and nc82 , respectively . Images were acquired as a z-stack , using FV1000 confocal microscope ( Olympus ) . Volume-rendered images were displayed using FluoRender ( http://www . fluorender . com ) . Total RNA was prepared from 20 fly heads of each genotype using TRIzol solution ( Invitrogen ) , and subjected to a reverse transcription reaction using a poly-dT20 primer and Superscript II enzyme ( Invitrogen ) , according to the manufacturer's instructions . The DopEcR cDNA sequence was amplified by PCR using the following primers: forward , 5′-ATGCAGGAAATGAGCTACCT-3′ and reverse , 5′-CTAGTCATCTGGGTCCAACC-3′ . rp49 was used as the internal control ( forward , 5′-ATGACCATCCGCCCAGCA-3′ and reverse , 5′-AATCTCCTTGCGCTTCTTGG-3′ ) . The gel images were processed using ImageJ software , to estimate the quantity of PCR products . The preparation of flies , stimulation , recording , and analysis of muscle responses were performed as described previously [36] , with some modifications . Electrical stimuli ( 0 . 1 msecond pulse ) were delivered across the brain through two uninsulated tungsten electrodes inserted in the eyes ( anode normally in the right eye ) . The action potentials in the left side leg extensor ( TTM ) and the right side wing depressor ( DLM ) were recorded as an indicator of GF pathway output [36] . Flies were given tissue paper balls ( less than 1 mm in diameter ) to inhibit flight , but were free to perform normal jump-and-escape reflexes . All recordings were carried out in an experimental Faraday cage covered with a black plastic sheet to reduce ambient light . To minimize the possible effects of handling and anesthesia , flies mounted for recording were rested for at least 1 hour in a humid chamber before recording . After being assessed for response thresholds ( during an inter-stimulus interval , ISI , of 30 seconds ) , flies were rested for 5 minutes before the habituation test . Three classes of responses , with progressively greater thresholds , were identified: long-latency , intermediate-latency , and short-latency . These responses could easily be distinguished in individual flies , and were used as an “internal gauge” on which to base the stimulation intensity for the habituation test . For each test , the stimulus intensity was set at the mean value of the thresholds for the long-latency and short-latency . To avoid causing artifacts by improper handling of flies , flies that had abnormally high activities or failed to respond more than twice , consecutively , were excluded from data analysis . Dishabituation stimuli ( air puffs ) were provided by gently squeezing a rubber bulb connected , by tubing , to a pipette nozzle mounted 2 cm to the anterior-left of the fly . All habituation data were recorded using the software pCLAMP 5 , and analyzed with clampfit in pCLAMP10 . The cumulative curves of the habituation responses were plotted with custom-designed software on the Matlab 7 platform . The courtship conditioning assay was performed at 25°C and 64% humidity , in an environment room under white light , as described previously [25] , with some modifications . All males were 3–5 days old at the time of testing . They were anesthetized with CO2 and stored in isolation for at least 24 hours prior to experiments . Females used as “trainers” in courtship conditioning were 3-days old and were fertilized a day before conditioning . In the conditioning phase , virgin males were placed with unreceptive , non-virgin females ( or alone in ‘pseudo-training’ experiments for naïve control males ) , in single-pair-mating chambers containing food medium ( 15 mm diameter×5 mm in depth ) , for 1 hour . After conditioning , males were rested individually for 30 minutes , in a glass tube ( 12 mm in diameter ×75 mm in depth , VWR International ) containing food medium . Memory tests were performed in a courtship chamber ( 15 mm in diameter ×3 mm in depth ) containing a freeze-killed virgin female . The male courtship behaviors were videotaped for a 10-minute test period , using DVD camcorder ( Sony DCR-DVD105 ) , and were manually scored for courtship index ( CI ) . The CI was defined as the proportion of time spent for courtship behaviors ( orientation , tapping , singing , licking and copulation attempts ) . We did not exclude from analysis males with a low courtship level . To compare CIs for conditioned and naïve males , we analyzed the data non-parametrically , using the Mann-Whitney U test , because the CI values were often not distributed normally . When CIs for conditioned and naïve males were significantly different ( P<0 . 05 ) , male courtship behavior was considered to be suppressed in an experience-dependent manner ( courtship memory ) . Experimental data are presented in the figures as the performance index ( PI ) , which was calculated using the following formula ( after CIs were subjected to arcsine square root transformation to approximate normal distributions ) : PI = 100× ( CIAve naïve−CIconditioned ) /CIAve naïve , where CIAve naïve and CIconditioned represent the averaged CI for naïve flies and a CI for each conditioned fly , respectively . Naïve courtship levels of Canton-S , DopEcRPB1 and DopEcR RNAi ( UAS-DopEcR RNAi/+; tub5-GS-Gal4/+ ) flies were shown in Table S1 . The CIs were not statistically different between Canton-S and DopEcRPB1 ( P = 0 . 086 ) and there was no statistical difference between the CIs of DopEcR RNAi males with or without RU486 treatment ( P = 0 . 8459 ) . Mann-Whitney U test . Flies carrying the RU486-inducible transgene ( GeneSwitch strains ) were fed food containing 500 µM RU486 ( Mifepristone , Sigma ) or vehicle ( ethanol; final concentration <2% ) for 3 days prior to the experiment . 20E was fed for 10 min using Kimwipe paper soaked in 1M sucrose solution containing a particular concentration of 20E ( Sigma ) . The 20E stock solution ( 25 mM ) was prepared in ethanol . 3-Iodotyrosine ( 3-IY ) was mixed into yeast paste with a final concentration of 10 mg/ml . Up to 10 newly eclosed flies were placed in vials containing fly food with 3-IY-yeast paste for 4 days . The change in cAMP levels was monitored using the genetically encoded cAMP reporter Epac1-camps [63] . This reporter was expressed in MB neurons using the c772-GAL4 driver . Two α-lobe tips and clusters of calix cell bodies were set as a region of interest ( ROI ) , and observed through the head cuticle . Test flies were immobilized on an observation plate by gluing the dorsal portion of the head and neck with nail polish . The observation plate was a large glass coverslip ( 24×60 mm ) attached to a small plastic coverslip ( 22×22 mm ) with a hole ( 7 mm diameter ) . The fly thorax was positioned at the edge of the hole so that the fly head was directly attached to the glass coverslip . Confocal images were obtained using a Plan-Neofluar 20× objective on a Zeiss 510 inverted confocal microscope ( Zeiss , Oberkochen , Germany ) . Epac1-camps fluorescence was scanned with a 458 nm Argon ion laser line . YFP-FRET and CFP-donor emissions were separated by means of a NFT545 dichroic mirror and BP475-525 and LP560 emission filters . YFP and CFP signals were scanned simultaneously onto separate photomultiplier tubes , and obtained every 20 seconds . After 3 minutes of baseline FRET ( YFP to CFP ratio ) measurement , the test fly was fed 20E-sucrose solution or vehicle control for 1 minute using a Kimwipe ( 10 mm×10 mm ) soaked with the solution . The 20E-sucrose solution contained blue food dye ( Acid Blue 9 , 0 . 125 mg/ml ) as an indicator of ingestion . The effects of 20E on FRET were observed for 30 minutes and analyzed as described by Shafer et al [63] . To compare the FRET time-course among different experiments , the YFP/CFP ratio values were normalized to the value of the first time-point . | The brain is a prominent target of steroid hormones , which control a variety of neurobiological processes and are critical to the regulation of behavior . Some effects of these hormones involve changes in gene expression and thus emerge slowly , over the course of hours or even days . Other responses to steroids occur rapidly and are independent of transcriptional regulation . Their functions and mechanisms of action are poorly understood , particularly in the context of steroid-mediated control of behavior . Here we show , using the genetic model organism Drosophila melanogaster ( the fruit fly ) , that an unconventional , membrane-bound receptor for the molting hormone ecdysone transmits a novel form of steroid signaling in the adult brain . Our study shows that this novel form of steroid signaling has a robust interface with the classical “memory genes” that encode central components of the so-called cAMP signaling pathway , which is universally important for neuronal and behavioral plasticity . These findings underscore the significance of steroid signaling in memory processing , and provide a foundation for the genetic analysis of rapid , unconventional steroid signaling in behavioral regulation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | A Novel Role for Ecdysone in Drosophila Conditioned Behavior: Linking GPCR-Mediated Non-canonical Steroid Action to cAMP Signaling in the Adult Brain |
Paracoccidioidomycosis ( PCM ) is a systemic mycosis , endemic in most Latin American countries , especially in Brazil . It is caused by the thermo-dimorphic fungus of the genus Paracoccidioides ( Paracoccidioides brasiliensis and Paracoccidioides lutzii ) . Innate immune response plays a crucial role in host defense against fungal infections , and neutrophils ( PMNs ) are able to combat microorganisms with three different mechanisms: phagocytosis , secretion of granular proteins , which have antimicrobial properties , and the most recent described mechanism called NETosis . This new process is characterized by the release of net-like structures called Neutrophil Extracellular Traps ( NETs ) , which is composed of nuclear ( decondensed DNA and histones ) and granular material such as elastase . Several microorganisms have the ability of inducing NETs formation , including gram-positive and gram-negative bacteria , viruses and some fungi . We proposed to identify NETs in tegumentary lesions of patients with PCM and to analyze the interaction between two strains of P . brasiliensis and human PMNs by NETs formation in vitro . In this context , the presence of NETs in vivo was evidenced in tegumentary lesions of patients with PCM by confocal spectrum analyzer . Furthermore , we showed that the high virulent P . brasiliensis strain 18 ( Pb18 ) and the lower virulent strain Pb265 are able to induce different patterns of NETs formation in vitro . The quantification of extracellular DNA corroborates the idea of the ability of P . brasiliensis in inducing NETs release . In conclusion , our data show for the first time the identification of NETs in lesions of patients with PCM and demonstrate distinct patterns of NETs in cultures challenged with fungi in vitro . The presence of NETs components both in vivo and in vitro open new possibilities for the detailed investigation of immunity in PCM .
Paracoccidioidomycosis ( PCM ) is a systemic mycosis considered an important cause of mortality and morbidity in most Latin American countries , especially in Brazil . Sporadic cases have been reported in European countries , United States of America ( USA ) and Japan , in individuals coming from endemic areas [1–5] . It is caused by the fungi of the genus Paracoccidioides ( Paracoccidioides brasiliensis and Paracoccidioides lutzii ) [6 , 7] , which share the same thermo-dimorphic features , developing as mycelium at room temperature and as yeast at body temperature [8 , 9] . P . brasiliensis infection occurs after propagules inhalation ( conidia presented in water , soil and plants ) [10 , 11] , which are deposited in the lungs and transformed into yeast cells , establishing the disease . From this stage on , infection could become latent ( PCM–infection ) , disseminate by lympho-haematogenic pathway to other organs , such as liver and spleen ( PCM–disease ) , or heal spontaneously [11] . Innate immune response is essential during early stages of fungal infections [12] . Phagocytic cells , such as neutrophils ( PMNs ) and macrophages , play crucial role in host defense , modulating the inflammatory response and fungicidal activity against P . brasiliensis [12–17] . In this context , studies have focused on the role of PMNs during PCM , since a massive infiltration of these cells is found in granulomas of the disease , after chemoattraction modulated by keratinocyte chemoattractant ( KC ) and macrophage inflammatory protein 1 alpha ( MIP-1α ) [18] . PMNs are short-lived cells that must be promptly recruited to the site of infection [19] . They can capture and kill microbes by oxygen dependent or independent mechanisms , by the action of NADPH enzyme or release of their granular components [19] . Reactive oxygen species ( ROS ) , produced by the action of NADPH enzyme are essential for the killing of fungi [14 , 15 , 20–23] . Previous studies demonstrated that non-activated PMNs do not have fungicidal activity , just showing fungistatic activity against P . brasiliensis [24] , with an increase in these functions after activation with cytokines such as interferon-gamma ( IFN-γ ) , tumor necrosis factor-alpha ( TNF-α ) , granulocyte monocyte colony-stimulating factor ( GM-CSF ) and interleukin-15 ( IL -15 ) [24–27] . The studies also showed that the effector mechanisms of activated PMNs against fungi involve superoxide anions and H2O2 participation . A novel PMN mechanism of action has been described as NETosis , which is an extracellular mechanism to kill microbes characterized by the PMN release of both granular and nuclear material and identified as Neutrophil Extracellular Traps ( NETs ) [28] . These structures are composed by a decondensed DNA backbone associated with histones and others antimicrobial proteins such as elastase , permeability increasing protein ( BPI ) and myeloperoxidase [28 , 29] . NETs can be triggered by gram-positive and gram-negative bacteria , fungi , protozoa and viruses , some molecules like interleukin-8 ( IL-8 ) , Phorbol Myristate Acetate ( PMA ) , lipopolysaccharide ( LPS ) and others cells as activated platelets [28 , 30–34] , showing until now , that several microorganisms are able to induce NETs formation . In some of them , NETs have antimicrobial activities , in others meanwhile , these structures have only temporary entrapment action , avoiding their dissemination [28 , 31 , 33–37] . Therefore , the aims of this study were to identify the presence of NETs in vivo , analyzing tegumentary lesions of patients with PCM , and in vitro , challenging human PMNs with P . brasiliensis yeast cells .
A prospective study was conducted to analyze skin tegumentary lesions of seven male patients between 51 and 75 years old , attended at clinical dermatology of the Botucatu Medical School , São Paulo State University . All patients had the chronic form of PCM with lesions localized at head , nose , hand , knee , foot and back . The diagnosis was confirmed by histopathological analysis performed by the Pathology Service/FMB . Patients were selected before treatment , excluding the immunocompromised ones and those with secondary infections . PMNs from peripheral blood of PCM patients with the chronic form of the disease and healthy volunteer donors between 20 and 30 years from FMB were also evaluated in this study . This investigation was conducted according to the principles expressed in the Declaration of Helsinki and was approved by the Research Ethics Committee of Botucatu Medical School , UNESP–São Paulo State University ( CEP—261/11 ) . Written informed consent was obtained from all participants . Peripheral blood from patients and healthy donors was collected by venous puncture and PMNs were separated by a density gradient centrifugation ( Histopaque 1119 and 1083g/mL—Sigma–Aldrich , St . Louis , USA ) at 460 g for 30 minutes followed by erythrocytes lysis with a hypotonic solution ( NaCl 0 , 2% ) . Cellular viability was assessed by trypan blue dye exclusion test , and purified PMNs ( ≥95% of the cells ) were then resuspended in complete medium ( RPMI medium 1640 supplemented with 10% inactivated fetal calf serum , both from Sigma–Aldrich ) and placed on ice until use . Cell culture was adjusted for 2x106 cells/mL before all procedures . Two different strains of P . brasiliensis were used throughout this study: P . brasiliensis strain 18 ( Pb18 , high virulence ) and strain 265 ( Pb265 , low virulence ) . The strains were submitted to weekly sub-cultivation on 2% glucose , 1% peptone , 0 . 5% yeast extract and 2% agar medium ( GPY medium ) ( all reagents from DIFCO , Franklin Lakes , NJ , USA ) , and used on the sixth day of culture . For preparation of P . brasiliensis suspension , yeast cells were removed from the cultivation medium , transferred to a sterile test tube containing glass beads and homogenized in a Vortex homogenizer ( two cicles of ten seconds ) . Yeast viability was determined by phase contrast microscopy and bright yeast cells were counted as viable while dark ones were considered as non-viable . Fungal suspensions containing more than 95% viable cells were used in the experiments . The yeast suspension was adjusted for 4x104 cells/mL before use . Tissue sections from biopsies of tegumentary lesions from seven PCM patients were fixed with buffered formalin , dehydrated in 70% alcohol and embedded in parafin . Samples ( 7μm thick ) were deparaffinized and stained with hematoxylin and eosin ( H&E ) , in attempt to identify the extracellular DNA , representative of NETs [38] . Sections of the same biopsies were stained with Gomori-Grocott to visualize yeast cells . Gomori-Grocott stain consists in oxidizing the sample with chromic acid 5% , bleaching with sodium bisulphite 1% , treating with methenamine silver solution until impregnation , toning with gold chloride 0 . 1% , fixing with sodium thiosulphate 3% and counterstaining with light green . Slides were scanned with the Pannoramic Digital Slide Scanners—Pannoramic MIDI ( 3DHISTECH Kft , Budapest , Hungary ) and analyzed using software Pannoramic Viewer 1 . 15 . 4 RTM ( 3DHISTECH Kft ) , from NTDP ( Digital Technologies in Pathology Facility , Dept . of Pathology–FMB ) . The same biopsies were analyzed by confocal laser scanning microscopy . Tissue sections ( 7μm thick ) were deparaffinized in two baths of 100% xylene and three baths of ethanol decreasing concentrations ( 100% , 90% and 70% ) . Samples were washed for 5 minutes in dH2O and incubated with a blocking buffer PBS-BSA 4% ( nonspecific binding block ) for 30 minutes . Tissues were incubated with anti-elastase ( Calbiochem—Merck Millipore—Merck KGaA , Darmstadt , Germany ) and anti-histone H1 ( Millipore—Merck Millipore—Merck KGaA , Darmstadt , Germany ) antibodies , followed by anti-rabbit-FITC ( Millipore ) and anti-mouse-Texas red ( Calbiochem ) antibodies , respectively . Slides were mounted using mounting medium for fluorescence with DAPI ( Vectashield-Vector Labs , Burlingame , CA , USA ) . Confocal images were taken in a Leica TCS SP5 microscope from the CME ( Electron Microscopy Center–Biosciences Institute—UNESP—Botucatu ) . Isolated PMNs ( 2x106 cells/mL ) from PCM patients and healthy donors were adhered on coverslips treated with Poly-L-Lysine 0 , 01% ( Sigma-Aldrich ) in 24-well flat-bottom plates ( Nunc Life Tech . , Inc . , MD , USA ) . In some cultures , after adherence , cells were pretreated with DNAse ( 100U/mL–Fermentas Life Science–St . Leon-Rot , Germany ) for 30 minutes and/or PMA ( 100ng/mL- Sigma–Aldrich ) as a negative and positive control respectively . PMNs were then challenged with Pb18 and Pb265 ( 4x104 cells/mL ) , using 50:1 cells/fungi ratio , and incubated for one or two hours in 5% CO2 at 37°C . Cocultures were fixed with 2 . 5% glutaraldehyde in 0 . 1 M cacodylate buffer , pH 7 . 2 , postfixed with 1% osmium tetroxide , and dehydrated with an ascending ethanol series . After dehydration and critical-point drying , samples were coated with gold and analyzed in a FEI QUANTA 200 scanning electron microscope from the CME ( Electron Microscopy Center–Biosciences Institute—UNESP—Botucatu ) . PMNs isolated from healthy donors were treated as described previously . For automated imaging acquisition , isolated PMNs ( 2x106 cells/mL ) were treated with PMA–positive control ( 100 ng/mL–Sigma Aldrich ) for 30 minutes or challenged with Pb18 or Pb265 ( 4x104 cells/mL – 50:1 cells/fungi ratio ) and incubated for two hours in 5% at 37°C . After this period , cells were stained with anti-elastase antibody ( Calbiochem ) , followed by anti-rabbit-FITC antibody ( Millipore ) , fixed with 4% formaldehyde solution and DNA was stained with DAPI ( Vectashield–Vector Labs ) . Following this , cultures were transferred into 96-well black/clear bottom microplate ( Corning ) at 2 × 105 cells/well in 100 μl microplates and were centrifuged at 2000 rpm for 5 minutes at 24°C . Images were acquired using the automated microscope ImageXpress Micro XL Widefield High-Content Screening System ( Molecular Devices , Sunnyvale , CA ) with a cooled 16-bit monochromes CMOS PCO camera ( 2160 × 2160 imaging array , 6 . 5 × 6 . 5 μm pixels ) using an 20× Super Plan Fluor ELWD , NA 0 . 45 Nikon objective . Exposure times were 200 msec for DAPI ( nuclear stain ) and 500 msec for FITC ( anti-elastase-FITC antibody ) . A total of 16 sites for each replicate ( n = 5 ) was acquired from wells containing non-treated PMNs , 100 ng/ml PMA-activated cells , and PMNs incubated with Pb18 or Pb265 ( 50:1 ratio ) . Cellular image analysis was performed using MetaXpress software version 5 . 3 . 0 . 5 . Average of total DNA stained by DAPI was quantified using a customized method with MetaXpress Custom Module Editor . Total area ( μm2 ) of the resulting NETs in response to Pb18 and Pb265 was analyzed by thresholding NETs stained with DAPI and anti-elastase-FITC antibody and measured by Integrated Morphometry Analysis . PMNs ( 2x106 cells/mL ) from healthy donors were incubated with or without Pb18 and Pb265 for two hours . In some assays , cultures were activated with PMA–positive control ( 100ng/mL—Sigma–Aldrich ) or challenged with Pb18 and Pb265 . After incubation , supernatants were collected and treated with restriction enzymes ( EcoR1 and HindIII , 15U/uL each; Invitrogen , Eugene , Oregon , USA ) , according to the manufacturer's instructions . After , extracellular DNA amounts ( NETs ) were quantified using the Picogreen dsDNA kit ( Invitrogen ) used by several authors [33 , 34 , 39–41] . The λ-DNA standard provided with the kit ( 100μg/mL ) was diluted with Tris-EDTA ( TE buffer ) to the concentration of 1ng/mL for the high range curve and received the same treatment with restriction enzimes . Plates were incubated at room temperature in the dark for 5 minutes prior reading on a SpectraMax M2 ( Molecular Devices ) using an excitation wavelength of 480 nm and emission wavelength of 520 nm . Measure of NETs area was analyzed by Wilcoxon’s test . Average of total DNA structures and quantification of extracellular DNA were compared by Friedman’s test followed by post-hoc Dunn’s test with the level of significance set at p<0 . 05 . All results were analyzed using GraphPad Prism 5 . 01 Software ( Graphpad Software Inc . , CA , USA ) .
In attempt to identify extracellular structures suggestive of NETs , histopathological analysis of skin tegumentary lesions of patients with PCM was performed . In Fig 1A and 1B , the image revealed basophilic material with characteristic extracellular filaments , indicating extracellular DNA ( positive for hematoxylin ) , suggestive of the formation of NETs , which are surrounding the yeasts at the lesion . Details of NETs and yeast cell were demonstrated in the highlighted area in Fig 1B ( 40x ) . In Fig 1C and 1D , the same tissue section was stained with Gomori-Grocott , as a positive control of the presence of P . brasiliensis in the site of lesion , corroborating the idea of the fungal participation on this process . After identification of the extracellular material suggestive of NETs by histopathological analysis , we seek to identify the individual components of NETs in biopsies from tegumentary lesions of patients with PCM ( Fig 2 ) . As expected , NETs were also visualized in these samples , and their constituents were evaluated individually . Decondensed DNA , the backbone of these structures , was identified after DAPI staining ( Fig 2A ) . Histones ( Histone H1 ) and elastase , other major NETs components , were identified combined with DNA after immunostaining with specific primary and secondary antibodies ( Fig 2B and 2C ) . Interestingly , Fig 2D is the overlay of these three images , confirming the co-existence of the three major components constituting the NETs . We also identified during the analysis of one section ( Fig 3 ) , two cells that appear to be in an early stage of NETs formation . In the box ( highlighted ) , we identified a nucleus labeled with DAPI ( Fig 3A ) , which had lost its normal format and had positive staining for elastase ( Fig 3B ) , demonstrating the colocalization of elastase with nuclear DNA . Elastase is transported to the nucleus , acting on histones to initiate the chromatin decondensation , even before it is released into the cytoplasm [42] . At the center of the field , we can observe a cell that appears to be releasing their decondensed DNA content ( labeled with DAPI ) ( Fig 3A ) , already bound to elastase ( Fig 3B ) , and the histone H1 in nuclear region ( Fig 3C ) , indicating the mobilization of these compounds in the formation of NETs . In Fig 3D , the overlap of 3 images was observed . These images are very interesting as they have been identified in lesions of patients showing an active response of human PMNs through NETs formation against P . brasiliensis in vivo for the first time , once the fungus was identified by Gomori-Grocott in the lesion . To visualize NETs formation in vitro by scanning electron microscopy , PMN cultures from patients and healthy donors were challenged with two strains of P . brasiliensis: Pb18 ( high virulence strain ) and Pb265 ( low virulence strain ) . Non-treated PMNs from healthy donors were not able to release NETs , but in the presence of PMA , these structures were formed and visible after 45 minutes of incubation . PMA was able to induce an intense release of NETs that completely covered the observed area ( Fig 4A ) . Analyzing fungi-PMNs interaction , Pb18 was able to trigger NETs formation by PMNs in one and two hours of incubation ( Fig 4B and 4C ) . Cultures with DNAse-1 treatment have not shown any evidence of NETs structures , confirming the enzyme’s degrading action upon the extracellular DNA structures ( Fig 4D ) . We also observed that patient´s PMNs when challenged with both strains of P . brasiliensis ( Pb18 and Pb265 ) released NETs in an attempt to entrap the fungi , possibly demonstrating the process of NETosis that may occur during in vivo infection with P . brasiliensis ( Fig 5 ) . However , distinct pattern of NETs in response to the different strains were detected . Analysis of cultures challenged with Pb18 revealed the presence of NETs in a large coverage area , and the structures seemed to be loose and scattered ( Fig 5A and 5B ) . Whereas , Fig 5C and 5D demonstrated a different pattern of NETs when cells were challenged with Pb265 . In these cultures , these structures looked smaller , denser and compacted when compared with Pb18 ( Fig 5C and 5D ) . We observed that in Pb18 slides , practically all area was covered by NETs , while in Pb265 slides , NETs were concentrated near to the fungus . To corroborate data obtained by Confocal Microscopy and Scanning Electron Microscopy , we analyzed PMN cultures with the automated microscope ImageXpress Micro XL Widefield High-Content Screening System ( Molecular Devices , Sunnyvale , CA ) . Once more , images showed an extracellular material stained with DAPI ( DNA ) and FITC ( elastase ) , which characterized the presence of NETs in the cultures activated with PMA or challenged with Pb18 or Pb265 ( Fig 6B , 6C and 6D , respectively ) . In Fig 6A , we observed non-treated PMNs , showing few PMNs in NETosis process , ratifying the idea that an activation is essential for NETs release . Whereas , in Fig 6B , when PMA was added to the culture , PMNs were able to release NETs to the extracellular environment . In Fig 6C and 6D , we observed Pb18 and Pb265 , respectively , demonstrating the formation of NETs covering the yeast cells , certifying that this defense mechanism is triggered in the infection with P . brasiliensis . In addition to the obtained images , the same PMN cultures were analyzed in attempt to quantify the number of extracellular DNA structures , which represents NETs released after the culture’s challenge with Pb18 , Pb265 or PMA activation . Analysis consisted in thresholding the structures stained with DAPI ( DNA ) . Total DNA structures were quantified using a customized method with MetaXpress Custom Module Editor and measured by Integrated Morphometry Analysis presented as an average of extracellular DNA structures for each well . Corroborating our images , total extracellular DNA structures were substantially higher in the cultures activated with PMA or challenged with Pb18 or Pb265 when compared to non-treated PMNs , showing a significantly increase of extracellular DNA structures staining in cultures challenged mainly with Pb18 ( p = 0 . 0016 ) ( Fig 7 ) . We also observed in these experiments , interesting different NETs patterns from cultures challenged with Pb18 or Pb265 . Analyzing NETs area ( μm2 ) , that consisted in thresholding the structures stained with DAPI ( DNA ) colocalized with FITC ( elastase ) , NETs from Pb18 coculture appeared to be bigger and more scattered than those presented by PMNs challenged with Pb265 , which seemed to be smaller ( p = 0 . 0625 ) ( Fig 8 ) , even though both are capable of entrapping the yeast cells as shown in Figs 5 and 6 . Fig 9 was included to demonstrate how the software selected and performed the analysis . We also analyzed NETs through quantification of the extracellular DNA by Picogreen dsDNA kit ( Invitrogen ) . PMNs from healthy donors were incubated for two hours with both strains of P . brasiliensis ( Pb18 and Pb265 ) . Corroborating the data shown in scanning electron microscopy and High-Content Screening System , both Pb18 and Pb265 were also able to induce release of NETs in the extracellular environment . However , there was no statistical difference between the two strains of P . brasiliensis . Non-treated PMNs released low levels of DNA when compared with cultures treated with PMA , Pb18 and Pb265 ( p = 0 . 0167 ) ( Fig 10 ) .
NETs action has been widely studied over the recent years . Several microorganisms are able to induce formation and release of these structures and in some cases , as in infections caused by Staphylococcus aureus , Shigella flexneri , Streptococcus pneumoniae , Leishmania amazonensis , Aspergillus fumigatus , Candida albicans , Aspergillus nidulans and P . brasiliensis , these NETs have antimicrobial activities [28 , 31 , 34 , 37 , 38 , 43–47] . Recent study demonstrated that PMNs selectively responds to pathogens that are too large to be phagocytosed via NETosis process [48] . However , some pathogens have evasion mechanisms that make the entrapment only temporary , enabling the recruitment of other immune cells for the site or inhibiting the microorganisms’ growth , as seeing in other infections [33 , 35–37 , 45 , 49] . The interaction between NETs and P . brasiliensis is still been investigated . In this study , we demonstrated for the first time NETs formation in vivo induced by P . brasiliensis yeast cells in tegumentary lesions of patients . Our results in vitro showed that yeasts are trapped by NETs , corroborating our previous study , in which were evidenced that P . brasiliensis yeast cells are able to induce NETs formation by PMNs and that these structures are involved in extracellular killing of the fungus [43] . However , our results also demonstrated different patterns of NETs in a dependence of the evaluated strain . This study identified NETs components , such as histone and elastase , in both analyzes , in patient's tegumentary lesions and in PMN cultures , by confocal laser scanning microscopy and High-Content Screening System . Furthermore , it was identified in some tissue sections , cells in the initial process of NETs formation , demonstrating the colocalization of nuclear DNA with elastase , which is transported to the nucleus and acts on histones to initiate the chromatin decondensation , even before it is released into the cytoplasm . Papayannopoulos et al . [42] showed that elastase is necessary for chromatin decondensation through degradation of histones , allowing NETs release to the extracellular environment . Thus , we believe that the weak labeling of histone on this image was due the elastase action upon this component ( Fig 3 ) . Interestingly , the decondensed nuclear material labeled with DAPI that appears to be released by cells in some images , still have certain conserved nuclear structure . This is consistent with what has been recently proposed by some authors , that NETs can be formed by viable PMNs and that cell death can occur subsequently , once cell death is not a requirement for the formation of such structures [50–52] . Histopathological analysis was performed using H&E and Gomori-Grocott stainings and was included in this study in an attempt to identify structures that could indicate the presence of yeast cells in the local of NETs formation , characterized by basophilic material with extracellular filaments , indicating extracellular DNA positive for hematoxylin . Urban et al . [38] also identified the presence of NETs in histopathological analysis of mice challenged with C . albicans , evidencing the presence of extracellular DNA positive for hematoxylin , as seen in this study , corroborating once more our findings on the lesion analysis by confocal laser scanning microscopy . In this manner , the presence of NETs in tegumentary lesions strengthens the results obtained in the in vitro experiments , proving that there is a release of these NETs against the fungus after PMNs recruitment in vivo , once P . brasiliensis was identified in the lesion . Scanning electron microscopy images showed similar structures as those first identified by Brinkmann et al . [28] and others [31 , 33 , 46] . In our images , the interaction between PMNs and P . brasiliensis yeast cells was evidenced , with consequent release of net like material , suggesting a role of NETs during PCM . These structures were similar to those seen in other microbe-NETs interaction like Mycobacterium tuberculosis , L . amazonensis and P . brasiliensis [33 , 34 , 43] and to images presented in previous studies with other fungi such as C . albicans , A . fumigatus , A . nidulans and Cryptococcus gattii [31 , 37 , 38 , 46 , 47 , 53] . However , when cells were challenged with different strains of P . brasiliensis , we identified a distinct pattern of NETs in a dependence of the evaluated strain . PMN cultures challenged with Pb18 showed presence of NETs in a large coverage area and these structures seemed to be loose and scattered , whereas NETs observed in PMN cultures challenged with Pb265 appeared to be smaller , denser and compacted , as shown in Scanning Electron Microscopy and by High Content analysis . We believe that this different NETs pattern might be related to an enzyme with DNAse like activity produced by Pb18 strain as a possible escaping mechanism avoiding a tight entrapment by NETs , which could contribute to the observed pattern . As demonstrated , in Pb18 genome ( ABKI00000000 . 2 ) [54] , there are two hypothetical proteins ( PADG_11161—Gene ID: 22587058 and PADG_08285—Gene ID: 22586608 ) that have DNAse activities . Besides , it was already identified a deoxyribonuclease gene ( PAAG_07587—Gene ID: 9093585 ) in P . lutzzi genome ( ABKH00000000 . 2 ) [54] . Studies to better explain these hypothesis are been conducted in our lab . Beyond that , Buchanan ( 2006 ) have shown the production of DNAse by group A Streptococcus as an escaping mechanism from NETosis [55] . Although NETs have two important effector functions against microorganisms , as temporary imprison of the pathogen that prevents its spread and a direct antimicrobial action on trapped microorganisms , studies have also related NETs with several diseases , correlating them with pathological effects . Some reports show that antimicrobial histones and peptides coating the NET-DNA have direct cytotoxic effect to tissue , and ineffective clearance of NETs is responsible for deleterious inflammation of host tissue in several disorders [41 , 56–64] . In acute respiratory distress syndrome ( ARDS ) , there is a massive influx of PMNs into the lungs causing neutrophilic inflammation and in acute lung injury ( ALI ) , there is an excessive activation and migration of PMNs into the lung . These cells are important contributors to the progression of ALI/ARDS , and higher PMN concentration in the bronchoalveolar lavage ( BAL ) fluid of patients with ARDS is often associated with greater severity of the disease [64 , 65] , while excessive PMNs and NETs contribute to the pathology of ALI , where NETs can directly induce lung epithelial cell death [62] , relating NETs to the pathogenesis of these important lung diseases [57 , 59 , 60] , as described above . Therefore , this mechanism could also be involved with the pathogenesis of PCM , once it was described the presence of PMNs in inflammatory infiltrates of granulomas and in lesions detected in disease experimental models [66–70] and now the evidence of NETs in the lesions . Thus , the real participation of NETs in defense against P . brasiliensis or in disease’s pathogenesis , as well as the fungal escape mechanisms involved needs to be further investigated . In conclusion , our data show for the first time the identification of NETs in patients’ tegumentary lesions . Beyond that , both strains of P . brasiliensis ( Pb18 and Pb265 ) are able to induce NETs formation by human PMNs in vitro and are related to different patterns of NETs . The presence of NETs components both in vivo and in vitro open new possibilities for the detailed investigation of the immunity in PCM . | Paracoccidioidomycosis ( PCM ) is an infectious disease caused by fungi of genus Paracoccidioides ( P . brasiliensis and P . lutzii ) . PCM is endemic in Latin America , with a greater incidence in Brazil , Colombia , and Argentina . Over the last years , studies are focusing on neutrophils’ ( PMNs ) actions against P . brasiliensis , due to the capacity of these cells to develop different defense strategies against pathogens . and especially due to constant presence of inflammatory infiltrates full of PMNs in the granuloma of the disease . As PMN release of both granular and nuclear material , identified as Neutrophil Extracellular Traps ( NETs ) , is a spectacular action mechanism against microbes , we seek to identify whether this process would be an important mechanism triggered against P . brasiliensis . Thus , we showed for the first time the identification of NETs in tegumentary lesions of patients with PCM by viewing the individual components of NETs . Beyond that , we demonstrated the entrapment of P . brasiliensis in vitro by these structures released from human PMNs of patients with PCM and healthy donors , with different patterns , in a dependence of the evaluated strain . Our data provides important new information regarding the role of PMNs against P . brasiliensis , opening new avenues for the research on immunity of PCM . | [
"Abstract",
"Introduction",
"Materials",
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"Methods",
"Results",
"Discussion"
] | [] | 2015 | Neutrophil Extracellular Traps Identification in Tegumentary Lesions of Patients with Paracoccidioidomycosis and Different Patterns of NETs Generation In Vitro |
Granulomas are complex lung lesions that are the hallmark of tuberculosis ( TB ) . Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment . Three fluoroquinolones ( FQs ) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . To date , insufficient data are available to support selection of one FQ over another , or to show that these drugs are clinically equivalent . To predict the efficacy of MXF , LVX and GFX at a single granuloma level , we integrate computational modeling with experimental datasets into a single mechanistic framework , GranSim . GranSim is a hybrid agent-based computational model that simulates granuloma formation and function , FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data . We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load , sterilization rates , early bactericidal activity and efficacy under non-compliance and treatment interruption . GranSim reproduces in vivo plasma pharmacokinetics , spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs . We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio . We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum . This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation . MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario . We conclude that MXF has a small but potentially clinically significant advantage over LVX , as well as LVX over GFX . We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB .
Tuberculosis ( TB ) , caused by Mycobacterium tuberculosis ( Mtb ) , is a global public health threat killing 1 . 5 million people annually [1] . Despite our arsenal of anti-TB antibiotics , effective treatment remains a challenge , requiring at least 6 months of combination therapy with up to four antibiotics . One obstacle to refining TB treatment is complex granuloma structures that develop in patient lungs following infection . Granulomas are dense collections of host immune cells , bacteria and dead host cell debris ( caseum ) ; and can be cellular ( without caseum ) , caseous , fibrotic or suppurative ( containing neutrophils in the core ) [2] . Granulomas isolate Mtb , enhance Mtb replication and provide a potential barrier for antibiotic penetration [3 , 4] . Fluoroquinolones ( FQs ) are a class of antibiotics typically used as second-line agents against multi-drug resistant TB ( MDR-TB ) [5] , or as preventive therapy for MDR-TB contacts [6 , 7] . One of three FQs is used in MDR-TB treatment: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . The choice of one FQ over another is essentially motivated by availability , cost and national guidelines . The WHO recommends use of LVX over MXF , and MXF over GFX [5] . In the absence of comparative clinical trials other than early bactericidal activity [8] , there are not sufficient data to declare that treatment with one FQ results in superior clinical outcome . Identifying the best FQ will require careful study of antibiotic dynamics and activity in granulomas . Recent studies have characterized pharmacokinetic ( PK ) and pharmacodynamic ( PD ) metrics of MXF , LVX and GFX ( Table 1 ) . The variety of mixed and conflicting data make it unclear whether one FQ is optimal . For example , PK metrics alone indicate: LVX and GFX have higher plasma exposure ( area under the concentration curve ( AUC ) ) , and MXF has higher concentrations in epithelial lung fluid or alveolar macrophages [9 , 10] . Examining PD metrics , GFX has lower MIC against intracellular Mtb , MXF and GFX have equivalent MICs against Mtb grown in liquid culture , MXF has higher bactericidal activity compared to LVX , and MXF and GFX can prevent resistance at lower concentrations than LVX [11–14] . According to clinical metrics , LVX has higher early bactericidal activity ( EBA , daily decrease in sputum bacterial burden ) ( day 0 to 2 ) , all three FQs have equivalent extended EBA ( day 2 to 7 ) , and MXF and LVX perform similarly on sputum culture conversion after 3 months , time to sputum culture conversion , and treatment success rate [8 , 15–18] . Based on these existing data it is not clear whether one FQ should be preferred for treatment of TB . The ability of an antibiotic to successfully treat TB depends on complex interactions along its path from dose , to plasma , to granuloma to bacterium [19] . Antibiotic concentrations in the blood determine how much antibiotic is available for distribution into granulomas . Antibiotics diffuse from blood vessels into lung tissue and granulomas , where spatial distribution is affected by uptake into host-cells , binding to caseum , and location of functional blood vessels . Once an antibiotic reaches bacteria , it must penetrate the bacterial cell wall and reach the molecular target in sufficient concentration to kill . Complicating things further is inter-individual host variability in plasma pharmacokinetics and lung pathology ( lesion type ) that must be considered when predicting antibiotic efficacy in TB . Both experimental and computational studies can be useful to identify antibiotic regimens that will effectively treat TB . Experiments can quantify antibiotic concentrations , spatial distributions , in vitro activity and in vivo efficacy in animal models or as part of background regimens in humans ( Table 1 ) . Computational approaches can combine datasets from multiple experimental systems , interpolate between experimental data points , and screen large numbers of treatment regimens more time- and cost-effectively [20–22] . Here we take a systems pharmacology approach , integrating state-of-the-art experimental and computational methods to predict FQ efficacy and compare FQs . We present a spatio-temporal computational model of granuloma formation , function and treatment that is calibrated to an exhaustive experimental dataset . Data include FQ dynamics in blood plasma , spatial and temporal distribution in granulomas , and activity in vitro . Our hybrid computational model , GranSim , tracks events at multiple spatial scales ( molecular , cellular and tissue ) and time scales ( seconds to months ) . Our systems pharmacology approach provides a unique format for predicting , at a single granuloma level , the potential effects of these three different FQs .
To predict and compare FQ efficacy in granulomas we use our mechanistic computational model , GranSim ( Fig 1 ) [23–26] . GranSim is a spatio-temporal model of granuloma formation and function that incorporates macrophage and T cell recruitment , migration and interaction; secretion and diffusion of chemokines and cytokines; Mtb growth and phagocytosis; and caseation . In the context of these in silico granulomas , GranSim simulates antibiotic plasma PK , tissue PK and PD [20–22] . GranSim is implemented as in [20–22] with three updates made in this work: 1 ) inclusion of fluoroquinolone dynamics ( previous versions include only isoniazid and rifampin ) ; 2 ) dynamic representation of cellular uptake of antibiotics ( previous versions assume pseudo-steady state ) ; and 3 ) antibiotic binding to caseum and normal lung tissue ( previous versions approximate binding by using effective diffusivity parameters ) . These changes were necessary for model calibration to experimental FQ data . We estimate GranSim parameters by calibrating to plasma PK , tissue PK , and PD data from in vitro and rabbit studies performed in this work , and from human studies described in the literature ( Table 2 ) . The plasma PK model within GranSim reproduces rabbit plasma concentrations of FQs ( Fig 2 ) . GranSim captures temporal concentration measurements in homogenized cellular and caseous rabbit granulomas ( Fig 3 ) , as well as qualitative differences in the spatial distribution of the FQs ( Fig 4 ) . PD parameters reproduce in vitro dose response curves specific to different bacterial subpopulations ( intracellular , extracellular replicating or extracellular non-replicating ) ( Figure in S1 Fig ) . Parameters used for simulations are listed in Table 3 . Interesting comparisons between the FQs emerge from the calibrated model . Plasma PK suggest higher peak concentrations of LVX and GFX ( Fig 2 ) , and faster inter-compartmental clearance for MXF ( Table 3 ) . Contrary to plasma PK , data from homogenized granulomas reveal higher MXF peak concentrations compared to GFX and LVX ( Fig 3 ) . This finding that MXF peak concentrations are lower in plasma and higher in granulomas compared to GFX and LVX highlights the need for more detailed PK studies in granulomas . Spatial distribution of FQs in rabbit and simulated granulomas reveal: poor penetration of GFX and MXF into caseum compared to LVX , GFX accumulation in cellular areas immediately surrounding caseum , and more evenly distributed MXF accumulation in cellular areas of granulomas ( Fig 4 ) . These spatial data suggest that average concentrations in homogenized granulomas might not represent antibiotic dynamics at specific locations where bacteria reside , e . g . caseum . PD parameter values show that MXF and GFX have similar C50 values ( concentration where 50% of maximum activity is achieved ) , while LVX has higher C50 values against both intracellular and extracellular Mtb . Model parameters also provide insight into the mechanisms behind FQ spatial distribution and function . Tissue PK parameter values indicate that: higher penetration of LVX into caseum is due to higher effective diffusivity in granulomas and slightly lower binding to caseum; accumulation of MXF in cellular areas is due to higher uptake into host cells; and GFX accumulation around caseum is due to slightly faster binding to caseum compared to MXF . PD parameter values indicate that MXF , LVX and GFX all have steep dose response curves , suggesting that these drugs would have very little effect at sub-C50 concentrations . Integration of multiple datasets ensures that our computational model captures spatial and temporal in vivo dynamics , while being consistent with in vitro and literature observations . Predictions for FQ efficacy in granulomas are now possible . For example , is better penetration of LVX into the caseum able to overcome its higher C50 values ( i . e . are spatial granuloma PK differences relevant in the context of PD differences ) ? Is the higher concentration of MXF in granulomas able to overcome its poor relative penetration into caseum ( i . e . are spatial granuloma PK differences relevant in the context of temporal PK differences ) ? We use our systems pharmacology model to address these questions . Before predicting FQ efficacy we explore how human vs . rabbit plasma PK affects the distribution of FQs in granulomas . There are notable differences in FQ plasma PK between rabbits and humans . To predict if human plasma PK would affect the observed spatial distribution of FQs within granulomas , we simulate treatment using human plasma PK parameters that we fit to existing data [9] . Parameter differences between rabbit and human data suggest faster absorption and slower clearance of all three FQs in humans compared to rabbits ( Table 3 ) . Spatial distributions in simulated granulomas are similar when using human or rabbit plasma PK parameters ( Figure in S3 Fig ) . Temporal differences in granulomas reflect differences in plasma PK between rabbits and humans , most notably more antibiotic accumulation within granulomas at 24 hours post dose with human plasma PK compared to rabbit plasma PK . This is a result of slower plasma clearance in humans ( Table 3 ) , resulting in slower movement of FQs out of the lung tissue into blood following peak concentrations . These results indicate that the qualitative spatial distribution of FQs is relatively insensitive to plasma PK . Taken together , our simulations suggests that the rabbit model provides an accurate representation of FQ spatial distribution within human granulomas if the granulomas are similar . To compare FQ efficacy , we simulate 6 months of daily therapy with each FQ in a collection of 210 in silico granulomas , starting at day 380 post-infection , using human plasma PK parameters . We use the following metrics to quantify FQ efficacy: bacterial load per granuloma during and after treatment , percentage of granulomas sterilized and time to sterilization . We also characterize FQ treatment in terms of immune responses within granulomas . Bacterial load per granuloma during and after treatment is similar for MXF and LVX ( Fig 5A ) , with higher bacterial load following GFX treatment . For all three FQs , treatment responses comprise a sharp initial decline followed by a slower decline . This biphasic response is widely observed during TB therapy [28] . Evaluating the response of specific bacterial subpopulations to treatment reveals that MXF sterilizes the intracellular bacterial subpopulation more quickly than LVX and GFX ( Fig 5B ) . The extracellular replicating subpopulation is effectively sterilized ( falling below an average of 1 bacterium per granuloma ) within 10 to 13 days by all three FQs ( Fig 5C ) . The non-replicating subpopulation ( residing in the caseum ) shows a very slow decline for all three FQs ( Fig 5D ) , and is responsible for the second phase in the biphasic kill curve in Fig 5A . Besides FQ effects on bacterial load within granulomas , we can also track immunological changes within granulomas during treatment . Upon infection , macrophages in the lung are activated in response to inflammatory cytokines ( TNF-α , IFN-γ ) and/or the presence of bacteria [29–31] . Furthermore , computational and non-human primate studies have shown that a balance between concentrations of inflammatory cytokines ( e . g . TNF-α ) and anti-inflammatory cytokines ( e . g . IL-10 ) is an important determinant in controlling bacterial growth in granulomas while limiting tissue damage [23 , 32] . The number of activated macrophages and the ratio of TNF-α concentration to IL-10 concentration in our simulation is therefore used as two metrics of inflammation . The inflammation metrics reflect the predicted bacterial differences between the FQs . MXF sterilizes the intracellular population more quickly than LVX and GFX , thereby eliminating infected macrophages , which are important drivers of host inflammation . As a result , numbers of activated macrophages and ratio of TNF-α concentration to IL-10 concentration both decline more quickly during MXF treatment compared to LVX and GFX ( Fig 5E and 5F ) . Other metrics of inflammation ( e . g . number of activated cytotoxic or IFNγ-producing T cells ) show little differences between FQs . These results suggest that bacterial killing by immune mechanisms continue to play a role during GFX and LVX treatment , and are less prominent during MXF treatment . The FQ concentration experienced by each of the bacterial subpopulations ( Fig 6 ) reveals the cause for the observed bacterial load dynamics . Intracellular bacteria are exposed to concentrations above C50 , BI for more than half of the dosing period for MXF , but not LVX or GFX ( Fig 6A ) . Extracellular replicating bacteria are exposed to FQ concentrations well in excess of their effective concentrations throughout most of the dosing period ( Fig 6B ) . We predict that non-replicating subpopulations see concentrations ~3-fold lower than their effective concentrations ( Fig 6C ) . Time to sterilization and percentage of granulomas sterilized are captured by Kaplan-Meier curves ( Fig 7 ) . Our simulations predict that MXF and LVX sterilize significantly higher percentages of granulomas compared to GFX . Based on these data we conclude that the three FQs are similar , with MXF having a slight advantage over LVX ( faster intracellular killing ) , and LVX a slight advantage over GFX ( more granulomas sterilized ) . To determine the effect of parameter uncertainty on our efficacy predictions , we quantify the effect of individual parameters on model outputs , as described in Sensitivity Analysis in Methods and in [33] . Briefly , we simultaneously sample all antibiotic parameters in ranges spanning values for the three FQs ( Table in S2 Table ) , and calculate partial rank correlation coefficients ( PRCCs ) between each parameter and model output ( Table in S3 Table ) . Consistent with previous results [20] , PRCCs reveal that plasma PK parameters ( plasma clearance rate constant , CL ) , tissue PK parameters ( cellular accumulation ratio , a , and permeability coefficient , PC ) and PD parameters ( maximum intracellular activity , Emax , BI , and C50 for intracellular Mtb , C50 , BI ) are drivers of infection and inflammation in the model . Outputs driven by these parameters include: bacterial load , macrophage activation , T cell activation , and TNF-α and IL-10 production . There is relatively low uncertainty in the plasma and tissue PK parameters , since they are estimated from calibration to multiple in vitro and in vivo data sets . PRCCs indicate that uncertainty in intracellular PD parameters influences model outputs , while extracellular and non-replicating PD parameters do not ( Table in S2 Table ) . This result is expected based on the concentration profiles in Fig 6 . Intracellular bacteria are exposed to antibiotic concentrations close to the measured ranges for C50 , BI , and therefore uncertainty in these values would affect predicted efficacy . I . e . if the in vivo C50 , BI values are significantly higher or lower than the in vitro measured values , the intracellular bacterial population would have a more or less significant role , respectively , in the long-term bacterial response to treatment in our simulations . It is not currently possible to directly measure intracellular PD parameters in vivo , and we therefore rely on in vitro measurements . The importance of these parameters in our efficacy predictions highlight the need for controlled in vivo efficacy studies that would allow for indirect estimation of PD parameters through calibration of bacterial loads to per-granuloma experimental data . Nonetheless , unless one FQ’s PD parameters are more sensitive to in vitro conditions than the others , our conclusion of MXF having an advantage over LVX and GFX should still hold . EBA , defined as the daily decrease in sputum bacterial burden measured in colony forming units ( CFU ) , is frequently used to assess the efficacy of a single drug in the first 7 or 14 days of treatment in clinical trials [34] . Here , we introduce a new term: ‘in silico EBA’ , which is defined as the daily decrease in simulated bacterial burden per granuloma . In silico EBA is also calculated for individual bacterial subpopulations , e . g . the ‘in silico intracellular EBA’ is the daily decrease in simulated intracellular bacteria per granuloma . Compared to clinical EBA measured from sputum [8] , in silico EBA is lower for all three FQs ( Table 4 ) . This is expected since our model tracks all bacteria in granulomas , whereas sputum samples contain a subpopulation of bacteria that may not fully represent the population in granulomas . This discrepancy between Mtb found in sputum vs . granulomas has been implicated in the poor ability of clinical sputum EBA to predict sterilization and long-term treatment outcomes [35] . In silico intracellular and extracellular replicating EBA more closely resemble clinical sputum EBA , compared to in silico non-replicating EBA , suggesting that intracellular and extracellular replicating subpopulations could be enriched in sputum . This is in agreement with human data showing high proportions of intracellular bacteria in sputum [36] . In silico intracellular EBA confirms that MXF is more efficacious than GFX and LVX . All three FQs have similar EBA against extracellular and non-replicating bacteria . With any TB treatment there is a risk of patient non-compliance ( inconsistent dosing throughout the treatment period ) or treatment interruption ( incomplete treatment ) , often arising from side effects and long treatment duration [37] . Our results show that both bacterial load and host immune responses decline more quickly during MXF treatment , compared to LVX and GFX treatment ( Fig 5 ) . This raises the question: is MXF treatment more sensitive to non-compliance or treatment interruption due to lower immune responses ? Or , is the bacterial population sufficiently controlled by the antibiotic such that the lower inflammatory response does not affect infection control , even during non-compliance or treatment interruption ? We predict the efficacy of each FQ during non-compliance by simulating 6 months of daily therapy with random skipping of 20% of the doses . This threshold is chosen because it is commonly used to define patients as ‘compliant’ in clinical trials [38] . Bacterial load after treatment shows increases under non-compliance conditions for GFX , MXF and LVX therapy , relative to full compliance ( Fig 8 ) . However , bacterial loads during non-compliant MXF and LVX treatment is lower than fully compliant GFX treatment . Kaplan-Meier curves comparing each FQ in the compliant vs . non-compliant scenario show noticeable , but statistically insignificant , differences for LVX and MXF ( Fig 9 ) . However , percentage of granulomas sterilized during non-compliant conditions for MXF ( 18% ) and LVX ( 20% ) treatments is still higher than during fully compliant GFX treatment ( 13% ) . These small differences at the granuloma level could manifest as clinically significant if we consider that each patient likely has multiple granulomas [2 , 39 , 40] . For example , if a single granuloma has a 5% probability of failing treatment , a person with 10 granulomas has a 40% probability of failing treatment . Studies in non-human primates indicate they have on average 46 granulomas [41] , so this is a significant factor . We predict the efficacy of each FQ under treatment interruption by simulating 10 or 70 days of daily treatment , after which we stop treatment for the rest of the 6-month period . We choose 10 days based on clinical studies suggesting that treatment interruptions start increasing around 2 weeks of treatment [42] , and our earlier results suggest that differences in immune response and bacterial load is most pronounced at this time . We choose 70 days for late interruption simulation time because at that point the immunological differences between FQs have largely disappeared ( Fig 5E and 5F ) . Bacterial load shows sharp increases following interruption of GFX and LVX treatment , and a return to the pre-treatment trajectory for all bacterial subpopulations ( Fig 10A–10D ) . In contrast , while interruption of MXF treatment results in an increase in all bacterial subpopulations , the increase is slower and the infection follows a slowed trajectory compared to pre-treatment . We can visualize the opposing forces of infection and immune responses by defining an immune score ( a collective metric of immune response ) and an infection score ( a collective metric of infection severity ) . The immune score is defined as: ( Activated macrophages* + Activated IFN-γ-producing T cells* + Activated cytotoxic T cells* + concentration of free TNF-α * ) /4; where ‘*’ indicates that the value is normalized to its value at the start of treatment . Similarly , the infection score is defined as: ( Infected macrophages * + Chronically Infected macrophages * + Extracellular Replicating Mtb* + Extracellular Nonreplicating Mtb* ) /4 . Tracking the immune and infection scores throughout treatment ( Fig 10E and 10F ) indicates that although the immune response is weaker during MXF treatment , it is sufficient to control the significantly reduced bacterial population . If treatment is interrupted after 70 days , the immune score and infection score remain stable following MXF and LVX interruption , while GFX interruption results in a slight increase in infection score ( Table in S4 Fig ) . Based on our in silico predictions that MXF-treated granulomas have lower bacterial loads and lower levels of treatment failure during non-compliance and treatment interruption , MXF is recommended over LVX and GFX in patients deemed at high risk of non-compliance or treatment interruption .
There are a number of new anti-TB antibiotics and antibiotic regimens in development and in various stages of clinical testing [43 , 44] . To maximize the useful lifespan of new and existing antibiotics , we need to optimize their implementation . Efficacy of any antibiotic depends on a combination of factors ( Table 1 ) , and in a nonlinear and often non-intuitive way . The complexity stems from granuloma pathology , host dynamics , pathogen interactions and drug properties [4] . Systems pharmacology approaches that combine host , pathogen and antibiotic dynamics are ideal tools to study these complexities in a single model system , and are valuable in identifying promising treatment regimens to advance to animal and clinical studies . We use a systems pharmacology approach to compare efficacy of three FQs in TB granulomas , concluding that MXF has a small but potentially clinically significant advantage over LVX , and LVX over GFX . MXF outperforms LVX and GFX in terms of total bacterial load , EBA and efficacy during non-compliance and treatment interruption . MXF and LVX each outperform GFX in terms of time to granuloma sterilization as well as percentage of granulomas sterilized . We would therefore recommend MXF over LVX , and LVX over GFX . Our predictions are currently being tested in rabbit models of TB , and will inform future studies in non-human primates . These studies will also be used to refine estimates of important in vivo intracellular PD parameters for future simulations . Our results could help guide FQ selection for MDR treatment as well as for future clinical trials for drug sensitive TB treatment . In addition to recommendations for future treatment and trials , our work also provides insight into clinical trial results . Recent phase III clinical trials explored the possibility of using MXF or GFX to shorten the 6-month treatment regimens prescribed for drug-sensitive TB [45–47] . All three trials failed to show non-inferiority compared to the standard 6-month regimen . In contrast , preclinical results in mouse models of TB showed FQs can improve cure rates [48 , 49] or bactericidal activity over shorter time scales [50 , 51] . Phase II clinical studies showed a larger decline in sputum bacterial load when FQs are substituted into the standard regimen [18 , 52 , 53] , but the 8 week time points evaluated in these phase II studies did not predict long term outcomes such as sterilization and recurrence that appeared in the phase III studies [45] . Our computational approach could explain why MXF and GFX failed to improve treatment outcomes in these 4-month regimens . In previous studies we found that granulomas that fail to sterilize with INH and RIF treatment contain mostly intracellular and non-replicating Mtb [20] . Our results here indicate that GFX and MXF would be unable to sterilize the non-replicating bacterial subpopulation . Taken together , these results suggest that MXF and GFX would be complementary to INH or RIF in the tested 4-month regimens by targeting the intracellular populations not eliminated by INH or RIF . However , the non-replicating populations that survive INH or RIF in the 4-month regimens would still persist in the context of MXF or GFX . Our results therefore predict that non-inferiority of these 4-month regimens could be due to non-replicating bacteria that persist throughout therapy , contributing to subsequent relapse . Toward the goal of optimizing TB treatment regimens that rely on multiple drugs , in future simulations we will explore the performance of these FQs in combination with INH and/or RIF and other anti-TB antibiotics . Combination therapy presents a number of challenges that can be studied using computational approaches . The ability of our method to track responses of different bacterial subpopulations to treatment will allow us to design and optimize combination therapies that effectively target all bacterial subpopulations . One particular challenge we can address is predicting the risk of ‘effective monotherapy’ . Effective monotherapy occurs when spatial or temporal windows of monotherapy arise , even under combination therapy , due to PK differences between the antibiotics given and could lead to inadvertent selection of drug resistant bacteria [19 , 54 , 55] . Beyond windows of effective monotherapy , optimizing multi-drug therapy is complicated by drug-drug interactions . The inherent properties of antibiotics ( e . g . how they are metabolized ) can result in complex networks of interaction ( synergy/antagonism ) [56 , 57] that also influence the selection of drug resistant bacteria [58] . While such detailed interaction networks are not currently available for Mtb , work in Mycobacterium marinum [57] could inform future optimization studies . We predict that FQ concentrations inside granulomas must be at least 3-fold higher than those simulated here to eliminate the non-replicating bacterial population . Indeed simulations with higher doses predict granuloma sterilization within 20 days ( data not shown ) . These doses would likely result in toxicity [59] . Targeted or inhaled drug delivery strategies could be used to increase the concentration within granulomas while lowering systemic distribution and therefore toxicity [22 , 60] . However , PK of antibiotics after release from such targeted delivery vehicles determines the feasibility of such approaches . For example , we previously predicted that INH is suitable for inhaled delivery , but RIF’s PK would require unrealistically high carrier loadings [22] . It is difficult to anticipate whether inhaled delivery would be a feasible alternative to oral dosing for FQs based on their plasma and tissue PK parameters derived here . Future studies would have to systematically explore the delivery vehicle parameter space to answer this question . EBA is a treatment outcome metric commonly used to compare antibiotic efficacy in early clinical trials , but the EBA has a poor ability to predict sterilization and long-term treatment outcomes [35 , 61] . The inability of bacterial load measurements in sputum to capture bacterial dynamics inside granulomas is supported by our model results that show lower in silico EBA compared to clinical sputum EBA . Systems pharmacology approaches could help extend the impact of EBA studies by predicting underlying bacterial dynamics . As with all computational models , the necessary assumptions made in our model place limitations on the predictions presented here . Due to the complexity of the system , the model has a large number of parameters that are known with varying degrees of certainty . When possible , we increase our confidence in parameter values by fitting to multiple experimental datasets ( e . g . LCMS and MALDI-MSI to estimate diffusivity ) and by including inter-individual variation in the calibration and prediction simulations . In vivo efficacy studies can assess the effect of these limitations on our current predictions , and help lessen these effects for future predictions . Systems pharmacology provides a platform to integrate sometimes-conflicting experimental data with computational modeling to further our understanding of PK/PD interactions in an in vivo setting . It also allows us to narrow the design space of combinations of drugs to better determine optimal treatments for patients . Here we have focused on FQs , but the approach can be applied in any setting of drug distributions in specific tissues . In TB , multiple drug regimens are used for long periods of time and thus the next necessary step to increasing our understanding is to perform virtual clinical trials that will allow us to predict the right combinations of drugs to shorten treatments .
All animal studies were carried out in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health , with approval from the Institutional Animal Care and Use Committee of the New Jersey Medical School , Newark , NJ , and the National Institute of Allergy and Infectious Diseases ( National Institutes of Health ) , Bethesda , MD . The experimental data we obtained for model calibration is outlined here , and detailed below . In vitro pharmacodynamic data comprise dose response curves in liquid culture media [27] as well as in Mtb-infected bone marrow derived macrophages from C57Bl/6 mice . In vitro pharmacokinetic data comprise drug uptake in human THP-1 cells and caseum binding . In vivo pharmacokinetic studies in rabbits include: temporal LC/MS-MS of antibiotic concentrations in plasma and homogenized granulomas between 0 . 5 and 24 hrs following dosing of each FQ; and MALDI-MSI ( matrix-assisted laser desorption ionization—mass spectrometry imaging ) providing semi-quantitative images of antibiotic spatial distribution within granulomas . Parameters used in the model are known with varying degrees of certainty depending on the process that the parameters describe and if this process is experimentally measurable . Estimation of specific parameters is discussed in more detail below , but briefly we take a combination of the following approaches: A summary of experimental data and how they are integrated into our computational framework is given in Table 2 . To quantify the influence of individual antibiotic parameters on model outcomes , we perform a sensitivity analysis ( SA ) . We sample all antibiotic parameters simultaneously and uniformly using Latin hypercube sampling ( LHS ) [33 , 69 , 70] . Parameters and ranges used for SA are listed in Table in S2 Table . Parameters were sampled 400 times , and each set of parameters was simulated in six unique granulomas . Simulations consisted of daily treatment between 380 and 410 days post infection with a dose of 400 mg/kg . Model sensitivity is quantified using Partial Rank Correlation Coefficients ( PRCC ) between model parameters and model outputs . Model outputs are averaged over the six granulomas tested . A p-value < 0 . 01 is considered significant for PRCC . | Tuberculosis ( TB ) is caused by infection with the bacterium Mycobacterium tuberculosis ( Mtb ) and kills 1 . 5 million people each year . TB requires at least 6 months of treatment with up to four drugs , and is characterized by formation of granulomas in patient lungs . Granulomas are spherical collections of host cells and bacteria . Fluoroquinolones ( FQs ) are a class of drug that could help shorten TB treatment . Three FQs that are used to treat TB are: moxifloxacin ( MXF ) , levofloxacin ( LVX ) or gatifloxacin ( GFX ) . To date , it is unclear if one FQ is better than the others at treating TB , in part because little is known about how these drugs distribute and work inside the lung granulomas . We use computer simulations of Mtb infection and FQ treatment within granulomas to predict which FQ is better and why . Our computer model is calibrated to multiple experimental data sets . We compare the three FQs by multiple metrics , and predict that MXF is better than LVX and GFX because it kills bacteria more quickly , and it works better when patients miss doses . However , all three FQs are unable to kill a part of the bacterial population living in the center of granulomas . Our results can now inform future experimental studies . | [
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"ve... | 2017 | Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach |
Noncoding RNAs ( ncRNAs ) are emerging as key regulators of cellular function . We have exploited the recently developed barcoded ncRNA gene deletion strain collections in the yeast Saccharomyces cerevisiae to investigate the numerous ncRNAs in yeast with no known function . The ncRNA deletion collection contains deletions of tRNAs , snoRNAs , snRNAs , stable unannotated transcripts ( SUTs ) , cryptic unstable transcripts ( CUTs ) and other annotated ncRNAs encompassing 532 different individual ncRNA deletions . We have profiled the fitness of the diploid heterozygous ncRNA deletion strain collection in six conditions using batch and continuous liquid culture , as well as the haploid ncRNA deletion strain collections arrayed individually onto solid rich media . These analyses revealed many novel environmental-specific haplo-insufficient and haplo-proficient phenotypes providing key information on the importance of each specific ncRNA in every condition . Co-fitness analysis using fitness data from the heterozygous ncRNA deletion strain collection identified two ncRNA groups required for growth during heat stress and nutrient deprivation . The extensive fitness data for each ncRNA deletion strain has been compiled into an easy to navigate database called Yeast ncRNA Analysis ( YNCA ) . By expanding the original ncRNA deletion strain collection we identified four novel essential ncRNAs; SUT527 , SUT075 , SUT367 and SUT259/691 . We defined the effects of each new essential ncRNA on adjacent gene expression in the heterozygote background identifying both repression and induction of nearby genes . Additionally , we discovered a function for SUT527 in the expression , 3’ end formation and localization of SEC4 , an essential protein coding mRNA . Finally , using plasmid complementation we rescued the SUT075 lethal phenotype revealing that this ncRNA acts in trans . Overall , our findings provide important new insights into the function of ncRNAs .
Eukaryotic cells express a wide variety of RNAs that do not code for proteins but contribute to the many essential functions within cells . The process of protein synthesis by translation requires ribosomal RNAs ( rRNAs ) to form the ribosomal subunits and transfer RNAs ( tRNAs ) to bring the amino acids to the ribosome [1 , 2] . Another class of RNAs called small nucleolar RNA ( snoRNAs ) predominantly catalyze the modification or processing of other RNAs , but additional novel functions for snoRNAs are emerging [3] . The small nuclear RNAs ( snRNAs ) of the spliceosome are required for the recognition and removal of introns from pre-messenger RNA [4] . The functions of most of these so called classical noncoding RNAs ( ncRNAs ) have been known for some time . More recently , expression analysis of eukaryotic genomes has established that pervasive transcription produces an abundance of ncRNAs whose functions are largely unknown [5–9] . In human cells , where some ncRNA functions are known , there tends to be three mechanistic themes for ncRNA function where ncRNAs act as either decoys to titrate proteins away from their binding sites , scaffolds to bring proteins together or guides to recruit proteins to DNA [10] . A number of methods to probe the functional significance of the numerous ncRNAs in humans have been utilized . For example , ncRNA gene deletion , targeting ncRNAs with RNAi and repression of ncRNA transcription with CRISPR based methods are just a few techniques used to investigate the functions of expressed human ncRNAs [11–15] . Mutations in ncRNAs are also increasingly being associated with human diseases [16–18] . In the yeast Saccharomyces cerevisiae , tiling arrays and strand-specific RNA sequencing analyses have identified novel classes of ncRNAs that are distinct from the classical ncRNAs . Two classes of ncRNAs were initially identified according to their half-life in the cell , the stable unannotated transcripts ( SUTs ) had a relatively long half-life whereas the cryptic unstable transcripts ( CUTs ) were RNAs with a short half-life and were revealed only after deletion of the exosome complex exoribonuclease Rrp6 [9 , 19] . Deletion of the cytoplasmic exonuclease Xrn1 , followed by RNA sequencing , revealed another class of ncRNAs termed Xrn1-sensitive unstable transcripts ( XUTs ) [20 , 21] , some of which overlap with either a SUT or CUT . Subsequently , depletion of the RNA binding factor Nrd1 revealed a fourth class of ncRNA termed Nrd1-unterminated transcripts ( NUTs ) [22] and deletion of the histone methyltransferase Set2 has identified yet another class of ncRNA called the Set2-repressed antisense transcripts ( SRATs ) [23] . With the numbers of these yeast ncRNAs in the thousands only a very small proportion have been ascribed a function to date . Where there are examples of ncRNA function in yeast one emerging theme is that ncRNA transcription can either induce or repress the expression of an adjacent gene [24–27] . One mechanism whereby ncRNA expression can induce or repress nearby gene expression is through chromatin modification [28] . An investigation into the influence of transcription by 180 anti-sense SUTs on the overlapping yeast genes found no direct relationship between antisense SUT transcription and protein abundance from the overlapping reading frame , indicating that the presence of an antisense SUT does not necessarily mean it regulates protein abundance from the sense protein coding gene [29] . Analysis of six intergenic SUTs using the synthetic genetic array ( SGA ) technology , to identify genetic interactions between deletions of these six SUTs and non-essential protein deletion strains , linked two SUTs to specific cellular functions and provided evidence that they may function in trans [30] . Many of the SUTs and CUTs are associated with specific RNA binding proteins within the yeast cell that are distinct from those bound by mRNAs to presumably allow them to carry out their specific function [31] . There is also evidence from ribosome profiling techniques that some yeast unannotated ncRNAs associate with ribosomes and can be translated into protein , so may not necessarily be noncoding [32–34] . As many of these studies have only investigated the function of a small subset of ncRNAs , a large scale analysis of ncRNA function in yeast would be useful for defining the role in the cell of the remaining ncRNAs . We have utilized the recently developed collection of ncRNA deletion strains [35] , which we have now expanded further , to carry out large-scale functional analysis of ncRNAs in yeast . In total 532 different ncRNA deletions were investigated encompassing tRNAs , snRNAs , snoRNAs , SUTs , CUTs and other annotated ncRNAs that do not overlap protein coding genes . Using both the heterozygous and haploid ncRNA deletion strain collections we have analyzed quantitatively , in a variety of growth conditions and phases , the influence that deletion of each ncRNA has on cellular fitness . This fitness analysis identified novel environmental-dependent haplo-proficient and haplo-insufficient growth phenotypes which provided key information on ncRNA function . Additionally , we have analyzed four essential ncRNAs of unknown function and have determined how deletion of these ncRNAs influenced surrounding gene expression . Moreover , we identified one ncRNA that works in trans and characterized a more detailed function for one of these ncRNAs in regulating the expression , 3’ end formation and localization of an essential protein coding mRNA . Overall , these data significantly expand the information available on the function of ncRNAs in yeast . Finally , the extensive catalog of functional data has been compiled into an easy to use website called YNCA providing an important resource for future ncRNA research .
The ncRNA deletion strain collections , as previously reported , contained 428 heterozygous diploid deletion strains in the reference strain BY4743 , 373 haploid ( MATa ) , 370 haploid ( MATα ) and 331 homozygous diploid ncRNA deletion strains giving a total of 1502 strains for functional analysis of ncRNAs [35] . Each ncRNA , that did not overlap with a protein coding gene , was deleted with the KanMX cassette while simultaneously introducing two unique molecular barcodes to allow identification of each deletion strain . We have now expanded this collection by the addition of 81 heterozygous diploid , 66 haploid ( MATa ) , 67 haploid ( MATα ) strains and 63 homozygous diploid ncRNA deletion strains to give a total of 1779 strains ( S1 and S2 Tables ) . Within these collections 532 different individual ncRNAs have been deleted in at least one strain background . These new combined collections of strains were utilized for fitness profiling to determine how ncRNA deletion affected the growth of cells under a variety of conditions . To quantify the impact of ncRNAs on cellular fitness , competition experiments were carried out using the heterozygous deletion collection , with the deletion strains pooled and grown in six different liquid media . Two biological repeats were carried out for each condition . After an initial batch phase , the strains were propagated in continuous culture ( steady state ) , an open system in which the amount of nutrients and pH are kept constant , allowing small fitness differences to be detected [36] . Specifically , cells were grown under carbon-limited and nitrogen-limited conditions at both 30°C and 36°C . Cells were also grown under carbon-limited and nitrogen-limited conditions at 30°C in the presence of 100mM LiCl which is known to inhibit the exoribonuclease Xrn1 and stabilize RNA [37] . Culture samples were removed for analysis at the beginning ( initial pool , P ) and end of the batch growth ( B ) , at early steady state ( ESS ) , mid steady state ( MSS ) and late steady state ( LSS ) time points ( Fig 1A ) to compare the composition of these populations with each other . Genomic DNA from each sample was isolated and the unique molecular barcodes identifying each deletion strain were amplified for next generation sequencing ( Bar-Seq ) [38–40] to determine the abundance of each ncRNA deletion strain in the population . As there were two biological repeats a total of four independent barcodes were sequenced for each ncRNA deletion strain . Under-representation of specific deletion strains highlights haplo-insufficient phenotypes , namely ncRNAs that are quantitatively important for phenotypic maintenance . Over-representation of deletion strains ( haplo-proficient phenotypes ) suggest that lowering the copy number of specific ncRNAs is beneficial in that particular environmental context . We first compared the population fitness profile between the initial pool and batch stage to identify strains that displayed either haplo-insufficiency or haplo-proficiency in the six different conditions tested ( Fig 1B and 1C; S3–S10 Tables ) . The tRNA , tR ( CCU ) J , also known as HSX1 , displayed an extreme haplo-insufficient phenotype between the pool and batch stages in all six conditions we tested . The tRNA tR ( CCU ) J is a single copy rare tRNA gene [41] and is clearly required for batch growth of yeast . The reduced fitness of the tR ( CCU ) J deletion strain from pool to batch indicates that the function of tR ( CCU ) J is critical when nutrients become limiting . Reduced fitness of the tR ( CCU ) J deletion strain was also validated in monoculture under nutrient rich ( YPD ) , carbon-limited and nitrogen-limited conditions at 30°C ( S1 Fig ) . Another tRNA , tA ( UGC ) O , displayed haplo-insufficiency between the pool and the batch stage in both carbon-limited and nitrogen-limited conditions , but only at 36°C ( Fig 1B and 1C; S10 Table ) , suggesting that this tRNA is required for fitness under conditions of heat stress . A number of the heterozygote deletion strains displayed better growth , haplo-proficiency , between the pool and batch stages . Interestingly , CUT248 deletion was haplo-proficient in all the conditions where nitrogen was limited ( Fig 1C; S10 Table ) which was confirmed in monoculture ( S1 Fig ) . CUT248 is located near DPS1 ( Fig 2A ) which is known to be up-regulated during yeast fermentation in the presence of diammonium phosphate [42] . Analysis of DPS1 expression by quantitative real-time PCR ( qRT-PCR ) confirms that deletion of CUT248 induces an increase in DPS1 expression in rich media and nitrogen-limiting conditions ( Fig 2A ) . CUT248 therefore appears to repress DPS1 transcription . Lowering the amount of CUT248 in a diploid background , allows increased DPS1 expression , which could be beneficial for growth in nitrogen-limited conditions . In contrast to the deletion of CUT248 , overexpression of the CUT248 RNA sequence from a plasmid in a wild-type haploid strain BY4741 results in a slow growth phenotype ( S2 Fig ) suggesting further that the levels of CUT248 are important for cellular fitness . A noticeable influence of temperature on fitness can be observed in the strain carrying the SUT340 deletion during the pool to batch transition ( Fig 1B and 1C ) . SUT340 displays strong haplo-insufficiency at 36°C in both carbon-limited and nitrogen-limited conditions but not in any of the conditions at 30°C , revealing that this ncRNA with no known function is required for growth at high temperature . Additionally , RNA stabilization through inhibition of Xrn1 with LiCl triggers haplo-proficiency of CUT873 and tT ( AGU ) J specifically in carbon-limited conditions between the pool and batch stages ( Fig 1B; S10 Table ) . We have also tested the deletion mutants tA ( UGC ) O , SUT340 , CUT873 and tT ( AGU ) J in monoculture using the same conditions in which haplo-insufficient and haplo-proficient phenotypes were observed , and reconfirmed their phenotypes . The tA ( UGC ) O and SUT340 deletion mutant strains which were haplo-insufficient are both significantly less fit than the WT strain when grown in monoculture ( S3A Fig ) . Similarly , the CUT873 and tT ( AGU ) J deletion mutant strains which were identified as being haplo-proficient are both significantly fitter than the WT strain when grown in monoculture ( S3B Fig ) . Deletion of the overlapping ncRNAs SUT233/CUT707 results in haplo-insufficiency in four of the six conditions in the pool to batch transition ( Fig 1B and 1C; S10 Table ) which was confirmed in monoculture ( S1 Fig ) . SUT233 lies upstream of the gene HAP4 which codes for a transcription factor involved in the diauxic shift in yeast [43–45] . CUT707 lies upstream of KTI12 which codes for a protein that in yeast associates with the elongator complex required for tRNA modification [46 , 47] . HAP4 expression is increased during the diauxic shift to allow the upregulation of the glyoxylate cycle with HAP4 inducing the expression of approximately 88% of the proteins made during the diauxic shift [43 , 45] . Analysis of HAP4 and KTI12 expression by qRT-PCR confirms that in nitrogen-limiting conditions deletion of SUT233/CUT707 reduces expression of both HAP4 and KTI12 ( Fig 2B ) . To show the number of haplo-insufficient and haplo-proficient ncRNA deletion strains in common between conditions in the pool to batch experiments UpSet diagrams of intersecting sets have been provided ( S4 Fig ) . We next compared the fitness of the heterozygote ncRNA deletion strains between the early LSS and ESS stages ( Fig 3; S3–S9 and S11 Tables ) . By keeping nutrients , pH and growth rate constant we were able to quantify smaller differences in fitness in response to changes in temperature . For example , deletion of SUT089 displayed haplo-proficiency in both carbon-limited and nitrogen-limited conditions at 30°C . However , this haplo-proficiency of SUT089 was significantly buffered in both carbon-limited and nitrogen-limited conditions at 36°C . Another striking example of temperature affecting the fitness of a heterozygous diploid ncRNA deletion strain is the large increase in fitness of SUT467 in nitrogen-limited conditions when temperature is increased from 30°C to 36°C . We have also found that SUT471 is haplo-proficient in all six conditions , therefore its presence clearly limits growth in continuous culture conditions . Under continuous culture conditions tR ( CCU ) J , which displayed severe haplo-insufficiency in the pool to batch growth phase , did not display any significant growth defect ( Fig 3A and 3B; S11 Table ) . Therefore , analysis of deletion strains under continuous culture conditions clearly reveals additional phenotypes not seen in traditional batch culture where nutrients become limiting . To show the number of haplo-insufficient and haplo-proficient ncRNA deletion strains in common between conditions in the ESS to LSS experiments UpSet diagrams of intersecting sets have been provided ( S5 Fig ) . Co-expression analysis has been used widely to infer functional relationships between protein encoding genes [48–51] . Here we apply a similar approach to our fitness data from eight different data sets to look for ncRNA deletion strains with similar fitness profiles and uncover phenotypic networks in the heterozygous ncRNA deletion collection . Four clusters were identified for a total of 226 deletion mutants which accounts for approximately 40% of the original dataset ( Fig 4; S12 and S13 Tables ) . Our results indicate that deletion strains within each cluster followed the same fitness pattern throughout the eight testing conditions . Cluster 1 and 2 are the biggest containing 149 and 65 strains , respectively . Within these clusters the ncRNA deletion strains are separated into smaller sub-groups , sub-cluster 1 and sub-cluster 2 , based on direction of fitness changes . The other clusters are relatively small ( 8 and 4 strains ) and consist mainly of tRNAs and SUTs ( S13 Table ) . Cluster 1 encompasses strains with primarily specific response to temperature ( Fig 4A ) . As shown in the heat map , this response to temperature is particularly evident in the initial pool ( P ) to batch ( B ) transition where , in any media considered , a change in fitness can be seen when the temperature is raised from 30°C to 36°C . Lowering the dosage of some ncRNAs either increases ( Fig 4A , sub-cluster 1 ) or decreases ( Fig 4A , sub-cluster 2 ) cell fitness with increasing temperature . The results suggest that ncRNAs in this cluster are involved in the optimal growth during heat stress and general nutrient deprivation . For example , SUT643 in sub-cluster 1 may have a function in transcriptional regulation of the neighbouring gene IME1 , which is essential for meiosis , and is required for repression of HSP82 [52–54] . Our data indicate that lowering the dosage of SUT643 has a positive impact on yeast growth at high temperature , suggesting that the repression on HSP82 is partially lifted ( the quantitative fitness profile for SUT643 is shown in S6A Fig ) . Cluster 2 encompasses strains with specific response to growth phases , such as transition from P to B ( batch phase with nutrient depletion ) and from ESS and LSS stage ( continuous culture phase with constant nutrients and pH ) , suggesting that ncRNA deletion strains in this cluster become important when nutrient levels are not constant ( Fig 4B ) . In this case , lowering the dosage of some ncRNAs either increases ( Fig 4B , sub-cluster 1 ) or decreases ( Fig 4B , sub-cluster 2 ) cell fitness with nutrient depletions . When cells are about to reach stationary phase , there is a decline in overall transcriptional activities and several changes in cellular metabolism occur to store complex carbohydrates such as glycogen and trehalose [55–57] . Based on our data , sub-cluster 2 ( Fig 4B ) encompasses ncRNAs which are crucial for survival during the pool to batch stage . We found that some ncRNAs in this sub-cluster 2 are located next to genes that are highly correlated with transition to stationary phase . For example , SUT471 is located downstream of SNF11 and upstream of TPS2 . TPS2 encodes for a phosphatase in the last step of the trehalose pathway , important for carbon storage and is activated sequentially after diauxic shift and is suppressed fully before entering stationary phase [58 , 59] . SNF11 encodes for a subunit of the SWI/SNF chromatin remodelling complex , which is involved in transcriptional regulation of several genes at the onset of stationary phase [60 , 61] . Another example is SUT509 which is located downstream of the medium chain fatty acyl-CoA synthetase gene FAA2 which has a transcriptional profile similar to that of the gene TPS2 . The effect of SUT471 and SUT509 deletion on the neighbouring genes may , therefore , be responsible for their haplo-insufficiency in the pool to batch transition ( the quantitative fitness profiles for SUT471 and SUT509 are shown in S6B and S6C Fig ) . We further analyzed all four clusters for representation of different ncRNA classes and found no bias in the distribution of SUTs or CUTs ( FDR > 0 . 05 ) . In addition , we could not identify any common biological functions using the gene ontology terms of neighbouring genes for the ncRNAs that comprise the different clusters . To determine the influence of complete removal of a ncRNA on cell fitness we individually arrayed each strain of the haploid deletion collections on rich media ( YPD ) plates at 30°C and assessed colony size compared to the wild-type strain . The haploid deletion collections exhibited significant variation in fitness on YPD and this variation was detected across all types of ncRNA ( Fig 5; S14 and S15 Tables ) . The deletion overlapping both SUT233 and CUT707 ( Fig 2B ) , which displayed significant haplo-insufficiency as a diploid heterozygotic deletion in most conditions in the pool to batch growth ( Fig 1 ) , is the least fit in the haploid deletion collection ( Fig 5 ) . Deletion mutants of tL ( CAA ) A and SUT339 are respectively , the second and third least fit strains in the haploid collection on YPD media . The tL ( CAA ) A tRNA is part of a family of tRNAs for the leucine CAA codon and deletion of tL ( CAA ) A has previously been shown to significantly impair growth on YPD , whereas other members of this tRNA family do not display a severe fitness defect [62] ( S14 Table ) . Our data support the idea that there are major and minor copies in tRNA gene families and loss of different members of a tRNA family affect cellular fitness differently [62] . From the genomic location of SUT339 there is no immediately obvious reason how its deletion is affecting fitness . The SNR75 , tD ( GUC ) J3 and tE ( UUC ) B deletion strains are the top three fittest strains in this plate assay showing increased growth . SUT471 deletion also had a significantly positive effect on fitness in the haploid background ( Fig 5 ) . This positive effect on fitness is consistent with SUT471 being haplo-proficient in all of the continuous culture conditions ( Fig 3 ) , supporting the theory that SUT471 expression limits cell growth . ncRNA deletions showing little fitness change in the heterozygote background , but significant effects in the haploid background , have also been identified here ( S15 Table ) . These data demonstrate that useful fitness data can be obtained from the plate array method of phenotyping on solid media . Moreover , some of the most dramatic phenotypes which were scored via colony size ( Fig 5 ) are also seen in our continuous culture experiments ( Figs 1 and 3 ) . For example , the SUT004 , SUT107 , CUT356 , SNR10 and tQ ( UUG ) L deletion mutants which were haplo-insufficient in at least one of the continuous culture conditions , also displayed significantly impaired fitness in the haploid fitness screen . Expanding this array method to a variety of other growth conditions should further our understanding of ncRNA function . The heterozygote ncRNA deletion strains were induced to sporulate and the haploid spores dissected to reveal whether individual ncRNA gene deletion was essential for growth . Three percent of the ncRNA gene deletions ( 17 of 532 ) were found to be essential in nutrient rich conditions ( YPD ) , with thirteen of these ( i . e . snRNAs , snoRNAs , tRNAs ) already known to be essential ( S2 Table ) . Four novel essential ncRNAs were identified , SUT075 , SUT367 , SUT527 and SUT259/691 , and were found to be essential in separate biological replicates of the deletion strains ( S7 Fig ) . One of these essential ncRNAs , SUT527 ( also annotated as RUF20 ) , overlaps by 140 base pairs with the 3’ untranslated region ( UTR ) of the essential gene SEC4 , a GTPase required for vesicle-mediated exocytic secretion and autophagy [63 , 64] ( Fig 6A ) . To determine whether SUT527 essentiality was derived from its overlap with the 3’ UTR of the essential SEC4 , two shorter deletions of SUT527 were constructed with 40bp overlap and no overlap with the SEC4 3’ UTR . The shorter SUT527 deletion , that still overlapped the SEC4 3’ UTR , resulted in a non-viable phenotype , whereas the strain containing a SUT527 deletion with no overlap with the SEC4 3’ UTR was viable ( S8 Fig ) . This viability indicates that SUT527 essentiality is derived from the overlap with the 3’ UTR of the essential gene SEC4 and that deletion in this region does not generally cause silencing of SEC4 transcription . Transformation of the original SUT527 diploid deletion strain with a plasmid containing an approximately 1 . 4kb DNA fragment containing the SEC4 sequence known to complement SEC4 function [64] restored strain viability after sporulation and tetrad dissection . To understand whether the essential phenotype was caused by the deletion of the SEC4 3’ UTR in itself or caused by the interaction of the SUT527 RNA with the SEC4 3’ UTR , we reduced SUT527 expression in a haploid strain using a regulated Tet promoter [65] . We found that SEC4 mRNA expression was greatly decreased ( Fig 6B ) and SEC4 3’ UTR formation was affected when SUT527 expression was suppressed ( Fig 6C ) . The SEC4 3’ UTR is required for localization of SEC4 mRNA [66] . Fluorescent in situ hybridization ( FISH ) revealed that SUT527 displayed a similar punctate localization to SEC4 mRNA and SEC4 mRNA was mislocalized when SUT527 expression was switched off ( Fig 6D and 6E ) . Analysis of data sets from a global sequence analysis of small RNAs from S . cerevisiae strains engineered for RNAi to reveal the presence of dsRNAs [21] , identified small RNAs produced from SUT527 in the region of overlap with the SEC4 3’ UTR ( S9 Fig ) . The presence of these small RNAs in the region of overlap between SEC4 and SUT527 indicates that in vivo there is dsRNA formation between SUT527 and the SEC4 3’ UTR . In fact , FISH of SEC4 mRNA and SUT527 in the same cells with different coloured detection probes revealed that in cells approximately 10% of the SEC4 mRNA ( red ) and SUT527 RNA ( green ) puncta were found next to each other ( Fig 6F ) . We have therefore defined a molecular function for the ncRNA SUT527 and suggest that the physical interaction between SUT527 and the 3’ UTR of SEC4 influences SEC4 3’ end formation and mRNA localization . Of the four essential ncRNAs identified here ( SUT527 , SUT075 , SUT367 and SUT259/691 ) SUT527 , SUT075 and SUT367 are located adjacent to essential genes . Deletion of the ncRNA with the KanMX cassette could potentially remove essential regulatory elements for a nearby essential gene , or the expression from the KanMX module may influence the expression of a nearby essential gene . To determine the influence of deleting a single copy of SUT527 , SUT075 , SUT367 or SUT259/691 on the expression of nearby genes , qRT-PCR was used to analyze expression of nearby genes in the diploid deletion strains compared to the wild-type diploid strain . Analysis of the diploid SUT527 deletion strain revealed greatly reduced expression of SEC4 as expected ( Fig 7A ) . Deletion of the overlapping SUT259/691 ncRNAs increased the expression of the upstream and downstream non-essential genes EMP46 and GAL2 which are both transcribed in the same direction as the KanMX ( Fig 7B ) . SUT690 is located between EMP46 and SUT259/691 . It is plausible that SUT690 might be the target of SUT259/691 regulation . However , analysis of SUT690 expression in the ΔSUT259/691 strain reveals that SUT690 expression levels are unchanged ( S10 Fig ) . The deletion of either EMP46 or GAL2 alone does not result in a lethal phenotype , but since a double deletion mutant of EMP46 and GAL2 shows positive epistasis [67] , it is possible that overexpression of both genes gives the opposite effect and hampers fitness ( see Discussion ) . To test this hypothesis , we have cloned both EMP46 and GAL2 into the pBEVY-GA plasmid , containing a bi-directional GAL1/10 promoter . Overexpression plasmids with EMP46 , GAL2 or both EMP46 and GAL2 were created and transformed into the BY4743 background strain . The comparative fitness of these overexpression strains were then examined using spot assays . Solitary overexpression of GAL2 or EMP46 was not lethal , however they resulted in impaired fitness , particularly the overexpression of GAL2 ( Fig 8 ) . The simultaneous overexpression of GAL2 and EMP46 resulted in no cell growth and is therefore lethal ( Fig 8 ) . This lack of growth supports the idea that the lethality , observed in the SUT259/691 knockout strain , is a result of a combined increase in EMP46 and GAL2 expression . The partial deletion of SUT075 caused a large decrease in the expression of the essential gene PRP3 which is transcribed in the opposite direction to SUT075 and the KanMX expression ( Fig 7C ) . The decreased expression of the essential PRP3 may be the explanation for SUT075 lethality . Deletion of SUT367 caused an increase in the expression of the essential gene RPL3 which is transcribed in the same direction downstream of SUT367 ( Fig 7D ) . Interestingly , RPL3 is one of the few ribosomal protein genes in yeast that is neither duplicated nor contains an intron , both properties that are associated with increased ribosomal protein gene expression [68] . Therefore , increased expression of RPL3 may be detrimental to cells providing a reason for SUT367 lethality ( see Discussion ) . Overall , we observed that deletion of ncRNAs can both positively and negatively influence the expression of nearby genes and that in some cases can explain the lethality . To investigate further the essentiality of ncRNAs SUT527 , SUT075 , SUT367 and SUT259/691 , we overexpressed these SUTs from plasmids to discover any that could recover the lethal phenotype of the corresponding deletion strain and identify trans ncRNA effects . We constructed centromeric plasmids with each SUT expressed in either the sense or antisense orientation from the GAL1 promoter . These plasmids were then transformed into the corresponding heterozygote diploid deletion strains . These strains were then sporulated and tetrads dissected . Successful generation of viable haploid strains , containing the deleted essential SUT , would indicate the ability of the overexpressed SUT to function in trans . The SUT367 , SUT527 and SUT259/691 sense or antisense plasmids were unable to reverse lethality of the corresponding ncRNA deletion in the haploid background following sporulation and tetrad dissection . However , deletion of SUT075 was no longer lethal when the sense orientation SUT075 plasmid was present ( Fig 9A and 9B ) but was still lethal with the antisense SUT075 . This complementation of the SUT075 deletion strain lethal phenotype by the ectopic expression of SUT075 RNA indicates that SUT075 functions in trans . It is , therefore , plausible that SUT075 regulates expression of distal genes in the genome , not just the neighbouring PRP3 gene . The deletion of one copy of SUT075 in the diploid background ( Fig 7C ) significantly reduced expression of the adjacent PRP3 gene . Expression of PRP3 was measured in the heterozygote diploid SUT075 deletion strain with the sense SUT075 plasmid expressing the SUT075 ncRNA , to determine if the rescue of the lethal phenotype in the haploid progeny ( Fig 9A and 9B ) was the result of PRP3 expression levels being returned to normal . PRP3 expression was found to be 8 . 3 fold greater in the heterozygote diploid SUT075 deletion strain , when the SUT075 expression plasmid was present ( Fig 9C ) . Recovery of PRP3 expression , to levels greater than in wild type cells , suggests that the GAL1 promoter is stronger than the native SUT075 promoter . Overall , these data confirm that expression of SUT075 from a plasmid is able to up-regulate PRP3 expression in trans and reverse lethality in strains deleted for SUT075 . To ease the use and access of our extensive functional fitness data for future research , we have built a publicly accessible online resource called the Yeast ncRNA Analysis ( YNCA ) ( http://sgjlab . org/ynca/ ) to host the heterozygote and haploid deletion fitness profiles for each of the deleted ncRNAs . Data are searchable by neighbouring genomic features , ncRNA type , essentiality , chromosomal position and growth phenotype for each growth medium used , as well as searchable by ncRNA name as classified in Xu et al . ( 2009 ) [9] . The types of searchable neighbouring genomic features are known open reading frames , tRNA genes , snoRNA genes , centromeric and telomeric regions , autonomous replicating sequences ( ARS ) , long terminal repeats ( LTR ) , pseudogenes , LTR retrotransposons and transposon internal genes . The user can download both raw experimental values and statistical significance values from a results table specific to the search performed . The list of barcode TAGs associated with each strain is also available on the website . We plan to progressively expand the YNCA database to include homozygote deletion strain fitness data under a variety of conditions and results from future analyses .
By utilizing the newly developed ncRNA deletion strain collections in the yeast Saccharomyces cerevisiae we have carried out large scale profiling of ncRNA function under a variety of growth conditions and phases . The extensive functional fitness data can be accessed via the database YNCA ( http://sgjlab . org/ynca/ ) where the influence of individual ncRNA deletion strains on cellular fitness has been catalogued in an easy to navigate and searchable website . This large scale functional profiling has now provided valuable functional information on the deletion of 532 different ncRNAs which includes tRNAs , snoRNAs , snRNAs , SUTs , CUTs and various other annotated ncRNAs . We have also investigated in more detail four novel essential ncRNAs and determined the mechanisms by which they result in a lethal phenotype when deleted . Yeast strains deleted for individual tRNA genes have been previously constructed with these deletion strains tested for both growth rate and growth yield under a number of conditions [62] . While there is significant overlap between our collection and that of Bloom-Ackermann et . al . , there are strains that are unique to each collection ( S16 Table ) . Where there is overlap between collections we have observed similar growth phenotypes of tRNA deletion strains . For example , our observation that the tR ( CCU ) J deletion strain displays decreased fitness in all the conditions we tested during the pool to batch growth phase was also observed for the growth rate of the tR ( CCU ) J deletion strain in four of the six growth conditions used by Bloom-Ackermann et al [62] . Within tRNA families major and minor tRNAs have been identified where deletion of the major tRNA influences the ability of a deletion strain to grow under different conditions more than one of the minor tRNAs in the same family [62] . For instance , the tRNAs tR ( UCU ) E and tR ( UCU ) M2 were identified as being major tRNAs that are influenced the most by different growth conditions in their family [62] . In the six conditions tested here we have also found that the tR ( UCU ) E deletion displays decreased fitness in all six conditions in the pool to batch transition . However , we have identified tR ( UCU ) B in the same family , a deletion novel to our collection , that also displays decreased fitness in all six conditions in the pool to batch transition and tR ( UCU ) G1 as less fit in four of the six conditions in the pool to batch transition ( S4–S10 Tables ) . In contrast , we did not observe a significant decrease in fitness with deletion of tR ( UCU ) M2 in any of the six conditions in the pool to batch transition . Analysis of the tRNA deletion strains in the tR ( UCU ) family under continuous growth conditions did not identify any tRNAs in the tR ( UCU ) family that displayed a consistently significant decrease in fitness , when deleted , in any of the conditions we tested . These results suggest that tRNA levels are more important in conditions where nutrients become limited . By using continuous growth conditions , we have uncovered additional phenotypes for ncRNA deletion strains that are not observed under growth conditions where nutrients become limiting . Specifically , we have observed changes in fitness associated with temperature changes that were not observed in the pool to batch growth phase . Additionally , phenotypes observed in the pool to batch stage where nutrients are limited were not observed in continuous culture . By observing the fitness of the deletions strains by two distinct methods of cell culture we have produced an extensive catalog of fitness data for each of the heterozygous diploid deletion strains . Combined with our analysis of the haploid deletion collections arrayed on solid media , overall we present the most extensive analysis of ncRNA requirements for cellular fitness to date . These data have been compiled into a database called YNCA . YNCA uses the more sensitive ESS-LSS fitness change for search based on the heterozygote fitness profile , but displays both pool-batch and ESS-LSS fold change values in the detailed ncRNA-specific page . Both sets of data are downloadable . For each growth medium , the user can retrieve strains which display any or one selected fitness phenotype ( after statistical analysis; gain/loss of fitness or haplo-proficient/insufficient ) or obtain a list of all strains with available experimental data , for raw data download . Given that only a few examples of CUTs and SUTs have a known function and that the analysis of ncRNA function sometimes focuses on the regulation of neighbouring genes , the YNCA website offers the option to search by nearby genomic feature , hence facilitating the selection of candidate ncRNAs as gene-specific or feature-specific regulators . In the construction and analysis of the ncRNA deletion strains we have identified ncRNAs that are essential for cell growth . Many of these essential ncRNAs have been previously identified and annotated as essential ncRNAs . The five snRNAs ( U1 , U2 , U4 , U5 and U6 ) required for pre-mRNA splicing , the snoRNAs snR128 ( U14 ) and snR30 ( U17 ) , the RNA component of RNase MRP ( NME1 ) , the RNA component of nuclear RNase P ( RPR1 ) and tRNAs tR ( CCG ) L , tR ( CCU ) J , tS ( CGA ) C and tT ( CGU ) K have all been previously shown to be essential . Besides the known essential ncRNAs we have identified four novel ncRNAs that are essential when deleted . These four novel essential ncRNAs are SUT075 , SUT367 , SUT527 and SUT259/691 . The essentiality of SUT527 is caused by its overlap with the 3’ UTR of the essential protein coding gene SEC4 , as making smaller deletions that did not overlap the annotated 3’ UTR of SEC4 did not result in a lethal phenotype . It appears that the overlap of SUT527 with the 3’ UTR of SEC4 is required for both the stability of the SEC4 mRNA and for the localization of SEC4 mRNA . SEC4 mRNA localization is determined by its 3’ UTR [66] . There is evidence that SEC4 3’ UTR/SUT527 RNA duplexes are formed within cells [21] and we have observed that the SEC4 mRNA and SUT527 RNA localize in close proximity . A cytoplasmic function for other SUTs is very likely as a proportion of SUTs are transported to the cytoplasm where they have been proposed to exert their function [31] . The ncRNA SUT367 was found to be essential when deleted , but analysis of the nearby essential gene RPL3 in the diploid heterozygous deletion strain revealed that RPL3 expression is increased ( Fig 7D ) . Large scale screens have previously identified that overexpression of RPL3 causes growth impairment , disrupts the cell cycle [69] and induces chromosome instability [70] . The mechanisms by which deletion of SUT367 leads to RPL3 overexpression or how overexpression of a ribosomal protein gene leads to chromosome instability/cell cycle disruption and lethality is not clear . However , other ribosomal protein genes have also been identified to cause chromosome instability/cell cycle disruption leading to cell lethality when overexpressed [69 , 70] . We show that deletion of SUT367 prevents spores from germinating after meiosis , and it is plausible that the resulting overexpression of RPL3 is responsible for the inability of the spores to grow . A deletion of SUT259/691 is lethal and this deletion results in the overexpression of two adjacent nonessential protein coding genes EMP46 and GAL2 , but not the adjacent SUT690 , in the diploid heterozygote ( Fig 7B , S10 Fig ) . Individual overexpression of either EMP46 or GAL2 displays a slow growth phenotype on their own ( [71] and Fig 8 of this a manuscript ) . When EMP46 and GAL2 are overexpressed simultaneously the cells are unable to grow ( Fig 8 ) . Therefore , SUT259/691 are essential for the regulation of EMP46 and GAL2 , and when deleted cause an overexpression of these genes which causes lethality . The SUT075 is expressed in the opposite direction to the essential gene PRP3 with the deletion we made of SUT075 being 230 nucleotides away from the start of PRP3 and 143 nucleotides away from the stop codon of the non-essential gene JIP4 ( Fig 7C ) . We successfully used complementation to determine that expressing the full length SUT075 RNA from a plasmid in trans could rescue the essential phenotype of SUT075 deletion . Therefore , we have identified another example of a ncRNA that works in trans . The action of SUT075 is in part locally as the trans expression of SUT075 increases the expression of the adjacent essential gene PRP3 , but there is also the possibility that SUT075 acts elsewhere in the cell . As transcription of SUT075 produces an RNA that works in trans we investigated yeast ribosome profiling data and found that SUT075 does not associate with ribosomes so is unlikely to be translated into protein [34] . To date only a few examples of ncRNAs working in trans have been identified . The Ty1 RTL CUT ncRNA has been found to regulate Ty1 expression in trans [72] and the ncRNA PHO84 can work in trans to silence genes [73] . Recent work has found two new trans acting SUTs suggesting that trans acting ncRNAs may be more prevalent than previously thought [30] . Our identification of SUT075 as another trans acting ncRNA supports the view that there may be more trans acting ncRNAs identified in the future . Overall , analysis of these ncRNAs , initially identified as being essential , has revealed that the transcription of ncRNA can both positively and negatively influence the expression of adjacent genes or produce an RNA that can function on its own , indicating that regions of the genome identified as producing SUTs and CUTs are functional and do not represent just transcriptional noise . By exploiting the yeast ncRNA deletion collections we produced a large array of phenotypic data which is a useful resource for providing a snapshot of ncRNA function in the cell . By expanding the number of conditions investigated it is hoped that a picture can be built of how ncRNAs contribute to the fitness of cells in different environments . Here by exploring individual examples of ncRNA we have determined the molecular function of SUT527 and also showed that SUT075 works in trans , expanding the repertoire of cellular functions that require ncRNAs . As the characterization of the numerous ncRNAs continues , the use of ncRNA deletion collections in large-scale functional and interaction studies will ultimately provide information on how ncRNAs fit into the functional framework of the cell .
All S . cerevisiae strains and primers used are listed in S17 Table and S18 Table . Individual deletion strains or the collection of deletions strains are available on request . Methods for the construction of the deletion strain collections have been previously described [35] . In preparation for chemostat continuous culturing of the heterozygote collection , a pool of the deletion strains was prepared . A -80°C stock of heterozygous ncRNA deletion strains were grown overnight at 30°C in liquid YPD and the OD600 of each strain in the microtitre plate was read using a FLUOstar OPTIMA plate reader ( BMG Labtech ) . Subsequently each strain was normalised to an OD600 of 0 . 1 and pooled . The diploid heterozygote ncRNA deletion collection pool was grown in chemically defined F1 medium limited for glucose ( carbon limitation ) or nitrogen at 30°C and 36°C . The pooled heterozygous deletion strains were also grown in F1 medium limited for glucose or nitrogen at 30°C in the presence of 100mM LiCl . The diploid heterozygote ncRNA deletion collection pool was grown in batch culture for 24hrs and then switched to continuous culture where it took about 42 hrs to reach steady state . Steady state growth conditions were maintained for 30 generations . Samples were taken at the Pool ( P ) , Batch ( B ) , Early Steady State ( ESS , 48 hours after switching to continuous culture ) , Mid Steady State ( MSS , after 20 generations ) and Late Steady State ( LSS , after 30 generations ) stages then processed for Illumina sequencing of the barcodes to determine the abundance of each strain . Two biological repeats were carried out for each condition . Details of growth medium and continuous culture in chemostats are as previously described [36] . Genomic DNA was isolated from samples using the Wizard Genomic DNA Purification Kit ( Promega ) according to the manufacturer’s protocol . UPTAGs and DOWNTAGs were amplified with primers compatible with multiplexed Illumina sequencing . For the UPTAGs the forward primer was 5’AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGCTCTTCCGATCTGATGTCCACGAGGTCTCT and the reverse primer was 5’CAAGCAGAAGACGGCATACGAGATNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTCGACCTGCAGCGTACG . For the DOWNTAGS the forward primer was 5’-AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCGAGCTCGAATTCATCGAT and the reverse primer was 5’CAAGCAGAAGACGGCATACGAGATNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGGTGTCGGTCTCGTAG . The NNNNNN represents the 6-mer indexing tag used for multiplexing the different samples . Amplified TAGs were quantified with the KAPA library quantification kit ( KAPA Biosysytems ) and 10nM of the TAG libraries was used for Illumina sequencing . Sequenced reads were trimmed to contain just the TAG sequence using Trimmomatic [74] . Trimmed reads were mapped to a database of the TAG sequences using Bowtie2 [75] . A TAG was deemed to be identified if the trimmed sequenced read aligned to the full length of that TAG with a maximum of 1 mismatch . Summed counts for each of the two TAGS for a deletion strain were used as input for DESeq [76] . The Log2 fold change was determined between different growth stages and the changes with a p value of < 0 . 05 and 1 . 5 fold change were identified . Two primers , RUF20_P1 and RUF20_P2 ( S18 Table ) were designed to amplify the KanMX-TetO7 cassette from plasmid pCM325 [65] . The resulting RUF20-KanMX-TetO7 cassette was transformed into the strain CML476 [65] . The two ends of the cassette were homologous to the start and 500bp upstream of the SUT527 gene to replace 500bp of the SUT527 promoter . The successful transformants were selected on 200μg/ml YPD-G418 plates , confirmed by PCR and the resulting strain was named CML/RUF20/tetO7 . For RT-PCR , mRNA was isolated from 200μg yeast total RNA prepared by the hot phenol method [77] with the SIGMA GenElute mRNA Miniprep kit according to the manufacturers protocol and eluted in 100μl of elution buffer . The polyadenylated mRNA sample was treated with 10 units of RQ1 DNase ( Promega ) in 1X DNase Buffer ( Promega ) and 200 units RNasin ( Promega ) at 30°C for 30 minutes . The reaction was then stopped by adding 2mM of EDTA and incubation at 65°C for 10 minutes . An equal amount of citrate buffered phenol ( pH 5 . 3 ) was added followed by vortexing for 1 minute and centrifugation at 15600g for 2 minutes . The polyadenylated mRNA was then precipitated from the aqueous phase by adding 0 . 1 volume of 3M sodium acetate ( pH 5 . 3 ) , 2 . 5 volumes of 100% ethanol and 10μg of glycogen . The sample was precipitated at -20°C for 30 minutes . The RNA was collected by centrifugation for 5 minutes and washed with 96% ethanol , pelleted again at 15600g and air dried . The pelleted sample was resuspended in 16 . 25μl of water to be used for the first strand cDNA synthesis using the OneTaq RT-PCR Kit ( New England Biolabs ) . The procedure for cDNA synthesis and PCR amplification was based on the manufacturer’s instructions . Primers for primer walking of the SEC4 and TUB2 coding sequence and 3’ UTR are listed in S18 Table . To determine the expression of genes , cells were grown to an OD600 of 0 . 5 prior to RNA extraction using the Qiagen RNeasy Mini Kit . RNA concentrations were determined with a NanoDrop Lite Spectrophotometer . The GoScript Reverse Transcriptase was used for cDNA synthesis with 200ng of RNA . Quantitative RT-PCR was performed on the cDNA using iTaq universal SYBR green Supermix ( BioRad ) in a CFX Connect Real-time PCR Detection System ( BioRad ) . qPCR cycling conditions were as follows: initial denaturation 95°C for 3 mins; 35 cycles of 95°C for 45 secs , 58°C for 45 secs and 72°C for 3 mins; final extension of 72°C for 5 mins . ACT1 was used as a reference gene . The Ct values were used to measure the expression of each gene according to the 2-ΔCt method [78] . Sequences for the oligonucleotides used can be found in the S18 Table . Using the ΔΔCт method and ACT1 as a reference gene , the fold change ( 2^ ) in expression , relative to the wild-type was calculated . Error bars are calculated using each of the three independent biological samples . P values were calculated using the Welch two sample t-test . The open reading frame plus 500bp upstream and 250bp downstream of SEC4 ( 129943–131331 ) , which contains the approximately 1 . 4kb BamHI/EcoRI fragment that complements SEC4 function [64] was amplified with Phusion DNA polymerase ( New England Biolabs ) and primers SEC4F-Bam and SEC4B-Eco ( S18 Table ) . The open reading frame plus 500bp upstream and 500bp downstream of SUT527 ( 13146–14586 ) was amplified with Phusion DNA polymerase and primers RUF20F-Bam and RUF20B-Xba ( S18 Table ) . PCR products were then cloned into pRS413 to produce plasmids pRS413-SEC4 and pRS413-RUF20 . The correct SEC4 and SUT527 ( RUF20 ) sequences were confirmed by sequencing . Plasmids pRS413-SEC4 and pRS413-RUF20 were used as templates for production of transcription templates for SEC4 and SUT527 ( RUF20 ) probes by PCR for digoxigenin or dinitrophenol labelling using primer pairs SEC4T7/SEC4B_prob and RUF20FT7/RUF20B-prob ( S18 Table ) . Digoxigenin and dinitrophenol labelled probes were made using 1μg of purified SEC4 or SUT527 ( RUF20 ) PCR template with 1X DIG RNA labelling mix ( Roche ) or an identical RNA labelling mix containing DNP-11-UTP in place of DIG-11-UTP in a transcription reaction at 37°C for 2 hours using T7 RNA polymerase ( Promega ) according to the manufacturer’s instructions . Two units of RQ1 DNase ( Promega ) were then added and the mixture incubated at 37°C for 15 minutes . The sample was then purified using the Qiagen RNA Easy kit following the manufacturer’s instructions . The RNA concentration was measured and 10μg of RNA probe was used for the hybridization step . Coverslips No . 1 glass 22mm X 22mm ( Fisher ) were boiled for 30min in 250ml water with 0 . 1N HCl . Cover slips were then rinsed 10X with deionised water and stored in 70% ethanol . Flamed coverslips were coated with 200μl of 1X poly-L-lysine solution ( Sigma ) for 2min then excess poly-L-lysine removed and the coverslips air-dried . Coverslips were washed three times with 250μl of water for 10 minutes and air dried . Slides were stored in single wells of a six-well tissue culture dish at room temperature after air drying . For cell fixation , cells were grown at 30°C in 50ml YPD with our without doxycycline ( 600μg/mL ) to OD600nm = 0 . 5 and fixed in 4% formaldehyde ( Sigma ) for 45min at room temperature . Cells were then centrifuged at 2 , 400g for 5min at 4°C then resuspended in 1ml buffer B ( 16mM KH2PO4 , 83mM K2HPO4 , 5 . 4% Sorbitol ) . Cells were then washed three times with buffer B . Washed cells were resuspended in 1ml freshly-prepared spheroblast buffer ( Buffer B with 20mM Vanadyl Ribonucleoside Complex ( NEB ) , 250 units lyticase and 0 . 002% β-mercaptoethanol ) and incubated at 30°C for 15 minutes . Cells were washed twice with 1ml ice cold buffer B and spun at low speed 2000g for 1 minute . Cells were resuspended in 1ml buffer B and 150μl of the cells were placed on coated coverslips and incubated at 4°C for 30 minutes to allow adherence of the cells to the coverslips . Cells were then washed with 5ml ice cold Buffer B and 5ml of 70% ethanol was added , cells were then stored at -20°C . The stored coverslips were immersed in 1ml of the hybridization mix ( 50% formamide , 5X SSC , 1mg/ml yeast tRNA , 100μg/ml heparin , 1X Denhardts , 0 . 1% Tween 20 , 0 . 1% CHAPS , 5mM EDTA ) in a six-well tissue culture dish . The dish was then sealed with parafilm and incubated at 50°C for 1 hour . Next , the hybridization mix was removed and another 2ml of the hybridization mix was added with 10μg probe ( either DIG-labelled probe alone for SEC4 or SUT527/RUF20 for single detection or DIG-labelled probe for SEC4 and DNP-labelled probe for SUT527/RUF20 for colocalization ) then incubated overnight at 50°C . Coverslips were washed with 2ml 0 . 2X SSC three times . Then 2ml of blocking buffer ( 1X PBS , 0 . 1% TritonX-100 and 10% horse serum ) was added to the coverslips and incubated at room temperature for 1 hour . For single localization of DIG-labelled probes coverslips were incubated for 2 hrs with HRP conjugated anti-digoxigenin monoclonal antibody ( Jackson Immuno Research ) diluted to 1:500 with 250μl blocking buffer . Coverslips were then washed three times with 1ml blocking buffer and incubated for 2 hours with Alexa Fluor 488-conjugated anti-HRP antibody ( Jackson Immuno Research ) diluted 1:100 with 250μl blocking buffer . For combined co-localization detection of DIG- and DNP-labelled probes coverslips were incubated for 2 hrs with goat anti-DIG antibodies ( Vector Laboratories ) and rabbit anti-DNP ( Vector Laboratories ) diluted to 1:500 with 250μl blocking buffer . Coverslips were then washed three times with 1ml blocking buffer and incubated for 2 hours with mouse anti-rabbit Alexa Fluor 488 antibody ( Jackson Immuno Research ) and mouse anti-goat Alexa Fluor 647 antibody ( Jackson Immuno Research ) each diluted 1:100 with 250μl blocking buffer . Coverslips were then washed three times with 1ml blocking buffer and the coverslips were placed on a slide with a drop of ProLong Gold antifade reagent with DAPI ( Molecular Probes by Life Technologies ) and allowed to set . For single localization slides were visualised with a Nikon Eclipse E600 microscope using a 100x/0 . 5–1 . 3 NA differential interference contrast oil Iris Apo objective . The images were captured using a Nikon DS-QilMc camera and NIS-Elements BR 3 . 2 software . To obtain the quantitative data on RNA localisation in each strain , 100 cells were scored and analyzed for the localization on whether RNA signals were localized to the cell membrane or not . Cells were scored from three technical repeats . For colocalization images were collected on a Zeiss Axioimager . D2 upright microscope using an Olympus UPlanFL 100x/1 . 30 Oil Ph3 0 . 17 objective and captured using a Coolsnap HQ2 camera ( Photometrics ) through Micromanager software v1 . 4 . 23 . Specific band pass filter sets were used to prevent bleed through from one channel to the next . Images were then processed and analyzed using Image J . In order to investigate the growth effects of the ncRNA deletions , strains were grown under rich ( YPD ) and minimal ( chemically defined F1 with carbon or nitrogen limitations ) media conditions at 30°C . F1 medium was prepared in accordance to Delneri [36] . Carbon and Nitrogen limited F1 media were modified to contain 0 . 25% glucose ( w/v ) and 0 . 46 g/liter ( NH4 ) 2SO4 , respectively . Growth measurements at OD595 were recorded using a BMG FLUOstar OPTIMA Microplate Reader , as previously described by Naseeb and Delneri [79] for up to 70hr incubation time . Cells were grown at 30°C from an OD600 0 . 1 and readings taken every 5min . Three technical replicates of three independent biological samples were used for each deletion mutant strain and six technical replicates for the wild type strain . Graphs were produced using the grofit package of the R program . Area under curve ( AUC ) measurements for the tA ( UGC ) O , SUT340 , CUT873 and tT ( AGU ) J deletion mutants were calculated as per Norris et al [80] , using the grofit::gcFitSpline R package . To account for plate and batch effects , two biological replicates ( MATa and MATα ) and four technical replicates of each haploid deletion mutant strain were prepared . Three technical replicates of each plate were performed . Strains were removed from -80°C storage and grown to saturation at 30°C in YPD , in 384 well microtitre plates . Using a Singer Rotor HDA , the 384 well cell cultures were stamped onto YPD plates and incubated at 30°C for 2 days . Plates were then imaged using a Bio-Rad Gel Doc XR system and images processed using SGAtools [81] . The average of the normalized colony size values for replicates of each biological were then combined and used for analysis . We assumed normal distribution on the dataset and used the standard EM algorithm to determine means and standard deviations from the mixture of strains with normal growth and others with reduced fitness using Mixtools [82] . The P values were calculated from the parameters that are closer to the wild-type and fitness differences considered significant with p < 0 . 05 . Sequencing data were normalized and converted to Log2 fold change to allow comparison between the pool and batch stage and between the early and late steady state across different media and temperatures using DESeq2 [83] . To classify the deletion strains based on the impact of growing conditions , we applied generalized linear model with normal approximation and selected those with significant response to our testing variables ( P value ≤ 0 . 05 ) . As a result , fitness profiles were simplified and clustered using the ad hoc partitioning around medroid method implemented in the R package cluster [84] . Finally , data analysis was conducted to evaluate enrichment of SUT/CUT using exact binomial test . False discovery rate ( FDR ) was calculated using R . The biological functions of neighboring genes to ncRNAs in each cluster were identified using GO Term Finder in SGD . The PGAL1 promoter was amplified from pAV1901 [85] using Gal1 . for and Gal1 . rev primers and cloned into the SalI site in pRS416 [86] . Next the CYC1 terminator was amplified from p426-GPD [87] using Cyc1 . for and Cyc1 . rev primers and cloned into the BamHI site creating pRS416Gal1Cyc1 . The sense and antisense ncRNA expression plasmids were created using the primer pairs in S18 Table . Phusion DNA polymerase ( New England Biolabs ) was used in all amplifications according to the manufacturers protocol using yeast genomic DNA from BY4742 as template . All sense and antisense ncRNA expression plasmids were cloned into pRS416Gal1Cyc1 via the HindIII restriction site using the Gibson cloning technique [88] . All constructs were verified by sequencing . Each ncRNA overexpression plasmid was transformed into the corresponding heterozygote diploid deletion strain and wild-type BY4743 . Cells containing the overexpression plasmids were selected for on SD media lacking uracil ( 0 . 67% Bacto yeast nitrogen base without amino acids , 2% glucose , 2% agar , 0 . 192% Yeast synthetic drop-out medium supplement without uracil ) . Strains were then sporulated in liquid sporulation medium lacking uracil ( 1% potassium acetate , 0 . 005% zinc acetate , 0 . 002% histidine and 0 . 003% leucine ) . Cultures were incubated for 5 days at 25°C followed by three days incubation at 30°C . Tetrad dissection was performed on SD media plates containing 2% Galactose and lacking uracil , using a Singer instruments MSM 400 microdissector . After 4 days incubation at 30°C , tetrad dissection plates were replica plated on to SD media containing 2% Galactose , 300mg/L G418 and lacking uracil . Haploids growing on the final plates were considered to contain the original ncRNA deletion cassette and the ncRNA overexpression plasmid . DNA was extracted ( QIAamp DNA Mini Kit ) from these haploids for PCR confirmation . The presence of the ncRNA overexpression plasmid was confirmed using universal pRS416 primers ( pRS416 F Primer ‘CATGGAGGGCACAGTTAAGC’ and pRS416 R Primer ‘ACCACATCATCCACGGTTCT’ ) . Deletion of SUT075 was confirmed using a primer specific to the kanamycin cassette ( kanC3 ‘CCTCGACATCATCTGCCCAGAT’ ) and a primer flanking the insertion site ( SUT075 confD ‘TGCAGGGAACAGATTTTAGATTT’ ) . PCR reaction mix contained: 0 . 5μM of each primer , 100ng of DNA template , 12 . 5μl MyTaq Red Mix ( Bioline ) and water to 25μl . Cycling conditions: initial denaturation at 95°C for 10min followed by 35 cycles of 95°C for 30sec; 57°C for 30sec; 72°C for 90sec and a final elongation of 72°C for 5min . PCR products were run on a 1 . 5% agarose gel . EMP46 and GAL2 overexpression plasmids were constructed following the same methodology as the ncRNA overexpression above , with a few adjustments . The pBEVY-GA plasmid , containing a bi-directional GAL1/10 promoter , was used [89] . EMP46 was inserted at the upstream site via the BamHI site and GAL2 was inserted at the downstream site via the XmaI site . Three plasmids were constructed containing: 1 ) GAL2; 2 ) EMP46 or 3 ) GAL2 and EMP46 . These plasmids were transformed separately into BY4743 and selected on SD media lacking uracil ( as above ) . Cultures of these overexpression strains were then serially diluted tenfold and stamped ( using the spot assay function of Singer Instrument’s ROTOR ) onto SD media containing either 2% Galactose ( promoter activate ) or 2% Glucose ( promoter inactivate ) . Plates were then imaged using a Bio-Rad Gel Doc XR system . Cultures of BY4743 ( +empty pRS416 ) , ΔSUT075 ( +empty pRS416 ) and the ΔSUT075 ( + sense SUT075 recovery plasmid ) heterozygote diploid strains were grown to an OD600 0 . 5 in liquid SD media containing 2% galactose and lacking Uracil . Three biological replicates of each strain were cultured . RNA was extracted and qRT-PCR was performed using the PRP3 forward and reverse primers ( S18 Table ) , following the methods previously described ( Quantitative Real Time-PCR ) . YNCA was developed in RStudio [90] , version 1 . 0 . 143 , with the use of the packages shiny [91] and rmarkdown [92] . Local server hosting relies on the open source version of Shiny-server . The underlying server-side data processing is written in R [93] , version 3 . 4 . 0 . Lists and positions of chromosomal features in S . cerevisiae are taken from the Saccharomyces Genome Database ( www . yeastgenome . org ) . The type of features included are: known opening reading frames , tRNA genes , snoRNA genes , centromeric and telomeric regions , autonomous replicating sequences ( ARS ) , long terminal repeats ( LTR ) , pseudogenes , LTR retrotransposons and transposon internal genes . | Genomes from different organisms produce noncoding RNAs that are not translated to make proteins and whose functions are largely unknown . There are approximately 2 , 000 noncoding RNAs that make up almost 25% of the yeast genome compared to the approximately 6 , 000 protein coding genes that make up 70% of the yeast genome . With this large number of ncRNAs there is a need for large-scale studies to determine the functional roles of ncRNAs . We take advantage of a recently developed resource of 532 yeast strains in which individual noncoding RNA genes have been deleted . We grow these yeast noncoding RNA deletion strains in different conditions and catalogue how each noncoding RNA contributes to cell growth . Improvement or inhibition of cell growth under particular conditions implicates the deleted RNA in cellular responses to those conditions . We have also investigated in more detail the function of specific noncoding RNAs , revealing examples of how a deletion influences nearby genes , and other examples of noncoding RNAs that regulate genes at distant genomic locations . We have made our extensive data on the fitness of noncoding RNA deletion mutants publically available for searching and bulk download in a new online resource , called Yeast ncRNA Analysis ( YNCA ) . This large-scale analysis of noncoding RNA deletion mutants reveals the importance of many noncoding RNAs in cellular function . | [
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"p... | 2018 | Large-scale profiling of noncoding RNA function in yeast |
SUMO-specific protease 2 ( SENP2 ) modifies proteins by removing SUMO from its substrates . Although SUMO-specific proteases are known to reverse sumoylation in many defined systems , their importance in mammalian development and pathogenesis remains largely elusive . Here we report that SENP2 is highly expressed in trophoblast cells that are required for placentation . Targeted disruption of SENP2 in mice reveals its essential role in development of all three trophoblast layers . The mutation causes a deficiency in cell cycle progression . SENP2 has a specific role in the G–S transition , which is required for mitotic and endoreduplication cell cycles in trophoblast proliferation and differentiation , respectively . SENP2 ablation disturbs the p53–Mdm2 pathway , affecting the expansion of trophoblast progenitors and their maturation . Reintroducing SENP2 into the mutants can reduce the sumoylation of Mdm2 , diminish the p53 level and promote trophoblast development . Furthermore , downregulation of p53 alleviates the SENP2-null phenotypes and stimulation of p53 causes abnormalities in trophoblast proliferation and differentiation , resembling those of the SENP2 mutants . Our data reveal a key genetic pathway , SENP2–Mdm2–p53 , underlying trophoblast lineage development , suggesting its pivotal role in cell cycle progression of mitosis and endoreduplication .
The first two distinct lineages to form in the mammalian embryos are the outer trophectoderm and the inner cell mass ( ICM ) of the blastocyst [1] . The trophectoderm initiates implantation and invasion of the uterus , processes that are essential for placental development [2] . This process depends on the differentiation of trophoblasts , the main and most important cell types in the placenta [3 , 4] . The trophoblast stem ( TS ) cells in the mural trophectoderm , distal to the ICM , stop dividing but continue to duplicate their genomes , a mechanism known as endoreduplication . The polyploid trophoblast giant cells ( TGCs ) then develop and eventually surround the entire fetus [5] . As development proceeds , the trophoblast progenitors give rise to three distinct layers in rodents—labyrinth , spongiotrophoblast and TGCs—to form a functional placenta acting as the maternal–fetal interface [6] . The fetal–placental blood vessels grow in from the allantois to generate the fetal parts of the placental vasculature where the chorioallantoic fusion has occurred [7] . The labyrinth is formed by extensive branching morphogenesis of the labyrinth trophoblast and endothelial cells [8] . The maternal blood passes through the small spaces of the labyrinth , directly contacting the fetal trophoblast cells to ensure exchange between the two blood systems . The labyrinth layer is supported structurally by the spongiotrophoblast cells , which are mainly derived from the ectoplacental cone and which form a layer separating the labyrinth from the TGC . The simplicity of placental cell lineages makes the placenta a valuable model system for understanding general aspects of development , including branching morphogenesis , lineage-specific determination , cell invasion , and polyploidy , crucial for cancer development and metastasis . SENP2 belongs to a family of proteases that remove a small ubiquitin-related modifier ( SUMO ) from protein substrates . SUMO ( also known as sentrin ) , which regulates posttranslational modification of proteins , is a member of the ubiquitin-like modifier family [9] . This covalent conjugation process is reversible and highly evolutionary conserved from yeasts to humans [10] . Unlike ubiquitination , which has a well-established role in targeting protein degradation , SUMO modification is involved in protein trafficking , cell cycle , cell survival , and cell death [11] . SUMO conjugation of proteins can alter their function , activity , or subcellular localization . Many sumoylated proteins have been shown to accumulate preferentially in specific complexes such as the nuclear pore and PML ( promyelocytic leukemia ) bodies [12] . Similar to ubiquitination , sumoylation requires processing , conjugation , and transfer . The transfer process , which covalently conjugates SUMO polypeptides to their targets , is catalyzed by E3 ligases [13] . The reverse desumoylation process is mediated by SUMO proteases . The hallmark of these proteases is the highly conserved carboxyl-terminal SENP domain of ∼200 amino acids . SENP2 , which is found in three different alternatively spliced forms , has been localized to the nucleus , cytoplasmic vesicles and PML nuclear bodies [14–16] . Although SENPs have been shown to catalyze SUMO modification in various physiological systems , their roles in mammalian development and pathogenesis are mostly unknown . We previously discovered an interaction of SENP2 with Axin [17 , 18] , a key signaling regulator for the canonical Wnt pathway . To determine the role of SENP2 in cellular signaling and the importance of SUMO modification in trophoblast development , we initiated a genetic analysis in mice . A SENP2-null mouse strain was created by gene targeting in embryonic stem ( ES ) cells . We found that the disruption of SENP2 leads to developmental defects in all three trophoblast layers . SENP2 is essential for the G–S transition of both the mitotic and the endoreduplication cell cycles , which control the expansion of trophoblast precursors and the maturation of TGCs , respectively . In the mutants , the loss of SENP2 caused a deregulation of Mdm2 , resulting in p53 stimulation . We also present evidence to support an essential role of SENP2 in modulating the p53–Mdm2 circuit that underlies genome replication in mitosis and polyploidy during trophoblast development .
To determine the role of SENP2 and the importance of SUMO modification in trophoblast development , we first examined its expression pattern . Strong expression of SENP2 was observed in extraembryonic tissues , including extraembryonic ectoderm , chorion and ectoplacental cone , at embryonic day ( E ) 7 ( Figure 1A ) . In extraembryonic ectoderm , its expression started diminishing by E7 . 5 ( Figure 1B ) . In addition to these stem cell niche sites , we also detected its transcript in TS cells ( Figure 1C ) . At E8 . 5 , SENP2 maintained its ubiquitous expression in trophoblast cells located in the chorion and ectoplacental cone ( Figure 1D–1F ) . By E9 . 5 and E10 . 5 , the SENP2 transcript was detected in all three trophoblast layers: labyrinth , spongiotrophoblast and TGC ( Figure 1H and 1L ) . SENP2 was expressed in the labyrinth trophoblast cells , which derive from the extraembryonic ectoderm and chorion , upon chorioallantoic fusion at E9 . 5 ( Figure 1I ) . In the E10 . 5 labyrinth layer , its expression was specifically localized to cytotrophoblasts ( mononuclear trophoblasts ) , adjacent to the maternal blood cells ( Figure 1M ) . Syncytiotrophoblasts , as well as endothelial and blood cells , appeared to be negative for the staining . In contrast , we found a uniform expression of SENP2 in spongiotrophoblasts and TGCs ( Figure 1J , 1K , 1N , and 1O ) , which are derivatives of the ectoplacental cone . TGCs include primary and secondary cells , derived from mural trophectoderm and ectoplacental cone ( derivatives of polar trophectoderm ) , respectively . The SENP2 transcript was detected in both the primary and secondary TGCs ( Figure 1G , 1K , and 1O ) . These results imply an important function of SENP2 in trophoblast progenitors and their development into all three major layers . A SENP2-null allele was created by the targeted insertion of a lacZ reporter with pgk-neo cassette into exon 2 and the deletion of exons 3 to 5 to inactivate all different forms of the SENP2 gene product ( see Materials and Methods for details ) . The targeted mouse ES cell clones heterozygous for SENP2 ( Figure S1A ) , obtained by homologous recombination , were then used to obtain the SENP2lacZ mouse strain ( Figure S1B ) . Mice carrying the targeted allele were subsequently bred with a Zp3-Cre transgenic strain to remove the pgk-neo cassette ( SENP2-null allele ) , as confirmed by PCR genotyping analysis ( Figure S1C ) . RT-PCR analyses further showed that the SENP2-null allele does not express the SENP2 transcript , but instead expresses the inserted lacZ gene ( Figure S1D ) . The SENP2-null heterozygous ( hereafter referred to as SENP2+/– ) mice were viable and fertile without any noticeable abnormalities . However , we were unable to find SENP2-null homozygous ( hereafter referred to as SENP2–/– ) newborns , implying that they died prematurely . These results prompted us to investigate whether the loss of SENP2 causes embryonic lethality . The SENP2–/– embryos appeared to be morphologically indistinguishable from their SENP2+/+ and SENP2+/– littermates at E9 . 5 ( Figure 2A and 2B ) . However , the SENP2–/– embryos were significantly smaller or underdeveloped compared with the SENP2+/+ and SENP2+/– littermates at E10 . 5 ( Figure 2C and 2D ) . We could not recover the SENP2–/– embryos after E11 . 5 . This phenotype is often associated with placental deficiencies , as the embryos begin to rely on maternal supplies upon allantoic fusion at mid gestation . Indeed , the SENP2–/– placentas were smaller and paler than the controls ( Figure 2E–2H ) . The average diameter of E10 . 5 placentas reduced from 5 . 2 mm in controls to 3 . 8 mm in mutants ( Figure 2S , p < 0 . 0001 , n = 7 ) . Histological analyses revealed a reduction of the TGC layer by E9 . 5 ( Figures 2I and 2J ) . By E10 . 5 , the thickness of all three trophoblast layers decreased drastically in the SENP2-null mutants ( Figures 2K and 2L ) . The TGC layer , which is the layer most severely affected by the SENP2 mutation , is almost completely missing . The data suggest that SENP2 has a pivotal role in development of all three trophoblast layers . The placental defects caused by SENP2 deficiency suggested that it is critical for trophoblast development . The stem cells derived from the trophectoderm develop into progenitors , which reside in the ectoplacental cone , the extraembryonic ectoderm , and the chorion . We therefore examined whether the SENP2 deletion interferes with formation of these niche sites . In situ hybridization of Tpbpa , a marker for the ectoplacental cone [19] , revealed a drastic reduction of trophoblast progenitors in the mutants ( Figure 2M and 2P ) . The number of trophoblast progenitors , marked by Cdx2 expression [20] was also decreased in the SENP2–/– chorion and extraembryonic ectoderm ( Figure 2N , 2O , 2Q , and 2R ) . The apparent developmental defects of trophoblast niche sites suggested that SENP2 might have a role in trophoblast stem cell development . A closer examination of the labyrinth layer was performed by analyzing the expression of Gcm1 , a labyrinth trophoblast marker that is specifically detected in the chorioallantoic invasion sites and later in the differentiated syncytiotrophoblasts [21] . No obvious difference between SENP2+/+ and SENP2–/– was observed at E9 . 5 ( Figure 3A and 3D ) . Gcm-1-positive trophoblast progenitors were clearly identified at the invasion sites . Therefore , fetal vascular invasion was not affected by the deletion . However , deficiencies in labyrinth development of SENP2–/– embryos were evident at E10 . The syncytiotrophoblasts positive for Gcm-1 exhibited punctated staining in the mutant instead of the continuous thin layers seen in the wild type , suggesting that their differentiation is defective ( Figure 3B and 3E ) . By E10 . 5 , the number of the Gcm1-expressing cells was dramatically reduced in the mutants ( data not shown ) . At this stage , SENP2 expression is restricted to the cytotrophoblasts ( Figure 1M ) , a subtype of TGCs [22] . Therefore , we examined whether cytotrophoblast development was affected by analyzing a cytotrophoblast marker , Ctsq [23] . Indeed , the Ctsq-positive cytotrophoblasts identified in the wild-type labyrinth were completely missing in the mutants ( Figure 3C and 3F ) . Histological analysis further showed that both maternal and fetal blood spaces were enlarged without formation of capillary structures in the SENP2 mutants ( Figure 3G and 3J ) . Immunostaining of laminin [24] , a basement membrane protein expressed by endothelial cells that highlight fetal blood spaces , further revealed a failure of branching morphogenesis in the SENP2-null fetal vasculature ( Figure 3H and 3K ) . This might be attributed to a deficiency in endothelial proliferation as the number of cyclin D1-positive cells ( proliferation marker only detected in endothelial cells ) was decreased in the mutants ( Figure 3I and 3L ) . These data demonstrated that SENP2 is essential for labyrinth trophoblast development in establishment of the maternal and fetal blood spaces . The presence of SENP2 in early trophoblast precursors might regulate the differentiation of specialized cell types at later stages . Alternatively , its function in the cytotrophoblasts could be crucial for proper development of syncytiotrophoblasts and endothelial cells . SENP2 is necessary for development of the labyrinth layer during placentation . We next examined the spongiotrophoblast layer that is affected by the SENP2 deletion . In situ hybridization analysis of Tpbpa [19] , a marker for the spongiotrophoblast , revealed that its expressing cells diminished significantly in the mutants at E9 . 5–E10 . 5 ( Figure 3M , 3N , 3P , and 3Q ) . Histology confirmed that a rapid expansion of this layer , found in the wild-type placenta , did not occur in the mutants ( Figure 3O and 3R ) . As a result , the SENP2-null spongiotrophoblast layer decreased significantly in volume . Based on the expression of SENP2 in spongiotrophoblasts ( Figure 1J and 1N ) and earlier in their precursors at the ectoplacental cone ( Figure 1A , 1B , and 1F ) , it is most likely that the abnormalities are primarily due to its deletion in these tissues . Therefore , spongiotrophoblast development requires SENP2 and its disruption induces abnormalities in the spongiotrophoblast layer . Consistent with its expression in early trophoblast development , histological analyses revealed a severe abnormality in the TGC layer ( Figure 4A–4H ) . The SENP2-null primary TGCs were reduced at E8 . 5 and completely missing at E9 . 5 ( Figure 4A , 4B , 4E , and 4F ) . Similarly , the number of secondary TGCs was decreased at E9 . 5 and almost disappeared at E10 . 5 ( Figure 4C , 4D , 4G , and 4H ) . In addition , the size of TGCs was significantly smaller in the SENP2 mutants ( Figure 4D and 4H ) . The analyses of TGC markers [19 , 25] , including PL-I ( Figure 4I–4P ) , PL-II ( unpublished data ) , and p450scc ( Figure 4Q–4V ) , confirmed that the TGC cell numbers were dramatically decreased in the SENP2 mutants at all stages examined . We next examined the initiation of TGC differentiation by in situ hybridization of Hand1 . Hand1 is required for cell fate determination of TGC , as mice without Hand1 lack TGCs [26] . Hand1 expression was detected in the SENP2-null TGCs , suggesting that the initial induction of TGCs was not affected by the loss of SENP2 ( Figure 4W–4Z and 4W′–4Z′ ) . However , later developmental processes of TGC were impaired in the mutants . The abnormal development of TGC caused by SENP2 deficiency was further tested using an in vitro differentiation analysis . The SENP2+/+ and SENP2–/– blastocysts were isolated at E3 . 5 , and cultured to induce TGC differentiation . TS cells growing out from the trophectoderm soon attached to the cultured plates , differentiated , and formed a single trophoblast layer . No noticeable difference was observed between the SENP2+/+ and SENP2–/– blastocysts before hatching ( Figure 5A and 5B ) . About equal amounts of ICM and trophoblast cells developed after 3 d in culture ( Figure 5C and 5D ) . However , although the differentiated TGCs were evident in the SENP2+/+ cultures , their number was significantly reduced in the SENP2–/– cultures after 6 d ( Figure 5E–5H ) . The average number of TGC dropped from 40 in the SENP2+/+ culture to 15 in the SENP2–/– ( Figure 5I , p = 0 . 005 , n = 6 ) . Consistent with our in vivo findings , these data suggest that TGC differentiation is severely affected by the loss of SENP2 . The results suggest an essential role for SENP2 in TGC development during early placentation . The SENP2 mutation led to abnormalities in trophoblast progenitors at niche sites and their development into all three major trophoblast lineages . These findings imply that SENP2 might have a general role in cellular regulations important for expansion of precursors and their differentiation . We speculated that decreases in the numbers of trophoblast progenitors and specialized cell types might be due to alterations in cell survival . However , we failed to detect differences in apoptosis caused by the mutation in trophoblast stem cell niches and all three major trophoblast layers in vivo , or in TS cell culture in vitro ( Figure S2 ) . We then examined whether SENP2 has an important function in the cell cycle . Investigating the expansion of trophoblast progenitors at the niches revealed a deficiency in their cell cycle progression . The expression of Ki67 , a marker detected in all phases of mitotic cells [18] , was detected in virtually all trophoblast progenitors in stem cell niches , including extraembryonic ectoderm , chorion , and ectoplacental cone ( Figure 6A , 6C , 6E , and 6G ) , suggesting that they are actively cycling cells . We next examined the cell cycle progression rate among actively cycling cells by measuring the DNA synthesis rate at S phase using BrdU labeling [18] for 1 h ( Figure 6B , 6D , 6F , and 6H ) . BrdU incorporation specifically measures the rate of cell cycle progression at S phase , whereas Ki67 identifies all phases of mitotic cells . The cell cycle progression index ( % BrdU-positive cells / % of Ki67-positive cells × 102 ) among actively cycling cells decreased 18 units in the mutants ( SENP2+/+ and SENP2+/– , 67; SENP2–/– , 49; p = 0 . 0001 , n = 6 ) in the stem cell niches ( Figure 6M ) . These data suggest a delay in cell cycle progression of trophoblast progenitors caused by the SENP2 deletion . Next , we determined whether similar deficiencies also affect development of the spongiotrophoblast layer . We found that this layer expanded rapidly in the wild-type placenta , but not in the mutants , between E9 . 5 and E10 . 5 ( data not shown ) . A portion of the SENP2+/+ spongiotrophoblasts exited the cell cycle at E10 . 5 ( Figure 6I ) , whereas almost all of the SENP2–/– spongiotrophoblasts remained Ki67-positive ( Figure 6K ) . In E10 SENP2+/+ and SENP2–/– placentas , spongiotrophoblasts were all positive for Ki67 , indicating that they are actively cycling cells ( Figure 6J and 6L ) . However , the cell cycle progression index , which mainly reflects the BrdU incorporation rate , was reduced from 72 in the controls to 53 in the SENP2 mutants ( p = 0 . 0005 , n = 3 ) ( Figure 6N ) . To examine whether cycling of TGC was also affected by SENP2 deficiency , we determined its cell cycle progression index ( Figure 6O ) . The cell cycle progression index of TGC decreased from 60 in SENP2+/+ to 41 in SENP2–/– ( p = 0 . 0034 , n = 4 ) . Taken together , these results suggest that cell cycle progression was defective in all stem cell niches , and the spongiotrophoblast and TGC layers , of SENP2 mutants . The SENP2 mutant cells were trapped or arrested in the cell cycle . To further examine stem cell expansion and development , we derived a number of SENP2–/– TS cell lines from blastocysts . Immunostaining analyses of Oct4 ( an ES cell marker ) [27] and Cdx2 ( a TS cell marker ) [20] confirmed that we were able to successfully establish the SENP2-null TS cell lines ( Figure S3 ) . The proliferation rate ( BrdU labeling for 1 h ) of the SENP2-null TS cells in vitro was also reduced , compared to that of the wild-type cells ( p = 0 . 013 , n = 3 ) ( Figure 7A ) . Although the deficiency in cell cycle progression was also demonstrated using the TS cells in vitro , the degree of severity was reduced compared with that seen in the in vivo studies . As we were aware , the in vitro system does not always recapitulate the dynamic developmental processes that occur in vivo . Nevertheless , because of the limited materials available from the early stages of placenta , the TS cell culture does provide a valuable system to further those of our investigations that are otherwise impossible to perform in vivo . To investigate whether a specific phase of the cell cycle was defective , we then determined the cell cycle profiles of the SENP2+/+ and SENP2–/– TS cells by flow cytometry analysis of PI ( propidium iodide ) stained cells . There was no significant difference in the cell population of G2–M between SENP2+/+ and SENP2–/– cells ( Figure 7B ) . However , in the SENP2 nulls , the percentage of cells in G0–G1 was increased ( p < 0 . 0001 , n = 4 ) but the percentage in S was decreased ( p = 0 . 0024 , n = 4 ) ( Figure 7B and 7C ) . This implied that the mutant cells were affected at the G1–S transition . To test this hypothesis , we used nocodazole , a microtubule depolymerizing agent , to block cell division at M phase . Nocodazole was effective in synchronizing the SENP2+/+ TS cells at G2–M after 6 h ( Figure 7D ) . However , if cells were arrested or trapped in the G1–S phase and unable to pass through the cell cycle , there would be a delay in synchronizing cells by the nocodazole treatment . Indeed , there were still ∼7% of the G0–G1 cells in SENP2–/– , but none in SENP2+/+ , 3 h after the treatment . After the 6 h treatment , a significant number ( 6 . 16% ) of the SENP2–/– TS cells remained in G0–G1 ( Figure 7D ) . Even after 24 h , this population arrested in G0–G1 was still present ( data not shown ) . The results suggest that SENP2 has a pivotal role in TS cell cycle progression and the G1–S checkpoint might be affected by the SENP2 ablation . Immunostaining of nuclear envelopes with lamin B [28] revealed that nuclei of the SENP2–/– TGCs were significantly smaller ( Figure 8A–8F ) . In addition , the mutant TGC nuclei contained smaller and fewer blue dots upon hematoxylin staining ( Figure 8A–8F ) , suggesting that the DNA content might be reduced . These abnormalities are likely caused by a deficiency in endopolyploidy . An important specialized process for TGC maturation is endoreduplication , whereby the genome is amplified without a complete mitosis . The endoreduplication cycle requires only the G and S phases [29] . To examine the possibility of a defect in endoreduplication , we induced the TS cells to undergo TGC differentiation in vitro by removal of FGF4 , heparin , and mouse embryonic fibroblast ( MEF ) -conditioned medium ( see Materials and Methods and [30] ) . Flow cytometric analysis of the differentiated cells stained with PI showed that the percentage of cells with higher DNA contents ( >4N ) was drastically reduced in the SENP2 mutants ( Figure 8G ) . The average percentage of polyploid cells reduced from 25% ( SENP2+/+ ) to 7% ( SENP2–/– ) ( p < 0 . 0001 , n = 5 ) ( Figure 8H ) . Therefore , the loss of SENP2 induced a severe deficiency in endopolyploidy . SENP2 apparently has a dual role in regulating the G–S transition of mitotic division and endoreduplication during TS cell proliferation and differentiation , respectively . The cell cycle defects led us to investigate potential downstream targets involved in trophoblast development . We specifically focused on those regulators shown to be conjugated by SUMO . Previous reports showed that SENP2 ( also known as Axam ) modulates the canonical Wnt pathway by interacting with its signaling molecules [14 , 31] . Even though this led us to identify SENP2 through its binding to Axin initially , we failed to detect any alteration of Wnt signaling in the SENP2 mutants . Nor were we able to show other alternative pathways critical for placentation , e . g . , MAPK and SAPK [32–36] , to be involved in the SENP2-dependent developmental processes . However , when p53 was examined by immunostaining , we detected an aberrant accumulation in the nuclei of the developing SENP2–/– TGCs at E8 . 5–E10 . 5 ( Figure 9D–9F ) . In contrast , the SENP2+/+ TGCs showed no detectable , or very low if any , p53 at these stages ( Figure 9A–9C ) . The results implied that there is a deficiency in p53 regulation caused by the SENP2 deletion . Degradation of p53 is mediated by ubiquitination-dependent proteolysis . Mdm2 , a RING finger E3 ubiquitin ligase that binds to p53 , has an essential role in this process [37–40] . We therefore tested whether the loss of SENP2 had an effect on Mdm2 . Immunostaining of Mdm2 revealed its localization in both the SENP2+/+ cytoplasm and the nucleus during early stages ( E8 . 5–E9 . 5 ) of TGC development ( Figure 9G and 9H ) . However , Mdm2 was mainly located to the nuclei of the terminally differentiated TGCs at E10 . 5 ( Figure 9I ) . The differential subcellular distribution of Mdm2 implies that it might be critical for development of TGCs . In contrast , Mdm2 accumulated in nuclei throughout TGC development in the absence of SENP2 ( Figure 9J–9L ) . The prominent cytoplasmic staining was lost in the mutants at E8 . 5–E9 . 5 ( Figure 9J and 9K ) . Furthermore , the loss of SENP2 also affected Mdm2 localization in the stem cell niche sites , such as extraembryonic ectoderm and chorion . Mdm2 clearly accumulated in the nuclei of the SENP2–/– trophoblast progenitors , but was evenly distributed in the whole cells of the controls ( Figure 9M and 9N ) . Similar nuclear accumulations of Mdm2 , affecting the p53 level , were also detected in the SENP2–/– labyrinth and spongiotrophoblast layers ( data not shown ) . Therefore , Mdm2 appeared to be aberrantly localized in the stem cell niches and all three major layers of trophoblast during early embryogenesis . The data suggest that SENP2 is required for proper localization of Mdm2 and degradation of p53 . Disturbance of SUMO modification by the SENP2 deletion thus causes deregulation of the p53–Mdm2 pathway , leading to deficiencies in mitotic and endoreduplication cell cycle progression and abnormal trophoblast development . The accumulation of p53 in the nuclei of SENP2-null placentas implied that SENP2 negatively modulates the p53–Mdm2 circuit . To determine the role of p53–Mdm2 in trophoblast development , we investigated whether SENP2 modulates Mdm2 and p53 at the posttranscriptional level . In addition to altering the subcellular distribution of Mdm2 , the loss of SENP2 had an effect on posttranslational modification of Mdm2 . The loss of SENP2 disturbed desumoylation of Mdm2 . In the SENP2–/– TS cells , Mdm2 accumulated in the SUMO conjugated state ( Figure 9O ) . The loss of SENP2 disturbed the ratio of Mdm2 and Mdm2–SUMO . The sumoylated Mdm2 could also be detected by an anti-SUMO-1 antibody ( Figure 9O ) as well as immunoprecipitation–immunoblot analysis using anti-Mdm2 and anti-SUMO-1 antibodies ( data not shown ) . We encountered a technical problem in determining the actual amount of the sumoylated Mdm2 by immunoprecipitation–immunoblot analysis . This is likely because desumoylation occurs rapidly in isolated cell extracts whereas immunoprecipitation requires proteins in a native conformation . Therefore , a straight immunoblot assay appears to be better suited for quantitative measurements . To determine whether SUMO modification of Mdm2 is regulated by SENP2 , a plasmid expressing a Myc-tagged SENP2 ( MT–SENP2 ) under the control of a CMV promoter was transiently transfected into the mutants . The reintroduction of SENP2 altered the ratio of Mdm2 and Mdm2–SUMO and diminished the level of Mdm2–SUMO , suggesting that its desumoylation is modulated by SENP2 ( Figure 9O ) . Immunoblot analysis also revealed an elevation of p53 caused by the SENP2 deletion in TS cells ( Figure 9P ) . Although p53 is known to be sumoylated , we did not detect obvious accumulations of the SUMO-conjugated form caused by the SENP2 ablation . We then tested whether SENP2 is required to mediate the downregulation of p53 by overexpression of MT–SENP2 . Consistent with our hypothesis , p53 levels were significantly reduced in the SENP2-null cells transiently transfected by MT–SENP2 ( Figure 9P ) . To further confirm that the loss of SENP2 was the primary cause of the trophoblast defects , we reintroduced MT–SENP2 into SENP2–/– cells . To determine the differentiation process affected by SENP2 at a more quantitative level , we examined the expression of a TGC marker , p450scc , by immunoblot analysis . The expression of p450scc was drastically reduced in SENP2–/– placentas , confirming the TGC developmental defects ( Figure 9Q ) . The expression of p450scc was not detectable in SENP2+/+ TS cells but was highly increased in the differentiated TGCs , suggesting the success of the in vitro culture system ( Figure 9Q ) . We did not detect a great induction of p450scc in the differentiated SENP2–/– cells , consistent with our in vivo findings ( Figure 9Q ) . The reintroduction of MT–SENP2 in the SENP2 mutants led to an induction of p450scc upon TGC differentiation ( Figure 9Q ) . The p450scc induction level did not reach that of the SENP2+/+ TGCs , most likely due to the transfection efficacy , in that not all of the mutants were transfected . Nevertheless , these data demonstrate that reintroducing SENP2 into the SENP2–/– TS cells can promote their differentiation into TGCs . This suggests that SENP2 inactivation is the cause of the trophoblast developmental defects observed in the mutants . An aberrant stimulation of p53 might be responsible for the SENP2-null defects in mitotic division and polyploidy . In the SENP2 mutants , the dislocation of Mdm2 implied that its distribution is regulated by the SUMO pathway . We therefore investigated whether Mdm2 localization is affected by SUMO . First , immunoblot analysis after cell fractionation showed that sumoylated Mdm2 is found preferentially in the nuclear fraction of SENP2–/– cells ( Figure 9R ) . Next , we examined whether SUMO conjugation alters the subcellular distribution of Mdm2 in live cells . GFP analysis of TS cells transiently expressing GFP-tagged Mdm2 or Mdm2–SUMO-1 , revealed their preferential localization . We found that Mdm2 mainly accumulated in the cytoplasm ( Figure 9S and 9V ) , with occasional distribution to the whole cell ( Figure 9T ) . However , Mdm2–SUMO-1 displayed a clear nuclear accumulation ( Figure 9U ) , with either a punctated ( Figure 9W ) or a nucleolar ( Figure 9X ) staining pattern . Similar results were also obtained by the use of Mdm2–SUMO-1GG96–97Δ , a mutant lacking the last two glycine residues of SUMO-1 , which prevent further conjugation that might affect subcellular distribution ( data not shown ) . Therefore , the SENP2 mediated SUMO modification of Mdm2 appears to be crucial for its subcellular trafficking . To address the importance of p53 in mediating the SENP2-null phenotype , we tested whether p53 activation is necessary and sufficient to affect trophoblast proliferation and differentiation . We used both gain-of-function and loss-of-function analyses . Nutlin-3 is a potent small-molecule antagonist of Mdm2 , which binds to the p53-binding pocket of Mdm2 and prevents its interaction , thereby stabilizing p53 . We first determined that the Nutlin-3 treatment of the SENP2+/+ cells could elevate p53 in a dosage-dependent manner , but , most importantly , to reach the level detected in the SENP2–/– TS cells ( Figure 10A ) . To examine whether the p53 elevation induced G1–S arrest , TS cells were treated with Nutlin-3 . A cell cycle profiling assay showed that the Nutlin-3 treatment caused the wild-type TS cells to accumulate in G0–G1 phase , similar to the SENP2–/– TS cells ( Figure 10C ) . Next , we examined whether the elevated level of p53 interfered with the differentiation process . In the SENP2+/+ TS cells induced for TGC differentiation , Nutlin-3 significantly reduced the expression of the TGC marker p450scc ( Figure 10E ) , and prevented TGC differentiation ( Figure 10F–10K ) . The average number of TGC decreased significantly in the presence of Nutlin-3 ( Figure 10L , p = 0 . 006 , n = 4 ) . These results support the hypothesis that stimulation of p53 by alteration in Mdm2 activity induces phenotypic defects in trophoblast proliferation and differentiation , resembling those observed in the SENP2 mutants . To determine whether downregulation of p53 was able to alleviate the trophoblast deficiencies caused by the SENP2 ablation , we knocked down its cellular levels using an RNA interference ( RNAi ) approach . First , immunoblot analysis showed that the p53 RNAi treatment successfully diminished its levels in the SENP2–/– TS cells ( Figure 10B ) . The p53 RNAi treatment also promoted the G1–S transition of the SENP2–/– TS cells arrested in G0–G1 ( Figure 10D ) . Furthermore , downregulation of p53 enhanced TGC differentiation of the SENP2–/– cells , as determined by the expression of p450scc ( Figure 10E ) . These data demonstrated that stimulation of p53 is not only necessary to mediate the SENP2-null defects , but is also sufficient to induce deficiencies in expansion of trophoblast stem cells and their maturation .
This study demonstrates an essential role of SENP2 in trophoblast lineage development during placentation . All three major trophoblast layers were affected by SENP2 deficiency . Our data provide an important connection between SENP2 and the p53–Mdm2 pathway in trophoblast development . The loss of SENP2 caused a deficiency in the G–S transition , which is required for both the mitotic cell cycle ( containing G1 , S , G2 , and M phases ) and the endocycle ( containing only the G and S phases ) during trophoblast proliferation and differentiation , respectively . The cell cycle regulators p53 and Mdm2 appear to be critical for SENP2-dependent trophoblast mitosis and polyploidy . We propose that the SENP2–Mdm2–p53 pathway has a dual role in the G–S checkpoint of mitotic division and endoreduplication ( Figure 11A ) . Although high levels of p53 induce a G1 arrest , a low level may be necessary to go through the rest of mitosis , such as through the tetraploid checkpoint . Because of the omission of M phase in endoreduplication , repression of p53 is essential to produce polyploid cells . Our findings further suggest that SENP2-dependent SUMO modification controls the subcellular localization of Mdm2 ( Figure 11B ) . Sumoylated Mdm2 , which preferentially accumulates in the nucleus , likely cannot modulate p53 , whereas desumoylated Mdm2 , which can move freely to the cytoplasm , is capable of p53 degradation . This study provides evidence to support an important function of p53 , as a guardian of the genome to control polyploidy . An endoreduplication deficiency was previously observed in embryos lacking cyclin E proteins [41] . In contrast to the SENP2-null deficiencies , the loss of cyclin E proteins did not affect TGC differentiation . It is conceivable that cyclin E , which functions in late G1 phase to promote S-phase entry , acts further downstream of the SENP2–Mdm2–p53 pathway . In the SENP2 mutants , we detected alterations of this regulatory pathway not only in the stem cell niche site , but also in the differentiated trophoblast layer . A recent report found that an increased number of TGCs were detected in the p53-null placentas [42] , further supporting our hypothesis . SENP2 might also be involved in a crucial step of p53-dependent aneuploidy , genome instability and tumorigenesis [43] . Polyploid cells have several different fates . They can arrest in the cell cycle mediated by the tetraploidy checkpoint , which then triggers apoptosis . However , the lack of p53 allows these cells , as they escape from the arrest to undergo multipolar mitosis , to become aneuploid [44–46] . The nature of trophoblast development provides a system to elucidate the regulatory mechanism underlying polyploidy . Because of the biochemical activity of SENP2 , the SENP2-null model offers a unique opportunity to further investigate the modulation of the p53–Mdm2 circuit by SUMO in normal developmental programming of polyploidy . The knowledge obtained here might be applicable to malignant transformation processes associated with polyploidy . SENP2 is also known as Axam , which has been shown to modulate Wnt signaling by interacting with Axin , a scaffold protein involved in targeting β-catenin for degradation [14 , 17] . Although biochemical studies suggested that SENP2 could regulate the canonical Wnt pathway by SUMO modulation of a LEF/TCF transcription factor [31] , there was no in vivo evidence to support this idea . We failed to detect alterations in Wnt–β-catenin signaling in the SENP2 mutant placentas ( SC and WH , unpublished data ) although this might occur in other tissues . SUMO modification of Axin has been shown to modulate its effects on JNK signaling [36] . Neither JNK , nor the related p38 and Erk1/2 factors that are important for placental function [32–35] , seem to be involved in the SENP2-mediated trophoblast development ( SC and WH , unpublished data ) . However , we identified the p53–Mdm2 pathway as a downstream target of SENP2 . Our data imply that SUMO modification mediated by SENP2 is required for proper localization and function of Mdm2 , which in turn controls p53 stability during trophoblast development . Not only does stimulation of p53 induce phenotypic defects resembling those of the SENP2 inactivation , but downregulation of p53 alleviates the trophoblast deficiencies caused by SENP2 deficiency . It is conceivable that Wnt or JNK/SAPK signaling regulated by SENP2 is critical for another cell type and lineage development . The generation of mouse models permitting conditional inactivation of SENP2 will aid these studies and determine its essential role in other developmental processes . The loss of SENP2 disturbs the balance of SUMO modification . Although sumoylation of Mdm2 has been described [47] , it was not clear whether this modification dictates subcellular distribution . Our data provide evidence that cellular distribution of Mdm2 is regulated by the SUMO pathway . Disruption of SENP2 , leading to an accumulation of Mdm2 in a hyper-sumoylated state , induces its mislocalization . Many sumoylated proteins , including PML , preferentially accumulate in specific complexes called PML nuclear bodies [12] . Sumoylation of PML is essential not only for these nuclear bodies to form but also for other sumoylated proteins to concentrate there . Although the biological function of PML nuclear bodies remains largely elusive , subsequent recruitment of proteins can modulate transcription activity . It has been shown that sumoylation of PML directs p53 to nuclear bodies , leading to a stimulation of its transcriptional and pro-apoptotic activities [48 , 49] . These effects can be regulated by sumoylation of p53 [11 , 50 , 51] . Because of technical limitations and , more importantly , SUMO regulation of a number of p53 regulators ( Mdm2 , MdmX , and PML ) , the functional consequences of sumoylation have been difficult to elucidate . As SUMO modification of PML and p53 is a key determinant for maintaining genome integrity [12] , our data imply that SENP2 might mediate this maintenance . Using a mouse model with disruption of SENP2 , this study suggests a novel role of SUMO modification in cell cycle progression and induction of polyploidy . Sumoylation , which dictates Mdm2 trafficking , is crucial for modulation of the p53–Mdm2 circuit . Further studies focusing on the detailed mechanistic switch of the SENP2–Mdm2–p53 pathway and its implications in other developmental and pathogenic processes promise important insights into the role of SUMO modification in mammalian development and disease .
Genomic DNA fragments containing the SENP2 gene ( Accession number NC_000082 ) were isolated by PCR and cloned into the pGEM vector . The 5′ arm contained sequences from the first coding exon to the beginning of the second coding exon , which encodes the first 49 amino acids of SENP2 . The 3′ arm included parts of the fifth intron and the sixth coding exon . A β-galactosidase cDNA was fused in-frame to the second coding exon of SENP2 . The SENP2lacZ /+ mutant ES cell lines were generated by electroporation of the targeting vector into CSL3 ES cells [52] . Correct homologous recombination at the SENP2 locus was confirmed by Southern blotting ( Figure S1A ) . ES cell clones were injected into blastocysts to generate chimeras that were bred to obtain mice carrying the targeted allele . Mice were genotyped by PCR analysis using primers ( G1: 5′-ctgttttctactgcagtggacac-3′ , G3: 5′-gatacttgtagaaaggcctagtat-3′ and K1: 5′-taaccgtgcatctgccagtttga-3′ ) to identify the wild-type and mutant SENP2 locus ( Figure S1B ) . To delete the neo cassette flanked by two loxP sites , the SENP2lacZ/+ strain was crossed with the Zp3-Cre strain as described [53] . PCR genotyping was performed to confirm the removal of neo and the presence of lacZ as described ( Figure S1C ) [52] . Care and use of experimental animals described in this work comply with guidelines and policies of the University Committee on Animal Resources at the University of Rochester . The pCS2-SENP2 clone , containing the Myc-tagged SENP2 cDNA , was generated by inserting a blunt-ended 1 . 7 kb Not1–Spe1 fragment into the blunt-ended Xho1–Xba1 sites of pCS2 vector [54] . The GFP-tagged Mdm2 expression vector ( pGFP-Mdm2 ) was generated by ligation of a full length Mdm2 [55] and GFP ( BD bioscience ) cDNA fragments . The GFP-tagged Mdm2–SUMO expression vector was created by insertion of a SUMO-1 fragment [51] into the pGFP-Mdm2 plasmid . To generate the pBS-SENP2 clone for making the RNA probes , a 400 bp BamH1–EcoR1 fragment of the pCS2-SENP2 clone was cloned into the same restriction sites in pBS vector ( Stratagene ) . To generate RNA probes for in situ hybridization , DNA plasmids pBS-Gcm1 , pBS-Hand1 , pCR4-PL-I , pCR4-Tpbpa , pBS-Ctsq , and pBS-SENP2 [19 , 21 , 23 , 56] were linearized and transcribed in vitro using RNA polymerases T3 , T7 , and SP6 ( Promega ) . Plasmid DNA transfection was performed by Lipofectamine 2000 ( Invitrogen ) -mediated transfer with 4 μg pCS2-MT–SENP2 , 1 μg pGFP-Mdm2 , 1 μg pGFP-Mdm2–SUMO-1 , or 10–100 nM p53 siRNA ( Santa Cruz ) . Cells were plated ( 1 . 5 × 105 cells in a 30 mm dish for protein extraction , 2 × 104 cells in a 24-well dish for GFP analysis , and 5 × 105 cells in a 60 mm dish for flow cytometry ) 24 h prior to the transfection procedure . The transfected cells were harvested after 48 or 72 h for further analyses . Total RNA , isolated using Trizol ( Invitrogen ) , was used to produce cDNA according to the manufacturer's instructions ( SuperScript III , Invitrogen ) . The reverse transcription products were subject to PCR amplifications of the SENP2-lacZ fusion transcript using primers 5′-cagtctctacaatgctgcc-3′ and 5′-ctgtcactctgatctttgg-3′ ( exons 3–5 ) , primers 5′-gtgagctgatgagttctgg-3′ and 5′-gtcgctccaataactttcg-3′ ( exons 4–6 ) , primers 5′-ggaggagcagaatcatgg-3′ and 5′-ctcaaaatctcatctggtgg-3′ ( exons 8–11 ) and primers 5′-cattaccagttggtctggtg-3′ and 5′-gctgcaataaacaagttccg-3′ ( lacZ ) . The PCR reaction was performed by denaturation at 94 °C for 5 min and 30 cycles of amplification ( 94 °C for 30 s , 53 °C for 30 s , and 72 °C for 45 s ) , followed by a 7-min extension at 72 °C . Mouse blastocysts were recovered and cultured in DMEM medium containing 15% FBS , 100 μM β-mercaptoethanol , 100 μM non-essential amino acid , and 100 μg/ml penicillin-streptomycin , in a humidified 5% CO2 incubator at 37 °C . Cultured embryos were hatched and attached to dishes after 24–36 h . The differentiated trophoblasts became identifiable in a few days . For genotyping , cultured cells were incubated in 10 μl buffer containing 25 mM NaOH and 0 . 2 mM EDTA , pH 12 for 1 h at 95 °C , followed by the addition of 10 μl buffer containing 40 mM Tris-HCl , pH 5 . 0 . Lysates were subject to PCR analysis . The SENP2 wild-type allele was detected by a nested PCR assay . Primers 5′- ctgttttctactgcagtggacac-3′ and 5′-gctgcctggagtttatctactgtag-3′ were used for the first PCR reaction , performed with 35 cycles of amplification ( 94 °C for 30 s , 60 °C for 30 s , and 72 °C for 2 min 30 s ) , followed by a 7-min extension at 72 °C . Subsequently , the first PCR products were subject to a second PCR reaction using the method described for genotyping the SENP2 wild-type mouse strain . For genotyping the SENP2 mutant culture , the same method for the SENP2 mutant mouse strain was used . To establish the TS cell lines [30] , blastocysts were recovered in TS medium ( RPMI-1640 medium containing 20% fetal bovine serum , 1 mM sodium pyruvate , 100 μM β-mercaptoethanol , 100 μg/ml penicillin–streptomycin ) , plus 25 ng/ml FGF4 and 1 ng/ml heparin . Briefly , each blastocyst was placed in a culture dish with mitomycin C-treated MEF feeders and cultured in a humidified 5% CO2 incubator at 37 °C . The blastocysts were hatched and attached to the dishes in 24–36 h . After 48 h , a small outgrowth from a blastocyst was formed and cultured in TS medium containing 25 ng/ml FGF4 and 1 ng/ml heparin . After 72–96 h , the outgrowths were ready to be disaggregated by the addition of 0 . 25% trypsin/EDTA and incubation for 3 min at 37 °C . The disaggregated cells were continuously cultured in TS medium with the presence of FGF4 and heparin . The TS cell colonies began to appear after days 6 to 10 , and continued to be cultured until they were about 50% confluent . After expanding the cultures on the feeders for one or two passages , MEF-free TS cells were obtained and maintained in media containing 70% MEF-conditioned medium , 30% TS medium , 37 . 5 ng/ml FGF4 , and 1 . 5 ng/ml heparin . To differentiate TS cells into TGC , cells were cultured in TS medium with no additions [30] . For BrdU labeling of the cultured cells , 30 μg/ml BrdU ( Sigma ) was added in the media for 1 h . The labeled cells were then fixed with methanol/acetone ( 1:1 ) , followed by immunostaining analysis . For cell cycle analysis by flow cytometry , 8 × 105 ( for mitotic cell cycle ) or 105 ( for endoreduplication cycle ) TS cells were cultured in 6 cm dishes in TS media plus FGF4 , heparin , and MEF-conditioned medium ( undifferentiated medium ) for 2 d , and TS media only ( differentiated medium ) for 6 d , respectively . Cells were then harvested by trypsinization and fixed in 70% ethanol at 4 °C for at least 24 h . Cells were then treated with RNase ( 1 mg/ml ) for 30 min , followed by PI staining ( 20 μg/ml ) for 10 min at room temperature . Samples were analyzed by an Epics Elite ESP ( Coulter Electronics ) set to collect 10 , 000 events . The percentage of cells in G0–G1 , S , G2–M or with polyploidy were determined using ModFit LT software . For synchronizing cells in M phase , 3 μM nocodazole was added to the media . Nuclear and cytoplasmic fractionations of TS cells were extracted using an NE-PER extraction kit according to the manufacturer's protocol ( PIERCE ) . Paraffin sections were treated with buffer containing 0 . 1 M Tris-HCl and 0 . 1 M EDTA ( pH 8 . 0 ) plus 1 μg/ml proteinase K for 30 min , and washed with the same buffer without proteinase K for 5 min at 37 °C . Samples were then incubated with buffer containing 0 . 2 M Tris-HCl ( pH 8 . 0 ) and 0 . 1 M glycine for 10 min at room temp , followed by post-fixing with 4% paraformaldehyde in PBS buffer for 20 min and a 20-min wash in PBS buffer at room temperature . The sections were incubated in buffer containing 0 . 1 M triethanolamine ( pH 8 . 0 ) for 10 min , followed by 0 . 25% ( v/v ) acetic anhydride in 0 . 1 M triethanolamine ( pH 8 . 0 ) buffer for 10 min and by 2× SSC ( 1× SSC: 0 . 15 M sodium chloride and 15 mM sodium citrate , pH 5 . 5 ) buffer for 10 min . After dehydration through ethanol gradients and air drying for 2 h , sections were incubated with digoxygenin-labeled probes ( 1 μg/ml ) in 5× SSC buffer containing 50% formamide , 50 μg/ml yeast tRNA and 1% SDS overnight at 70 °C . Samples were then washed three times with 5× SSC buffer for 15 min at 70 °C and 2× SSC buffer containing 50% formamide for 10 min at 45 °C before incubating with buffer containing 20 μg/ml RNase A , 5 U/ml RNase T1 , 0 . 5 M sodium chloride , 10 mM Tris ( pH 8 . 0 ) and 1 mM EDTA ( pH 8 . 0 ) for 30 min at 37 °C . After washing with 2× SSC for 10 min at 37 °C and 0 . 1× SSC for 10 min at 45 °C , samples were incubated in MBST buffer containing 60 mM maleic acid , 0 . 15 M sodium chloride , and 0 . 1% Tween-20 , pH 7 . 5 for 10 min and blocked with 10% goat serum in MBST for 2 h at room temperature . After incubating with anti-digoxygenin antibody ( Roche ) in the blocking buffer for overnight at 4 °C , sections were washed with NTMT buffer ( 100 mM sodium chloride , 100 mM Tris , pH 9 . 5 , 50 mM magnesium chloride and 0 . 1 % Tween 20 ) and incubated in NTMT plus 2 mM levamisole overnight at 4 °C . To visualize the bound signals , samples were incubated with BM-purple ( Roche ) for 2 h to several days . The reaction was stopped by incubating in PBS buffer , followed by counterstaining with nuclear fast red . Samples were fixed , paraffin embedded , sectioned , and stained with hematoxylin/eosin for histological evaluation as described [57] . Tissue sections were subject to immunological staining with avidin:biotinylated enzyme complex as described [18 , 58] . Proteins were extracted from TS cells using M-PER reagent ( PIERCE ) with the addition of protease inhibitor cocktail ( Sigma-Aldrich ) , 1 mM sodium molybdate , 1 mM sodium vanadate , and 10 mM N-ethylmaleimide , or SDS lysis buffer ( 2% SDS , 10% glycerol , and 50 mM Tris , pH 6 . 8 ) . Protein extracts were subject to immunoblotting as described [54] . Bound primary antibodies were detected with horseradish peroxidase-conjugated secondary antibodies ( Vector Lab ) , followed by ECL-mediated visualization ( GE HealthCare ) and autoradiography . Mouse monoclonal antibodies anti-actin ( Thermo Fisher; 1:1 , 000 ) , anti-BrdU ( Thermo Fisher; 1:300 ) , anti-Cdx2 ( BioGenex; 1:1 ) , anti-MDM2 ( Santa Cruz; 1:100 ) , and anti-SUMO-1 ( Zymed; 1:2 , 000 ) ; rabbit polyclonal antibodies anti-calnexin ( Stressgene; 1:2 , 000 ) , anti-cyclin D1 ( Neomarker; 1:100 ) , anti-Ki67 ( Neomarker; 1:400 ) , anti-laminin ( Sigma-Aldrich; 1:25 ) , anti-Myc tag ( CalBioChem; 1:400 ) , anti-Oct4 ( Santa Cruz; 1:200 ) , anti-p53 ( Santa Cruz; 1:50 ) , and anti-p450scc ( Chemicon; 1:200 ) ; and goat polyclonal antibody anti-lamin B ( Santa Cruz; 1:100 ) were used as primary antibodies . BrdU incorporation analysis was performed by intraperitoneal injection of BrdU ( 250 μg/g of body weight ) into pregnant females for 1 h . Placentas were recovered , fixed , embedded , sectioned , and subject to immunostaining as described [18 , 57] . | Genome replication is essential for both expansion of stem cell numbers through mitosis and their maturation into certain specialized cell types through endoreduplication , a unique mechanism for multiplying chromosomes without dividing the cell . An important function of p53 as a guardian of the genome ensures that the genetic information is properly propagated during these processes . In this study , we discovered that mice with disruption of SENP2 , an enzyme that removes small molecular signals ( called SUMO ) that modify a protein's behavior and stability , are unable to form a healthy placenta as a result of deficiencies in the formation of various trophoblast cell types that give rise to the placenta . In the mutants , SUMO modification of Mdm2 , a protein that monitors the cellular levels of p53 , is deregulated . The loss of SENP2 causes dislocation of Mdm2 , leading to aberrant stimulation of p53 . The precursor cells known as trophoblast stem cells rely on p53 to proliferate and differentiate into specialized polyploid cells , which contain multiple copies of chromosomes . In SENP2 mutants , all three trophoblast layers were substantially defective , with the layer containing mainly the polyploid cells most severely affected and diminished . This study reveals a key genetic pathway , SENP2–Mdm2–p53 , which is pivotal for the genome replication underlying trophoblast cell proliferation and differentiation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"cell",
"biology",
"obstetrics",
"genetics",
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"genomics"
] | 2008 | SUMO-Specific Protease 2 Is Essential for Modulating p53-Mdm2 in Development of Trophoblast Stem Cell Niches and Lineages |
BK polyomavirus ( BKPyV ) is an emerging pathogen whose reactivation causes severe disease in transplant patients . Unfortunately , there is no specific anti-BKPyV treatment available , and host cell components that affect the infection outcome are not well characterized . In this report , we examined the relationship between BKPyV productive infection and the activation of the cellular DNA damage response ( DDR ) in natural host cells . Our results showed that both the ataxia-telangiectasia mutated ( ATM ) - and ATM and Rad-3-related ( ATR ) -mediated DDR were activated during BKPyV infection , accompanied by the accumulation of polyploid cells . We assessed the involvement of ATM and ATR during infection using small interfering RNA ( siRNA ) knockdowns . ATM knockdown did not significantly affect viral gene expression , but reduced BKPyV DNA replication and infectious progeny production . ATR knockdown had a slightly more dramatic effect on viral T antigen ( TAg ) and its modified forms , DNA replication , and progeny production . ATM and ATR double knockdown had an additive effect on DNA replication and resulted in a severe reduction in viral titer . While ATM mainly led to the activation of pChk2 and ATR was primarily responsible for the activation of pChk1 , knockdown of all three major phosphatidylinositol 3-kinase-like kinases ( ATM , ATR , and DNA-PKcs ) did not abolish the activation of γH2AX during BKPyV infection . Finally , in the absence of ATM or ATR , BKPyV infection caused severe DNA damage and aberrant TAg staining patterns . These results indicate that induction of the DDR by BKPyV is critical for productive infection , and that one of the functions of the DDR is to minimize the DNA damage which is generated during BKPyV infection .
BK polyomavirus ( BKPyV ) was first isolated in 1971 from a renal transplant patient [1] and has gained much interest in the past two decades due to its disease prevalence in immunocompromised patients [2] . Infection with BKPyV is ubiquitous in healthy individuals but does not lead to any known clinical disease . Under immunosuppressed conditions , especially in renal transplant and bone marrow transplant recipients , the virus can reactivate from a persistent state to lytic infection , which results in severe disease including polyomavirus-associated nephropathy ( PVAN ) and hemorrhagic cystitis ( HC ) , respectively [2] . Unfortunately , there is currently no FDA-approved , specific anti-BKPyV drug available for treating these diseases . The common approach to control BKPyV reactivation is palliative care for HC patients , or combining immunosuppression reduction with drugs that inhibit viral DNA replication for PVAN , although there are often conflicting outcomes with these treatment options [3] . Much of the knowledge about the polyomavirus lytic life cycle comes from research performed on Simian Virus 40 ( SV40 ) . The viral genome , a circular double-stranded DNA molecule , is delivered into the nucleus . Following nuclear entry , early proteins including the large T antigen ( TAg ) are expressed . TAg sets up the host environment for viral DNA replication by inducing cells into S phase and , at the same time , inhibiting the p53-dependent apoptotic pathway [4] . Initiation of viral DNA replication requires the concerted efforts of TAg , replication protein A ( RPA ) , DNA polymerase alpha-primase ( Pol-prim ) , and topoisomerase I [5] . Newly replicated viral DNA is encapsidated by the capsid proteins VP1 , VP2 , and VP3 , and this is followed by viral egress and cell lysis , thus completing the life cycle . Although SV40 is well-studied , there are differences between it and BKPyV . There is still much that is unknown with regard to the interaction between host nuclear components and viral factors during BKPyV replication . Identification and characterization of these interactions is extremely important , since it may reveal novel anti-viral therapeutic targets . The DNA damage response ( DDR ) is emerging as a cellular process that is targeted by a number of DNA and RNA viruses [6] , [7] . The DDR involves signaling cascades that are initiated when cells experience various types of DNA damage , and it coordinates many cellular processes including cell cycle arrest , chromatin modification , and DNA repair to allow cells to repair the damaged DNA [8] , [9] . This response is largely orchestrated by two major phosphatidylinositol 3-kinase-like kinases ( PI3KKs ) : ataxia telangiectasia mutated ( ATM ) , and ATM and Rad3-related ( ATR ) kinase . ATM mainly responds to double-stranded breaks ( DSBs ) resulting from conditions such as ionizing irradiation ( IR ) . The Mre11-Rad50-NBSI ( MRN ) protein complex serves as a sensor for DSBs , and is crucial for the activation of ATM upon DSB damage [10] , [11] . ATR , on the other hand , is activated by single-stranded DNA lesions and is important for resolving replication stress from conditions such as stalled replication forks and ultraviolet ( UV ) light [8] . Both kinases , when activated , can phosphorylate numerous downstream targets that are involved in DNA repair and cell cycle arrest , including Chk1 , Chk2 , and a histone variant H2AX [8] , [12] . The phosphorylated H2AX ( serine 139 , referred to as γH2AX ) is considered a hallmark of the DDR , and it is crucial for recruiting and maintaining downstream mediator and repair proteins to sites of damage [13] , [14] . There is accumulating evidence suggesting that the DDR is closely linked to polyomavirus infections . Mouse polyomavirus ( MPyV ) infection increases phosphorylated ATM ( pATM ) , and using either an ATM inhibitor or ATM-deficient cells , it has been demonstrated that an ATM-mediated DDR is required for MPyV replication [15] . ATM phosphorylates SV40 TAg , and knockdown of ATM decreases the level of TAg-pS120 and viral DNA synthesis [16] . The ATM-mediated DDR has also been reported to be required for SV40 infection , and SV40 infection is found to lead to the proteasome-dependent degradation of MRN complex [17] , [18] . Another human polyomavirus , JC polyomavirus ( JCPyV ) , also activates the ATM DDR . It is suggested that DDR activation induces G2 arrest to promote JCPyV replication [19] . The contribution of the ATR-mediated DDR to polyomavirus infection is less clear . Although knockdown of ATR during JCPyV infection results in a decrease in TAg levels , ATR knockdown does not seem to affect SV40 DNA replication [18] . This lack of effect on viral replication has been attributed to incomplete knockdown of ATR and kinase redundancy [18] . Using a cell line that expresses a dominant negative form of ATR , another group showed that ATR is required for the activation of ATR-Δp53 signaling pathway during SV40 infection [20] . The activation of this pathway is believed to lead to intra-S checkpoint activation and promote TAg- Pol-prim complex formation [20] . Finally , a recent microarray analysis has shown that several genes that are involved in DNA damage repair are also upregulated with BKPyV infection , indicating that the DDR may be important during BKPyV infection [21] . In this study we examined the roles of both the ATM and ATR-mediated DDR during lytic BKPyV infection in a primary human renal proximal tubule epithelial ( RPTE ) cell culture model [22] . Our results showed that BKPyV activates both branches of the DDR . Using small interfering RNA ( siRNA ) knockdowns , we demonstrated that ATM and ATR each contribute to DDR activation caused by BKPyV infection . Our data also clearly suggested the importance of ATR during lytic BKPyV infection , and more importantly , that both proteins function additively to ensure efficient viral DNA replication and synergistically to affect production of viral progeny . In the absence of either kinase , severe DNA damage accumulated during BKPyV infection , indicating that part of the roles that ATM and ATR play is to repair DNA damage caused by BKPyV infection .
To investigate the relationship between BKPyV and the DDR , we began our studies with the ATM-mediated branch of DDR , because it has been implicated in many polyomavirus infections [15]–[18] . To examine whether BKPyV infection activates an ATM-mediated DDR , we harvested total protein lysates from mock- and BKPyV-infected RPTE cells over a 3-day time course . The proteins were immunoblotted for markers that are indicative of ATM-mediated DDR activation ( Figure 1A ) . The gradual increase in the viral early protein TAg allowed us to monitor the progression of infection . In our Western blots for TAg , we routinely observe two bands using a monoclonal anti-TAg antibody , which are labeled as TAg and TAg* ( filled arrowheads ) . We think these are two forms of TAg because TAg is known to undergo multiple post-translational modifications [23]–[25] . While the total level of ATM remained constant throughout the course of infection , the level of ATM-pS1981 increased dramatically by 2 days post infection ( dpi ) . Concomitant with the increase of ATM-pS1981 , there was an induction of NBSI-pS343 , γH2AX , Chk2-pT68 , RPA32-pS4/8 , and p53-pS15 , all of which are downstream targets of active ATM-pS1981 [8] , [12] . The induction of both ATM-pS1981 and γH2AX was similar to that seen when RPTE cells were treated with IR ( data not shown ) , suggesting that our detection of DDR markers is specific . There was an increase in total p53 levels following BKPyV infection , consistent with previous reports of TAg stabilizing p53 [26]–[28] . In contrast to SV40 [17] , BKPyV did not cause degradation of MRN components . In addition , we detected that BKPyV infection resulted in unique γH2AX and Mre11 staining patterns ( Figure S1 ) . There was a marked increase in both bright foci and pan-nuclear staining of γH2AX , as well as bright nuclear foci of Mre11 . Some , but not all of these foci co-localized with small TAg foci . Together , these results demonstrated that the ATM signaling pathway is activated upon BKPyV infection in RPTE cells . We next determined whether BKPyV infection led to cell cycle dysregulation ( Figure 1B ) , which is commonly seen with DDR activation . Flow cytometry analysis of cells stained with propidium iodide ( PI ) showed that starting from 2 dpi , in cells that were infected with BKPyV , there was a gradual increase of a cell population with >G2 DNA content ( Figure 1B and 1C ) . The accumulation of these polyploid cells persisted until 6 dpi ( data not shown ) , whereas in mock-infected cells , most cells were in the G0/G1 phase throughout the time course . These data indicated that multiple rounds of DNA replication can occur within a single cell cycle in BKPyV-infected cells , and are consistent with the activation of the DDR . To address the functional roles of the ATM-mediated DDR in productive BKPyV infection , we knocked down ATM using siRNA and assessed the effects of the knockdown on BKPyV infection . RPTE cells were first transfected with siRNAs that targeted ATM , followed by infection with BKPyV when the knockdown of ATM was achieved at 3 days post transfection ( dpt ) ( Figure 2 ) . By Western blotting , ∼90% of the total ATM was knocked down , which was also confirmed by immunoblotting against ATM-pS1981 ( Figure 2A and data not shown ) . The knockdown lasted throughout the course of infection and was not affected by BKPyV replication ( Figure 2A ) . Moreover , knockdown of ATM did not affect the morphology or viability of RPTE cells compared to cells that did not receive siRNA or cells that were transfected with non-targeting siRNA control ( data not shown ) . We first compared the level of TAg in ATM knockdown cells to control cells using immunoblotting . The expression of TAg was similar among all the cells over a 3-day time course , suggesting that ATM was not required for viral early gene expression ( Figure 2A ) . We then examined whether ATM knockdown resulted in a defect in viral DNA replication or infectious progeny production . Low molecular weight DNA was extracted from the samples and real-time PCR was employed to measure the viral DNA load ( Figure 2B ) . The knockdown led to an ∼60% decrease of viral DNA compared to control cells by 3 dpi ( Figure 2B ) . Consistently , we also observed an approximately 50% decrease of viral infectious progeny in ATM knockdown cells ( Figure 2C ) . These data suggested that ATM contributes to optimal BKPyV DNA replication and viral growth . To ask whether ATM was responsible for the activation of the DDR we observed during BKPyV infection , total proteins from mock- and BKPyV-infected ATM knockdown cells were immunoblotted for Chk2-pT68 and γH2AX and compared to control cells ( Figure 2D ) . ATM knockdown partially abolished the induction of Chk2-pT68 caused by BKPyV infection ( Figure 2D ) , consistent with ATM being the main kinase that phosphorylates Chk2 during the DDR [8] , . γH2AX , however , was still induced in BKPyV-infected , ATM knockdown cells ( Figure 2D ) . These data suggested that ATM may not be the sole contributor to the BKPyV-induced DDR . The fact that ATM knockdown did not completely eliminate the BKPyV-activated DDR suggested that there are other DDR factors involved . Both ATM and ATR share a number of substrates and therefore it is possible that the DDR activation we observed during BKPyV infection was , in part , a result of ATR activation . To determine whether the ATR-mediated DDR is also activated during BKPyV infection , we immunoblotted proteins from mock- and BKPyV-infected RPTE cells for ATR , Chk1-pS317 , and Chk1-pS296 ( Figure 3A ) . ATR levels remained relatively unchanged during infection; however , both Chk1-pS317 and Chk1-pS296 increased starting at 2 dpi . Chk1-pS317 phosphorylation is a direct result of ATR activation upon DNA damage , which is followed by Chk1 autophosphorylation on S296 [29] . The strong induction of both Chk1-pS317 and Chk1-pS296 clearly suggested that an ATR-mediated DDR is also activated by BKPyV infection . Next , we used siRNAs to either singly knock down ATR or doubly knock down both ATM and ATR , and examined the effects of these knockdowns on the DDR activation during BKPyV infection . Quantitative Western blotting showed that the knockdowns were ∼90% complete , and no significant cellular viability changes were noticed in knockdown cells ( Figure 3B and data not shown ) . Having established that , we went on to determine whether ATR single knockdown or ATM+ATR double knockdown could eliminate the BKPyV-induced DDR in RPTE cells . Total proteins from mock- and BKPyV-infected control or knockdown cells were immunoblotted for Chk1-pS317 , Chk1-pS296 , Chk2-pT68 , and γH2AX ( Figure 3B ) . ATM knockdown reduced Chk2-pT68 in infected cells , whereas ATR single or ATM+ATR double knockdown almost completely abolished the induction of both Chk1-pS317 and Chk1-pS296 . ATR knockdown , however , did not significantly alter the induction of Chk2-pT68 . These data suggested that ATM was mainly responsible for the activation of pChk2 upon BKPyV infection , while ATR was more important in inducing pChk1 . γH2AX induction was still evident even in ATM+ATR double knockdown cells ( Figure 3B ) , suggesting that ATM and ATR may not be the only kinases contributing to the phosphorylation of this DDR marker . We then investigated the role that ATR plays alone and in conjunction with ATM during productive BKPyV infection . We first measured viral gene expression in these knockdown cells . At 2 dpi , we did not detect a significant difference in the level of TAg between any knockdown cells and control cells ( Figure 4A and S2 ) , but a third TAg band appeared in the ATR and double knockdown cells ( Figure 4A , open arrowhead ) . This band was no longer present at 3 dpi . In contrast to the ATM knockdown alone , at 3 dpi the ATR knockdown displayed a more marked difference in TAg and TAg* . There was about a 50% decrease of TAg in ATR knockdown cells , and an ∼80% decrease of TAg* . The ATM and ATR double knockdown sample had a pattern similar to the ATR knockdown alone ( Figure 4B and 4C ) . None of the knockdowns , however , had a major effect on late gene VP1 expression ( Figure 4D and S2 ) . To ensure that we did not saturate the cells with large amounts of BKPyV , we repeated these experiments with a low multiplicity of infection ( MOI ) ( 0 . 01 IU/cell vs . 0 . 5 IU/cell ) . Quantitative Western blotting showed that there was no significant difference in either TAg or VP1 levels among all the knockdown and control cells ( Figure S3A and S3B ) at 2 dpi , suggesting that the lack of difference in TAg and VP1 levels at 2 dpi is not due to compensation from a high MOI infection . TAg is required for the initiation of viral DNA synthesis and it provides a conducive host replication environment by inactivating the retinoblastoma susceptibility family proteins and p53 [4] . We therefore determined whether a change in TAg in ATR knockdown cells was accompanied by a defect in viral DNA replication . Using quantitative PCR , we found that ATR or ATM knockdown caused a similar decrease in viral DNA synthesis at 2 dpi ( Figure 4E ) . The double knockdown cells , however , exhibited a more severe defect in viral DNA levels at both high and low MOIs ( Figure 4E and Figure S3C ) , suggesting that ATM and ATR function additively to ensure full viral DNA replication . Finally , we measured infectious viral progeny production under these knockdown conditions ( Figure 4F ) . ATR single knockdown led to a slightly larger decrease in viral titer compared to ATM single knockdown . The double knockdown cells displayed a dramatic decrease ( ∼90% ) compared to the control cells . To confirm the importance of the ATR/Chk1 pathway in BKPyV infection , we treated RPTE cells with the Chk1 inhibitor UCN-01 at 1 dpi ( at which time the viral genome is delivered into the nucleus ) [30] . The presence of UCN-01 slightly reduced TAg and VP1 levels ( Figure 5A ) . Treatment with UCN-01 also resulted in a partial decrease in viral DNA replication and a severe reduction in viral titer ( Figure 5B and 5C ) , consistent with ATR/Chk1 pathway being required for BKPyV productive infection . We have also tested the effect of the ATM inhibitor KU55933; however , this drug seemed to have some off-target effects in RPTE cells and therefore we could not draw conclusions from these experiments ( data not shown ) . It has previously been shown that another PI3KK that is involved in nonhomologous end-joining DNA repair , DNA-dependent protein kinase ( DNA-PKcs ) , is able to phosphorylate H2AX independent of ATM and ATR [31] , [32] . Additionally , for certain viruses such as adeno-associated virus ( AAV ) , the induction of the DDR in infected cells is mostly mediated through DNA-PKcs [33] . We therefore tested the effect of DNA-PKcs single knockdown and ATM/ATR/DNA-PKcs triple knockdown on BKPyV infection and the induction of γH2AX ( Figure 6 ) . The DNA-PKcs-targeting siRNAs efficiently knocked down DNA-PKcs , but they also reduced ATM ( Figure 6A ) . The single knockdown slightly increased TAg , TAg* , and VP1 , with a concomitant ∼2 fold increase in viral DNA and viral titer ( Figure 6C and 6E ) . The impact of the ATM/ATR/DNA-PKcs knockdown is similar to that of the ATM/ATR double knockdown . There is an ∼60% drop in viral DNA and a more striking difference in viral titer ( Figure 6D and 6F ) . Surprisingly , with all three kinases absent , we could still detect a strong induction of γH2AX in BKPyV-infected cells ( Figure 6B ) . These data suggested that additional cellular kinases may be involved in the activation of DDR during BKPyV infection . To further dissect the roles that ATM and ATR play during BKPyV infection , we next examined the localization of TAg in knockdown and control cells ( Figure 7 ) . In all the mock-infected cells , the nuclei appeared normal judged by DAPI staining ( data not shown ) . With BKPyV infection , however , we observed some abnormal DAPI staining in ATM/ATR single or double knockdown cells ( Figure 7A ) . In these cells , we observed that the nuclei sometimes appeared smaller or fragmented ( open arrowheads , Figure 7A , and Figure 7B ) , which are very similar to micronuclei that arise when chromosomes are broken or damaged [34] . At the same time , we also observed aberrant TAg staining patterns in the knockdown cells ( Figure 7A ) . While TAg appeared mostly nuclear in control cells , in ATM and ATR knockdown cells we often observed cells that had diffuse TAg staining , sometimes throughout the whole cytoplasm ( Figure 7A , filled arrowheads , and Figure 7C ) . In addition , in cells that received ATR siRNA , some of the cells showed fragmented TAg staining patterns ( Figure 7A , arrows , and Figure 7C ) , similar to the fragmented DAPI staining . To confirm that the observed aberrant DAPI and TAg staining is indeed associated with DNA damage , we performed metaphase chromosome analysis in control and knockdown cells , with or without BKPyV infections ( Figure 8 ) . In all the mock-infected cells ( including all the knockdown cells ) , or BKPyV-infected cells with no knockdowns , most chromosome spreads were normal . In ATM or ATR knockdown cells , there were a very high percentage of metaphases showing a shattered phenotype , with ATR single knockdown showing the most severe defect ( Figure 8A and 8C ) . These numbers might even be underestimated considering that we could not distinguish between uninfected and infected cells in the metaphase spreads . Shattered chromosomes were never observed in uninfected cells . Among the metaphases that were not shattered , the average number of chromosome breaks per cell was also higher in BKPyV-infected , ATM/ATR knockdown cells ( Figure 8B ) . These data suggested that ATM , and to a greater extent , ATR , contribute to repairing DNA damage that is triggered by BKPyV infection .
In this report we characterized the relationship between the ATM- and ATR-mediated DDR and lytic BKPyV infection . Our results indicated that both branches of the DDR were activated by BKPyV; moreover , ATM and ATR functioned in parallel and contributed to the activation of individual DDR pathways ( Figure 9A ) . In particular , ATM was mainly responsible for activating pChk2 , whereas ATR was more important in activating pChk1 and also might be important for regulating TAg and its modified forms . Our results showed that both ATM and ATR were required to achieve maximal viral DNA replication and infectious progeny production . This is the first such report for any polyomavirus family member to our knowledge . Knocking down each individual kinase resulted in a partial defect in viral replication and the double knockdown had a more dramatic effect . When comparing ATM and ATR double knockdown cells to control cells , the degree of decrease in viral titer was consistently greater than the decrease in viral DNA , suggesting that there may be some post-DNA replication regulation that involves both ATM and ATR function . For example , ATR may be involved in alleviating the DNA replication stress generated during the resolution of the two daughter circular BKPyV chromosomes , and therefore ATR knockdown caused a more marked defect in viral progeny production than in DNA replication . Our results also showed that in the absence of ATM or ATR , severe chromosome damage accumulated upon BKPyV infection . These data point out that another possible function of ATM and ATR during BKPyV infection is to help repair the DNA damage caused by BKPyV ( Figure 9B ) . The exact role of ATM during polyomavirus early gene expression remains unclear , but it may be different depending on the specific virus and cell type . For example , one report showed that ATM knockdown in African green monkey CV1 cells reduced the level of SV40 TAg-pS120 , but it did not have much effect on the total level of TAg [16] . In another report using BSC40 monkey kidney cells , the ATM inhibitor KU55933 reduced total SV40 TAg levels [17] . Our ATM knockdown data suggest that ATM is not required for TAg expression during BKPyV infection . Consistent with this , BKPyV TAg expression was similar in wild-type and ATM-knockout mouse embryonic fibroblast cell lines ( data not shown ) . Unfortunately we cannot study the entire BKPyV life cycle in mouse cells due to a block to DNA replication [35] . Our data demonstrate the importance of ATM and ATR during BKPyV DNA replication , DDR activation , and progeny production . It is possible that ATM , ATR and downstream effectors directly participate in replication-related events . For example , ATM has been reported to phosphorylate SV40 TAg in vivo and therefore is required for optimal SV40 replication [16] . In our ATR single knockdown or ATM+ATR double knockdown cells , we observed a decrease in a modified form of TAg ( TAg* ) at 3 dpi , and the appearance of a third band of TAg at 2 dpi ( Figure 4A ) . Preliminary experiments indicated that phosphatase treatment did not affect the level of TAg or TAg* ( data not shown ) , but this does not definitively rule out the possibility that TAg* may represent a phosphorylated form of TAg . Additionally , proteins involved in homologous recombination or the Fanconi anemia pathway ( required for repair of stalled replication forks ) have been found to be necessary for SV40 replication [18] . It is possible that DDR proteins that are downstream of ATM and ATR directly contribute to BKPyV DNA replication . Alternatively , instead of being directly involved in viral replication , ATM and ATR may participate indirectly in BKPyV infection by affecting cell cycle status . For example , ATM and ATR activation leads to G2 arrest in JCPyV-infected human neuroblastoma cells and oligodendrocytes [19] . It is hypothesized that G2 arrest contributes to JCPyV replication by maintaining the cellular replication machinery and preventing mitosis [19] . Our cell cycle analyses also showed that BKPyV infection of RPTE cells results in the accumulation of polyploid cells , consistent with the idea that mitosis is inhibited to allow for maximal DNA replication . A number of DDR proteins including γH2AX and Mre11 were re-localized into small TAg-containing foci during BKPyV infection . Similar co-localization between DDR proteins and TAg foci is seen during SV40 infection , although the MRN proteins are eventually degraded during SV40 infection [17] , [18] . It has been hypothesized that these foci represent sites of viral DNA replication because they also contain proteins that are required for viral replication including RPA and Pol-prim , but not Pol-prim-associated host replication factor [17] . In addition to the small , bright foci of γH2AX , there was also an increase in pan-nuclear staining of γH2AX in BKPyV-infected cells . Although the functional significance of this increase is not clear , it has previously also been reported in other DNA virus infections such as adenovirus and AAV [33] , [36] , [37] . It is thought that this pan-nuclear staining increase may represent modification of histones on cellular chromatin and that the modification can be stimulated by viral replication . Intriguingly , the induction of γH2AX by BKPyV still occurred in ATM , ATR , and DNA-PKcs triple knockdown cells , suggesting that there might be other cellular kinase ( s ) responsible for phosphorylating this molecule . Alternatively , it is possible that BKPyV infection alters a cellular phosphatase activity that , together with residual PI3KK activity due to incomplete knockdown , leads to an increase in the steady state level of γH2AX . One phosphatase candidate is PP2A , which has been demonstrated to dephosphorylate γH2AX in an ATM- , ATR- , and DNA-PKcs-independent manner [38] . Polyomavirus small T antigen is well known for its interaction with PP2A and its ability to inhibit PP2A enzymatic activity [39] . It will be interesting to determine the mechanism of γH2AX induction and its functional significance during BKPyV infection in the future . What triggers the activation of both the ATM- and ATR-mediated DDR during BKPyV infection requires more careful examination . It is possible that a viral protein ( s ) alone is able to achieve the induction . For example , expression of SV40 TAg without a viral replication origin in normal human BJ/tert fibroblasts induces both an ATM- and ATR-mediated DDR , and this induction is dependent on the interaction of TAg with Bub1 , a mitotic spindle checkpoint kinase [40] . Moreover , polyomavirus TAg alone is able to induce cellular DNA damage as judged by comet assays and cytogenetic analyses [18] , [41]–[44] . For JCPyV , the ability of TAg to associate with cellular DNA is important for TAg induction of G2 cell cycle arrest [19] . It is also possible that either incoming or replicating viral genomes serve as the trigger for the DDR . For example , both wild-type and UV-inactivated AAV2 , but not recombinant AAV2 vectors , are capable of inducing a DDR , suggesting that it is the viral DNA sequence , but not the viral capsid , that is responsible for the activation of DDR [37] . In conclusion , our results demonstrate the unique activation of various DDR components upon BKPyV infection and the essential roles of both ATM and ATR for viral replication and growth . The study of BKPyV infection and the DDR not only reveals novel knowledge about the cellular pathways with which the virus needs to interact in order to complete the lytic life cycle , but may also have important clinical implications for BKPyV reactivation and its related disease . For example , BKPyV reactivation is a severe problem in bone marrow transplant patients , who might have experienced radiation as part of the preparative regimen . Thus , research focusing on BKPyV and DDR may shed light on the analysis of the causality of BKPyV reactivation in these patients .
RPTE cells ( Lonza ) were maintained in renal epithelial cell growth medium ( REGM ) as previously described [45] . All cells were grown at 37°C with 5% CO2 in a humidified incubator . BKPyV ( Dunlop ) was grown , purified , and titered using an infectious unit ( IU ) assay as previously described [46] . For infections , RPTE cells were pre-chilled for 15 min at 4°C . The cells were then exposed to purified BKPyV diluted in REGM at the indicated MOIs and incubated for 1 h at 4°C . Infection was initiated by replacing the viral inoculum with pre-warmed REGM and transferring the cells to 37°C . Total cell proteins and viral lysates were harvested as previously described [45] . UCN-01 ( Sigma ) was reconstituted according to the manufacturer's recommendations . The drug was added at 1 dpi at 100 nM and was left on for the time of the experiment . A cell metabolism WST-1 assay ( Roche ) was used to ensure that the drug treatment did not cause significant cytotoxic effects ( data not shown ) . Total cell proteins were harvested , quantified , and immunoblotted as previously described [46] . For quantitative blots using the Odyssey Infrared Imaging System , the membrane was processed according to the manufacturer's instructions ( LI-COR ) . The membrane was scanned using the Odyssey Infrared Imaging system , and the relevant bands were quantified using the Odyssey software . See Table S1 for a list of antibodies and concentrations used in this study . Mock or BKPyV-infected cells were trypsinized , resuspended in PBS , and fixed with 100% cold ethanol . DNA was labeled with 50 µg/ml PI+100 µg/ml RNAse in PBS at room temperature for 30 min . Samples were analyzed with a BD FACSCalibur flow cytometer and the cell cycle data were modeled using ModFit LT software . At the indicated times post infection , RPTE cells were fixed and immunostained as previously described [47] with antibodies listed in Table S1 . For standard fluorescence microscopy , samples were examined using an Olympus BX41 microscope with a Plan 40×/0 . 65 objective and processed using the Olympus DP manager software . For laser-scanning confocal microscopy , all images were obtained using a Zeiss LSM 510 confocal microscope with a 63×/1 . 2 objective and 1 µm optical section . Images were analyzed and processed using LSM image browser ( Zeiss ) . siRNA ON-TARGET plus SMART pools were purchased from Thermo Scientific Dharmacon: Non-targeting ( D-001810-10-05 ) ; ATM ( L-003201-00-0005 ) ; ATR ( L-003202-00-0005 ) ; and DNA-PK ( L-005030-00-0005 ) . siRNAs were resuspended in 1× siRNA buffer ( Dharmacon ) to 20 µM stocks . RPTE cells were reverse transfected with indicated siRNAs using Lipofectamine RNAiMAX transfection reagent ( Invitrogen ) according to manufacturer's instructions . siRNAs were diluted in REGM without serum or antibiotics and mixed with Lipofectamine RNAiMAX ( 2–4 µl per well of a 12 well plate , or 33 µl per T75 flask ) . The complexes were allowed to form at room temperature for 15 min , followed by the addition of RPTE cells ( 60 , 000 cells per well , or 660 , 000 cells per flask ) . The optimal final concentrations of siRNA were determined empirically . ATM and DNA-PK siRNAs were used at 10 nM , and ATR siRNA was used at 20 nM . For double or triple knockdowns , non-targeting siRNAs were added to ensure that the total concentrations of siRNAs in all the samples were the same . Transfection complexes were washed out 1 dpt and replaced with REGM containing serum and antibiotics [45] . The cells were infected with BKPyV at 3 dpt as described above . For some batches of RPTE cells , siRNA transfection resulted in uneven cell death among different wells . Under these circumstances , cells were trypsinized , counted , and re-plated prior to infection to ensure that same number of cells were present in all samples for infection . To quantify the viral DNA load in cells , low molecular weight DNA was isolated using a modified Hirt protocol [47] , real-time PCR reactions were performed , and data were analyzed as previously described [47] . Cells were harvested for chromosome preparations with a modified protocol [48] . Briefly , cells were treated with colcemid ( 50 ng/ml ) for 1 h followed by an 18 min incubation in 0 . 8% sodium citrate at 37°C and multiple changes of Carnoy's fixative ( 3∶1 methanol∶acetic acid ) . Cells were dropped onto slides and slides were baked overnight at 55°C before staining with Giemsa ( Sigma ) . Metaphase chromosomes were observed using an Olympus BX41 microscope with a Plan 100×/1 . 25 oil objective or a Nikon OPTIPHOT microscope with a Plan 100×/1 . 40 oil objective . | BK polyomavirus ( BKPyV ) is a human pathogen that establishes a persistent sub-clinical infection in healthy humans . When patients are immunosuppressed , particularly in kidney and bone marrow transplantation , the virus can reactivate and result in severe disease . BKPyV-related disease has risen due to the use of newer immunosuppressive regimens and an increase in the number of transplants performed each year . We are interested in understanding the interactions between BKPyV and host cell components or pathways , with the aim of developing more BKPyV-specific antiviral treatment options . In this study we characterized the relationship between BKPyV infection and the cellular DNA damage response ( DDR ) , a signaling cascade that is initiated by cells to repair damaged DNA molecules . Our study indicated that BKPyV activates and hijacks the DDR to facilitate its infection and that various components of the DDR may play distinct roles during this process . These data suggest that the DDR may provide a potential host target to control BKPyV reactivation in transplant recipients . | [
"Abstract",
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"Results",
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"Materials",
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] | [
"virology",
"biology",
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] | 2012 | Roles of ATM and ATR-Mediated DNA Damage Responses during Lytic BK Polyomavirus Infection |
Echolocation is the ability to use sound-echoes to infer spatial information about the environment . Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks . The first step of human biosonar is the transmission ( mouth click ) and subsequent reception of the resultant sound through the ear . Existing head-related transfer function ( HRTF ) data bases provide descriptions of reception of the resultant sound . For the current report , we collected a large database of click emissions with three blind people expertly trained in echolocation , which allowed us to perform unprecedented analyses . Specifically , the current report provides the first ever description of the spatial distribution ( i . e . beam pattern ) of human expert echolocation transmissions , as well as spectro-temporal descriptions at a level of detail not available before . Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth , but levels drop gradually at further angles , more than for speech . In terms of spectro-temporal features , our data show that emissions are consistently very brief ( ~3ms duration ) with peak frequencies 2-4kHz , but with energy also at 10kHz . This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz , which were based on less detailed measurements . Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential , with angular attenuation by a modified cardioid . We provide model parameters for each echolocator . These results are a step towards developing computational models of human biosonar . For example , in bats , spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour . The data we present here suggest similar research opportunities within the context of human echolocation . Relatedly , the data are a basis to develop synthetic models of human echolocation that could be virtual ( i . e . simulated ) or real ( i . e . loudspeaker , microphones ) , and which will help understanding the link between physical principles and human behaviour .
Echolocation is the ability to use sound reverberation to get information about the distal spatial environment . It has long been established that certain species of bats or marine mammals use echolocation , e . g . to navigate and locate prey [1] . Research has also demonstrated that humans are capable of echolocation [2–4] . In fact , there are some blind people who have trained themselves to use mouth-clicks to achieve extraordinary levels of echolocation performance , in some cases rivalling performance of bats [5] . Human echolocation is a biosonar system , and thus relies on both signal transmission ( mouth-click ) and signal reception ( the ears ) . Head related transfer functions ( e . g . HRTF data bases ) can be used to model characteristics of signal reception . But , to date there is no description of transmitted mouth clicks other than approximations of their duration or peak frequencies in the straight ahead direction [6 , 7 , 8] . For the current report , we collected a large database of click emissions with three blind people expertly trained in echolocation , which allowed us to perform unprecedented analyses . Specifically , here we provide the first ever descriptions of acoustic properties of human expert echolocation clicks in the spatial domain ( i . e . the emission beam pattern ) , as well as descriptions in spectral and time domains at a level of detail not previously available in the literature [6 , 7 , 8] . We also provide model fits to our measurements , and introduce a method to synthesize artificial clicks at various positions in space and for each of our three expert echolocators . Combined with existing HRTF databases this can be used for synthetic echo-acoustics . The data we present here open avenues for future research . For example , in bats , the spatial distribution of emissions have been used to formulate and test model based hypothesis about behaviour [9 , 10] and similar might be possible in humans . Also , the question arises if people may adapt their emissions pending situational demands , as it has been observed in bats [9–16] . Relatedly , the data are a basis to develop synthetic models of human echolocation that could be virtual ( i . e . simulated ) or real ( i . e . loudspeaker , microphones ) , and which will help understanding the link between physical principles and human behaviour . For example , understanding characteristics of click echoes from various objects could be used to understand human echolocation behaviour in tasks such as localising or recognising an object , navigating around it etc . To undertake this type of work large amounts of data are required ( for example , a radar reflectivity measurement of a single object typically requires thousands of measurements ) , which are impractical to ask from human subjects , and where synthetic models are needed . In the following sections we describe our measurement set-up , data analysis and results . We finish with the description of click synthesis , before discussion of limitations and implications of our work .
Three blind people with expertise in echolocation participated . EE1: male , 49 years at time of testing; enucleated in infancy because of retinoblastoma; reported to have used echolocation on a daily basis as long as he can remember . EE2: male , 33 years at time of testing; lost sight aged 14 years due to optic nerve atrophy; reported to have used echolocation on a daily basis since he was 15 years old . EE3: male , 31 years at time of testing; lost sight gradually from birth due to Glaucoma; since early childhood ( approx 3 yrs ) only bright light detection; reported to have used echolocation on a daily basis since he was 12 years old . All participants had normal hearing as assessed via pure tone audiometry ( 250-6000Hz ) . EE1 through EE3 use echolocation to go about their daily life , including activities such as hiking and travelling unfamiliar cities , playing ball and riding bicycles . There are also previous data on echo-acoustic angular resolution for EE1-EE3 . EE1 and EE2 had previously taken part in a 2-interval 2-alternative forced choice echo-acoustic localization test [17] and had obtained 75% thresholds of 4° and 9° , respectively ( for method details see [17] ) . All participants had also taken part in an echo-acoustic Vernier acuity test [5] and had obtained thresholds of 1 . 4° , 7 . 6° and 1 . 2° , respectively ( for details see [5] ) . The experiment was conducted in a sound-insulated and echo-acoustic dampened room ( approx . 2 . 9m x 4 . 2m x 4 . 9m , 24dBA noise-floor; lined with acoustic foam wedges that effectively absorb frequencies above 315 Hz ) . Participants were positioned in the centre of the room . The elevation of a participant’s mouth with respect to the floor was: EE1: 154cm . EE2: 170cm . EE3: 143cm . The floor was covered with foam baffles . Recordings were made with DPA SMK-SC4060 miniature microphones ( DPA microphones , Denmark ) ( with protective grid removed ) and TASCAM DR100-MKII recorder ( TEAC Corporation , Japan ) at 24bit and 96kHz . A reference microphone was placed 50cm in front of the participant , at mouth level , whilst the other microphone was moved around the participant to capture variation in clicks as a function of azimuth and elevation . In the horizontal plane ( mouth level ) we measured a span of 270° in 10° steps starting to the right of the participant at both 40cm and 100cm distance . In the vertical plane we measured a span of 260° in 10° steps starting 40° below the mouth level plane to the front at 40cm distance . Participants were not allowed to move their head during recording so as not to introduce error into microphone placements , as these were done with respect to the mouth . To achieve this we used a custom made set of tactile markers so that participants could move in between trials , but could reliably place their head in the correct position and orientation for recording . Participants were instructed to make clicks as they normally would in their daily life . The room was empty except for the microphones , and the participants knew this . All analysis were done using Matlab ( The Mathworks , Natick , USA ) and custom written routines . The frequency content of the click , the spatial form of the click ( how the click power distributes in space ) , and the time-domain envelope of the click were considered . Individual clicks were extracted from audio files by peak detection , and isolating 300 samples prior to the peak and 399 post the peak . Visual inspection confirmed accurate selection of clicks as well as rejection of bad samples ( e . g . clipping ) . The numbers of clicks that passed criteria for EE1 were 1280 ( azimuth , 100cm ) , 1199 ( azimuth , 40cm ) and 885 ( elevation ) , for EE2 they were 1577 ( azimuth , 100cm ) , 1441 ( azimuth , 40cm ) and 1065 ( elevation ) , and for EE3 they were 816 ( azimuth , 100cm ) , 756 ( azimuth , 40cm ) and 560 ( elevation ) . The average numbers of clicks for any spatial position for EE1 , EE2 and EE3 were 40 . 5 ( SD: 8 . 9 ) , 49 . 2 ( SD: 13 . 5 ) and 25 . 7 ( SD: 5 . 2 ) , respectively . Supporting S1 Table provides a complete breakdown . Average inter-click intervals for EE1 , EE2 and EE3 were 526ms ( SD: 112 , median: 496 ) , 738ms ( SD: 58 , median: 721 ) and 682ms ( SD: 71 , median: 672 ) , respectively . Fig 1 illustrates waveforms of three representative clicks for each of the three echolocators . It is important to note that the waveforms of clicks produced by a single echolocator are replicable , but that there is also some click to click variability . Correlation coefficients calculated in the time-domain between any two extracted clicks for EE1 were 0 . 98 ( max ) , 0 . 14 ( min ) , 0 . 77 ( median ) , 0 . 74 ( mean ) , for EE2 0 . 99 ( max ) , 0 . 11 ( min ) , 0 . 78 ( median ) , 0 . 75 ( mean ) , for EE3 0 . 96 ( max ) , 0 . 12 ( min ) , 0 . 53 ( median ) , 0 . 54 ( mean ) . Analyses on spectral content were carried out on recordings from the reference microphone , for all clicks for 100cm azimuth conditions for each echolocator . The reference microphone was always placed at 50cm straight ahead from the echolocator , even if the target microphone moved to various positions . For each click the discrete Fourier transform and spectrogram were calculated and used to obtain average power spectral density ( PSD ) estimates and spectrograms . Spectrograms were calculated using a Kaiser-Bessel window ( β = 3 ) of 192 samples ( 2ms ) , and 191 samples overlap . The directivity pattern in the horizontal plane ( ϕ = 0° , θ = {−90° , −80° , … , 180°} ) and in the vertical plane ( ϕ = {−40° , −30° , … , −140°} , θ = 0° ) was evaluated . To suppress unsystematic click-to-click variation , the power of signals measured at the target microphone were normalized by the corresponding signal powers measured at the reference microphone . As several clicks were produced at each angular position the mean power ratio was calculated for each position as shown in Eq 1 . In Eq 1 , which calculates the total power directivity pattern as the mean ratio of target to reference powers at each angular position , C ( t ) n , sig is the nth click recorded at the target microphone and C ( t ) n , ref is the same click recorded at the reference microphone . N ( θ , ϕ ) is the total number of clicks at a given azimuth and elevation position , and T is the click duration in samples . Subsequently , azimuthal directivity patterns were fitted in order to mathematically describe them . A sufficient fit was found to be a modified cardioid fit , i . e . pure cardioid ( numerator ) modified by an ellipse ( denominator ) . This is given in Eq 2 , where α and β are constants which varied between echolocators , and that were estimated by performing a non-linear least squares fit with a trust-region algorithm implemented in the Matlab optimization toolbox [18] . A similar analysis was performed to investigate the directionality of different frequency components for more detailed reproduction of the clicks . Processing for this was similar to that used to form the total directivity patterns , but substituted the total click power for the power contained within specific frequency bands . This power can be estimated by summing the PSD estimate calculated over an appropriate range of frequencies as shown in Eq 3 . In Eq 3 , which calculates frequency-dependent directivity patterns as the mean ratio of target to reference power contained within a given frequency band at each angular position , P ( f ) n , sig and P ( f ) n , ref are the powers contained within each frequency f in the interval [flo , fhi] , for the nth clicks recorded at the target and reference microphones , respectively . Typically the envelope of a signal is evaluated by low-pass filtering the signal , but this assumes a smoothly varying signal and performs poorly on the echolocators’ click by smoothing out their rapid-onset . To resolve this issue the click envelope was estimated by taking the absolute value of each click time sample , calculating peak positions , and interpolating the envelope between the peaks using a Piecewise Cubic Hermite Interpolating Polynomial ( pchip ) method implemented in Matlab [19] . Peaks were excluded if their height or prominence fell below 2% of the maximum peak height . This envelope estimate was then fitted with an exponential decay function mediated by a step function according to Eq 4 . In Eq 4 , H ( t ) is the Heaviside step function , and a , b , c are rise magnitude ( a ) , decay time constant ( b ) , and onset time ( c ) , i . e . constants which varied between echolocators , and that were estimated by performing a non-linear least absolute residual fit with a trust-region algorithm implemented in the Matlab optimization toolbox [18] .
Average spectrograms and PSD estimates shown in Fig 2 for EE1 , EE2 and EE3 demonstrate that main frequency components are present and remain unchanged in frequency over the duration of the click . Grey shaded areas denote +/- 1SD around the average PSD ( middle panels ) . To further illustrate the variation that each echolocator makes from click to click the foreground of the bottom plots of Fig 2 show a subset of the click PSD estimates for each echolocator , from which it can be seen that for EE1 , while the main component at 3 . 39 kHz is present and remains relatively unchanged between clicks , there is variation in frequency content between the clicks elsewhere in the spectrum . In the background of the bottom plots of Fig 2 the averaged PSD estimates for the entire set of echolocator clicks are shown . Comparing PSD and spectrograms across individuals it is also visible that there are differences across EE1 , EE2 and EE3 in terms of the spectral content of their clicks . Specifically , both EE1 and EE3 appear to have higher centre frequencies and broader spectral content when compared to EE2 . Yet , peak frequencies for EE1-EE3 are all within 2-4kHz range , and all echolocators also had energy at ~10kHz . Even though energy at 10kHz was low compared to energy at peak , it was a local increase , as opposed to a smooth drop-off from peak towards the high end of the spectrum , for example . Table 1 provides information about peak frequencies from Fig 2 in numerical format . It is interesting to note , that within our three participants those who have emissions with higher frequency content had obtained better angular resolution in previous behavioural tests . For example , angular resolution thresholds for EE1 vs . EE2 based on [17] were 4° and 9° respectively , and for EE3 , EE1 and EE2 based on [5] were 1 . 2° , 1 . 4° and 7 . 6° , respectively . Fig 3 top and middle rows present the average directivity diagrams produced for the echolocators in the horizontal plane for overall sound energy at 100cm and 40cm respectively using Eq 1 . These figures are relative intensity plots , normalised to the maximum average intensity found in each data set . The figures show that click intensity is at a maximum in the forward direction ( θ = 0° ) and stays fairly constant within a 60° cone emanating from the mouth , and smoothly and gradually decreases towards the reverse direction ( θ = 180° ) . Patterns are left-right symmetric . These patterns were fitted with the modified cardioid given in Eq 2 . Fig 3 bottom row presents the diagrams produced for the echolocators in the vertical plane for overall sound energy at 40cm . The vertical plane directivity diagrams show that the behaviour in the vertical plane is similar to that in the horizontal plane , but with more variation ( likely due to the shape of the head which is not front-back symmetric ) . Data are available in supporting S2 Table . For comparison , Fig 4 shows directivity patterns for speech based on data published in [20] , and [21] , and superimposed the directivity patterns of clicks . It is evident that directivity of clicks exceeds directivity of speech . Fig 5 top , middle , and bottom rows show frequency-dependent directivity patterns for horizontal and vertical planes respectively ( horizontal measured at 40cm , top , and 100cm , middle ) . One can see that EE1 exhibits higher click directivity in azimuth for the high frequency band compared to the low frequency band . These figures also show that EE3 exhibits higher click directivity in elevation for the high frequency band compared to the low frequency band . Data are available in supporting S3 Table . Fig 6 shows three sample EE1 clicks along with the estimated envelope , demonstrating that the implemented algorithm performs well in estimating the envelope . The median mean squared error ( MSE ) of the envelope estimates for each echolocator were . 0133 ( EE1 ) , . 0084 ( EE2 ) and . 0485 ( EE3 ) . Subsequently , the envelope function in Eq 4 was fitted to envelope estimates . R2 values of fits for EE1 were . 9989 ( median ) , . 9996 , ( max ) , . 9887 ( min ) , . 9987 ( mean ) , for EE2 they were . 9983 ( median ) , . 9995 ( max ) , . 9885 ( min ) , . 9979 ( mean ) , for EE3 they were . 9969 ( median ) , . 9992 ( max ) , . 5757 ( min ) , . 9958 ( mean ) . Table 2 shows median estimates for rise magnitude ( a ) , decay time constant ( b ) , and onset time ( c ) for EE1-EE3 based on envelope fits . Based on these results duration of EE1 , EE2 and EE3’s clicks is 2 , 3 and 2ms , respectively ( i . e . time for sound energy to drop to 5% of its original magnitude ) , or 3 , 4 and 3 ms ( time to drop to 1% of original magnitude ) . Results gained from click analysis were used to derive artificial clicks . The aim was not to approximate a single click , but rather to create a click that is typical of the general set for EE1 , EE2 , and EE3 at various azimuth angles . The synthetic click for EE3 is less representative than the synthetic click for EE1 and EE2 due to the larger variation of EE3’s main frequency components . The clicks were modelled as sum of monotones mediated by an envelope function E ( t ) in a process developed from [22] . Specifically , Eq 5 was used to build synthetic clicks by extracting typical click parameters from the database of clicks . The parameters that were extracted for each echolocator were coefficients of the envelope function E ( t ) ( rise magnitude ( a ) , decay time constant ( b ) , onset time ( c ) ) , monotone centre frequencies ( f ) , monotone magnitudes ( N ) , monotone phases ( ϕ ) , and modified cardioid parameters ( α and β ) . All parameter values are given in Table 2 . Eq 5 provides the monotones model for a synthetic click . To extract monotone centre frequencies and magnitude parameters from the click database , peak frequencies and amplitudes were extracted for each click from the PSD estimate within a set of manually-selected frequency bands ( EE1: 2–4 . 5kHz , 4 . 5–5 . 8 kHz , 5 . 8–8 . 2kHz , 8 . 2–11 kHz , 11-13kHz; EE2: 1-3kHz , 5 . 5-9kHz , 9–12 . 4kHz , 12 . 4-16kHz; EE3: 2-6kHz , 7 . 5-12kHz ) . The median value of frequency and amplitude for each band were then used . The envelope function parameters were determined by fitting the function to envelope estimates , and then using median values of the parameter distribution obtained from these fits . Cardioid parameters α and β were estimated for each echolocator by performing a non-linear least squares fit with a trust-region algorithm implemented in the Matlab optimization toolbox [18] ( compare section 2 . 4 . Description/Analysis of Clicks ) . Fig 7 shows synthetic clicks for EE1 , EE2 , and EE3 at 0° azimuth . Matlab code to synthesize the clicks is available in supporting S1 Code .
The current report provides the first description of the spatial characteristics ( i . e . beam pattern ) of human echolocation transmissions based on measurements in three blind human echolocators , as well as spectro-temporal descriptions at a level of detail not available before . A model to generate the transmission as a function of angle for each echolocator is also provided . We found that acoustics of transmissions were consistent across echolocators in particular with respect to duration ( ~3ms ) and directionality . We also found that directionality of clicks exceeded directionality of speech ( as reported by [20] and [21] ) . Peak frequencies varied across echolocators , but nonetheless were all within the 2-4kHz range , and all echolocators also had energy at ~10kHz . Even though energy at 10kHz was low compared to energy at peak , it was a local increase , as opposed to a smooth drop-off from peak towards the high end of the spectrum , for example . EE1 , EE2 and EE3 produced clicks with average inter-click intervals of 526ms , 738ms and 682ms , respectively . The analysis and synthesis methods we have used here are new ( i . e . sum of monotones modulated by a decaying exponential with angular attenuation provided by a modified cardioid ) , and only possible because of the detailed measurements we had obtained . The models fit emissions well and are a viable method for synthetic generation . Interestingly , within our three participants those who had emissions with higher frequency content had obtained better angular resolution in previous behavioural tests . Angular resolution thresholds for EE1 vs . EE2 based on [17] were 4° and 9° respectively , and for EE3 , EE1 and EE2 based on [5] were 1 . 2° , 1 . 4° and 7 . 6° , respectively . This is in agreement with previous studies that have found relationships between spectral features of clicks and performance , e . g . [7] . The fact that echolocators in our study consistently made clicks ~3ms duration does not imply that this would be an ‘optimal’ duration for human echolocation . Rather , 3ms might be the minimum duration humans can achieve considering their vocal apparatus and the tissues involved in generating the click . We may speculate that perhaps , in general , briefer emissions may present an advantage for expert human echolocators , for example in terms of reproducibility , immunity to noise , and/or in terms of spatial resolution . Echolocators in our study had been instructed to make clicks as they usually would during their everyday activities . The room was empty . In this way the task was a ‘non-target’ task , i . e . echolocators did not actively echolocate a target . Bats can adjust their emissions dynamically , for example , some species may shift spectro-temporal aspects of their calls ( i . e . duration , spectrum , pulse rate ) pending on the environmental conditions [10–14] , or they may adjust the direction and/or width of their sound beam when they lock onto a target [9 , 10 , 15 , 16] . The question arises if blind human expert echolocators may adjust their clicks as well . Our current report does not speak to this issue because we only measured clicks in a ‘non-target’ setting . Nonetheless , in regards to the beam pattern it is important to point out that the anatomy of the human head , mouth and lips poses severe limitations on the flexibility of the width of the spatial distribution of a click ( and speech as well ) . On the other hand , the direction into which a click is pointed can be varied easily by head-rotation . In regards to spectro-temporal characteristics there is some flexibility , for example by changing the shape of the lips or simply clicking at a higher rate ( i . e . reducing inter click intervals ) . Therefore , based on research in bats and our finding that the click beam pattern is oriented forwards with energy fairly constant within a 60° cone , we might for example expect that people exhibit more variability in head rotation angle when they scan for a target as compared to when they approach a target , and changes in head rotation behaviour might be accompanied by changes in click peak frequency or clicking rate . In sum , our results suggest that future research should address dynamic emission adjustments in people . There have been previous approximations of duration and peak frequencies of human echolocation emissions in the straight ahead direction [6 , 7 , 8] . These investigations did not provide any directivity or rate measurements and range of estimates was wide ( duration: 3-15ms; peak frequencies: 2-8kHz ) , likely due to the fact that samples included sighted people who do not use echolocation on a daily basis . Rojas and colleagues [8] also commented on signal properties such as replicability , and immunity to noise , but they did not provide empirical data to support arguments made . Our analysis of inter-click correlations suggests that indeed the clicks made by human expert echolocators have a high degree of replicability . Importantly , in bats it has been shown that spatio-temporal properties of the emission can explain aspects of echolocation behaviour , e . g . [9 , 10] and even properties of neural activity , e . g . [23] . The same might be possible in people , highlighting the importance of the data reported here for investigating human echolocation in a hypothesis driven way . Human biosonar consists not only of the transmission ( e . g . mouth click ) , but also of the reception of the resultant sound through the ear . It follows , therefore , that only combining these two elements will permit precise predictions for echolocation performance , for example , based on signal strength . One might expect that target detection should be better at angles with stronger received signal strength as compared to angles with lower received signal strength . The model of the human biosonar emission we provide here , together with existing HRTF databases , makes future hypothesis-driven work of this kind possible . There have been prior studies trying to measure precision and acuity of human echolocation , but these have exclusively focused on performance in the median plane ( see [2–4] for reviews ) . The current results clearly suggest that there is merit in characterizing performance at farther angles also . The data presented here are a basis to develop synthetic models of human echolocation , which will help understanding the link between physical principles and human behaviour . Understanding characteristics of click echoes from various objects could be used to understand human echolocation behaviour in tasks such as localising or recognising an object , navigating around it etc . To undertake this type of work large amounts of data are required ( for example , a radar reflectivity measurement of a single object typically requires thousands of measurements ) . These are impractical to ask from human subjects . One could also build instrumentation ( e . g . loudspeakers ) that can create beam patterns either matching those of human echolocators , or not , which can then be used to systematically measure effects of beam patterns on performance . Building of synthetic models and instrumentation requires understanding of the properties of the click waveform itself and its spatial distribution after transmission , which is the purpose of this paper . Echolocation can provide humans with information about the distal environment that is not limited to spatially localising an object . Specifically , the same echolocation process is used to reveal information about size , shape and material of objects as well as their spatial location ( for reviews see [2 , 3 , 4] ) . Developers of artificial sonar and/or radar systems might therefore benefit from our results via use of synthetic models because they might be useful for development of artificial systems that provide multifaceted information about the distal environment . Human sonar emissions are well within the audible spectrum . In contrast , echolocating bats or toothed whales can produce emissions in the ultrasonic range ( >20kHz ) . Whilst frequency sweeps are a common emission in bats , some bat species also use clicks and demonstrate remarkable echolocation abilities [24] . Based on physics , higher sound frequency translates into better spatial resolution . As such , one might suspect human echolocators to be at a disadvantage compared to bats based on acoustics of the emissions alone . Nonetheless , people have shown to be able to resolve lateral position of objects separated by less than 2° , with best performers having shown thresholds between 1 . 2° and 1 . 9° [5] . This compares favourably to the acuity of some bats when measured in a similar way [25] . Again , the emission models we provide here in combination with existing HRTF data bases can be used to build echo-acoustic models to investigate how this human level of performance might be possible . Virtual echo-acoustic models permit stimulus control not possible in natural environments and can therefore be a useful tool for understanding echolocation processes , e . g . [26 , 27] . For humans in particular they are also ideal to investigate neural processes in environments that are not suitable for ‘real’ echolocation due to constraints on space and/or body movement ( e . g . fMRI , MEG , EEG ) [28] . Yet , at present , virtual echo-acoustic models for investigating human echolocation have no empirical basis for their choice of directional propagation of click emissions . It follows that models of emissions such as those provided here are required to use accurate virtual echo-acoustic models to further advance understanding of human echo-acoustic processing . | Echolocation is the ability to use sound-echoes to infer spatial information about the environment . It is well known from certain species of bats or marine mammals . Remarkably , some blind people have developed extraordinary proficiency in echolocation using mouth-clicks . Human echolocation work has built on scant theoretical foundations to date . The current report characterizes the transmission ( i . e . mouth click ) that people use for echolocation , and in this way provides data that can be used to advance the field in a theory guided way . We collected a large database of mouth clicks with three blind people expertly trained in echolocation . This allowed us to perform unprecedented analyses . Specifically , the current report provides the first ever description of the beam pattern of human expert echolocation transmissions , as well as spectro-temporal descriptions at a level of detail not available before . Based on our measurements we also propose a mathematical model to synthesize transmissions . Thus , the data are a basis to develop synthetic models of human echolocation , which are essential for understanding characteristics of click echoes and human echolocation behaviour in tasks such as localising or recognising an object , navigating around it etc . | [
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"animal",... | 2017 | Mouth-clicks used by blind expert human echolocators – signal description and model based signal synthesis |
Significant advances have been made in the discovery of genes affecting bone mineral density ( BMD ) ; however , our understanding of its genetic basis remains incomplete . In the current study , genome-wide association ( GWA ) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel ( HMDP ) to identify and functionally characterize novel BMD genes . In the HMDP , a GWA of total body , spinal , and femoral BMD revealed four significant associations ( −log10P>5 . 39 ) affecting at least one BMD trait on chromosomes ( Chrs . ) 7 , 11 , 12 , and 17 . The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes . This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous ( NS ) SNPs . This analysis revealed that the most significant BMD SNP on Chr . 12 was a NS SNP in the additional sex combs like-2 ( Asxl2 ) gene that was predicted to be functional . The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD . To begin to unravel the mechanism through which Asxl2 influenced BMD , a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains . Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells . In bone , osteoclasts are bone-resorbing cells of myeloid origin , suggesting that Asxl2 may play a role in osteoclast differentiation . In agreement , the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts . This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits .
Osteoporosis is a common disease characterized by bone fragility and an increased risk of fracture [1] . One of strongest predictors of fracture is low bone mineral density ( BMD ) [2] and while BMD is influenced by both genetic and environmental factors , most ( between 60% and 80% ) of its variance is heritable [3] . Thus , the identification of novel BMD genes is critical for the discovery of new pathways and gene networks that will advance our understanding of basic bone biology and identify new therapeutic targets with the potential to combat osteoporosis . Due in large part to its many advantages , such as the ability to experimentally cross genetically defined strains and perturb candidate genes , the mouse has played an instrumental role in the genetic analysis of BMD [4] . However , progress has been limited by low-resolution linkage-based quantitative trait loci ( QTL ) mapping approaches and the difficulties inherent to QTL cloning [5] . As a result , mouse linkage approaches have lead to the identification of only three BMD quantitative trait genes , Alox12 [6] , Sfrp4 [7] and Darc [8] , even though hundreds of QTL have been mapped [9] . In part due to the success of genome-wide association ( GWA ) in humans , several groups have investigated the prospects of high-resolution association mapping approaches in the mouse . One of the main challenges facing GWA in the mouse has been determining the most ideal population for such analyses . To date , mouse GWA studies have been performed with varying success using small sets of classical laboratory strains [10] , advanced intercross lines [11] , heterogeneous stocks [12] , outbred mice [13] , [14] and the Hybrid Mouse Diversity Panel ( HMDP ) [15] . One of the most promising is the HMDP , a collection of ∼100 classical laboratory and recombinant inbred ( RI ) strains that have been genotyped at ∼135 , 000 SNPs [15] . The primary advantage of the HMDP is that mapping resolution is over an order of magnitude higher than with linkage . We have recently demonstrated through simulations that associations explaining 5% of the phenotypic variance in a trait have 95% confidence intervals ( CIs ) of ∼2 . 6 megabases ( Mb ) [15] . This is in comparison to CIs for mouse linkage studies that are typically in the range of 40–60 Mb [13] . Additionally , statistical power in the HDMP has been found to be adequate to map variants affecting complex traits [15] . Moreover , phenotypes can be mapped in the HMDP without the need for costly genotyping and one can collect multiple specimens ( e . g . tissues , individual cell-types , etc . ) from strains for molecular profiling that are difficult or impossible to collect from a single mouse [15] . A drawback of both mouse and human GWA studies ( and all “genotype to phenotype” mapping approaches ) is their inability to provide information on how associated genes actually influence disease [16] . In many cases , it takes years to decipher the underlying biology of novel gene discoveries . One way to begin to provide functional information is through the use of systems genetics [17] . Systems genetics is an approach that incorporates molecular phenotypes , most commonly gene expression microarray profiles , into the genetic analysis of clinical phenotypes . One way that systems genetics can be used to functionally annotate genes of unknown function is through the generation of gene co-expression networks . Co-expression networks are created by clustering genes based on patterns of co-expression across a series of perturbations , such as the differing genetic backgrounds in the HMDP [18] . Co-expressed gene clusters or “modules” have been shown to be enriched for genes involved in the same general function [19] , [20] , [21] , allowing one to annotate genes through “guilt-by-association” [22] . For example , if an uncharacterized gene is co-expressed with genes known to be involved in a biological process such as “apoptosis” , then it is more likely than not the unknown gene is also involved in “apoptosis” . The use of network analysis of systems genetic data can help to inform GWA discoveries by providing clues as to a gene's function in a physiologically-relevant context . The goal of the current study was to identify and functionally characterize novel BMD genes using GWA and systems genetics in the HMDP . This approach implicated additional sex combs like-2 ( Asxl2 ) as the gene responsible for a BMD association on chromosome ( Chr . ) 12 . This was further strengthened by the observation that Asxl2 knockout mice had reduced BMD . Furthermore , gene co-expression analysis of bone transcriptomic data predicted that Asxl2 was involved in the differentiation of bone-resorbing osteoclasts . In support of this prediction , osteoclastogenesis was impaired in bone marrow macrophages in which Asxl2 expression was reduced by RNA interference . Together , these data are consistent with the hypothesis that Asxl2 is a novel regulator of BMD and osteoclastogenesis .
A detailed description of the HMDP , including strain selection and evaluation of statistical power and mapping resolution , is provided in [15] . Towards identifying genomic regions associated with BMD , we phenotyped 16-week old male mice ( N = 879 ) from 97 HMDP strains ( N = 9 . 1 mice/strain ) for total body ( TBMD ) , lumbar spine ( SBMD ) and femur ( FBMD ) areal BMD ( Table S1 ) . A wide range of BMD values were observed across the HMDP with differences of 1 . 4 , 1 . 6 and 1 . 6-fold between the lowest and highest strains for TBMD , SBMD and FBMD , respectively ( Figure 1 ) . To identify associations for the three BMD phenotypes we used the Efficient Mixed-Model Association ( EMMA ) algorithm [23] . Adjusted association P-values were calculated for 108 , 064 SNPs with minor allele frequencies >5% . We have previously demonstrated that the P<0 . 05 genome-wide equivalent for GWA using EMMA in the HMDP is P = 4 . 1×10−6 ( −log10P = 5 . 39 ) [23] . At this threshold , associations on chromosomes ( Chrs . ) 7 , 12 and 17 were identified influencing TBMD ( Figure 2A ) . A fourth unique association on Chr . 11 was identified for SBMD ( Figure 2B ) and the only significant locus for FBMD was the Chr . 7 association also affecting TBMD ( Figure 2C ) . The details of each association are provided in Table 1 . Importantly , each of these regions overlap with the location of QTL for areal BMD measures previously identified by linkage in F2 crosses [9] . We have previously shown that the 95% confidence interval ( CI ) for the distribution of distances between the most significant and true causal SNPs , for simulated associations that explain 5% of the variance in the HMDP , is ∼2 . 6 Mb [24] . Therefore , we used this interval to conservatively define the boundaries of the four associations ( Table 1 ) . Within each association there were a total of 112 ( Chr . 7 ) , 14 ( Chr . 11 ) , 18 ( Chr . 12 ) and 19 ( Chr . 17 ) unique RefSeq genes ( a full list of genes is provided in Table S2 ) . We next identified those genes possessing functional alterations that might underlie the associations . Genes were selected based on whether they were regulated by a local expression QTL ( eQTL ) in the HMDP or if they harbored a non-synonymous ( NS ) SNP that was predicted to have functional consequences . For the eQTL analysis , we generated gene expression microarray profiles using RNA isolated from cortical bone in 95 of the 97 HMDP strains ( N = 1–3/arrays per strain ) . EMMA was then used to perform an association analysis between all SNPs and array probes mapping within each region . A total of 74 genes were represented by at least one probe , after excluding probes that overlapped SNPs present among the classical inbred strains used in the HMDP ( see Methods ) . Of these , 11 genes ( 8 within the Chr . 7 association and 3 within the Chr . 17 association ) were identified with at least one probe whose expression was regulated by a significant ( P≤5 . 1×10−4; Bonferroni corrected for the number of probes tested ) local eQTL ( Table 2 and data for all genes is provided in Table S2 ) . In addition , we identified a total of 19 NS SNPs in 14 genes that were predicted to be either “Possibly Damaging” or “Probably Damaging” by PolyPhen [25] , [26] ( Table 3 and a list of all NS SNPs is provided in Table S3 ) . A nonsense SNP was also identified in the meprin A , alpha ( Mep1a ) gene ( Table 3 ) . Therefore , of the 163 positional candidate genes , 26 were found to be regulated by a local eQTL in bone or harbored potentially functional NS SNPs . The number of functional candidate genes within each association was 12 ( Chr . 7 ) , 2 ( Chr . 11 ) , 3 ( Chr . 12 ) and 9 ( Chr . 17 ) . We also determined if any of the 163 genes implicated by GWA have been previously implicated in bone development . The associations on Chrs . 11 and 12 did not harbor known bone genes . In contrast , three ( Fosb [27] , RelB [28] and Apoe [29] ) of the Chr . 7 genes have been linked to the regulation of bone mass . All three were located in close proximity to the association peak . Fosb was 124 kilobases ( Kb ) upstream , Relb was 180 Kb downstream and Apoe was 270 Kb downstream of rs32149600 , the most significantly associated SNP on Chr . 7 . Additionally , of the 19 Chr . 17 genes , Runx2 , the “master regulator” of osteoblast differentiation [30] , was located 1 . 0 Mbp downstream of rs33294019 , the most significant SNP . None of the known bone genes were regulated by local bone eQTL or harbored potentially functional NS SNPs . All 26 of the genes identified above are candidates for the BMD associations and warrant further investigation . However , the goal of this analysis was to identify a gene ( s ) that was the most likely causal gene for an association . Due to Chr . 7 and Chr . 17 possessing multiple functional candidates ( 12 and 9 , respectively ) , we could not identify the most likely candidate based on the existing data for either of the associations; therefore , we focused on the Chr . 11 and Chr . 12 associations due to the presence of only 2 and 3 functional candidates , respectively . All five of these genes harbored potentially functional NS SNPs . The expression of these genes was not regulated by local eQTL . Of the five , the additional sex-combs like 2 ( Asxl2 ) was the most compelling candidate due to the fact that rs29131970 , a NS SNP in Asxl2 that was predicted to be functional , was also the peak SNP for Chr . 12 BMD association ( Table 3 and Figure 3 ) . Rs29131970 results in a phenylalanine ( F ) to serine ( S ) substitution at amino acid 1191 of the mouse ASXL2 protein . The mouse reference genome ( the C57BL/6J strain ) harbors the “C” allele , which codes for S; whereas , the rat , human , orangutan , dog , horse , opossum and chicken reference genomes all have the “T” allele , which codes for F . HMDP strains homozygous for the “C” allele had lower BMD relative to strains with the “T” allele ( data not shown ) . A role for the other four candidates possessing NS SNPs on Chr . 11 and Chr . 12 appear to be less likely because of the modest linkage disequilibrium ( LD ) ( r2 between 0 . 09 and 0 . 36 ) among the HMDP classical inbred strains between these SNPs and the SNPs most significantly associated with BMD for each region ( Table 3 ) . Therefore , based on the existing data we hypothesized that Asxl2 was the causal gene for the Chr . 12 association . To directly test the hypothesis that Asxl2 was involved in the regulation of BMD we characterized TBMD , SBMD and FBMD in Asxl2 knockout mice ( Asxl2−/− ) . The mice used for this experiment were between the ages of 2–4 months and as previously reported [31] a significant ( P<0 . 05 ) reduction in body weight in Asxl2−/− mice was observed ( data not shown ) . To evaluate bone mass in the absence of these confounding effects we evaluated the BMD residuals across genotype after adjusting for age and body weight within each sex . This analysis revealed significant ( P<0 . 05 ) reductions in TBMD , SBMD and FBMD residuals in male Asxl2−/− mice as compared to wild-type ( Asxl2+/+ ) controls ( Figure 4A–4C ) . In addition , male heterozygous ( Asxl2+/− ) mice demonstrated an intermediate phenotype for all BMD measures , although the differences were not statistically different from either homozygous genotype ( Figure 4A–4C ) . We also observed similar decreases in TBMD , SBMD and FBMD residuals in female Asxl2−/− mice as compared to Asxl2+/+ controls ( Figure 4D–4F ) . These data confirm that Asxl2 is a regulator of BMD and are consistent with the hypothesis that Asxl2 is responsible for the genetic association on Chr . 12 in the HMDP . We next used systems genetics to begin to identify a potential function for Asxl2 in bone . For this analysis we utilized the cortical bone gene expression data from the HMDP strains . An analysis of Asxl2 expression revealed that while its expression was not under regulation by detectable local ( described above ) or distant eQTL ( data not shown ) , Asxl2 was expressed in cortical bone ( in the top 10% of probes based on average expression ) and most importantly , its expression differed by 1 . 5-fold between the lowest and highest expressing HMDP strains ( Figure S1 ) . We reasoned that its high level of expression and variation in expression among strains would allow us to identify biologically meaningful co-expression relationships between Asxl2 and genes sharing similar functions , even though a difference in its expression does not underlie the Chr . 12 association . To identify the genes that were co-expressed with Asxl2 in bone , genes were grouped into “co-expression modules” using Weighted Gene Co-expression Network Analysis ( WGCNA ) [20] . We used WGCNA to generate a bone co-expression network comprised of the 3600 most variable and highly connected genes ( see Methods ) . The 3600 genes were subsequently partitioned into eight gene modules ( Figure 5A ) . Of the eight , we focused our attention on the blue module that contained Asxl2 along with 1334 other genes ( full list is provided in Table S4 ) . The DAVID knowledge base ( http://david . abcc . ncifcrf . gov/ ) was used to determine if the blue module was enriched for specific gene ontology ( GO ) categories . We were most interested in identifying enrichments in specific gene functions; thus , we restricted the analysis to the GO biological process and molecular function categories and excluded the cellular component category . DAVID's functional annotation clustering tool was used to identify 14 significant ( enrichment score ( ES ) >3 . 0; see Methods ) gene clusters containing highly related GO terms ( Table S5 ) . The top four clusters contained genes involved in: 1 ) the cell cycle/chromosome/DNA replication/cell division , 2 ) hematopoiesis/myeloid cell differentiation , 3 ) ATP binding and 4 ) chromosome organization/chromatin organization ( Table 4 ) . Asxl2 is thought to regulate the function of Polycomb ( PcG ) and Trithorax ( trxG ) protein complexes , which are involved in the establishment and maintenance of chromatin [31] . Its membership in a module enriched for genes involved in the GO terms “chromosome” ( Bonferroni corrected enrichment P = 3 . 2×10−7 ) and “chromatin organization” ( Bonferroni corrected enrichment P = 9 . 2×10−3 ) is consistent with its known function and suggests that this module is comprised of biologically meaningful co-expression relationships . We next characterized the nature of the genes most closely connected to Asxl2 in the blue module . For this purpose , a network view was generated for the 34 , 690 strongest connections among 1256 ( 94% ) of the 1335 blue module genes ( Figure 5B ) . In this view of the blue module , Asxl2 was connected to a single node , the apoptotic peptidase-activating factor 1 ( Apaf1 ) gene . Apaf1 , in turn , was connected to 149 genes; all located within the cluster of genes on the left side of the network depiction Figure 5B . To examine the gene composition of the cluster connected to Asxl2 , genes were colored based on their membership in three of the four top enrichments described above , including the “cell cycle” , “myeloid cell differentiation” and “chromatin organization” ( Table 4 ) . We excluded the “ATP-binding category” since genes in this category are involved in a wide-range of biological processes . The blue module as a whole was enriched for all three categories ( Table 4 ) ; however , the cluster of genes most closely connected to Asxl2 contained most ( 75%; enrichment P = 2 . 0×10−3 ) of the genes involved in myeloid cell differentiation ( red nodes in Figure 5B ) and very few cell cycle ( green nodes ) or chromatin organization ( yellow nodes ) genes . These data indicate that in our bone network , Asxl2 is most closely connected with genes involved in myeloid cell differentiation and may play a role in this process . The GO category “myeloid cell differentiation” is comprised of genes that are involved in the general process of myeloid precursors acquiring characteristics of downstream cell lineages . With respect to bone cells , osteoclasts are bone-resorbing cells of myeloid origin; thus , we hypothesized that Asxl2 played a role in the differentiation of pre-osteoclasts . Additionally , many of the blue module genes in this category have been implicated in osteoclastogenesis , such as Inpp5d ( aka SHIP ) [32] , Smad5 [33] , Id2 [34] , Plcg2 [35] , Twsg1 [36] , among others . To test the hypothesis that Asxl2 was involved in osteoclastogenesis , we infected bone marrow macrophages ( BMMs ) with lentiviral constructs expressing short-hairpin RNAs ( shRNA ) targeting Asxl2 . BMMs were infected with a vector only control ( NC ) or one of five lentiviral constructs ( A1–A5 ) expressing distinct shRNAs targeting Asxl2 . After five days of culture , Asxl2 expression was particularly lower in cells infected with the A3 and A5 lentiviral constructs ( Figure 6A ) . Osteoclastogenesis was induced in infected BMMs by culturing in the presence of M-CSF and RANKL . After five days of culture the number of TRAP+ ( tartrate-resistant acid phosphatase , a marker of osteoclasts ) multinuclear cells ( MNCs ) was significantly ( P<0 . 05 ) reduced in cultures infected with lentiviral constructs A3 and A5 compared to NC treated cells ( Figure 6B and 6C ) . We observed a strong positive correlation ( r = 0 . 74 , P = 0 . 04 ) between the relative expression of Asxl2 and TRAP+ MNCs across the six treatments ( Figure 6D ) . These data are consistent with our network inference and confirm that Asxl2 is a regulator of osteoclastogenesis and strengthen the hypothesis that Asxl2 is a regulator of BMD .
The mouse has numerous advantages for the genetic analysis of BMD; however , historically mapping approaches in the mouse have been plagued by the lack of resolution . Additionally , GWA approaches provide no information on the function of associated genes . We have addressed both limitations through the use of high-resolution GWA in the HMDP to identify associations confined to narrow genomic intervals and gene co-expression analysis of bone microarray data to provide insight on gene function . This novel analytical paradigm resulted in the discovery of Asxl2 as a regulator of BMD and osteoclastogenesis . This study identified a new gene and possibly an entire network of genes that play an important role in BMD and osteoclast function . Polycomb ( PcG ) and trithorax ( trxG ) are highly conserved protein complexes that are involved in the repression and activation of gene expression , respectively , through the establishment and maintenance of chromatin modifications at specific target genes [37] . With respect to bone cells , PcG and trxG have been implicated in cell proliferation [38] , myeloid precursor maturation [39] and osteoblast differentiation [40] . In Drosophila , Additional sex combs ( Asx ) belongs to a group of proteins known as the Enhancers of trxG and PcG ( ETP ) [41] . Although its specific mechanism is unknown , Asx is thought to promote both PcG-mediated silencing and trxG-mediated activation of gene expression . In humans and mice there are three Asx homologues , Asx-like 1 , 2 and 3 [42] , [43] , [44] . Mutations in ASXL1 result in myleoproliferative neoplasms [45] and little is known regarding the function of ASXL3 . Recently described Asxl2−/− knockout mice display a global reduction in the PcG-associated histone modification trimethylation of histone H3 lysine 27 . This is consistent with a conserved function as an ETP in mammals [31] . Additionally , Asxl2−/− mice develop anterior and posterior transformations of the axial skeleton [31] , which further supports our observation that Asxl2 is involved in bone development . An important open question is whether Asxl2 impacts bone development through its expression in other cell-types , such as osteoblasts . If Asxl2 functioned exclusively in osteoclasts , we would expect based on the in vitro osteoclastogenesis data , that loss of Asxl2 function would impair osteoclast function and bone resorption in vivo , resulting in increased BMD . In contrast , we observed a decrease in BMD in Asxl2−/− mice . It has been shown in a number of mouse models that loss of specific genes can lead to an impairment of both osteoblast and osteoclast function . This results in a condition referred to as low-turnover osteopenia in which bone formation by osteoblasts and resorption by osteoclasts are impaired with a net loss of bone . As examples , osteoblasts and osteoclasts from mice deficient in klotho [46] , JunB [47] and Akt1 [48] have impaired in vitro differentiation . These mice also have reduced BMD due to low-turnover osteopenia . In addition , Synaptotagmin VII ( Syt11 ) has been shown to alter protein secretion in osteoblasts and osteoclasts , resulting in decreased bone mass in Syt−/− mice [49] . In data not presented we observed ( using publically available data from the BioGPS browser; http://biogps . gnf . org , probes 1460597_at and 1439063_at ) high and ubiquitous expression of Asxl2 in 96 different mouse tissues and cell-lines , including primary osteoclasts and osteoblasts . In addition , while the blue module was highly enriched for genes involved in myeloid differentiation there were also a number of genes such as Bmp4 , Chrd , Hdac5 and Igf2 that play a role in osteoblast differentiation ( Table S3 ) . These data suggest that the decreases in BMD seen in Asxl2−/− mice may be due to deficiencies in both osteoclast and osteoblast function . Further work is needed to clarify the precise role of Asxl2 in bone . We have previously characterized the HMDP as a novel population for GWA in the mouse [15] . This study extends our original observations by demonstrating the feasibility of identifying associations affecting additional complex phenotypes . In contrast to more traditional mouse linkage mapping strategies , we used association in the HMDP to identify four associations containing a relatively small number of genes . Based on prior work , the boundaries of the associations were defined as the 2 . 6 Mb region surrounding the most significant SNP . We expect that these intervals are conservative and would likely be smaller if based on region specific LD patterns , as shown for Chr . 12 ( Figure 3 ) . However , defining the associations in this way allowed us to be confident that the regions contained the causal gene ( s ) . This study also highlights the other key advantage of the HMDP; the ability to collect and molecularly profile tissues , such as bone , that are difficult or impossible to collect from a single mouse or in human populations . In the future , the accumulation of many bone-related clinical and molecular phenotypes in the HMDP will enable the large-scale systems-level analyses that should provide significant insight on bone physiology and genetics . Additionally , the HMDP will provide the opportunity to address the genetic basis of extremely important phenotypes , such as bone loss , nanostrucutal properties of bone and bone cell aging that are difficult to address in humans . Moreover , as we have used systems genetic to gain functional insight for a gene identified by mouse GWA , it is also possible that the HMDP could be used to dissect the function of genes identified in human GWA studies . However , the HMDP is not without limitations . First , the statistical power to detect effects of subtle variants is modest . We have previously estimated that for highly heritable phenotypes , such as BMD , the power to detect variants explaining 10% of the trait variance is 50% [15] . The power drops precipitously for variants explaining less than 10% of the variance . Thus , this version of the HMDP ( with ∼100 strains ) is unable to identify the many more variants with subtle effects that are undoubtedly affecting BMD in this population . This problem will be less of an issue for future HMDP panels containing a larger number of strains . Additionally , one of the side effects of the breeding history of inbred mouse strains is the presence of LD between markers on separate chromosomes ( i . e . non-syntenic LD ) . It is thought that this is due to selection for allelic combinations that confer increased fitness during the inbreeding process [50] . False-positive associations can arise if a region associated with a phenotype is in LD with other regions of the genome . Although this is always a potential pitfall when using the HMDP , it is easily identifiable and we did not observe LD ( r2<0 . 4 ) between any of the four BMD associations ( data not shown ) . Most GWA studies stop at gene discovery . However , without physiologically relevant functional information it is often difficult to be confident that the true causal gene has been identified and to begin unraveling the mechanistic underpinnings of significant associations . Recently , some GWA studies have begun to incorporate gene expression information to determine if a significant variant regulates expression . A positive finding in such an analysis suggests that the genotype dependent differential gene expression is the basis of the association . This approach has recently been used to discover that variants in the promoter of the serine racemase ( SRR ) gene regulate its expression and BMD [51] . We have taken that application one step further and developed a bone transcriptional network to aid in the functional annotation of genes of unknown function . Using this network , we were able to determine that Asxl2 was closely connected to genes with links to osteoclast differentiation . This simple connection allowed us to test the hypothesis that Asxl2 was involved in a bone specific function . Another important point is that , as we demonstrated for Asxl2 , a gene's expression does not have to be under genetic regulation for this approach to work . In conclusion , we have used mouse GWA and gene co-expression network analysis to identify Asxl2 as a novel regulator of BMD and osteoclastogenesis . Our analysis has revealed a new gene and pathway that play an important role in bone development . Additionally , this study demonstrates the feasibility of using the HMDP for the dissection of complex genetic traits .
The animal protocol for the HMDP mice was approved by the Institutional Care and Use Committee ( IACUC ) at the University of California , Los Angeles . The animal protocol for the Asxl2−/− mice was approved by the Animal Care Committee ( ACC ) at the University of Illinois at Chicago . Approximately nine male mice for each HMDP strain ( Table S1 ) were purchased from the Jackson Labs ( Bar Harbor , ME ) . Mice were between 6 and 10 weeks of age and to ensure adequate acclimatization to a common environment the mice were aged until 16 weeks before sacrifice . All mice were maintained on a chow diet ( Ralston-Purina Co , St . Louis , Mo ) until sacrifice . Inbred strains were previously genotyped by the Broad Institute ( available via www . mousehapmap . org ) . Genotypes of recombinant inbred strains were imputed as previously described [15] . Of the 140 , 000 SNPs available at the Broad Institute , 108 , 064 were informative with an allele frequency ≥5% and less than 20% missing data , and were used for the association analysis . All carcasses were stored at −20°C after sacrifice and then thawed overnight at 4°C prior to BMD scans . The entire thawed carcass was scanned . BMD scans were performed using a Lunar PIXImus II Densitometer ( GE Healthcare , Piscataway , NJ ) . The PIXImus II was calibrated daily using a phantom of known BMD . BMD was calculated for the entire carcass minus the skull , the lumbar spine and the left femur . At sacrifice the diaphysis of the right femur was excised and cleaned free of soft tissue . Bone marrow was removed by flushing with PBS using a 22-guage needle and 3 ml syringe . The bone was then flash frozen in LN2 and stored at −80C . Total RNA was isolated using the Trizol Plus RNA Purification Kit ( Invitrogen , Carlsbad , CA ) following homogenization of the whole bone sample . RNA integrity was confirmed using the Agilent 2100 Bioanalyzer ( Agilent , Palo Alto , CA ) . Microarray expression profiles were generated ( N = 1–3 per strain ) using the Illumina MouseWG-6 v1 . 1 BeadChips ( Illumina , San Diego , CA ) by the Southern Genotyping Consortium at UCLA . Biotin-labeled cRNA was synthesized by the total prep RNA amplification kit from Ambion ( Austin , TX ) . cRNA was quantified and normalized to 77 ng/µl , and then 850 ng was hybridized to Beadchips . The expression values were transformed using the Variance Stabilizing Transformation ( VST ) [52] , and normalized with the Robust Spline Normalization ( RSN ) algorithm using the LumiR R package [53] . After normalization , the ComBat software was used to adjust for batch effects using an empirical Bayes methods [54] . Microarray data has been submitted the the NCBI Gene Expression Omnibus ( GEO ) database ( GSE27483 ) . Population structure is a major confounding for genome-wide association analyses in the HMDP . This is due to the fact that many phenotypes correlate with the phylogeny of HMDP strains ( i . e . genetically similar strains have similar phenotypes ) and any SNP that correlates with these strain relationships will be falsely associated with the phenotype . The Efficient Mixed-Model Association ( EMMA ) algorithm has been shown to effectively reduce this confounding [23] . We applied the following linear mixed model to perform association mapping between BMD or a gene's expression and a marker under the confounding effect from population structure [23] , [55] , [56]; , where is a phenotypes of each mouse , is their genotype , is an incidence matrix mapping each mouse to corresponding strain , is a random effect accounting for population structure effect with , and is the uncorrelated random effect with . The Efficient Mixed Model Association ( EMMA ) [23] is used for efficient and reliable estimation of restricted maximum likelihood ( REML ) parameters and hypothesis testing under the linear mixed model . After estimating REML parameters , a standard F test is used to test the statistical significance of the marker-phenotype association . To characterize the genes located in each BMD association , we downloaded all RefSeq genes in the four regions from the USCS genome browser ( http://genome . ucsc . edu/cgi-bin/hgGateway ) using the NCBI Build37 genome assembly . From the Illumina MouseWG-6 microarray we identified all probes corresponding to the 163 RefSeq genes . Probes were excluded if they overlapped with SNPs ( dbSNP 128 ) to avoid the hybridization artifacts that can arise due [57] , [58] . EMMA was used to calculate association P-values for all probes corresponding to the 163 RefSeq genes . Only SNPs mapping to each associated region were used in this analysis . Known non-synonymous SNPs within each region were downloaded from the Mouse Phenome Database ( http://phenome . jax . org/ ) using a set of over 7 million genotyped and imputed SNPs . We only selected SNPs that were variant in at least one of the classical inbred strains represented in the HMDP . Prediction of the functional effect of these SNPs was performed using the PolyPhen tool ( http://genetics . bwh . harvard . edu/pph/ ) . R2 was calculated for each non-synonymous SNP and the peak BMD SNP within the 30 classical inbred strains in the HMDP . The generation and initial characterization of Asxl2−/− gene trap mice has been previously described [31] . These mice harbor a gene trap cassette downstream of exon 1 . Homozygotes for the gene trap allele show little ( ∼3% ) Asxl2 expression whereas heterozygotes expressed Asxl2 at ∼50% of wild type levels . BMD in littermate male and female mice ( 2–4 months of age ) of varying Asxl2 genotype was measured as described above . Age and weight at sacrifice were recorded . To assess the effects of Asxl2 deficiency on BMD independent of age and weight we generated BMD residuals using a simple linear regression . A Student's t-test was used to test the significance of the differences in BMD residuals in the different genotypes . Network analysis was performed using the WGCNA R package [59] . An extensive overview of WGCNA , including numerous tutorials , can be found at http://www . genetics . ucla . edu/labs/horvath/CoexpressionNetwork/ . To begin , we filtered the array data to remove lowly and non-expressed genes by selecting probes based on a detection P-value of <0 . 05 in 95% of the samples . Next , we selected the 8000 most varying genes based on variance across the 95 samples and then selected the most connected ( based on k . total described below ) 3600 genes for network analysis . Our group and others have used this number of genes previously ( as examples [19] , [60] ) , mainly because most other genes have very low k . total values . If multiple probes existed for a given gene only the most connected probe per gene was included in the list of 3600 . To generate a co-expression network for the selected probes , we first calculated Pearson correlation coefficients for all gene-gene comparisons across the 95 microarray samples . The matrix of correlations was then converted to an adjacency matrix of connection strengths . The adjacencies were defined as where and are the and gene expression traits . The power was selected using the scale-free topology criterion previously outlined by Zhang and Horvath [18] . Network connectivity ( k . total ) of the gene was calculated as the sum of the connection strengths with all other network genes , . This summation performed over all genes in a particular module was defined as the intramodular connectivity ( k . in ) . Modules were defined as sets of genes with high topological overlap [18] . The topological overlap measure ( TOM ) between the and gene expression traits was taken as , where denotes the number of nodes to which both and are connected , and indexes the nodes of the network . A TOM-based dissimilarity measure was used for hierarchical clustering . Gene modules corresponded to the branches of the resulting dendrogram and were precisely defined using the “Dynamic Hybrid” branch cutting algorithm [61] . Highly similar modules were identified by clustering and merged together . In order to distinguish modules each was assigned a unique color . Whole bone marrow was extracted from femora of mice with α-MEM and cultured overnight in α-MEM ( Sigma-Aldrich , St Louis , MO ) containing 10% heat-inactivated FBS , 100 IU/ml penicillin , and 100 µg/ml streptomycin ( α10 medium ) . The nonadherent cells were collected by centrifugation and re-plated in a new 10-cm petri dish in α10 medium . To generate osteoclasts , 100 ng/ml RANKL and 1/100 vol CMG 14-12 culture supernatant were added to α10 medium for 4–5 days . Osteoclasts were stained for TRAP as described by the manufacturer's instructions ( Sigma-Aldrich ) . The SHCLNG-NM_172421 MISSION lentiviral shRNA ( Sigma-Aldrich ) clone set ( containing five separate lentiviral shRNA clones each expressing a distinct Asxl2 targeting sequence ) was used to transduce BMMs according to manufacturer's specifications . The MISSION pLKO . 1-puro control vector was used as a negative control . Total RNA was isolated from cultures using the RNeasy Mini Kit ( Qiagen ) . Purified RNA was DNase treated using the DNA-free kit ( Ambion ) . The High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems ) was used to synthesize cDNA in a volume of 20 µl . The reaction mixture was adjusted to 200 µl with dH2O and 5 µl of the dilute cDNA was used for PCR . PCR was performed for 22 cycles for Asxl2 and Actb using the following primers ( Asxl2-F , 5′-ACCCACCATTCCAGCAAGTA-3′ , Asxl2-R , 5′-TGGCTGCTTTGACAGTCTTG-3′ , Actb-F , 5′-CCAACCGTGAAAAGATGACC-3′ , Actb-R , 5′-ACCAGAGGCATACAGGGACA-3′ ) . PCR products were separated on a 1 . 5% agarose gel containing 0 . 5 mg/ml ethidium bromide . Band densitometry was performed using the Image J software ( NIH ) . Normalized Asxl2 expression levels were determined by subtracting lane specific backgrounds for each sample and dividing by Actb intensities . | Osteoporosis is a disease of weak and fracture-prone bones . The characteristic of bone that is most predictive of fractures is low bone mineral density ( BMD ) , a trait primarily controlled by genetics . In recent years , significant advances have been made in the discovery of genes affecting BMD; however , our understanding of its genetic basis is still primitive . In this study , we used genome-wide association in the mouse to identify additional sex combs like-2 ( Asxl2 ) as a novel BMD gene . In confirmation of our genetic analysis , mice deficient in Asxl2 had reduced BMD . To evaluate its function in bone , the expression levels of Asxl2 and tens of thousands of other genes were measured in bone in a large number of inbred mouse strains . Asxl2 demonstrated a pattern of expression indicative of genes that play a critical role in osteoclasts , the cells that are responsible for bone resorption . Further study of Asxl2 may reveal novel therapeutic targets for the treatment and prevention of osteoporosis . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
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"Methods"
] | [
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] | 2011 | Mouse Genome-Wide Association and Systems Genetics Identify Asxl2 As a Regulator of Bone Mineral Density and Osteoclastogenesis |
Planning interventions to respond to cholera epidemics requires an understanding of the major transmission routes . Interrupting short-cycle ( household , foodborne ) transmission may require different approaches as compared long-cycle ( environmentally-mediated/waterborne ) transmission . However , differentiating the relative contribution of short- and long-cycle routes has remained difficult , and most cholera outbreak control efforts focus on interrupting long-cycle transmission . Here we use high-resolution epidemiological and municipal infrastructure data from a cholera outbreak in 1853 Copenhagen to explore the relative contribution of short- and long-cycle transmission routes during a major urban epidemic . We fit a spatially explicit time-series meta-population model to 6 , 552 physician-reported cholera cases from Copenhagen in 1853 . We estimated the contribution of long-cycle waterborne transmission between neighborhoods using historical municipal water infrastructure data , fitting the force of infection from hydraulic flow , then comparing model performance . We found the epidemic was characterized by considerable transmission heterogeneity . Some neighborhoods acted as localized transmission hotspots , while other neighborhoods were less affected or important in driving the epidemic . We found little evidence to support long-cycle transmission between hydrologically-connected neighborhoods . Collectively , these findings suggest short-cycle transmission was significant . Spatially targeted cholera interventions , such as reactive vaccination or sanitation/hygiene campaigns in hotspot neighborhoods , would likely have been more effective in this epidemic than control measures aimed at interrupting long-cycle transmission , such as improving municipal water quality . We recommend public health planners consider programs aimed at interrupting short-cycle transmission as essential tools in the cholera control arsenal .
Cholera transmission during an outbreak is known to occur via both ‘short-’ ( for example , locally-mediated via food or household water ) [1–3] and ‘long-cycles’ ( environmentally-mediated via natural or manmade water and sanitation systems ) [4 , 5] . Although long-cycle waterborne transmission is often considered the archetypical cholera transmission route [6 , 7] , there is a growing interest in understanding the importance of short-cycle pathways [8–10] . Identifying the relative contributions of these different pathways has important public health implications for designing effective cholera interventions [11] , yet remains difficult to ascertain from epidemiological data alone [12–14] . The proportion of cases infected via long-cycle relative to short-cycle transmission is probably highly context dependent , but the difficulty in distinguishing between the relative contributions of each route means that little data exists for any individual context [15 , 16] . The resulting uncertainty hinders planning of appropriate interventions . For example , spatially targeted interventions ( e . g . targeted hygiene/sanitation or reactive vaccination programs ) may be effective against short-cycle transmission [17] , but suboptimal against long-cycle transmission [18] . To better characterize the relative importance of long-cycle vs short-cycle cholera transmission and mitigate parameter identifiability issues , high-resolution , high-quality epidemiological data can be augmented with data detailing the flow of drinking water in a specific setting . We use detailed data from an 1853 cholera outbreak in Copenhagen , Denmark as a case example . This outbreak has three key advantages from a modeling perspective: ( 1 ) this was likely an immunologically naive population as this was the first reported cholera outbreak in Copenhagen , ( 2 ) the outbreak was largely unmitigated by control measures as no effective treatments or interventions were implemented , and ( 3 ) historical datasets provide detailed information about the city’s hydraulic network . The water supply of Copenhagen was composed of a network of hollowed wooden tree-trunks under low water-pressure , and thus vulnerable to outside contamination ( S1 Fig ) . Additionally , there was no sewage system; rather , the street gutters functioned as light sewage drainage , with most human waste stored in unsealed cellars in each building and removed by night-men twice annually for use as fertilizer for food crops in nearby fields [19] . The piped drinking water was reported to be contaminated by seepage from these cesspools [20] , thus elevating the risk of drinking contaminated water for downstream users during the cholera epidemic . Most piped water was sourced from nearby ( <1 km ) semi-artificial lakes , while some public fountains were supplied from water sources >10 km upstream [21] . Here , we use newly uncovered historical epidemiologic data from the 1853 epidemic along with modern statistical methods to investigate the relative contribution of short-cycle versus long-cycle transmission in a cholera outbreak . Fitting a time-series meta-population model to data from the 1853 cholera outbreak in Copenhagen , we characterize the spatio-temporal transmission dynamics and assess the signal of long-cycle environmentally-mediated transmission in the progression of the epidemic .
Weekly cholera morbidity and mortality data for each city neighborhood and outlying communities were obtained from datasets compiled by contemporary physicians conducting active surveillance during the 1853 cholera epidemic in Copenhagen [22] . A corps of physicians traveled door-to-door to residences , hospitals ( “sick-houses” ) , and “poor-houses” diagnosing and tabulating cases . Hospitalized cases were assigned to the neighborhood of residence , unless they were already in hospital prior to diagnosis , in which case they were geolocated to the hospital’s address . Six of the 13 neighborhoods were aggregated into two neighborhoods labeled “Combined upper” ( Nørre and Klædebo ) and “Combined lower” ( Frimands , Strand , Snarens and Vester ) due to low case counts and small geographic area . We excluded 621 ( 9% ) cases that could not be geolocated , consisting of 122 ( 2% ) cases from docked ships , and 499 ( 7% ) from scattered outlying communities . Cholera cases were defined as patients with rice-water diarrhea and evidence of severe dehydration [23] , making the historical diagnostic criteria stricter than the current WHO suspected cholera case definition [24] . Population size for each neighborhood was interpolated between the 1850 and 1855 census assuming a linear growth model . Population density was estimated for each neighborhood by georeferencing the neighborhoods using a Geographic Information System ( GIS ) and calculating area . Hydraulic data was digitized from a contemporary map [21] showing the layout and direction of flow of all water-pipes supplying drinking water for the city ( Fig 1A ) . We created a binary asymmetric transition matrix of hydraulic connectivity describing the flow of water between neighborhoods , and a binary symmetric matrix describing neighborhoods that share a border ( as a proxy for sewage runoff and human connectivity ) ( S1 Table and S2 Table ) . All GIS work was done in QGIS [25] . To model the number of infectious people at each day , we fit a discrete time susceptible-infected-recovered ( SIR ) model to imputed daily case data We used the basic model structure from Azman et al . [17] , with modifications as described below . In order to simulate daily case data ( necessary because of the short generation interval for cholera ) , weekly cases were randomly reallocated to the seven days preceding the reported date . This reallocation was repeated 10 times to give 10 possible realizations of the epidemic . Each of the nine neighborhoods were considered as a discrete population . The model-predicted number of infected people in each neighborhood at each time-step was Poisson distributed where the mean was a function of the fraction susceptible in that neighborhood and a sum of the internal and external forces of infection . We constructed a series of nested models in which the infection rate of susceptible individuals living in neighborhood i , or the force of infection , λi , was the sum of an internal force of infection , βi , and an external force of infection from neighborhood j upon neighborhood i , αj , i ( Fig 2 ) , such that: λi=βiIi+∑j≠iαj , iIj Each model had differing assumptions about α and β . Starting from the simplest base-case model to the most complex saturated model , we allowed: ( 1 ) a single β and single α for all neighborhoods such that βi = β and αj , i = α , ( 2 ) an individual βi for each neighborhood and a single α for all neighborhoods such that αj , i = α , and ( 3 ) an individual βi for each neighborhood and an asymmetric αj , i such that αj , i ≠ αi , j ( S1 Text ) . In order to estimate the effect of the water supply on the epidemic , we used two methods . First , we fit a linear regression model using the log of the median cross-neighborhood transmission coefficients ( αj , i ) from model 3 ( saturated model ) as the outcome and tested whether the hydraulic-transition and geographic-proximity variables were significant predictors of between-neighborhood transmission at a significance level of 0 . 05 ( S1 Text , S1 Table and S2 Table ) . Second , we incorporated the hydraulic-connectivity and geographic-proximity matrices into model 2 , producing models 2b and 2c , respectively . To assess the effect of hydraulic connectivity ( model 2b ) , we allowed αj , i to vary depending on whether the neighborhoods were connected via water pipes , such that: αj , i={α0ifnowaterconnectionj→iα0+α1ifwaterconnectionj→i To incorporate geographic proximity ( model 2c ) , we added an additional term if the neighborhoods shared a border . The resulting αj , i can be described as: αj , i={α0ifnosharedborderorwaterconnectionj→iα0+α1ifnosharedborderbutwaterconnectionj→iα0+α1+α2ifsharedborderandwaterconnectionj→i Parameter estimation was done using Markov chain Monte Carlo ( MCMC ) methods in JAGS 4 . 2 . 0 [26] in R 3 . 3 . 1 [27] . Each model was run once on each of the 10 possible realizations of the imputed daily epidemic data . We used four MCMC chains with a burn-in of 40 , 000 iterations and sampled the subsequent 60 , 000 iterations . Chain convergence was assessed with a potential scale reduction factor ( R^ ) cutoff of 1 . 05 and a visual assessment of the trace plots . Posterior samples from all four chains and all 10 data realizations were pooled and summarized ( S1 Text ) . Model preference was assessed using the average Watanabe-Akaike Information Criterion ( WAIC ) ; a difference of five in WAIC was considered significant , translating to a fold difference of e5/2 = 12 . 2 in terms of posterior probability on the model state space [28] . WAIC was chosen due to recent developments in this field suggesting WAIC is better able to capture the tradeoffs between model complexity and fit in a Bayesian framework than the more commonly used DIC [29 , 30] . We validated our model by simulating the outbreak in each neighborhood . To do so , we selected a parameter set from the joint posterior distribution and then drew from the number of cases at the next time step from the appropriate Poisson distribution in each neighborhood . Point-wise prediction intervals were constructed by taking the 2 . 5 and 97 . 5 percentiles at each time-step . We then used the simulated data to refit the model parameters to test if the model could recapture the original parameter estimates . To assess the heterogeneity of transmission efficiency by neighborhood , we calculated the internal reproductive number ( Rint ) , the outflowing reproductive number ( Rout ) , the inflowing reproductive number ( Rin ) , and the total reproductive number ( Rtot ) within each neighborhood using the force of infection coefficients . The Rint , Rout , and Rtot can be interpreted as the number of cases a single infectious case produces within its own neighborhood , in all other neighborhoods , and in the whole city respectively , while the Rin represents the number of cases produced within one neighborhood by a single infectious case in the rest of the city .
The epidemic began on June 12 with two reported cases in the Nyboder neighborhood among individuals working on ( or in close contact with ) ships . The epidemic soon spread to other neighborhoods ( Fig 3A and 3B , S1 Dataset ) . The epidemic was declared over on October 1 , although four cases were reported subsequently in October . A total of 7 , 173 cases were reported , of which 5 , 953 ( 83% ) were community acquired and 1 , 220 ( 17% ) were acquired in hospitals or poor-houses . A total of 4 , 717 died , resulting in a case fatality ratio ( CFR ) of 66% . The CFR among hospital/poor-house-acquired infections was 77% , as compared to 63% in community-acquired infections . A total of 6 , 552 cases ( 91% ) occurred within the city walls and were included in this analysis . The outbreak was spatially heterogeneous over the city , despite its small geographic size . Attack rates within the city walls ranged from 2 . 1 to 9 . 6 per 100 , and the CFR ranged from 76% to 54% ( Table 1 , Fig 1C and 1D ) . The neighborhood attack rates were not associated with the neighborhood’s population density as assessed by a linear regression ( p = 0 . 99 ) . Model selection carried out using WAIC ( Table 2 ) indicated the best model allowed for asymmetric and heterogeneous transmission between neighborhoods ( model 3 ) ; this heterogeneity could not be explained by hydraulic and geographic connectivity ( model 2b and 2c ) . Similarly , the linear regression on the median of the cross-neighborhood transmission coefficients ( αj , i ) ( Fig 4 ) from the fully saturated model ( model 3 ) provided no support that a hydraulic connection between neighborhoods was significantly associated with the force of transmission between neighborhoods , even after controlling for geographic proximity ( Table 3 ) . The internal forces of transmission in each neighborhood were not strongly associated with the neighborhood’s population density ( p = 0 . 11 ) . Using the fully saturated model ( model 3 ) , we simulated the outbreak in each neighborhood one day ahead , drawing a new parameter combination from the joint posterior distribution for each simulation ( Fig 5 ) . The Rint ranged from 0 . 2 ( 0 . 0–0 . 5 ) in Østerto to 1 . 7 ( 1 . 5–1 . 8 ) in St . Annæ Vester ( Fig 6 ) . One of the nine neighborhoods ( St . Annæ Vester ) , had Rint >1 , meaning it could maintain epidemics without infections from outside; in three other neighborhoods ( St . Annæ Øster , Christianshavn and Nyboder ) , Rint was above 1 but the credible interval spanned 1 . 0 , and in all remaining neighborhoods the Rint was below 1 . 0 . To validate the model , we re-estimated the model parameters from the simulated data for each of the 10 epidemic realizations . In seven of the 10 realizations , the 95% CI estimated from the simulated data overlapped 95% CI fitted from the original data for all 83 parameters . In the remaining three realizations overlap occurred in 82 , 81 and 80 parameters respectively .
There is growing momentum towards re-thinking the dominance of long-cycle waterborne transmission in cholera outbreaks [31] . Using a spatially explicit metapopulation model , we captured the essential spatial-temporal dynamics of a major urban cholera epidemic in Copenhagen in 1853 . Our analysis indicates that although transmission occurred between the different sections of the city , the data do not support an association between the trajectory of cases across neighborhoods and the flow of water/sewage from neighborhood to neighborhood . The lack of a signal of long-cycle transmission in the data , suggests the importance of short-cycle transmission . The data do not match what would be expected for an epidemic primarily disseminated via long-cycle , waterborne transmission . There are three pieces of evidence for this: ( 1 ) we found the attack rates to be highly heterogeneous within the city despite all neighborhoods sharing common sources for drinking water and extensive water-pipe connections between the neighborhoods; ( 2 ) neighborhoods , such as Øster , Kjøbmager and Combined Lower , that were downstream of highly affected neighborhoods , such as St . Annæ Vester and Nyboder , and ( 3 ) a model fit to the between-neighborhood transmission coefficients from the fully saturated model ( model 3 ) did not show evidence that water-flow between neighborhoods was associated with the force of infection between neighborhoods . The transmission heterogeneity seen in Copenhagen has been documented in other cholera outbreaks over a range of spatial scales [17 , 32] and was unlikely to be confounded by socioeconomic status ( SES ) . We suspect this because SES did not have a spatial structure at the city level; the rich and poor lived on the same city blocks with the rich living facing the streets and the poor inside interior courtyards [33] . Furthermore , population density was not associated with attack rate or the force of internal transmission . In terms of differential reporting , the relative uniformity of the case fatality ratio ( Fig 1D ) implies that all neighborhoods were similarly likely to report cases . This suggests that the transmission heterogeneity seen is a true phenomenon rather than an artifact of confounding . The lack of support for long-cycle waterborne transmission in the Copenhagen epidemic has important public health implications for responding to present-day cholera outbreaks . Our analysis indicates that interrupting transmission by interventions targeting the centralized drinking water system would likely have had little effect . Despite the historical nature of our data , the Copenhagen outbreak can be a proxy for contemporary resource-constrained settings where water infrastructure is poor . Our results corroborate other research , both in historical and contemporary settings , where short-cycle transmission was described as a critical element in certain epidemic settings [1 , 10 , 31 , 34 , 35] . Taken together we propose public health practitioners need to more thoroughly investigate alternative transmission routes when investigating cholera outbreaks and planning interventions . A stronger focus on interrupting short-cycle transmission , including targeted reactive vaccination programs and sanitation and hygiene related interventions , could significantly reduce an epidemic’s impact [36–38] . There are several limitations to this analysis . Although we found no support for long-cycle transmission driving transmission between neighborhoods , our analysis could not rule out this transmission pathway entirely . Our results show some transmission did occur between neighborhoods , although it did not correlate with the piped water supply or neighborhood proximity ( as a proxy for sewage run-off ) . Additionally , despite the high spatial-resolution of our data , long-cycle transmission within a single neighborhood is possible and would instead be captured by the within-neighborhood transmission coefficient ( βi ) . It is probable that the epidemic resulted from multiple disparate transmission routes , a hypothesis consistent with recent models of the Haitian outbreak [34 , 39] . Our analysis could not investigate any specific alternative pathway , although contemporary reports of human waste used as fertilizer for food crops highlights one possible alternative route , which has been observed in other outbreaks as well [40] . Additionally , the available incidence data did not have the ideal temporal resolution for the analysis; it was aggregated into weekly time-steps , yet the generation time for cholera is suspected to be closer to 3–5 days [41] . To address this limitation , we randomly redistributed the weekly cases over the preceding week , thereby propagating uncertainty in the relationship among weekly-aggregated cases in terms of who transmitted to whom . In regard to hydraulic data , our measure of hydraulic connectivity was reduced to a binary value and does not fully capture the gradations of neighborhood water connections . Although we argue the 1853 Copenhagen outbreak was not driven by long-cycle transmission , it may have played a larger role in outbreaks in other settings , at least in their initial stages [5 , 42–44] . In Haiti , for example , there is evidence that the initial outbreak , along with the continuing transmission , entailed significant long-cycle transmission based on hydrological transport of the pathogen [4 , 35 , 45 , 46] . The factors determining which mode of transmission will dominate new epidemics are not understood , but perhaps consists of a combination of cultural and environmental factors . The ability to classify the dominant transmission mode early on in an epidemic will be key to enacting effective contagion control strategies . Using a unique and highly detailed dataset , we have shown that that long-cycle , environmentally-mediated transmission is not a prerequisite for explosive , large-scale cholera outbreaks . While an exact quantification of each pathway’s contribution remains difficult , our findings—taken together with previous research [10 , 17 , 31 , 35 , 36 , 38]—suggest spatially-targeted cholera interventions , such as reactive vaccination and hygiene/sanitation programs , are important tools to combat epidemics with significant short-cycle transmission . Moreover , programs targeting long-cycle waterborne transmission may not be effective in all outbreak settings . | John Snow’s seminal work on the London cholera epidemic and Broadway pump helped establish cholera as a quintessential waterborne ( long-cycle ) pathogen . However , there is renewed interest in the role that short-cycle ( e . g . food-borne and household ) transmission plays in epidemic contexts . The distinction between transmission pathways can be important , as they may demand different interventions . However , disentangling these pathways requires high-quality epidemiological and contextual data that is rarely collected in outbreak situations . Here we use detailed cholera incidence and municipal infrastructure data from a cholera outbreak in 1853 Copenhagen to estimate the relative contributions of short- and long-cycle transmission to the epidemic . We find transmission between neighborhoods during the epidemic did not follow water pipe connections , suggesting little evidence of long-cycle transmission . Instead , we suggest that short-cycle transmission was likely critical to the propagation of the outbreak . Interventions targeting short-cycle transmission are important tools that merit further consideration by public health officials combating epidemic cholera . | [
"Abstract",
"Introduction",
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] | [
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"modeling"... | 2017 | The importance of thinking beyond the water-supply in cholera epidemics: A historical urban case-study |
Snakebite is an important medical emergency in rural Nepal . Correct identification of the biting species is crucial for clinicians to choose appropriate treatment and anticipate complications . This is particularly important for neurotoxic envenoming which , depending on the snake species involved , may not respond to available antivenoms . Adequate species identification tools are lacking . This study used a combination of morphological and molecular approaches ( PCR-aided DNA sequencing from swabs of bite sites ) to determine the contribution of venomous and non-venomous species to the snakebite burden in southern Nepal . Out of 749 patients admitted with a history of snakebite to one of three study centres , the biting species could be identified in 194 ( 25 . 9% ) . Out of these , 87 had been bitten by a venomous snake , most commonly the Indian spectacled cobra ( Naja naja; n = 42 ) and the common krait ( Bungarus caeruleus; n = 22 ) . When both morphological identification and PCR/sequencing results were available , a 100% agreement was noted . The probability of a positive PCR result was significantly lower among patients who had used inadequate “first aid” measures ( e . g . tourniquets or local application of remedies ) . This study is the first to report the use of forensic genetics methods for snake species identification in a prospective clinical study . If high diagnostic accuracy is confirmed in larger cohorts , this method will be a very useful reference diagnostic tool for epidemiological investigations and clinical studies .
In rural Nepal snakebite is an important public health problem . A survey conducted in the 1980s showed that about 20’000 people were bitten each year , resulting in over 1’000 deaths [1] . These official figures , however , significantly underestimate the true burden [2–7] . Annual incidence and mortality figures of 1’162/100’000 and 162/100’000 , respectively , have been reported in some regions of Nepal [4] . Children are among the primary victims [2 , 8 , 9] . Most snakebites occur in the southern plains of Terai , a region with a hot tropical climate and high population density . Most bites occur during the rainy season , from June to September , which corresponds to the peak period for agricultural work . Eighty nine snake species have been recorded in Nepal , of which 17 are known to be venomous [10] . The Elapidae family includes two species of cobra ( Naja naja and Naja kaouthia ) , the king cobra ( Ophiophagus hannah ) , one species of coral snake ( Sinomicrurus macclellandii ) and six species of krait ( Bungarus bungaroides , Bungarus caeruleus , Bungarus fasciatus , Bungarus lividus , Bungarus niger , and Bungarus walli ) . The Viperidae are represented by seven species . The most dangerous of these , Russell’s viper ( Daboia russelii ) , appears to be rare in Nepal . Pitvipers ( Gloydius himalayanus , Himalayophis tibetanus , Ovophis monticola , Protobothrops spp . Trimeresurus albolabris and Trimeresurus septentrionalis ) , on the other hand , are widespread from the lowlands to the high mountains . Non-venomous species are also very common and may be involved in bites . Some of these non-venomous species are easily mistaken for venomous ones [10] . For example , rat snakes ( Ptyas and Coelognathus species ) may be confused with cobras , while wolf snakes , which are common inside and around houses , have a colour pattern similar to that of kraits [11–13] . Bites can therefore be inflicted by a variety of species , in all kinds of environments . Neither the geographical distribution of these species nor their contribution to snakebite mortality and morbidity have been systematically studied in Nepal . Snakebite envenoming can be life-threatening , and recognizing early signs of systemic envenoming is crucial for the optimal management of patients . Depending on the species of snakes , different organs and tissues can be affected [14] . In envenoming following the bites of elapid snakes , neurotoxicity with progressive descending paralysis is characteristic , and patients usually die of respiratory failure if not adequately ventilated [15–17] . Clinical prognosis and response to antivenom depend on the species , hence knowing which snake is responsible for the bite is of primary importance [14] . In South Asia appropriate tools for species identification are not available . The snake is rarely seen , and if it is , its description by the victim is often misleading [18] . Hospital personnel are generally not trained to properly identify the biting species , even when it is killed and brought by the victim [19–21] . The morphological resemblance of venomous by non-venomous species complicates this task . Several approaches have been investigated to improve snake species identification in peripheral health centres . Immunodiagnosis of circulating venom antigen can be used to identify the biting species or ‘immunogroups’ of antigenically similar , cross-reacting venoms [22–25] , but commercial point-of-care venom detection kits have been marketed only in Australia , for species occurring on that continent [26] . In Sri Lanka , syndromic approaches have been proposed [20] and clinical scores developed based on a systematic analysis of features of envenom envenoming in cases where snake specimens brought to hospital were expertly identified [27] . However , the extent to which these approaches can also be applied to the Nepal setting is not known . The present study aimed at investigating the accuracy , feasibility and usefulness of different , complementary approaches to species identification in patients bitten by snakes in rural Nepal . The main objectives were ( 1 ) to clarify the contribution of different snake species causing envenoming and non-envenoming bites through morphological analysis of preserved snake specimens and the use of forensic methods for DNA-based identification , and ( 2 ) to explore the utility , diagnostic performance and feasibility of DNA-based identification by forensic methods .
The study was conducted in three centres , namely the Snake Bite Treatment Centre of Damak Red Cross Society and the Snake Bite Management Centre of Charali , both in Jhapa district , and Bharatpur District Hospital , Bharatpur , Chitwan district . All three centres are located in the Terai lowland region of Nepal . All snakebite victims presenting to the Damak Red Cross Centre and to the Charali Snake Bite Management Centre during the study period were offered to participate in the study , irrespective of the presence and nature of envenoming signs . Hence , victims of bites by elapids , viperids and non-venomous snakes were included . Patients were excluded from the study if they were below 5 years of age or if they had already received antivenom prior to admission . Patients unable or unwilling to give consent were also excluded . In Bharatpur District Hospital , additional exclusion criteria were applied due to the contemporary conduct of a randomized controlled trial on neurotoxic envenoming ( clinicaltrials . gov number NCT01284855 ) . In this centre , only snakebite patients presenting with signs of systemic neurotoxic envenoming were included in the study . Those presenting more than 24 hours after the bite , pregnant or breastfeeding women and patients with proven viper bite envenoming were excluded . The recruitment period in Bharatpur was shorter and extended from 1st April 2011 to 31st October 2012 . Dead snakes brought by bite victims were systematically labelled with patient number , initials , date of birth and date of admission , and preserved in 70% ethanol . Morphological identification was conducted by taxonomic experts ( UK , DP ) who remained blind to the circumstances of bites and to envenoming status of the victims . For each preserved specimen , morphological characters of scalation and dentition were analysed by comparison to relevant reference specimens in museum collections and an existing database [28 , 29] . Additionally , genetic information from tissue samples of killed snakes were obtained in the form of DNA ‘barcodes’ of the mitochondrial cytochrome b gene [28 , 29] . Whenever the bite site could be located , trace DNA of the biting snake was collected by rubbing the cotton swab of a Prionix evidence collection tube on the bite site ( see Standard Operating Procedure in supplementary material ) . The sample was left to dry at room temperature . For DNA extraction , one half of the cotton bud was cut off and subjected to lysis in CTAB ( cetyl trimethylammonium bromide ) buffer with proteinase K at 56°C , followed by one extraction with phenol-chloroform-isoamyl alcohol and then with chloroform-isoamyl alcohol . DNA was precipitated with 98% ethanol and 3M sodium acetate ( pH 5 , 2 ) and stored overnight at -20°C before washing with 70% ethanol , drying and dissolving in TE-buffer . For PCR we used primers flanking a 400 bp sequence of the mitochondrial cytochrome b gene ( cytb ) which preferentially amplify snake rather than human cytb sequences . For nested PCR we designed primers with cytb binding sites upstream and downstream of the PCR primers . PCR was performed in 30 μl volumes containing 6 μl of template DNA solution , 0 . 3 pM of each primer , 83 pM of each dNTP , 3 μl 10 × PCR buffer without MgCl2 ( Fermentas ) , 3 . 25 mM MgCl2 , 1 unit TrueStart Hot Start Taq polymerase ( Fermentas ) , and 13 . 7 μl H2O . PCR products were visualized by SYBRGreen ( Invitrogen ) staining in 1% agarose gels and ultraviolet ( 302 nm ) light illumination . Their lengths were estimated using a 100 bp ladder ( Fermentas ) . Cycle sequencing of both strands was performed with 0 . 16 μl of BigDyeTerminator 3 . 1 reaction mix ( Applied Biosystems ) , 1 μl primer , 1 . 92 μl 5 × sequencing buffer ( Applied Biosystems ) and 5 . 92 μl milliQ H2O ( Millipore ) . Products of the sequencing reaction were separated on an ABI 3730 sequencer ( Applied Biosystems ) operated with a 50 cm capillary , 8 sec injection time and 1500 V injection voltage . After visual checks of electropherograms , correction of base-calls and comparison of complementary strands using BioEdit and SequenceScanner software , sequences were submitted to BLAST searches for comparison with the sequences in the GenBank , ENA and DDBJ nucleotide sequence database to determine the snake species . New DNA sequences obtained in the course of this study were deposited in this database . A full description of the methods is included in Melaun and Kuch [30] . Local envenoming was defined as the presence of one or more of the following: ( 1 ) necrosis , ( 2 ) bullae or blisters , ( 3 ) enlarged regional lymph nodes plus either local bleeding or ecchymosis or swelling and ( 4 ) swelling extending at least halfway between two articulations . Snakebite victims presenting with moderate local swelling ( i . e . , not extending farther than one articulation ) were not considered as locally envenomed . Systemic envenoming was defined as the presence of one or more of the following: ( 1 ) Incoagulable blood as indicated by the 20 Minutes Whole Blood Clotting Test ( 20’WBCT ) , ( 2 ) spontaneous and continuous bleeding from the bite site , IV line , gums or old wound , ( 3 ) gastrointestinal bleeding or blood in urine , ( 4 ) neurotoxic sign ( s ) including inability to frown , bilateral ptosis , inability to open the mouth , inability to protrude the tongue beyond incisors , inability to clear secretions , broken neck sign , skeletal muscle weakness , gag reflex loss and paradoxical breathing . Neurotoxic envenoming was defined as the presence of one or more of the above-mentioned neurotoxic signs . Envenoming status was determined retrospectively , by one of the authors ( EA ) who was blinded to the results of snake species identification . The clinical management of snakebite victims followed the Nepal national protocol and WHO SEARO guidelines [14] . All patients presenting with systemic envenoming received antivenom , as did patients presenting with severe local envenoming ( e . g . , edema extending over full limb ) . Antivenoms available in Nepal are all manufactured in India and target four species , the Indian spectacled cobra ( Naja naja ) , the common Indian krait ( Bungarus caeruleus ) , the saw-scaled viper ( Echis carinatus ) and Russell’s viper ( Daboia russelii ) . Additional treatments included anticholinesterases and assisted ventilation for those patients experiencing respiratory paralysis . Patients who were asymptomatic on admission were kept under observation for 24 hours . A standard Case Report Form ( CRF ) was designed to prospectively collect data on the circumstances of the bite , the participants’ demographic characteristics , and the clinical features on admission . First aid measures , whether appropriate or not , were recorded , as well as features of clinical management after admission . To ensure harmonization of data collection , study staff were trained on CRF completion and were instructed to follow Standard Operating Procedures ( SOP ) . Separate forms were used for the results of morphological identification and molecular analysis . As these analyses were performed well after recruitment was over , their results did not influence the assessment of snakebite victims by the care-providers . The researchers performing the PCR and DNA sequence analyses were also blind to the results of morphological identification and patient data . Characteristics on admission were described using percentages for categorical variables and by calculating means and standard deviations or median and Inter Quartile Ranges ( IQR ) for continuous variables . The Case Fatality Rate ( CFR ) was calculated as the percentage of patients who died among those showing signs of envenoming . Descriptive analyses were restricted to participants with snake species identified by PCR or morphology . Determinants of PCR positivity were analysed for all patients for whom a PCR was done , as follows: continuous variables were compared using Student’s t test , or the Wilcoxon- rank sum test for non-normally distributed variables . Categorical variables were compared using the Χ2 test with continuity correction or the Fisher exact test , as appropriate . For categorical variables with more than one category , a test for trend was used . Associations were examined at the p<0 . 05 level of significance . To assess the probability of a positive PCR , Risk Ratios ( RR ) with 95% Confidence Interval ( 95% CI ) were calculated . The study was conducted in accordance with the Declaration of Helsinki 1964 , as revised in Seoul , 2008 , and in compliance with the protocol , Good Clinical Practices ( GCP ) and Nepal regulatory requirements . The B . P . Koirala Institute of Health Sciences ( BPKIHS ) Ethics Committee , the Nepal National Health Research Council ( NHRC ) and Geneva University Hospital Ethics Committee approved the study prior to its start . All participants provided written informed consent before being included in the study .
The baseline characteristics and circumstances of the bites of the 194 snakebite victims for whom a species could be ascertained , are summarized in Table 2 . The majority of snakebite victims were male ( 52 . 6% ) and the median age was 30 . 8 years ( IQR = 18–40 ) . There were 28 ( 14 . 4% ) children ( 5–15 years ) . A clear seasonal pattern was observed in all three study sites , with the incidence of bites being higher during the rainy season ( June to September ) . The median time needed to reach the treatment centre was 85 minutes ( IQR = 50 to 120 minutes ) , with a longer time observed in Bharatpur ( 194 minutes; IQR: 11 to 269 minutes ) . Only four ( 2 . 1% ) snakebite victims visited a traditional healer before reaching the study centre . Use of first aid measures was more common in Damak ( n = 91/93; 97 . 8% ) and Charali ( n = 72; 98 . 6% ) compared to Bharatpur ( n = 11; 39 . 5% ) . Tourniquets were by far the most common first aid method applied . S1 Table compares baseline characteristics , circumstances of the bite and first aid measures between snakebite victims with identified and unidentified snake species . Among snakebite victims presenting to the treatment centres in Damak or Charali during the study period ( n = 676 ) , 191 ( 27 . 4% ) showed signs of envenoming . Twenty three ( 24 . 7% ) patients in Damak and four ( 5 . 5% ) in Charali presented with neurotoxic signs . As expected by local inclusion criteria , all 73 patients recruited in Bharatpur had signs of systemic neurotoxic envenoming on admission , and among those , seven also had local signs ( see S1 Text ) . The signs and symptoms presented on admission by the 194 snakebite victims for whom a species identity could be ascertained are described in Table 3 . Among 87 victims for whom a venomous species was identified , 16 ( 18 . 4% ) had not developed any sign of envenoming . Among the 73 patients for whom an elapid snakebite could be ascertained , 64 ( 87 . 7% ) developed signs of envenoming . As expected , none of the 107 patients bitten by non-venomous species exhibited local or systemic signs of envenoming . Swelling of the bitten limb was present in 43 out of 194 patients ( 22 . 2% ) , and bleeding from the bite site was present in 60 ( 30 . 9% ) . Of note , both signs also occurred in patients bitten by non-venomous species , in particular 25 out of 88 X . piscator bite cases ( 28 . 4% ) presented with bleeding from the bite site . The 20 Minutes Whole Blood Clotting test was performed on all victims presenting to one of the study centres during the study period . Out of those with incoagulable blood on admission , the snake species could be determined in 7 cases: 5 were N . naja and 2 were white-lipped pit vipers ( Trimeresurus cf . albolabris ) . No snakebite victim presented with gum bleeding , gut bleeding , or blood in urine on admission . S2 Table compares clinical features on admission of snakebite victims with identified and unidentified snake species . As the number of cases in which the snake species could be identified by PCR sequencing was low , we investigated baseline characteristics and circumstances of the bite that may influence the sensitivity of this method . Results are summarized in Table 4 . The median time to reach the center was significantly longer in patients with a negative PCR . The probability of a positive PCR result was significantly lower among patients who had used first aid measures . In particular , applying local remedies ( e . g . , herbs , honey , etc . ) was associated with a 2-fold decrease in the probability of a positive PCR . Patients bitten on the upper limb were 60% less likely to have a positive PCR compared to those bitten on the lower limb . Local bleeding also increased the probability of a positive PCR by 1 . 45 fold . Whereas bite by a venomous species did not affect the chances of a positive PCR ( RR = 1 . 004 , 95%CI: 0 . 893–1 . 128 ) , showing signs of envenoming significantly increased the chances of a positive result ( RR = 1 . 444 , 95% CI: 1 . 091–1 . 912 , p = 0 . 017 ) . A total of 67 ( 34 . 5% ) snakebite victims received antivenom . Eight were put under mechanical ventilation and nine were transferred to a tertiary care centre . Among patients treated with antivenom the median dose was 2 vials ( inter-quartile-range = 1–3 ) . In total there were 5 deaths ( Case Fatality Rate = 5/194 = 2 . 6% ) : one in Damak , one in Charali and three in Bharatpur ) . Of these 5 deaths , 4 had been bitten by a B . caeruleus and 1 by a N . naja .
Between the 1st of April 2010 and the 31st of October 2012 , 749 individuals with a history of snakebite presented to one of three study centres in southern Nepal . In 194 ( 25 . 9% ) patients , the responsible snake species could be ascertained , by either morphological identification or PCR plus DNA sequencing . Most species identified were non-venomous ones . The non-venomous checkered keelback ( X . piscator ) was the most frequently identified species , followed by the spectacled cobra ( N . naja ) and the common krait ( B . caeruleus ) . Other venomous species contributing to the snakebite burden in this study comprised several pitvipers ( O . monticola , Trimeresurus sp . , T . albolabris , and T . popeiorum ) as well as various additional elapid snakes , including the first cases of envenoming by the greater black krait ( Bungarus niger ) and the king cobra ( O . hannah ) ever reported in Nepal . As few victims ( 11 . 6% ) brought the dead snakes to the study centres , the use of PCR to amplify snake trace DNA from bite-site swabs increased to 25 . 9% the proportion of victims for whom the snake species could be ascertained . Morphological identification of preserved specimens by a qualified herpetologist is the gold standard for species identification . However , this method is seldom used , as snakes are rarely captured and preserved , and as care-providers working in snakebite treatment centres generally lack the appropriate expertise [19–21] . Alternative approaches must therefore be developed to complement morphological identification . Molecular techniques have shown promising results in animal models [30–33] , and the present study shows that PCR amplification of a mitochondrial gene region from snake trace DNA is feasible in field setting . Sampling is straight forward and requires minimal training ( see SOP in supplementary material ) , and the storage and transport conditions for the Prionix evidence collection tubes ( room temperature and protected from light ) can easily be met . The PCR yielded a positive result in 27 . 1% of the cases only . This could probably be improved if several factors shown in this study to be associated with a lower sensitivity , such as inappropriate first-aid measures ( use of tourniquet or application of local remedies on the bite site ) or prolonged time to reach the treatment centre , were corrected or improved by public health interventions . Although the sensitivity of the PCR did not seem to be affected by whether the species was venomous or not , envenoming status did have an effect . This may be due to confounding effects of venom injection . Bites associated with venom injection may go deeper in tissues , and snakes may deposit more trace DNA on bite sites during envenoming bites compared to ‘dry’ bites or bites by non-venomous snakes e . g . , snake DNA can also be recovered from snake venom [33] . In all cases where both snake morphological identification and DNA sequence from the bite-site swab were available ( n = 21 ) , a 100% agreement was observed between the two methods , suggesting a high specificity of molecular identification . An on-going study conducted by the authors is expected to complement this preliminary data and validate PCR-aided sequencing of snake trace DNA on a larger cohort of patients . Although molecular tools are not yet appropriate for point-of-care ( POC ) testing and hence cannot be used to guide clinical management , the encouraging results presented here , if confirmed in larger studies , suggest that they could be used as reference tests in future epidemiological and clinical studies . Progress in the development and validation of POC tests for snake species has indeed been hindered by the difficult implementation of the diagnostic gold standard ( morphological identification ) in rural regions where most bites occur . Besides , molecular tools could be very useful in clarifying the contribution of different snake species to the snakebite burden , and help identify new medically important species . The species found to have caused neurotoxicity in this study were the two species of cobra ( N . naja and N . kaouthia ) and three different species of krait ( B . caeruleus , B . lividus , and B . niger ) . Of these , only two ( N . naja and B . caeruleus ) are included in the production of the Indian polyvalent antivenoms that are available in Nepal . No pre-clinical data exist on the efficacy of Indian polyvalent antivenoms to neutralize the venoms of these two species in Nepal , and no clinical data have been published so far . In this study , patients bitten by a krait had significantly higher chances of being put under mechanical ventilation and being transferred to an intensive care unit compared to those being bitten by a cobra ( see supplementary information ) . This is consistent with published literature which suggests that krait bites often result in poorer outcomes for patients , and high mortality rates [34 , 35] . The efficacy of the available Indian antivenom in reversing envenoming by kraits is increasingly being questioned , and several case series have reported little or no benefit of immunotherapy [17 , 36–38] . The frequency of neurotoxicity observed in our study is consistent with elapid snake species being most commonly involved in envenoming bites in Nepal . The fact that we did not observe late-appearing signs such as broken neck sign , muscle weakness and loss of gag reflex may be due to the early presentation of victims to the health centre and the prompt initiation of antivenom therapy . Interestingly , among those patients who presented with incoagulable blood on admission were five victims of N . naja bites . This is not the first report of apparent coagulopathy following bites by this species [39] , and a few in vitro studies have reported anticoagulant activities in N . naja venoms [40–42] , however , these results need to be interpreted with caution . In fact , the 20 Minutes Whole Blood Clotting Test can give erroneous results if performed incorrectly , in particular if the tubes used bear traces of detergent [14] . The present study has several limitations , the principal one being that the study population differed between study sites . Snakebite victims admitted to Bharatpur District Hospital were only included if they presented with signs of neurotoxic envenoming . It is therefore not surprising that all snakes identified in this centre were venomous , resulting in an overestimation of the contribution of venomous species ( and in particular elapids ) to the snakebite burden . When Bharatpur was excluded from the analysis , venomous species accounted for only 35 . 6% of identified bites . The checkered keelback and the spectacled cobra remained the most common species identified ( see supplementary information ) . Another limitation relates to the geographical coverage of the study . The list of species identified here is not representative of all snakes causing bites in Nepal . The three study centres are located in the lowlands of the Terai region where most snakebites occur , but their catchment areas do not cover all of Nepal’s great biogeographical diversity . In particular , species found in mountain regions ( although present , e . g . , O . monticola ) were probably under-represented in our study . The fact that the biting species could be identified only in a relatively small proportion of patients ( 25 . 9% ) could in theory lead to bias . Although we cannot exclude that some selection bias occurred in the present study , its impact is likely to be minimal . We compared bite circumstances and baseline characteristics of snakebite victims with or without identification of snake species ( S1 and S2 Tables ) . Differences were seen with regard to season of bite , location and activity at the time of bite and consultation of a traditional healer . However , the magnitude of these differences was minimal . Moreover , the epidemiological characteristics of our study population are consistent with other published reports [2–5 , 8 , 43 , 44] , further ruling out the possibility of selection bias and reaffirming the external validity of our findings . Finally , morphological identification and molecular analysis results were both available in only 21 cases , limiting our ability to evaluate the diagnostic performance of the molecular diagnosis method . Findings presented here thus need to be interpreted with caution . A follow-up prospective validation study is ongoing in Nepal and Myanmar to address this issue . Snakebite envenoming is an important health problem in Nepal , accounting for up to 39 . 7% of poisoning cases admitted to emergency units in certain regions [6] . Neurotoxicity following the bites of elapid snakes is of particular concern . This study for the first time addresses the distribution and medical importance of snake species contributing to the burden of snakebite in Nepal . It provides crucial information for clinicians and health workers involved in the management of snakebite victims in Nepal . It notably highlights that the majority of bites are caused by non-venomous snakes , and that the diversity of venomous snake species involved in bites is greater than previously believed . Finally , this study provides initial evidence on the utility of forensic DNA-based methods in the identification of biting snake species . | Snakebite is an important medical problem in sub-tropical and tropical regions , including Nepal where tens of thousands of people are bitten every year . Snakebite can result in life-threatening envenoming , and correct identification of the biting species is crucial for care providers to choose appropriate treatment and anticipate complications . This paper explores a number of methods , including molecular techniques , to assist care providers in identifying the species responsible for bites in rural Nepal . Out of 749 patients with a history of snakebite , the biting species could be identified in 194 ( 25 . 9% ) . Out of these , 87 had been bitten by a venomous snake , most commonly cobras ( n = 42 ) and kraits ( n = 22 ) . This study is the first to report the use of molecular techniques for snake species identification . The diagnostic accuracy of this method appears high but needs to be confirmed in larger studies . | [
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"diseases",... | 2016 | Use of Molecular Diagnostic Tools for the Identification of Species Responsible for Snakebite in Nepal: A Pilot Study |
Despite extensive genetic diversity of HIV-1 in chronic infection , a single or few maternal virus variants become the founders of an infant’s infection . These transmitted/founder ( T/F ) variants are of particular interest , as a maternal or infant HIV vaccine should raise envelope ( Env ) specific IgG responses capable of blocking this group of viruses . However , the maternal or infant factors that contribute to selection of infant T/F viruses are not well understood . In this study , we amplified HIV-1 env genes by single genome amplification from 16 mother-infant transmitting pairs from the U . S . pre-antiretroviral era Women Infant Transmission Study ( WITS ) . Infant T/F and representative maternal non-transmitted Env variants from plasma were identified and used to generate pseudoviruses for paired maternal plasma neutralization sensitivity analysis . Eighteen out of 21 ( 85% ) infant T/F Env pseudoviruses were neutralization resistant to paired maternal plasma . Yet , all infant T/F viruses were neutralization sensitive to a panel of HIV-1 broadly neutralizing antibodies and variably sensitive to heterologous plasma neutralizing antibodies . Also , these infant T/F pseudoviruses were overall more neutralization resistant to paired maternal plasma in comparison to pseudoviruses from maternal non-transmitted variants ( p = 0 . 012 ) . Altogether , our findings suggest that autologous neutralization of circulating viruses by maternal plasma antibodies select for neutralization-resistant viruses that initiate peripartum transmission , raising the speculation that enhancement of this response at the end of pregnancy could further reduce infant HIV-1 infection risk .
Despite the wide success of antiretroviral therapy ( ART ) in lowering mother-to-child transmission ( MTCT ) risk of HIV-1 below 2% , each year more than 150 , 000 children become infected worldwide [1] . Even if 90% maternal ART coverage is reached , approximately 138 , 000 infant HIV-1 infections will still occur annually [2 , 3] due to factors that include: drug non-adherence , breakthrough infections , development of drug resistant viral strains , late presentation of pregnant women to clinical care , and acute infection during late pregnancy or breastfeeding . Mother to child transmission of HIV can occur through three distinct modes: antepartum ( in utero ) , peripartum ( around the time of delivery ) , or postpartum ( via breastfeeding ) . Interestingly , only 30–40% of infants born to HIV infected mothers acquire HIV-1 in the absence of ART [4] . Thus , maternal factors , such as maternal Env-specific antibodies , may contribute to protecting infants from HIV infection . Maternal factors that are associated with HIV transmission risk include: low maternal peripheral CD4+ T cell count , and high maternal plasma viral load , delivery mode , and infant gestational age [5–7] . Yet , the role of maternal Env-specific antibody responses and their association with reduced MTCT risk still remains unclear . Previous studies have reported an association between the magnitude of maternal antibody responses and reduced risk of MTCT [8–10] . However , this association has not been universally observed [11–15] . Moreover , it has been observed that variants transmitted to infants can be resistant to neutralization by maternal plasma [16] , although other studies have failed to replicate these observations [17–19] . These conflicting results may be due to the small number of subjects included in these studies and study designs that inconsistently control for viral and host factors known to impact transmission risk , such as maternal peripheral CD4+ T cell counts , plasma viral load , non-identification of T/F viruses , and ART use [20 , 21] . Moreover , autologous viruses from a large cohort of HIV-1 infected , transmitting mothers for assessment of the impact of maternal plasma neutralization activity against her own viruses has not to our knowledge , been investigated . Thus , despite considerable effort , it remains unclear whether maternal antibody responses impact the risk of perinatal transmission of HIV . We recently completed a maternal humoral immune correlates of protection analysis to identify maternal humoral immune responses associated with protection against peripartum HIV-1 infection using samples from the US-based Women and Infants Transmission ( WITS ) study [22] . The WITS cohort was enrolled prior to the availability of ART prophylaxis as the clinical standard of care in HIV-infected pregnant mothers and their infants , thereby eliminating the strong impact of ART on perinatal HIV-1 transmission risk and outcome [23 , 24] . Additionally , we controlled for established maternal and infant risk factors associated with perinatal transmission , including maternal peripheral CD4+ T cell count , maternal plasma HIV-1 viral load , infant gestational age , and delivery mode by propensity score matching of transmitting and non-transmitting women . The results of this immune correlate analysis indicated an association between high levels of maternal antibodies against the HIV-1 Env glycoprotein third variable loop ( V3 ) and reduced MTCT risk [22] . In addition , and more surprisingly , the ability of maternal plasma to neutralize tier 1 ( easy-to-neutralize ) viruses but not tier 2 ( difficult-to- neutralize ) viruses , also predicted decreased risk of perinatal transmission of HIV-1 . Yet , vertically transmitted HIV variants have been characterized as more difficult to neutralize tier 2 variants [17 , 25–28] . Thus , it was surprising that tier 1 virus neutralizing antibodies were associated with decreased transmission risk . More interestingly , maternal V3-specific monoclonal IgG antibodies obtained from a non-transmitting mother neutralized a large proportion of maternal autologous viruses obtained from her plasma[22] , leading to the conclusion that maternal V3-specific non-broadly neutralizing antibodies , which were previously thought to be ineffective at preventing HIV-1 transmission , might indeed play a role in preventing MTCT . In fact , Moody et . al [29] showed that V3 and CD4 binding site ( CD4bs ) specific monoclonal antibodies obtained from non-pregnant chronically HIV-infected individuals could neutralize a large proportion of autologous circulating viruses obtained from plasma . These V3 and CD4bs-specific autologous virus-neutralizing mAbs exhibited tier 1 neutralization activity but limited heterologous tier 2 virus neutralization , suggesting that measurement of tier 2 heterologous virus neutralization potency of mAbs or plasma does not predict autologous virus neutralization capacity . In contrast to the extensive genetic diversity of HIV-1 variants in a chronically infected host , acute HIV infections are established by a limited number of T/F viruses [17 , 18 , 30–33] . This viral genetic bottleneck suggests the selective transmission of a single or homogeneous group of viruses [4] . In the setting of MTCT , maternal or infant immunologic and virologic factors that drive the selective transmission of one or a few HIV variants are not established [34] . As maternal viruses co-circulate with maternal HIV Env-specific antibodies , it is possible that maternal antibodies play a role in selecting maternal escape viruses that may initiate infection in the infant . Therefore , studying unique features of infant T/F viruses and their neutralization-sensitivity determinants to maternal autologous virus neutralizing antibodies may provide insights of the molecular events that lead to virus escape from maternal humoral responses . The use of broadly neutralizing antibodies as a treatment and/or prevention strategy is currently being explored in adult and infant clinical trials [35 , 36] . Among the new generation of bNAbs , VRC01 ( antibody recognizing CD4bs region ) has been able to neutralize about 80% of diverse HIV-1 strains [37 , 38] . This has led to studies of VRC01 impact on HIV-1 infection in adults and infants when infused passively , with a phase I study of pharmacokinetics and safety of VRC01 in HIV-exposed newborns currently underway [39] . However , the susceptibility of infant T/F viruses to bNAbs like VRC01 does not seem to define infant T/F viruses from maternal non-transmitted viruses [17] and administration of a bNAb to chronically-infected mothers is likely to lead to rapid development of resistant viruses [36 , 40] . Thus , defining the role of autologous neutralization in MTCT is critical to establishing the utility of active maternal vaccination to further reduce and eliminate infant HIV infections . In this study , we characterized maternal non-transmitting and infant T/F viruses from 16 HIV-1 clade B infected peripartum transmission mother-infant pairs from the WITS cohort and defined the role of concurrent maternal autologous neutralizing antibodies in selecting for infant T/F viruses . We sought to define if neutralization resistance to paired maternal plasma was a defining feature of infant T/F viruses compared to other circulating non-transmitted maternal variants , which will inform the development of maternal or infant vaccination strategies to further reduced MTCT risk to achieve an HIV-free generation .
We selected HIV-1 infected mother-infant transmission pairs from the WITS cohort that met the inclusion criteria of peripartum transmission ( infants tested negative for HIV-1 infection at birth by HIV-1 DNA PCR , yet had HIV-1 DNA detectable at one week of age or older; Table 1 ) . These HIV-exposed infants had not reportedly been breastfed [24] . Infant plasma samples available for sequencing were between 16–74 days of age . Five HIV-infected infants with heterogeneous virus populations were excluded from the study due to our inability to confidently infer the infant T/F viral sequences , and selected a total of 16 mother-infant transmission pairs for this study . The maternal plasma viral load of the selected transmitting women ranged from 4 , 104 to 368 , 471 copies/mL , and peripheral blood CD4+ T-cell counts ranged from 107 to 760 cell/mm3 . Infant plasma viral loads varied between 11 , 110 and 2 , 042 , 124 copies/mL , and CD4+ T-cell counts were between 1 , 872 and 7 , 628 cell/mm3 . All infants were born via vaginal delivery except for three infants ( 100014 , 100155 , 100307 ) born via Cesarean section , thus potentially representing late in utero transmission . Five infants ( 100014 , 100155 , 100307 , 102149 , and 101984 ) were born prematurely ( 31 , 34 and 36 weeks respectively ) , and the remaining infants were born between 37 and 40 weeks of gestation . Infant plasma samples were used to obtain 465 env gene sequences ( Table 2 ) . Neighbor-joining phylogenetic trees and highlighter plots of the env sequences from each infant were used to define infant T/F viruses . These analyses showed within-lineage low diversity populations in infant Env sequences and high diversity populations in maternal env sequences ( Fig 1 , S1 Fig and S2 Fig ) . In 6 out of 16 ( 37% ) infants we detected 2 or 3 ( in case of 100002 ) genetically distinct T/F variants , one of which was present at higher frequency ( primary T/F ) , while the rest were present at lower frequency ( secondary T/Fs ) . In 10 other infants ( 67% ) , we observed only one T/F virus . With the exception of two infants , all of our samples had over 20 infant sequences , giving us a 90% confidence that we were able to sample all variants with a population frequency of at least 10% . For the two infant samples in which we only had 15 and 18 sequences respectively , we were 90% confident that we were able to sample all variants with a population frequency of 15% or more [26] . A total of 463 env genes were obtained from 16 maternal plasma samples ( collected at time of delivery ) from transmitting mothers as previously described [26] . Using an algorithm described in the Methods , out of these 463 maternal env variants , we selected 134 SGA variants for env pseudovirus production ( 5–12 per mother ) to represent the env genetic diversity found in the plasma of each transmitting mother at the time of delivery . As the infants were selected for peripartum transmission , their age at sampling ( in days ) was also the post-infection time . To confirm the time of infection , all infant alignments were analyzed using the LANL Poisson Fitter tool [41] . For infants that had more than one T/F , only the sequences in the major T/F lineage were used for this analysis . When recombinants and APOBEC enrichment were detected , the timing was calculated after removing recombinants and/or positions enriched for hypermutation [26 , 41] . After removing hypermutated sequences and /or recombinants , all but one infant ( 102605 ) yielded a good Poisson fit , indicating that the amount of diversity found in these samples was compatible with a random accumulation of mutations as observed in acute infections . The time since the most common ancestor was consistent with transmission at delivery in 9 out of 16 pairs ( 57% ) , within the 95% confidence interval of the Poisson Fitter time estimate ( Table 2 ) . For the remaining infants , discrepancies between actual vs . predicted transmission timing could be due to a number of factors , including late in utero infection , postpartum infection from unreported breastfeeding , and the model being designed to evaluate an adult rather than infant HIV-1 evolution , which could gather mutations more rapidly due to more robust T cell responses . Infant 102605 env SGA sequences did not yield a good Poisson fit due to non-random accumulation of non-synonymous mutations ( which breaks the model assumption of random accumulation of mutations ) at HXB2 positions 752–754 ( S3 Fig ) . According to the LANL immunology database ( https://www . hiv . lanl . gov/content/immunology/index ) , there are five different human CTL epitopes in this region , suggesting that the non-random mutations found in infant 102605 were likely due to selection pressure by T cell responses . Twenty-one infant T/F env amplicons including 16 primary T/Fs and 5 secondary T/Fs were used to generate pseudoviruses and their neutralization sensitivity to paired maternal plasma ( collected at the time of birth ) and a panel of bNAbs was assessed . None of the mothers with the exception of two ( 100014 and 100504 ) had non-specific neutralization activity as assessed by neutralization activity against a murine leukemia virus ( MLV ) . Eighteen out of 21 infant T/F Env pseudoviruses ( 86% ) were resistant to paired maternal serum ( ID50<40 ) . Sensitivity of 2 Infant T/F pseudoviruses’ ( 100014 and 100504 ) against paired maternal plasma could not be determined with confidence due to higher plasma reactivity against MLV . The T/F virus of infant 100046 was sensitive against paired maternal plasma ( Fig 2 ) . When infant T/F pseudoviruses were tested against the autologous plasma ( plasma from the same infant ) , only 2 T/Fs ( from infants 100046 and 100155 ) showed some sensitivity as per our criteria ( ID50> 3X that of MLV ) , while others were completely resistant . Some infant T/F pseudoviruses did show sensitivity against their own plasma but were not considered as sensitive due to high reactivity against MLV ( S4 Fig ) . To determine whether these infant T/F env variants were globally resistant to heterologous plasma neutralization , we performed neutralization tier phenotyping using a standardized panel of heterologous plasma of HIV-1 infected individuals [42] . Thirteen ( 62% ) of 21 infant T/F Env pseudoviruses were classified as tier 2 neutralization phenotype while 3 ( 14% ) of 21 were classified as tier 3 neutralization phenotype , as expected for infant T/F viruses ( Fig 2 ) . Remarkably , the remaining 5 ( 24% ) tested were classified as the easier-to-neutralize tier 1b or tier 1a sensitivity , possibly because these variants were uniquely resistant to their paired maternal plasma ( Fig 2 ) . In contrast to the relative resistance to paired maternal plasma neutralization , all the infant T/F viruses were relatively sensitive to second-generation HIV-1 broadly neutralizing antibodies , such as VRC-01 , ( IC50 range 0 . 12–5 . 0 μg/ml ) , PGT121 ( IC50 range 0 . 01–0 . 13 μg/ml ) , NIH 45–46 ( IC50 range 0 . 01–0 . 28 μg/ml ) and first generation bNAb 10E8 ( IC50 range 0 . 05–0 . 76 μg/ml ) ( Fig 3 and S5 Fig ) . Not surprisingly , the infant T/F Env pseudoviruses were less neutralization sensitive to the less potent first generation broadly neutralizing antibodies b12 ( IC50 range 2 . 97–25 μg/ml ) , 4E10 ( IC50 range 1 . 31–18 . 73 μg/ml ) and 2F5 ( IC50 range 1 . 01–19 . 09 μg/ml ) ( Fig 3 ) . Importantly , all the infant T/F viruses were neutralization sensitive to VRC-01 , a bNAb currently being evaluated in clinical trials for use in HIV exposed infants . However , V3 glycan-specific bNAbs , which clustered together in neutralization sensitivity , and NIH45-46 mediated the most neutralization breadth and potency against the infant T/F viruses ( Fig 3 ) . We next calculated the geometric means of both the breadth and potency of the panel of bNAbs against the infant T/F viruses and compared with their potency against other HIV variants as documented in CATNAP ( Compile , Analyze and Tally NAb Panels ) [43] , the Los Alamos National Laboratory ( LANL ) interface that collects all published immunological data . In general , potency and breadth of the bNAbs against infant T/F viruses followed the mean potency and breadth calculated in CATNAP ( p = 0 . 013 and 0 . 02 respectively , Spearman correlation test ) , with one exception: bNAb 2G12 displayed more potent and broad responses in the infants than in the CATNAP collective data ( Fig 3 ) . Pseudoviruses were prepared from a total of 134 non-transmitted maternal env variants using the promoter PCR method [44] and assessed for neutralization sensitivity against paired maternal plasma , including the maternal non-transmitted variant that was most closely related to the infant T/F variant . Variable neutralization sensitivity to paired maternal plasma was observed in non-transmitted maternal variants , with some of the variants exhibited neutralization sensitivity while others showed complete neutralization resistance ( Fig 4 ) . Comparison of neutralization sensitivity between infant T/F Env variants and the identified closest maternal variant within each mother-infant pair revealed no consistent pattern . Over a 2-fold increase in sensitivity was observed for Env pseudoviruses of non-transmitted maternal variants that were most closely related to infant T/F for 6 infants ( 100002 , 100307 , 100052 , 102149 , 102407 and 102605 ) . In contrast , infant T/F Env pseudoviruses from 3 infants ( 100014 , 100046 and 100504 ) were more sensitive to maternal plasma than their most closely related maternal variants . Yet , the infant T/F viruses were generally more resistant to the maternal plasma at delivery than the non-transmitted viruses from mothers within each maternal-infant pair , with the exception of 100046 . However , since there were only 1 or 2 T/F viruses in each infant , we could not perform statistical analysis to determine if the differences were statistically significant within each pair . To determine whether infant T/F viruses were overall more resistant to maternal plasma than the paired non-transmitted maternal viruses , we employed a 1-sided permutation test to compare the neutralization sensitivity of maternal non-transmitted variants to the infant T/F Env variants . Remarkably , infant T/F Env variants were overall significantly more resistant to paired maternal plasma collected at delivery than non-transmitted maternal Env variants ( p = 0 . 012 ) . Even when excluding the mother-infant pairs with high MLV neutralization ( 100014 and 100504 ) , the infant T/F Env variants remained more resistant to neutralization than non-transmitted maternal variants ( p = 0 . 005 ) . To assess whether any particular epitope-specific neutralization sensitivity was distinct in infant T/Fs compared to matched maternal variants , we determined the neutralization sensitivity of 4 bNAbs targeting distinct vulnerable epitopes on HIV-1 Env: VRC-01 ( CD4bs-specific ) , PG9 ( V2 glycan-specific ) , DH429 ( V3 glycan-specific ) , and DH512 ( membrane proximal external region–MPER-specific ) ( Fig 5 and S6 Fig ) . We used the same 1-sided permutation test described above to assess for differences in neutralization sensitivity to these bNAbs in infant T/Fs versus non-transmitted maternal sequences . Interestingly , we found that infant T/F viruses were significantly more resistant to DH512 ( MPER-specific ) compared to non-transmitted maternal sequences ( p = 0 . 025 by 1-sided permutation test; p = 0 . 045 when excluding the two mothers with non-specific neutralization ) , while all other comparisons yielded no statistical significance ( Fig 5 and S6 Fig ) . Because DH512 binds to the MPER region , we investigated the amino acid positions within this epitope ( positions 662–683 ) and identified 4 positions that were either associated with higher maternal plasma neutralization ID50 ( at position 662 , amino acid A , K , Q , or S were significantly more resistant than the wild type E , p = 9 . 9e-05 , 1-sided permutation test ) , lower DH512 IC50 ( at position 667 and 676 , p = 9 . 9e-04 and 0 . 003 , respectively by 1-sided permutation test ) , or both ( at position 683 , amino acid R was significantly more resistant than the wild type K the most frequent AA at this position , p<1e-04 by 1-sided permutation test ( Fig 6 ) . To understand the role of amino acids at position 662 and 683 in resistance of infant T/F viruses against maternal plasma , two gp41 mutants R683K and Q662E were generated for 100997i and 102407i T/F env genes respectively . No impact on neutralization sensitivity was observed for the R683K mutant of 100997i whereas the Q662E mutant 102407i T/F was more sensitive to paired maternal plasma than the wild type virus ( S7 Fig ) . However , when we looked at the Env sequences in individual mother-infant pairs , these amino acid residues that associated with DH512 neutralization resistance were equally distributed across non-transmitted maternal sequences and infant T/F viruses . Therefore , we could not determine if T/F viruses carrying the above resistance-conferring amino acids were more apt to be transmitted compared to non-transmitted maternal variants . This could be due to the low sequence number within pairs , or suggests that the wild type amino acids at these positions are associated with DH512 neutralization resistance , but not necessarily transmission . However , infant T/F viruses and maternal variants that have the same sequence in the MPER region can have more than three fold differences in DH512 MPER-associated antibody neutralization , indicating that there are residues in the flanking regions that impact MPER-associated neutralization . As maternal V3-specific IgG binding and tier 1 virus neutralizing responses were predictive of MTCT risk in this cohort [22] , we explored possible signatures of neutralization resistance to paired maternal plasma in the V3 region . We examined the highly variable N and C-terminal region amino acid residues K305Q , I307T , H308T , R315Q , F317L , A319T , and D322R ( Fig 7 ) , 3 of which ( K305Q , I307T , and H308T ) have previously been identified critical targets of the V3-specific IgG responses associated with reduced MTCT risk [45] . Comparing non-transmitted maternal sequences and infant T/F viruses , we found position K305R to be significantly associated with higher sensitivity to paired maternal plasma ( p<0 . 001 by 1-sided permutation test ) . At position 308 , sequences carrying mutations from the consensus amino acid H ( N , P , S or T ) were significantly associated with higher neutralization resistance to paired maternal plasma ( p<0 . 001 by 1-sided permutation test ) . This discrepancy could partially be due to the heterogeneity at amino acid residue position 308 . Yet , 6 out of 14 transmitting mother-infant pairs exhibited distinct amino acid residues at position 308 from the more frequently occurring histidine ( Fig 7 ) , suggesting variability at this position could be overrepresented in transmitting pairs . However , as for the MPER residues , we observed that these amino acids were equally distributed across non-transmitted maternal and infant T/F virus sequences , indicating that none of these amino acid residues were directly involved with transmission risk .
While maternal and infant ART has considerably reduced rates of MTCT , pediatric HIV infection remains a significant public health problem in areas of high HIV prevalence , with up to 16% of HIV-infected women still transmitting the virus to their infant globally [1] . It is likely that a maternal or infant HIV-1 vaccine will be required to eliminate pediatric HIV [46] . However , a better understanding of factors that affect HIV-1 transmission in the setting of MTCT will be required to develop vaccination strategies that can block HIV-1 transmission . Recent findings published by our group demonstrated that maternal V3 loop-specific and tier 1 virus-neutralizing antibody responses both correlated and were independently associated with of reduced MTCT risk . Moreover , we established that V3-specific antibodies in maternal plasma could neutralize maternal autologous viral variants circulating in plasma [22 , 29] . To examine the potential role of maternal Env-specific Ab responses in driving the viral genetic bottleneck of MTCT , we aimed to define if neutralization resistance to maternal autologous virus neutralizing antibodies is a defining feature of infant T/F viruses compared to circulating maternal non-transmitted plasma variants . Although , env gene sequences derived from the same T/F viruses were very homogenous in all infants , we observed evolutionary selection in some infant env sequences as indicated by the presence of same non-synonymous mutations in multiple env gene sequences ( Fig 1B ) . Six out of 16 ( 37% ) peripartum-infected infants were infected by at least 2 T/F viruses . Infection with multiple T/F viruses occurs in approximately 19–24% of heterosexual HIV infections [26 , 47 , 48] , and 12–38% of homosexual infections [49–51] , whereas up to 60% of infections that occur through intravenous drug use involve multiple T/Fs [52] . Thus , the rate of multiple T/F transmissions in this mother-infant cohort is in line with or slightly higher than sexual transmission modes , but lower than that of transmission via intravenous drug use . While it is well established that a genetic bottleneck occurs in the setting of MTCT , the determinants that drive the selection of one or multiple T/F viruses are less clear [4] . Importantly , the lack of maternal ART prophylaxis around the time of delivery in the WITS cohort could contribute to the observed high rate of multiple T/F viruses , potentially stemming from a larger virus inoculum in this cohort compared to ARV-treated mothers . Notably , these infant T/F viruses uniformly represented a minor variant of the maternal viral population Env variants , indicating that maternal antibodies that can block infant virus transmission will need to target minor circulating variants . A greater understanding of virologic characteristics of infant T/F viruses will also be important for developing immune-based strategies to prevent MTCT . BNAb neutralization analysis of all infant T/F viruses showed that they were significantly more resistant to DH512 ( MPER region specific antibody ) than non-transmitted maternal variants , we investigated role of MPER region based neutralizing antibodies of maternal plasma in evolution of infant T/F [53] . Signature sequence analysis of the MPER sequences showed that E662 and K683 were associated with sensitivity of infant T/F viruses to maternal plasma . Site Directed mutagenesis analysis in the MPER region of two infant T/F env genes from 100997i R683K and 102407i Q662E and sensitivity against paired maternal plasma was tested . Results showed that identified residues in the MPER region epitope indeed had a role in escape of infant T/F viruses for one infant T/F ( 102407i ) but not the other T/F virus ( 100997i ) . However , roles of other regions or structures in recognition of this MPER epitope could not be ruled out . This result supports our signature sequence analysis and yet also indicates the potential importance of other Env regions in escape of infant T/F viruses from the maternal plasma selection pressure . As maternal V3-specific IgG responses predicted reduced risk of transmission in this cohort , we investigated V3 loop residues in the maternal and infant viruses and how they related to paired maternal plasma neutralization sensitivity . Despite the association between maternal V3-specific IgG responses targeting the C terminal region and reduced MTCT risk in this cohort [45] , we did not find amino acid residues within the C terminal region to be associated neutralization resistance to paired maternal plasma . Instead , we found that maternal non-transmitted and infant T/F viruses carrying N , P , S or T at the N terminal region amino acid residue position 308 were more neutralization resistant to paired maternal plasma . These seemingly disparate findings could partly be explained by several reasons . Firstly , N terminal amino acid residues 308 and 309 have been shown to interact with C terminal amino acid residue 317 , and this interaction leads to the stabilization of the V3 loop [54 , 55] . Thus , the disruption of intra-peptide interactions at either the N and or C terminal region could lead to altered neutralization sensitivity of viruses to paired maternal V3-specific IgG plasma responses . Secondly , it should be noted that in this study , we compared maternal non-transmitted circulating viruses to infant T/F viruses in 16 transmitting mother infant pairs , whereas we previously defined the potentially-protective role of maternal V3-specific IgG binding and neutralizing responses by comparing transmitting and non-transmitting women in a larger number ( n = 248 ) of subjects in the WITS cohort [22 , 45] . Therefore , there may be differences in the fine-specificity and neutralizing function of V3-specific IgG responses within these 16 peripartum transmitting mothers compared to n = 165 non-transmitting women from the WITS cohort . In addition , there could be differences in the V3 loop accessibility in these 16 transmitting mothers compared to non-transmitting women in this cohort and thus explaining the lack of a V3 loop amino acid residue signature that is associated with neutralization resistance . Previous work has shown that V3 loop accessibility to maternal neutralizing antibodies may be modulated by distal amino acid residues within gp120 or gp41 [15] . For example , specific glycosylation sites within the V1 loop may alter V3 loop accessibility to V3-specific neutralizing antibodies [56] . Moreover , interactions between C2 and V3 may stabilize the structure of the HIV-1 Env [57] , as demonstrated with the recent crystal structure elucidation of the SOSIP trimer [58] . In contrast to previous studies that examined the neutralization sensitivity of randomly selected or non-paired infant or maternal viruses , here we defined the neutralization sensitivity of paired infant T/F viruses and maternal non-transmitted variants . Moreover , this study accounted for phylogenetic relationships of infant T/F viruses and maternal non-transmitted variants to represent the diverse maternal virus lineage pools . Furthermore , we carefully controlled for confounders such as transmission mode and disparate maternal and infant sample testing . Moreover , as the WITS cohort was enrolled and followed prior to the availability of ART to prevent MTCT , viral evolution in this cohort is not influenced by ART selection pressure . With this robust study design , our analysis demonstrated that infant T/F viruses are mostly resistant to concurrent maternal plasma , suggesting that infant T/F viruses are defined by neutralization resistance to paired maternal neutralizing antibodies . This work confirms previous studies that have made this prediction based on smaller studies or with less well-defined maternal and infant virus variants [16 , 59 , 60] . Yet , Miligan et . al [61] recently showed that neutralization resistant viruses do not predict MTCT risk in a breastfeeding transmission setting . However , as our analysis focused on peripartum transmission , there may be distinct virologic or immunologic determinants in peripartum and postpartum HIV transmission . Yet , an important and novel observation gleaned from this study is that infant T/F viruses’ neutralization resistance to maternal plasma is not predictive of neutralization resistance to heterologous plasma . Remarkably , the tiered categorization of infant T/F viruses ranged from easy to neutralize tier 1a viruses , to very difficult to neutralize tier 3 viruses , suggesting that heterologous plasma neutralization resistance is not a defining feature of infant T/F viruses . Specifically , 24% of infant T/F viruses characterized in this study were classified as tier 1b or tier 1a variants by a standard panel of heterologous plasma [42] , consistent with the hypothesis that these infant T/F viruses may be specifically resistant to maternal antibodies that co-evolved with the transmitted variants . The majority of infant T/F viruses were neutralization sensitive to a number of second generation broadly neutralizing antibodies ( Fig 5 ) . Clinical studies to analyze safety and pharmacokinetics ability of VRC01 showed that it was well tolerated [40] . Further , VRC01 was able to protect non-human primates from infection [62 , 63] . Our findings are clinically relevant , as they suggest that infant passive immunization with second generation broad and potent bNAbs to prevent HIV-1 transmission could be an effective strategy to block MTCT . The uniform sensitivity of these newly characterized clade B infant T/F viruses to VRC01 neutralization indicates that these viruses would be effectively neutralized by VRC01 , and that clade B infant virus transmission may be blocked by VRC01 . Interestingly , there is an ongoing passive immunization clinical trial of high-risk , HIV-exposed infants with VRC01 ( https://clinicaltrials . gov/ct2/show/record/NCT02256631 ) . To our knowledge , this is the largest study that has characterized infant T/F and maternal viruses and their neutralization sensitivity to maternal neutralizing responses . Our study specifically addresses whether infant T/F viruses are defined by their neutralization sensitivity to maternal autologous virus neutralizing antibodies in peripartum MTCT of HIV . MTCT is a unique setting in which protective antibodies only need block autologous viral variants circulating in blood to which the infant is exposed . The observation that infant T/F viruses are neutralization resistant compared to non-transmitted maternal variants suggests that the development of a maternal vaccine that boosts maternal autologous virus neutralizing responses may be a viable strategy to further reduce MTCT risk . Maternal Env immunization regimens with closely related , but not identical , Envs to maternal circulating virus populations may elicit antibodies that target maternal autologous virus pool through the well-described immune phenomenon of ‘original antigenic sin’ [64 , 65] . Our central finding that resistance to maternal plasma neutralization is more common in infant T/F viruses than non transmitted maternal viruses has important implications in designing maternal Env vaccination strategies that can enhance maternal autologous virus neutralization and synergize with current ART treatment strategies help achieve a reduced rate of perinatal virus transmission .
Maternal and infant pairs from the WITS cohort that met the following criteria were selected: peripartum transmission , infant plasma samples from < 2 . 5 months of age , and maternal samples available from around delivery . Peripartum transmission was defined by a negative PCR result or negative culture from peripheral blood samples collected within 7 days of birth with subsequent positive result 7 days after birth ( Table 1 ) . Samples used in this study were obtained from an existing cohort named as Women Infant Transmission Study ( WITS ) . WITS cohort samples were received as de-identified material and were deemed as research not involving human subjects by Duke University Institutional Review Board ( IRB ) . The reference number for that protocol and determination is Pro00016627 . Viral RNA was purified from the plasma sample from each patient by the Qiagen QiaAmp viral RNA mini kit and subjected to cDNA synthesis using 1X reaction buffer , 0 . 5 mM of each deoxynucleoside triphosphate ( dNTP ) , 5 mM DTT , 2 U/mL RNaseOUT , 10 U/mL of SuperScript III reverse transcription mix ( Invitrogen ) , and 0 . 25 mM antisense primer 1 . R3 . B3R ( 5’-ACTACTTGAAGCACTCAAGGCAAGCT TTATTG-3’ ) , located in the nef open reading frame . The resulting cDNA was end-point diluted in 96 well plates ( Applied Biosystems , Inc . ) and PCR amplified using Platinum Taq DNA polymerase High Fidelity ( Invitrogen ) so that < 30% of reactions were positive in order to maximize the likelihood of amplification from a single genome . A second round of PCR amplification was conducted using 2μl of the first round products as template . 07For7 ( 5’-AAATTAYAAAAATTCAAAATTTTCGGGTTTATTACAG-3’ ) and 2 . R3 . B6R ( 5’- TGA AGCACTCAAGGCAAGCTTTATTGAGGC -3’ ) were used as primer pair in the first round of PCR amplification step , followed by a second round with primers VIF1 ( 5’- GGGTTTATTACAGGGACAGCAGAG -3’ ) ( nt 5960–5983 in the HXB2 tat coding region ) and Low2c ( 5’- TGAGGCT TAAGCAGTGGGTT CC -3’ ) ( nt 9413–9436 in HXB2 nef ) . PCR was carried out using 1X buffer , 2 mM MgSO4 , 0 . 2 mM of each dNTP , 0 . 2μM of each primer , and 0 . 025 U/μl Platinum Taq High Fidelity polymerase ( Invitrogen ) in a 20μl reaction . Round 1 amplification conditions were 1 cycle of 94°C for 2 minutes , 35 cycles of 94°C for 15 seconds , 58°C for 30 seconds , and 68°C for 4 minutes , followed by 1 cycle of 68°C for 10 minutes . Round 2 conditions were one cycle of 94°C for 2 minutes , 45 cycles of 94°C for 15 seconds , 58°C for 30 seconds , and 68°C for 4 minutes , followed by 1 cycle of 68°C for 10 minutes . Round 2 PCR amplicons were visualized by agarose gel electrophoresis and sequenced for envelope gene using an ABI3730xl genetic analyzer ( Applied Biosystems ) . The final amplification 3’-half genome product was ~4160 nucleotides in length exclusive of primer sequences and included all of rev and env gp160 , and 336 nucleotides of nef . Partially overlapping sequences from each amplicon were assembled and edited using Sequencher ( Gene Codes , Inc ) . Sequences with double peaks per base read were discarded . Sequences with one double peak were retained as this most likely represents a Taq polymerase error in an early round of PCR rather than multiple template amplification; such sequence ambiguities were read as the consensus nucleotide . Sequence alignments and phylogenetic trees were constructed using ClustalW and Highlighter plots were created using the tool at https://www . hiv . lanl . gov/content/sequence/HIGHLIGHT/highlighter_top . html . SGA sequences generated for each mother-infant pair were deposited in Genbank under accession numbers MH012257- MH013187 ( http://www . ncbi . nlm . nih . gov/Genbank/ ) . All maternal and infant envelope sequences were aligned using the Gene Cutter tool available at the Los Alamos National Laboratory ( LANL ) website ( http://www . hiv . lanl . gov/content/sequence/GENE_CUTTER/cutter . html ) and then refined manually . Full-length envelope sequences were manually edited in Seaview [66] . The infant T/F env virus sequences were identified by analysing phylogenetic trees and highlighter plots , and infant consensus sequences of the major T/F lineage were generated using the LANL Consensus Maker tool ( http://www . hiv . lanl . gov/content/sequence/CONSENSUS/consensus . html ) . For infants that were infected by 2 or more distinct T/F viruses , the highlighter plots and phylogenetic trees were rooted on the consensus of the major variant . Maternal and infant envelope alignments were characterized using Bio-NJ phylogeny ( Mega 6 Software ) and highlighter plot . The number of infant T/F viruses was determined by visual inspection of both phylogenetic trees and highlighter plots of infant-maternal env sequence alignments . Hypermutation was also evaluated using the tool Hypermut ( http://www . hiv . lanl . gov/content/sequence/HYPERMUT/hypermut . html ) . Sequences with significant hypermutation ( p<0 . 1 ) were removed from the alignment and not included in further analysis . When a sample was found to be overall enriched for hypermutation [26] , positions within the APOBEC signature context were removed ( Table 2 ) . All 16 infant infections were acute and we were able to time the infection using the Poisson Fitter method after removing putative recombinants and/or hypermutated sequences as described above . Days since infant infection were calculated using the Poisson Fitter tool ( http://www . hiv . lanl . gov/content/sequence/POISSON_FITTER/ pfitter . html ) which estimates the time since infection based on the accumulation of random mutations from the most recent common ancestor ( MRCA ) [41] . For infants infected with 2 or more T/F viruses , only the major variant was analyzed to obtain the time since the infection . The defined mutation rate was 2 . 16e-5 . Values were reported in days with a 95% confidence interval and a goodness-of-fit p-value . Amplicons from the first round PCR product that matched the infant consensus sequence ( T/F virus sequence ) were ligated into pcDNA3 . 1 Directional Topo vectors ( Invitrogen ) by introducing a–CACC 5’ end via a PCR reaction with the primers Rev19 ( 5’- ACTTTTTGACCACTTGCCACCCAT-3’ ) and Env1A ( 5’-caccTTAGGCATCTCCT ATGGCAGGAAGAAG-3’ ) . Phusion High-Fidelity PCR Master Mix with HF Buffer was used according to the manufacturer’s instructions ( New England BioLabs ) . Plasmids were then transformed into XL10 gold chemically competent Escherichia coli cells . Cultures were grown at 37°C for 16 hours . Colonies were selected for growth , and plasmids were minipreped and quality controlled by restriction enzyme digestion using BamHI and XhoI ( New England BioLabs ) . Plasmids containing an insert of correct size were sequenced to confirm 100% sequence homology with the original env infant consensus sequence . Plasmids were then prepared by Megaprep ( Qiagen ) kit and re-sequenced to confirm . For three infants , 100046 , 100383 and 101580 , no SGA sequences matched 100% of nucleotides in the consensus sequence . Therefore site-directed mutagenesis on a single nucleotide was performed to generate an env gene identical to the consensus sequence . Primers for site directed mutagenesis were designed using Agilent’s QuikChange primer design program and Agilent’s QuikChange II XL kit was used . Sequencing of the clones was done to ensure 100% homology with the infant consensus sequence . Env pseudoviruses were prepared by transfection in HEK293T ( ATCC , Manassas , VA ) cells with 4μg of env plasmid DNA and 4μg of env-deficient HIV plasmid DNA ( SG3Δenv ) using the FuGene 6 transfection reagent ( Roche Diagnostics ) in a T75 flask . Two days after transfection , the culture supernatant containing pseudoviruses was harvested , filtered , aliquoted , and stored at -80°C . An aliquot of frozen pseudovirus was used to measure the infectivity in TZM-bl cells . 20μl of pseudovirus was distributed in duplicate to 96-well flat bottom plates ( Co-star ) . Then , freshly trypsinized TZM-bl cells were added ( 10 , 000 cells/well in Dulbecco’s modified Eagle’s medium ( DMEM ) -10% fetal bovine serum ( FBS ) containing HEPES and 10μg/ml of DEAE-dextran ) . After 48 h of incubation at 37°C , 100μl of medium was removed from the wells . 100μl of luciferase reagent was added to each well and incubated at room temperature for 2 min . 100μl of the lysate was transferred to a 96-well black solid plate ( Costar ) , and the luminescence was measured using the Bright-Glo luminescence reporter gene assay system ( Promega ) . Neutralizing antibody activity was measured in 96-well culture plates by using Tat-regulated luciferase ( Luc ) reporter gene expression to quantify reductions in virus infection in TZM-bl cells . TZM-bl cells were obtained from the NIH AIDS Research and Reference Reagent Program , as contributed by John Kappes and Xiaoyun Wu . Assays were performed with HIV-1 Env-pseudotyped viruses as described previously [67] . Test samples were diluted over a range of 1:20 to 1:43740 in cell culture medium and pre-incubated with virus ( ~150 , 000 relative light unit equivalents ) for 1 hr at 37°C before addition of cells . Following a 48-hr incubation , cells were lysed and Luc activity determined using a microtiter plate luminometer and BriteLite Plus Reagent ( Perkin Elmer ) . Neutralization titers are the sample dilution ( for serum/plasma ) or antibody concentration ( for sCD4 , purified IgG preparations and monoclonal antibodies ) at which relative luminescence units ( RLU ) were reduced by 50% compared to RLU in virus control wells after subtraction of background RLU in cell control wells . Serum/plasma samples were heat-inactivated at 56°C for 1 hr prior to assay . Pseudovirus made with Env glycoprotein from a murine leukemia virus ( SVA . MLV ) was used as a negative control [42] . A response was considered positive if the plasma ID50 against infant T/F viruses was at least 3 times higher than the ID50 of the SVA . MLV pseudovirus . Neutralization titers ( ID50s ) were determined essentially as described above using five plasma samples from HIV+ individuals in chronic infection . The geometric mean titer ( GMT ) was calculated in Microsoft Excel and tier phenotype was determined by comparing these values to the GMTs of standard panels of viruses representing tier 1A , tier 1B and tier 2 viruses [42 , 68] using the same five HIV+ plasma samples . To select maternal non-transmitted variants and capture the most divergent sequences from the infant T/F , we devised an algorithm as follows . The algorithm finds the most variable positions in the amino acid alignment and ranks all sequences with respect to the frequencies at these positions . Sequences are then selected starting from the most divergent based on motif coverage as observed in the alignment and in the phylogenetic tree ( in other words , if a group of diverging sequences all share the same motif , only one in the group and/or tree node is selected ) . To test whether infant transmitted viruses were statistically significantly more resistant to maternal plasma than non-transmitted maternal sequences , we devised a 1-sided permutation test . At each iteration , we randomly assigned the “transmitted” status to any one sequence in each infant-mother pair , and then ranked the remaining sequences in the pair according to maternal plasma responses . All ranks across all pairs were then summed . We repeated this randomization 1 , 000 times and then calculated the p-value as the percentage of sum of ranks that were above the observed sum of ranks , out of all randomizations performed . This method is robust , as it does not make any underlying assumption of the distribution of the maternal plasmas , and it preserves the within mother-infant correlation of the data . The same algorithm was used to test whether specific amino acid positions conferred resistance to maternal plasma and/or antibodies . This time the “transmitted” status that was reshuffled at each iteration , was the wild type amino acid . All programming for the sequence selection algorithm and permutation test was done on the R platform [69] . | Mother to child transmission ( MTCT ) of HIV-1 can occur during pregnancy ( in utero ) , at the time of delivery ( peripartum ) or by breastfeeding ( postpartum ) . With the availability of anti-retroviral therapy ( ART ) , rate of MTCT of HIV-1 have been significantly lowered . However , significant implementation challenges remain in resource-poor areas , making it difficult to eliminate pediatric HIV . An improved understanding of the viral population ( escape variants from autologous neutralizing antibodies ) that lead to infection of infants at time of transmission will help in designing immune interventions to reduce perinatal HIV-1 transmission . Here , we selected 16 HIV-1-infected mother-infant pairs from WITS cohort ( from pre anti-retroviral era ) , where infants became infected peripartum . HIV-1 env gene sequences were obtained by the single genome amplification ( SGA ) method . The sensitivity of these infant Env pseudoviruses against paired maternal plasma and a panel of broadly neutralizing monoclonal antibodies ( bNAbs ) was analyzed . We demonstrated that the infant T/F viruses were more resistant against maternal plasma than non-transmitted maternal variants , but sensitive to most ( bNAbs ) . Signature sequence analysis of infant T/F and non-transmitted maternal variants revealed the potential importance of V3 and MPER region for resistance against paired maternal plasma . These findings provide insights for the design of maternal immunization strategies to enhance neutralizing antibodies that target V3 region of autologous virus populations , which could work synergistically with maternal ARVs to further reduce the rate of peripartum HIV-1 transmission . | [
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"i... | 2018 | Infant transmitted/founder HIV-1 viruses from peripartum transmission are neutralization resistant to paired maternal plasma |
Extensive shielding by N-glycans on the surface of the HIV envelope glycoproteins ( Env ) restricts B cell recognition of conserved neutralizing determinants . Elicitation of broadly neutralizing antibodies ( bNAbs ) in selected HIV-infected individuals reveals that Abs capable of penetrating the glycan shield can be generated by the B cell repertoire . Accordingly , we sought to determine if targeted N-glycan deletion might alter antibody responses to Env . We focused on the conserved CD4 binding site ( CD4bs ) since this is a known neutralizing determinant that is devoid of glycosylation to allow CD4 receptor engagement , but is ringed by surrounding N-glycans . We selectively deleted potential N-glycan sites ( PNGS ) proximal to the CD4bs on well-ordered clade C 16055 native flexibly linked ( NFL ) trimers to potentially increase recognition by naïve B cells in vivo . We generated glycan-deleted trimer variants that maintained native-like conformation and stability . Using a panel of CD4bs-directed bNAbs , we demonstrated improved accessibility of the CD4bs on the N-glycan-deleted trimer variants . We showed that pseudoviruses lacking these Env PNGSs were more sensitive to neutralization by CD4bs-specific bNAbs but remained resistant to non-neutralizing mAbs . We performed rabbit immunogenicity experiments using two approaches comparing glycan-deleted to fully glycosylated NFL trimers . The first was to delete 4 PNGS sites and then boost with fully glycosylated Env; the second was to delete 4 sites and gradually re-introduce these N-glycans in subsequent boosts . We demonstrated that the 16055 PNGS-deleted trimers more rapidly elicited serum antibodies that more potently neutralized the CD4bs-proximal-PNGS-deleted viruses in a statistically significant manner and strongly trended towards increased neutralization of fully glycosylated autologous virus . This approach elicited serum IgG capable of cross-neutralizing selected tier 2 viruses lacking N-glycans at residue N276 ( natural or engineered ) , indicating that PNGS deletion of well-ordered trimers is a promising strategy to prime B cell responses to this conserved neutralizing determinant .
The HIV-1 envelope glycoprotein ( Env ) trimer is the sole target for neutralizing antibodies on the surface of the virus , mediating both receptor attachment and entry . Recently , high resolution structures of the native and native-like HIV-1 trimer revealed the extensive N-linked glycan shielding that has evolved to protect most of the underlying polypeptide surface from access by B cells and most antibodies [1–4] . However , the past decade has identified multiple broadly neutralizing antibodies ( bNAbs ) from selected HIV-infected individuals [5] , demonstrating that the human immune system can elicit antibody responses that can penetrate and , in some cases , recognize the glycan shield . These studies reveal several cross-neutralizing epitopes , including those localized to the gp120 V2 apex [6–10] , the V3-proximal N332 super site [10 , 11] , the CD4 binding site [12–16] , the gp120-gp41 interface site [17–20] and membrane proximal external region ( MPER ) -directed site [11 , 20] . Antibody selection pressure to HIV-1 has evolved considerable host-derived N-glycan masking , occluding most conserved potential neutralizing determinants . Multiple bNAbs isolated from chronic HIV-1 patients are directed against the HIV-1 Env conserved primary CD4 receptor-binding site ( CD4bs ) [12–16] . The CD4bs surface itself is devoid of N-linked glycosylation but is shrouded by N-glycans around its periphery . Presumably , the shielding restricts antibody access but is sufficient to allow the critical function of CD4 receptor engagement to initiate viral entry [21–27] . Therefore , in this study we sought to determine if trimers with targeted N-glycan deletion would more efficiently activate B cells and better elicit neutralizing antibodies . Since the CD4bs is partially accessible , we selected this site to test targeted N-glycan deletion to prime B cell responses and neutralizing antibodies . The known CD4bs-directed bNAbs isolated from chronically infected individuals are divided into two major classes depending upon their mode of recognition of the CD4bs and their VH family usage [14] . One class is comprised of the variable heavy ( VH ) -restricted bNAbs that include the VRC01-class antibodies . These VRC01-like antibodies use the VH1-2*02 or VH1-46 heavy chain gene segments and contact the CD4bs primarily with complementarity determining region 2 ( HCDR2 ) -encoded residues and are less dependent on the HCDR3 than most antibodies [15 , 28] . The light chains of these bNAbs , usually kappa , also display common properties by possessing relatively short or flexible CDRs , often a 5 amino acid LCDR3 . The second class of CD4bs-directed bNAbs are not VH-restricted and use their diverse HCDR3s to contact the CD4bs [22 , 23] . The bNAbs from both classes bind the CD4bs with roughly similar lateral angles of approach ( Fig 1 ) , which is associated with their breadth and potency [28] . Other CD4bs-directed monoclonal antibodies ( mAbs ) , which are broad but less so than the VRC01-like class , such as CH103 [23] or b12 [29] , display more vertical or less optimal angles of approach to the CD4bs . Presumably , a major restriction for activation of both of these classes of bNAbs is efficient engagement of the corresponding naïve B cell receptors . Access to the CD4bs is limited due to its recessed location , obstructed by extensive glycan shielding and tight quaternary packing of the antibody-selected Env trimeric spike [30] . The conservation and the fact that N-linked glycans are not part of the contact surface [31] , along with the isolation of multiple bNAbs against this site from several HIV-infected patients , elevates the CD4bs as an attractive target for HIV-1 vaccine design . Here , guided by CD4bs antibodies and Env structures [2 , 32 , 33] , we selectively deleted potential N-linked glycans ( PNGS ) proximal to the CD4bs on the well-ordered soluble clade C 16055 NFL TD CC trimers because of their high degree of stability and homogeneity [33 , 34] . Our goal was to enhance in vivo engagement by naïve B cells specific for this conserved neutralizing determinant regardless of the genetic properties of their B cell receptors ( BCRs ) , similar in concept to two recent studies performed in parallel [35 , 36] . We generated a series of N-glycan-deleted variant trimers that maintained native-like trimer conformation without significant loss in stability . We used a panel of CD4bs-directed bNAbs from both the VH gene-restricted and the CDRH3-using classes that demonstrated better accessibility of the CD4bs on N-glycan-deleted trimer variants , while maintaining conformational or steric occlusion defined by the trimer quaternary structure . We also introduced a subset of the PNGS-deletions into the full-length 16055 Env to generate pseudoviruses , and demonstrated that they retained resistance to non-neutralizing mAbs . We performed rabbit immunogenicity experiments using two approaches comparing glycan-deleted to fully glycosylated NFL trimers . The first was to delete four PNGS sites and then boost with fully glycosylated Env; the second was to delete the four sites and gradually re-introduce these N-glycans in subsequent boosts , an approach previously not yet tested in the context of native-like trimers . These experiments revealed that the PNGS-deleted trimers more rapidly elicited neutralizing antibodies for CD4bs-PNGS-deleted viruses and more potent responses against fully glycosylated wt virus . We demonstrated that part of this activity was CD4bs-directed and could be boosted with fully glycosylated trimers to elicit weak but detectable cross-neutralization . The analysis presented here indicates that targeted N-glycan deletions is a promising approach to more efficiently elicit antibodies directed toward the conserved CD4bs .
To preferentially increase recognition of the gp120 CD4bs , while maintaining well-ordered trimeric native-like structure , we selected a highly stable and homogeneous soluble trimer 16055 NFL TD CC ( T569G ) , as the parental backbone for targeted N-glycan deletions , designated as “PT” for “Parental Trimer” for the remainder of this manuscript . This soluble trimeric protein is derived from an Indian subtype C HIV-1 Env sequence that was isolated from a patient following acute infection [37] . The original NFL trimer design [38] consists of a 10 residue ( G4S ) flexible linker between the REKR-deleted Env gp120 C-terminus and the unmodified gp41 N-terminus , contains a I559P mutation in gp41 and is truncated at residue 664 . The NFL TD , for trimer-derived , possesses substitutions at residues E47D , K49E , V65K , E106T , I165L , E429R , R432Q , A500R to increase trimer formation and stability [34] and a T569G substitution that increases homogeneity and yields [33] . An engineered intra-protomer disulfide I201C-A433C ( CC ) prevents CD4-induced conformational rearrangements that expose non-neutralizing determinants [34 , 39] . Guided by Env trimer structures [2 , 32 , 40] , we deduced that several N-linked glycosylation sites occlude the gp120 CD4bs within the quaternary packing of trimer ( Fig 1a and 1b ) . In addition , by inspecting the angles of access determined for several CD4bs-directed bNAbs [14 , 23 , 41 , 42] , we reasoned that deleting one set of PNGSs , by genetic alteration of this motif , would increase access for most bNAbs approaching the CD4bs with a VRC01-like lateral path ( Group A , Fig 1a ) without allowing access by non-broadly neutralizing CD4bs-directed mAbs such as F105 . Although we included the VRC01-like antibodies as design guides , we also included the non-VH-gene-restricted class of CD4bs-directed bNAbs such as VRC13 or VRC18 [43] , with the objective to open access to the CD4bs unfettered by VH or VL gene-restricted requirements . The PNGSs revealed by this analysis include N234 , N276 , N360 and N463 amongst others ( Group A , Fig 1a ) . We chose to not alter PNGSs at the V-cap trimer apex ( i . e . N386 ) because we showed previously that non-broadly neutralizing CD4bs-directed mAbs bind this region by a vertical angle that allows access to the CD4bs on some tier 1 viruses ( HXBc2 ) , that is occluded by N-glycans on tier 2 viruses [44] . We also determined that deletion of the additional N-glycans N197 , N262 and N301 would potentially open access to the CD4bs for antibodies displaying a similar angle of approach as the bNAb , b12 ( Group B; Fig 1b ) . Following lectin purification , we analyzed trimer production by size-exclusion chromatography ( SEC ) relative to the PT as the first criterion to assess PNGS-deleted trimer integrity . Single ( A1 , B1 ) , double ( A2 ) and triple ( A3 ) glycan-deleted trimer variants were analyzed ( S1 Fig ) . In parallel , we investigated the conformational state of the selected glycan-deleted variants by negative stain EM as a second criterion to assess PNGS-deleted trimer integrity ( S1 Fig ) . As a third criterion , we analyzed trimer stability and homogeneity by DSC to assess trimer integrity harboring the targeted genetic PNGS deletions ( S2 Fig ) . These biophysical analyses are detailed in the Supplementary materials and our findings can be summarized as follows . We determined that mutations N276Q , N301Q and the combinations of mutations N276Q/N360Q , N276Q/N463Q and N276Q/N360Q/N463Q minimally affected the trimer yields and thermostability and allowed native-like trimer conformation ( S1 and S2 Figs ) . On the other hand , the PNGS mutations N197Q , N234Q and N262Q affected trimer integrity . Deletion of N262 PNGS resulted in extremely low trimer expression ( S1 Fig ) . Similar effects were observed when N234Q was introduced in the combination with mutations N276Q and N463Q ( S1 Fig ) . In the case of the N197Q substitution , we observed a substantial loss of both the propensity to form well-ordered trimers and protein thermostability ( S2 Fig ) . Therefore , we further focused our analysis on PNGS modifications that did not affect trimer integrity , namely , N276Q , N301 , N360Q , and N463Q . As mentioned above , the 16055 Env naturally lacks a PNGS at residue N332 , located in the gp120 outer domain . However , this N-glycan site is generally well-conserved across HIV Env strains and is central to the 332N-glycan “supersite” that is the target of many bNAbs such as 2G12 , PGT128 and PGT135 [27 , 45] . We reasoned that , in addition to restoring an important neutralizing determinant , that genetic restoration of this N-glycan might impact overall trimer stability , thereby allowing us to delete additional PNGS from Group A ( Fig 1a ) . Accordingly , we introduced the PNGS at residue 332 in the 16055 PT by a K334S mutation . We termed this N332-glycan-restored trimer as “+N332 PT” , where the italicized N refers to the N-glycan , not the asparagine residue common to both trimer-types . To confirm conformational integrity , we compared the thermal transition midpoints ( Tms ) and the EM 2D class averages for the two trimeric proteins with and without the PNGS at residue 332 ( S3a and S3b Fig ) . The +N332 PT trimer was minimally more stable than the isogenic PT lacking the 332 N-glycan , displaying a Tm increase of +0 . 3°C ( S3a Fig ) . EM analysis showed nearly identical populations of native-like trimers for both proteins . We demonstrated that there were no significant differences for the binding by a panel of CD4bs-directed mAbs ( S3c Fig ) and no difference in binding by the trimer-preferring bNAbs , PGT145 , PG9 and PG16 . Restoration of the N332 supersite was confirmed by efficient binding by the bNAbs , PGT135 and PGT128 ( S3c Fig ) . Expression and yields of the PNGS-deleted NFL trimeric proteins for both Group A and B were not affected by the glycan alterations and EM analysis revealed that the trimeric glycoproteins retained a native-like conformation ( Fig 2 ) . DSC analysis of both sets of N-glycan deleted trimers showed that their Tms remained practically identical suggesting that the N-glycan alterations did not affect stability of the proteins ( Fig 2B ) . These analyses allowed us to select the best combination of N-glycan deletions proximal to the CD4bs in the native-like NFL context . To examine the effects of N-glycan deletion on antibody accessibility at the CD4bs , we analyzed binding of a set of CD4bs-directed bNAbs to specific N-glycan-deleted variants compared to their respective parental trimers . For this analysis , we used a His-capture ELISA , to maintain native-like trimer confirmation to assess bNAb recognition as previously described [34] . Preservation of a native-like trimer conformation was confirmed by efficient recognition by the trimer-dependent bNAb , PGT145 ( S5 and S6 Figs ) [46] , and by poor recognition by the non-broadly neutralizing , CD4bs-directed mAb , F105 [47] . We selected a panel of monoclonal antibodies based on their differential ability to neutralize 16055 pseudovirus and their different modes of Env recognition . Access to the CD4bs was assessed to determine whether specific targeted N-glycan deletions rendered this region more accessible for mAbs of different origin , angles of approach and neutralizing capacity . We demonstrated increased binding by the bNAbs VRC01 , VRC03 , VRC06b , VRC18b ( VH1-2-derived; [12 , 14 , 48] ) and 1B2530 and 8ANC131 ( VH1-46-derived; [24] ) to the N276Q/N463Q glycan-deleted variants with or without N332 restored ( Fig 3 , S4 and S6 Figs ) . Increased binding by the bNAbs VRC01 , VRC03 , VRC06b , VRC18b , 1B2530 and 8ANC131 was also detected to the +N332 N276Q/N360Q/N463Q and +N332 N276Q/N360Q/N463Q/N301Q triple and quadruple N-glycan-deleted variants compared to the fully glycosylated +N332 PT backbone ( Fig 3 , S6 Fig ) . For the +N332 N301Q glycan-deleted variant , the difference in binding was less pronounced ( Fig 3 , S6 Fig ) . We next assessed recognition by the set of HCDR3-using CD4bs-directed mAbs , VRC13 , VRC16 and HJ16 . Binding to the +N332 N301Q glycan-deleted variant was enhanced in comparison with +N332 PT for all three antibodies ( Fig 3 , S6 Fig ) . As expected , HJ16 binding was impaired when the PNGS at residue 276 was altered , consistent with its known ( Fig 3 , S4 and S6 Figs ) N276 glycan-dependence [49] . VRC13 recognition was similarly impaired by deletion of the N463 PNGS and is likely dependent upon the presence of this N-glycan for efficient Env recognition ( Fig 3 , S6 Fig ) . Both of these changes in recognition are consistent with deletion of the N-glycans at residues 276 and 463 by altering PNGS motif . With the four N-glycans eliminated in the 16055 trimers , we tested binding by the germline-reverted antibodies VRC01gl , VRC13gl , VRC16gl but as expected , did not detect binding ( S5 and S6 Figs ) . To complete the antigenic analysis of the N-glycan-deleted trimer variants , we detected efficient recognition by the trimer-preferring V2-apex-directed bNAbs , PG9 and PG16 , confirming that the trimer native-like conformation was not affected by the N-glycan deletions ( S5 and S6 Figs ) . No binding differences were observed for the N332-glycan “supersite” antibodies PGT121 and PGT135 , whereas , 2G12 [11] displayed slightly decreased recognition for the 301 N-glycan-deleted trimer variants ( S5b and S6 Figs ) . In sum , targeted N-glycan deletions preferentially enhanced antibody recognition by the majority of CD4bs-directed antibodies without significantly altering bNAb recognition of other Env regions . Next , we used BLI ( Octet ) to assess the effect of N-glycan deletion on the binding efficiency of the CD4bs-directed bNAb , VRC03 . Since the bivalent VRC03 IgG can potentially bind CD4bs epitopes on multiple trimers , creating avidity , we generated the VRC03 Fab to permit precise determination of the affinity of this interaction with trimer . Using the Fab as the monomeric analyte in solution , we found that the N276Q/N463Q trimer , when captured in the sensor surface , was recognized by the VRC03 Fab approximately 30-times more efficiently compared to the PT “backbone” trimer ( Fig 4 ) . In case of glycan-deleted variants of +N332 PT , there was a 10- and 8-fold difference , respectively , in affinity for the +N332 N276Q/N360Q/N463Q and +N332 N276Q/N360Q/N463Q/N301Q variants compared to the backbone protein . The binding of +N332 N301Q variant was two-fold lower in comparison with the +N332 PT backbone . Following the detected increase in VRC03 Fab affinity for the four-position N-glycan-deleted trimer , we assessed the effect of this N-glycan deletion on stoichiometry by negative-stain EM . We generated complexes and obtained 2D class averages and 3D reconstructions of the +N332 N276Q/N360Q/N463Q/N301Q variant compared to the backbone +N332 PT trimer . We found that despite the large affinity increase of VRC03 Fab for the N-glycan-deleted trimer detected by BLI ( and ELISA ) , the stoichiometry of the interaction was not altered relative to the +N332 PT backbone as determined by EM ( S7 Fig ) . To evaluate Ab responses elicited by the PNGS-deleted trimer immunogens , we generated full-length 16055 Env expression plasmids encoding matching CD4bs-proximal N-glycan deletions . We generated 16055 HIV-1 pseudoviruses that we named “wt” for the fully glycosylated Env and “Δ followed by a numeral” to specify N-glycan deletions at the stated Env positions and assessed their properties of entry and neutralization sensitivity . For example , a pseudovirus with Env possessing two N-glycan deletions at positions 276 and 463 is designated 16055Δ276Δ463 . Consistent with the observations made for the soluble Env trimers , pseudoviruses lacking two to four N-glycans were more sensitive to neutralization by VRC01 , VRC03 and VRC06b and , as expected , less sensitive to the N276-glycan-dependent bNAb , HJ16 ( Fig 5 ) . In the 16055 virus context , each of the glycan-deleted pseudoviruses displayed a tier 2-like phenotype as defined by selected mAbs and HIVIG ( HIV Immunoglobulin , lot# 140406 ) . In particular , deletion of the N-glycan residue N301 often causes a”global opening” or tier 1 phenotype for other pseudoviruses with this same mutation ( i . e . , YU2 , JRFL and SS1196 ) [50 , 51] , but it did not cause the same effect in the 16055 context . All 16055 pseudoviruses deleted of their Env CD4bs-proximal PNGS remained insensitive to the non-neutralizing mAbs , b6 , F105 , GE136 , 17b , 447-52D and 19b ( Fig 5 ) , as well as to polyclonal HIVIG derived from a pool of HIV-infected individuals . This analysis indicated that the same N-glycan deletions that were tolerated in the context of soluble PT and +N332 PT proteins also did not affect the native Env conformation on the pseudovirus , while increasing bNAb access to the CD4bs ( Fig 5 ) . We observed that the pseudovirus 16055Δ276Δ463 was the most sensitive to the CD4bs-directed bNAbs , and less sensitive to PGT145 , in comparison with other N-glycan-deleted viruses , even those variants with additional N-glycan modifications . This set of Env N-glycan-modified pseudoviruses recapitulated the trimer antigenic profiling of our N-glycan-deleted soluble trimers and represents a useful set of tools to characterize antibody responses generated by such trimers . To assess if N-glycan-deletion at the CD4bs altered the elicited B cell response and serum antibodies compared to unmodified trimers following vaccination , we performed an immunogenicity experiment in rabbits . We tested two different immunization regimens that involved priming animals with N-glycan-deleted trimers . We then compared each of these regimens to the control immunization regimen , where all animals were immunized with fully glycosylated trimers ( Group 1 ) . The rabbits from this control Group 1 were immunized four times with the parental trimer 16055 NFL TD CC ( T569G ) , to which the N332 glycan had been introduced as described above ( Fig 6a ) . For simplicity of the nomenclature , we will refer to this trimer as the “wt” control immunogen for the remainder of the study . The rabbits in Group 2 were immunized twice with the N-glycan deleted +N332 N276Q/N360Q/N463Q/N301Q trimer ( from now on , referred to as “ΔGly4” ) and boosted two times with the wt immunogen ( Fig 6a ) . The rabbits in Group 3 were immunized sequentially with the three N-glycan-deleted trimer variants: ΔGly4 , then ΔGly2 ( +N332N276Q/N463Q ) , then ΔGly1 ( +N332 N276Q ) and lastly with wt trimer ( Fig 6a ) [52] . To enhance immune responses , we arrayed all trimers on liposomes at high-density as previously described [52] . We have demonstrated that this multivalent presentation of trimers on the surface of liposomes more effectively generates germinal centers B cells and serum neutralizing antibodies [52 , 53] . Animals from each group were immunized via the subcutaneous route at weeks 0 , 4 , 12 and 24 with 30 μg of each trimer arrayed on the liposomes ( Fig 6b ) and formulated in ISCOMATRIX adjuvant ( CSL ) . We confirmed the quality of each trimer-liposome preparation by EM negative stain analysis prior to each immunization ( Fig 6b ) . Bleeds were obtained on the day of immunization and 2 weeks after each immunization , except following the first inoculation ( Fig 6a ) . After completion of the full regimen , we tested serum IgG binding titers against the +N332 PT trimer by anti-His capture ELISA ( See Methods and Fig 6c ) . There was no statistical difference in geometric mean binding titers ( GMT ) between Group 2 or Group 3 compared to Group 1 , although the values obtained for the rabbits in Groups 2 and 3 displayed less variance following the fourth immunization ( Fig 6c ) . We then analyzed the antibody neutralizing response of all animals in a longitudinal manner following the second , third and fourth immunization ( post 2 , post 3 and post 4 , respectively ) . In terms of neutralizing capacity , the most striking difference for either Group 2 or Group 3 compared to Group 1 was observed with the N-glycan-deleted viruses . Specifically , we first analyzed the serum neutralizing capacity against the pseudoviruses with matching N-glycan deletions relative to the trimeric immunogens for Groups 2 and 3 . Following two inoculations , all animals from Group 2 could neutralize the 16055Δ276Δ360Δ463Δ301 and the 16055Δ276Δ463 pseudoviruses and five of six animals from Group 3 neutralized these viruses . In contrast , only one animal in Group 1 weakly neutralized the 16055Δ276Δ463 virus after two immunizations . These differences were statistically significant ( Fig 7a ) . The differences in neutralization capacity of the 16055Δ276Δ360Δ463Δ301 and 16055Δ276Δ463 pseudoviruses between Groups 2 or 3 compared to Group 1 were also significant following the third immunization . After the fourth immunization , when the animals from Groups 2 and 3 were both inoculated with the fully glycosylated wt trimers , there was a trend to higher titers against 16055Δ276Δ360Δ463Δ301 and 16055Δ276Δ463 viruses for Group 2 compared to Group 1 . The difference for Group 3 in comparison to Group 1 for the four-N-glycan deleted ( 16055Δ276Δ360Δ463Δ301 ) virus was statistically significant ( Fig 7a ) . Because the pseudoviruses with multiple glycan deletions were better neutralized by the serum derived from Group 2 or 3 animals compared to those from Group 1 , we assessed neutralization against each of the 16055 singly-N-glycan-deleted virus Δ276 , Δ360 , Δ463 and Δ301 to define clearly the neutralization specificity in the polyclonal serum . That is , we sought to pinpoint if the elimination of single N-glycan would reflect the neutralization capacity detected against the multiple N-glycan deleted viruses ( Fig 8 ) . Several animals from Group 2 or Group 3 elicited weak , but detectable , neutralizing activity against all four of the single N-glycan-deleted viruses after the second immunization ( week 4/post 2 ) , while only the highest responder in Group 1 , showed weak neutralization against 16055Δ360 at that time point ( Fig 8 ) . More animals in Group 2 or 3 , compared to Group 1 , exhibited neutralization serum activity against the singly glycan-deleted viruses after the third immunization ( week 12/post 3 ) . There was a statistically significant difference in titers between Groups 1 and 3 against the 16055Δ276 pseudovirus . After the fourth immunization ( week 24/post 4 ) , the neutralization titers against single glycan-deleted 16055 pseudoviruses increased substantially in all three groups although the tendency to display higher titers against single glycan-deleted viruses in either Groups 2 or 3 , in comparison with Group 1 , remained . In terms of the specific viruses , titers against the 16055Δ301 pseudovirus did not increase more than two-fold in comparison with the titers against 16055wt , indicating that this N-glycan had a minimal effect in regards to neutralizing activity ( Figs 7 and 8 ) . In terms of specific animals from Group 2 , the 16055Δ463 pseudovirus was better neutralized by the rabbit #2–3 ( that is , animal number 3 , from Group 2 ) . This might be due to the peripheral location of the N463 glycan relative to the CD4bs providing better accessibility to the underlying protein surface ( Fig 1a ) . Animals from Group 3 displayed high titers against the 16055Δ276 pseudovirus , and the difference in the responses between Group 1 and 3 was statistically significant after three immunizations . There was also a strong trend of more potent neutralization of the 16055Δ463 virus in this group after three immunizations , while the neutralization titer pattern for other single N-glycan-deleted viruses ( 16055Δ301 and 16055Δ360 ) was similar to the wt virus neutralization pattern at this time point . These results were consistent with a neutralizing antibody response focused toward the proximity of residue N276 by the ΔGly4 , ΔGly2 and ΔGly1 sequential immunization , while responses proximal to residues 301 and 360 diminished , likely due to restoration of these N-glycans in the immunogens . The trend of more potent and consistent neutralization elicited by the N-glycan deleted viruses was also detected when assessed against the autologous tier 2 fully-glycosylated 16055wt virus . The differences in 16055wt pseudovirus neutralization were detectable as well following the third immunization ( post 3 , Fig 7a ) . Four animals from Group 2 and five animals from Group 3 displayed neutralizing activity against the 16055wt , compared to only two animals from Group 1 . After the final boost ( post 4 ) , five animals from Group 2 and six animals from Group 3 showed neutralization against 16055wt virus ( Fig 7a ) . In terms of potency , four animals from each of these groups displayed autologous serum titers above 100 , while only two animals displayed titers above 100 in Group 1 ( Fig 7a ) . In general , the responses in the animals from Group 1 were less potent than those in either Groups 2 or 3 , with only one animal achieving 100% neutralization against the wt autologous virus after four immunizations ( Fig 7b ) , whereas , four animals in either Groups 2 or 3 achieved 100% wt virus neutralization ( Fig 7b ) . These data suggest that genetic deletion of PNGS proximal to the CD4bs on the Env trimeric immunogens may eliminate steric barriers imposed by the presence of N-glycans that normally limit the B cells responding to this conserved epitope . In our study , the elimination of these barriers led to a more consistent and robust neutralizing antibody response when the N-glycan-deleted immunogens were used to prime the immune response . The analyses described in the previous section indicated that the neutralizing antibody responses were directed proximal to the CD4bs , especially in the sequential N-glycan-restored Group 3 animals . To determine by another means if the elicited neutralizing antibody response was in part directed to the CD4bs , we generated a pair of 16055gp120-based TriMut probes as previously described for the HXBc2 TriMut proteins [54] . Both 16055 gp120 variants possess three mutations ( I423M , N425K , and G431E ) in the bridging sheet ( hence , TriMut ) that allow recognition by CD4bs-directed antibodies , but eliminates binding to the primary HIV receptor , CD4 ( S8 Fig ) . These modification permit the addition of the TriMut gp120 glycoproteins directly into the neutralization assays ( “dump-in” ) without affecting entry by the normal high-affinity binding of wt gp120 to CD4 [55 , 56] . The gp120 TriMut possesses an unmodified CD4bs , while the paired probe incorporates two additional mutations , D368R/M474A , which prevent binding by most CD4bs-directed antibodies ( S8 Fig ) . These two isogenic proteins can be used to determine neutralization specificity directed toward the CD4bs by differential adsorption or depletion . We first validated the differential depletion assay using known bNAbs that can neutralize 16055 , detecting a decrease in VRC13 and HJ16 neutralization upon the addition of the TriMut gp120 , but not the isogenic 368R/474A variant ( S9a Fig ) . The differential between the two proteins confirmed their capacity to map neutralization specific for the CD4bs ( S9b Fig ) . We then analyzed total polyclonal IgG isolated from selected hyperimmune rabbit anti-sera using this assay . Following IgG isolation , we established the concentration for each sample that could neutralize 80% of virus entry . Using this concentration of IgG , we then performed the adsorption assay . We determined that increasing amounts of the TriMut gp120 could deplete neutralizing activity of the wt 16055 virus , while the 368R/474A TriMut gp120 depleted only a portion of this activity ( Table 1 , S9 Fig ) . This differential indicated that some of the 16055-neutralizing activity was CD4bs-directed ( Table 1 , S9b Fig ) . We quantitated this differential neutralization at the CD4bs as a difference between TM and TM368R/474A area under the curve ( AUC ) values , normalized by the control AUC value ( Table 1 ) . We observed CD4bs-directed activity in rabbit #1–3 , the highest responder from Group 1 , after third and fourth immunizations ( termed “post 3 and 4”; Table 1 , S9c Fig ) . Rabbit #1–6 from Group 1 also showed partial CD4bs-directed neutralization activity . Rabbit #2–1 from Group 2 displayed a small fraction of neutralization directed to the CD4bs after the fourth immunization ( Table 1 , S9d Fig ) , while more than 50% of the total IgG neutralization in rabbit # 2–4 was directed against the CD4bs at this time point ( Table 1 , S9d Fig ) . In Group 3 , however , two rabbits ( #3–2 and #3–4 ) demonstrated partial CD4bs-directed neutralization following just the third immunization ( post 3 , S9e Fig , Table 1 ) . Rabbit #3–4 displayed partial CD4bs-directed neutralization after fourth immunization , as well , whereas for rabbit #3–2 the CD4bs-directed differential was no longer detectable at this time point . In addition , following the fourth inoculation , three other rabbits from Group 3 displayed partial CD4bs-directed neutralizing activity ( Table 1 , S9e Fig ) . In sum , we observed CD4bs-directed activity in several animals from all three groups . Compared to animals from Group 1 , animals from Group 3 showed more consistent CD4bs-directed neutralizing antibody responses following four immunizations . With indications that there was some CD4bs-directed neutralizing activity proximal to the CD4bs ( and the proximal N-glycan at residue ) , we performed neutralization assays using the purified polyclonal IgG purified from serum of the rabbits that demonstrated weak serum neutralization against a selected panel of heterologous viruses . We analyzed neutralization of a small set of pseudoviruses with PNGS N276 deleted , namely BG505Δ276 , JRFLΔ276 , IAVIC22Δ276 , along with their respective wt pseudoviruses . We also analyzed IgG neutralization of several pseudoviruses naturally lacking the N276 PNGS , Q259 and 62357 , another Indian clade C pseudovirus from the same cohort as 16055 , 1428 , and the pseudoviruses 1086 and CE1176 . We used the SIV pseudovirus as a negative control for neutralizing specificity as this virus is not recognized or neutralized by HIV Env-specific antibodies . For these experiments , we titrated the purified IgG starting at a relatively high initial concentration of 2 mg/ml because even in a hyper-immunized animal only a minor fraction of circulating IgG is antigen-specific ( ~5–10% ) , and , of that , only a subset is neutralizing . As a negative control , we used purified IgG isolated from a rabbit that was immunized similarly with blank liposomes in adjuvant , at the same concentrations , to rule out non-specific IgG effects in the cross-neutralization assay . We were able to detect weak cross-neutralization activity exclusively in IgG derived from animals in Group 2 or 3 that had been immunized with different variants of the N-glycan-deleted trimers ( Fig 9 ) . Most cross-neutralization was detected in the IgG isolated from the animals in Group 3 with three animals displaying detectable activity . Rabbit #3–3 displayed neutralization of the BG505Δ276 pseudovirus ( Fig 9a and 9b ) , while rabbits #3–5 and #3–6 were able to neutralize both the BG505Δ276 and IAVIC22Δ276 pseudoviruses . In addition , rabbit #3–5 showed neutralization even against both the wt , fully glycosylated BG505 and 1086 pseudoviruses ( Fig 9a and 9b ) . Following three immunizations , rabbit #3–6 neutralized BG505Δ276 and this activity increased following four inoculations . Two animals from Group 2 displayed some detectable cross-neutralizing activity . Rabbit #2–5 was able to neutralize the IAVIC22Δ276 and 1086 pseudoviruses following three immunizations and this activity increased against the IAVIC22Δ276 pseudovirus following the fourth immunization ( Fig 9a and 9b ) . Rabbit #2–4 very weakly neutralized the 62357 ( NIH15 ) pseudovirus after the fourth immunization ( Fig 9a and 9b ) , which naturally lacks the N-glycan at residue 276 . None of the IgGs derived from the Env-trimer-immunized cross-neutralized the control SIV pseudovirus , confirming HIV cross-neutralization specificity . Finally , even though we observed some CD4bs-directed neutralization in two animals from Group 1 in the previously described depletion assay , we were not able to detect cross-neutralizing serum activity in any IgG isolated from animals in this group . To further confirm specificity of the cross-neutralization , we performed a depletion assay with the 16055 gp120 TriMut probe for the animals displaying the highest IgG IC50 values , i . e . rabbit #2–5 for the IAVIC22Δ276 pseudovirus and rabbit #3–5 for the BG505Δ276 pseudovirus ( Fig 9c ) . We demonstrated that the cross-neutralizing activity was adsorbed substantially by pre-incubation of IgG with the 16055 gp120 TriMut protein , indicating that , in those animals , this activity was HIV Env-specific . Overall , cross-neutralization was consistent with the CD4bs mapping for the animals from Group 2 and Group 3 , thus , most animals with CD4bs-directed IgG neutralizing activity showed some level of cross-neutralization ( marked with * in Table 1 ) , except one rabbit from Group 3 ( #3–5 ) . This animal displayed generally low autologous neutralization and therefore the response could not be analyzed in the mapping experiment . Together , these data suggest that the sequential ΔGly4 to ΔGly2 to ΔGly1 immunization did better than the other two regimens at directing the neutralizing antibody response to the CD4bs .
Coupled with quaternary packing , N-linked glycosylation prevents most naïve B cells from gaining a “foothold” against the underlying Env trimer polypeptide surface to prime neutralizing Ab responses . Accordingly , in this study , we generated well-ordered and highly stable 16055 NFL trimers possessing targeted PNGS deletions proximal to the CD4bs to better expose this conserved neutralizing determinant for BCR access and B cell activation . We demonstrated that up to four specific PNGS can be deleted without altering trimer conformational integrity as determined by SEC , DSC , EM and by efficient recognition by selected trimer-specific bNAbs . We further showed that these same PNGS can be deleted in the context of full-length Env to generate pseudoviruses that maintain a tier 2-like phenotype as determined by selected antibodies and HIVIG . In a rabbit immunogenicity study , we demonstrated that PNGS-deleted 16055-stabilized NFL trimers more efficiently prime neutralizing antibody responses , and that there was a statistically significant difference in the capacity to neutralize the glycan-deleted pseudoviruses between the regimens that incorporated PNGS-deleted trimers and the control regimen that incorporated only wt trimers . We also detected a tendency to have more potent neutralization against the tier 2 autologous 16055wt pseudovirus in the animals immunized with the PNGS-deleted trimer variants . In addition , even though we only used a single Env strain in our immunogenicity experiment , we observed some cross-neutralization activity in several immunized animals from both groups “primed” with the CD4bs N-glycan-deleted trimers , notably in some animals that were sequentially boosted with the PNGS-restored immunogens . We initially visited the approach to delete PNGS proximal to the CD4bs in the context of gp120 [50] . Here , we generated PNGS deletions in the context of well-ordered trimers , to eliminate steric barriers for antibody recognition imposed by the N-glycans surrounding the CD4bs , while maintaining the steric trimer constraints imparted by the trimeric nature of our trimeric Env . The fact that the same PNGS that we eliminated in our NFL immunogens can be altered in the context of native 16055 Env when pseudo-typed as viruses to mediate functional entry is reassuring concordance between the NFL trimer design and native Env . N-glycan deletions that were not compatible with native trimer formation were often highly conserved PNGS that were previously shown to be critical for folding of gp120 itself [57] . The fact that the 16055 NFL TD CC ( T569G ) trimers can tolerate the described N-glycan deletions attest to their stable design [33 , 34] . In the 16055 NFL trimers described here , deletion of the PNGS at N197 was detrimental , in contrast with the results recently described deletion of this N-glycan in the BG505 SOSIP context [35 , 58] . Deletion of N-glycan 301 in the 16055 Env context does not make the virus more globally sensitive , in contrast with results reported previously for YU2 or JRFL or SS1196 pseudoviruses [50 , 51] , which become sensitive to the non-broadly neutralizing mAbs , F105 and 447-52D upon removal of the N-glycan at residue 301 . The aim of the trimer redesign by targeted N-glycan deletion is to enhance B cell access to the CD4 binding loop and proximal elements to ultimately generate a cross-reactive antibody response when used as immunogens . One caveat to this approach is that removal of the N-glycan will expose the underlying protein surface , potentially rendering it immunogenic . We would envision that although some of the immune response will be elicited to epitopes that will no longer be accessible in the context of wt virus with a fully intact glycan shield , a fraction of the B cell response will be able to access the CD4bs . And that there is a general advantage of increasing B cell activation to the glycan-denuded region so that some of these responses can be driven to accommodate the shield by gradual restoration of N-glycans in either a homologous or heterologous context . In this regard , our immunogenicity results suggest that sequential restoration of N-glycans proximal to the CD4bs may help to focus the antibody response on either the available protein epitope free of glycans and/or to the precise CD4bs itself . This interpretation is consistent with the data as we observed a statistically significant difference in neutralization of 16055Δ276 pseudovirus from Group 3 samples following three immunizations compared with the control group , Group 1 . More animals from the Group 3 demonstrated partial CD4bs-directed neutralization compared to animals from Group 1 , again suggestive of B cell focusing at the conserved CD4bs . For a more definitive answer to this issue , isolation of individual CD4bs-directed B cells and cloning of monoclonal antibodies is needed . Importantly , we detected weak but specific cross-neutralization of selected heterologous viruses , mostly lacking N-glycan 276 , which is known to be a major impediment toward potent vaccine-elicited neutralization at the CD4bs , even in gL-reverted transgenic mice [59] . We detected weak neutralization of wt BG505 pseudovirus in one rabbit from Group 3 suggesting that this impediment can be overcome . Strategies to boost these heterologous responses are needed to increase the robustness of this approach . Other investigators have explored the effect of glycan-shield disruption at the CD4bs on B cell activation and germline reverted antibodies binding enhancement in vitro or in germline transgenic or chimeric mice [59–61] . In some cases , the stimulation of germline reverted BCRs in vitro and in vivo was observed [60 , 61]; however , with limited autologous neutralization [59] . Two recent studies performed in parallel to ours used similar glycan-deleted immunogens [35 , 36] in outbred animals , but without the boosting regimens we described here . Crooks et al . used JRFL Env based trimer VLPs both possessing ( wt ) and lacking the N-glycan at N362 . They detected some autologous neutralization and mapping to CD4bs-proximal N-glycans [36] , but with small numbers of animals per group it was not possible to determine statistical difference in the responses against wt or N362 glycan-deleted JRFL virus [36 , 62] . Zhou et al . analyzed four well-ordered SOSIP trimers possessing targeted N-glycan deletions at the CD4bs including those derived from 16055-based chimeric trimer . Homologous 16055 wt virus neutralization was observed in two out of four 16055–2 . 3-chim . DS . SOSIP . ΔGly4–immunized animals after three immunizations , where their “ΔGly4” included N197 , N463 and N276 PNGSs modifications with N362 naturally missing [35] . Differences in 16055 autologous neutralization responses might be attributed to our use of trimers arrayed on liposomes and slightly different N-glycan deletions between the two immunogens . This study also detected some cross-neutralization of N-glycan deleted pseudoviruses , consistent with the results presented here . Note that there are substantial differences between these studies such as our regimen used trimer-liposomal array , included the gradual restoration of the deleted N-glycans and we used more animals per group to allow better statistical analysis . In addition , our regimen consisted of four immunizations , and a long interval between the third and fourth immunizations , which we have shown previously enhances neutralizing antibody responses [63] . In sum , the targeted N-glycan approach outlined in this study shows promise to focus B cell responses to the CD4bs . Targeted N-glycan deletion may be applicable to other neutralizing determinants present on this extensively glycan-shrouded critical protein complex , thereby allowing recognition and engagement of naïve B cells that otherwise would not be efficiently activated by the fully glycosylated trimeric complex .
The described Env DNA substitutions were introduced via site-directed mutagenesis PCR using a QuikChange Lightning Multi Site-Directed Mutagenesis kit ( Agilent Technologies ) into NFL expressing plasmids ( CMV-R , where CMV is cytomegalovirus ) [34] or into the pcDNA plasmid , containing codon-optimized 16055 env sequences . In brief , single primers were designed for each mutation . We used up to three primers per reaction mixture to introduce multiple substitutions simultaneously . Reaction products were transformed into competent bacteria and plated onto Luria broth agar plates for colony selection , subsequent plasmid DNA isolation , and sequencing . To map serum neutralizing activity directed toward the CD4bs , TriMut and TriMut 368R/474A proteins were generated as described previously [54] . Briefly , three mutations , I423M , N425K and G431E , were introduced to make a triple mutant 16055 gp120 protein ( TriMut ) that eliminates CD4 binding but does not affect recognition by CD4bs-directed mAbs . For the receptor-binding-defective protein , TriMut 368R/474A , two additional mutations , D368R and M474A , were introduced to eliminate CD4 binding . The Env NFL trimeric proteins and TriMut proteins were produced as previously described [38 , 64] . Briefly , the 16055 Env proteins were transiently expressed as soluble glycoproteins in 293F ( Free-style 293-F Cells , Thermo Fisher Scientific ) cells from codon-optimized sequences under the control of the CMV promoter/enhancer [34] . Cell culture supernatants were harvested at day 5 post-transfection , and the Env-derived glycoproteins were purified by affinity chromatography using a Galanthus nivalis lectin-agarose column ( Vector Laboratories ) . Bound glycoproteins were eluted with phosphate buffered saline ( PBS ) containing 500 mM NaCl and 500 mM methyl-α-D-mannopyranoside and then concentrated with an Amicon filter ( 30-kDa ) to 1 ml . The lectin-purified proteins were subsequently purified by size-exclusion chromatography ( SEC ) using a HiLoad Superdex 200 16/60 column to separate the trimer and gp120 monomer fractions . Thermal stability of the soluble 16055 trimer and its N-glycan-deleted variants were evaluated using MicroCal VP-Capillary differential scanning calorimetry instrument ( General Electric ) . Protein samples were dialyzed in PBS , pH 7 . 4 , and the concentrations were adjusted to 0 . 125 mg/ml . Scans were collected at a rate of 1°C per min over a temperature range of 20–100°C , while pressure was maintained at 3 . 0 atm throughout the scan period . DSC data were analyzed after buffer correction , normalization , and baseline subtraction using CpCalc software provided by the manufacturer . The purified NFL trimers were analyzed by negative-stain electron microscopy ( EM ) following the same protocol previously described [34] Data were collected using an electron dose of ~30e-/Å2 . All the data were processed as previously published [34] . Briefly , particles were picked and assembled into a stack using the Appion software package [65] Iterative multivariate statistical analysis ( MSA ) /multireference alignment ( MRA ) ) was used to obtain 2D classes . Using EMAN2 [66] we obtained EM volumes of the trimers in complex with the VRC03 Fab . We used 2475 particles to obtain the 3D volume of the +N332 PT in complex with 3 VRC03 Fabs and 3250 particles for the asymmetric volume bound to 2 VRC03 . For the 3D reconstruction of the +N332 N276Q/N360Q/N463Q/N301Q trimer bound to 3 VRC03 Fabs , 2448 particles were used . His-capture ELISA was performed as previously described [34] . In brief , MaxiSorp plates ( Thermo ) were coated overnight at 4°C with 1 . 5 μg/ml of a mouse anti-His tag monoclonal antibody ( mAb ) ( R&D Systems ) in PBS , pH 7 . 5 . The next day the plates were incubated at 4°C in blocking buffer ( 2% BSA in PBS , pH 7 . 5 ) for 2 h and the Env-derived soluble trimers was added to the plate at a concentration of 3 μg/ml in PBS and incubated at RT for 40 min . Serially diluted mAbs at a maximum concentration of 10 μg/ml or sera from vaccinated animals were added into wells , and following incubation and washing , the secondary antibodies of peroxidase-conjugated goat anti-human IgG or goat anti-rabbit IgG were added to all wells . Following incubation and washing , the signals were developed by addition of the 3 , 3’ , 5 , 5;-tetramethylbenzidine chromogenic substrate solution ( Life Technologies ) and detected at 450 nm . For direct-coat ELISA , trimers were added directly to the wells at 3 μg/ml and analyzed for antibody binding as described above . The kinetics of VRC03 Fab binding to glycan-deleted trimer varians were performed with an Octet RED96 system ( ForteBio Inc , Menlo Park , CA ) by BLI in a 96-well format . The trimers were subjected to SEC to remove undesired oligomeric forms where applicable . Then trimers were captured by anti-His biosensors ( HIS2; ForteBio ) at concentration 10 μg/ml and VRC03 Fab were used as analytes in solution ( 1000 nM–15 . 6 nM ) . Ab-Env associations ( on-rate , Kon ) were measured over a 2 min interval , followed by immersion of the sensors into wells containing buffer to measure dissociation ( off-rate , Kdis ) . KD values ( in nanomolar units ) were calculated as off-rate/on-rate ( Kdis/Kon ) . The sensograms were corrected with the blank reference and fit with the software ForteBio Data Analysis 7 using a 1:1 binding model with the global fitting function ( grouped by color , Rmax ) . The rabbit immunogenicity study was performed at The Scripps Research Institute ( TSRI ) , a site approved by the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) . The animal inoculation protocols were approved by TSRI’s Institutional Animal Care and Use Committee ( IACUC ) . protocol #10–0002 , which was designed and conducted in strict accordance with the recommendations of the NIH Guide for the Care and Use of Laboratory Animals , the Animal Welfare Act and under the principles of the 3Rs . All efforts were made to minimize discomfort related to the inoculations and blood collection . For the immunogenicity experiment New Zealand White female rabbits ( six per group ) were immunized at weeks 0 , 4 , 12 and 24 with 30 μg of each trimer arrayed on the liposomes as described in [52] . Briefly liposomes were prepared using mixture of DSPC ( 1 , 2-distearoyl-sn-glycero-3-phosphocholine ) , cholesterol , DGS-NTA ( Ni2 ) in molar ratio 60:36:4 , respectively . The components were dissolved in chloroform , mixed and placed overnight in a desiccator under vacuum to yield a lipid film . The lipids were hydrated in PBS for 2 hr at 37°C , with constant shaking followed by vigorous sonication . The liposomes were extruded by sequentially passing across a series of membrane filters ( Whatman Nuclepore Track-Etch membranes ) with pore sizes of 1 . 0 , 0 . 8 , 0 . 2 , and 0 . 1 m , respectively . The liposomes were incubated overnight with trimer proteins ( 900 μg protein to 300 μl liposomes ) and passed over a S200 size-exclusion column to separate the protein-coupled liposomes from unbound protein . Quality of each trimer-liposome preparation was confirmed by EM negative stain analysis prior to each immunization . Trimer-coupled liposomes were formulated with 75 units of ISCOMATRIX adjuvant ( CSL , Australia ) and used for rabbits immunization via the subcutaneous route . Serum was collected on the day of inoculation and 2 weeks after each immunization to assess binding and neutralization titers . Standard TZM-bl-based neutralization assays were performed as previously described [67 , 68] using 16055 full-length Env natural sequence to complement the Env-deleted plasmid to generate clade C pseudovirus [69] and its deglycosylated variants . Titrated 16055 pseudovirus was used to evaluate sensitivity and inhibition of entry ( neutralization , IC50s ) to a panel of mAbs ( VRC01 , VC03 , VRC06b , HJ16 , F105 , b6 , GE136 , 17b , PGT145 , 447-52D , 19b ) and HIV Immunoglobulin ( HIVIG , lot# 140406 ) , derived from a pool of HIV-infected individuals . Once characterized , the 16055 pseudoviruses were pre-incubated with serum samples derived from the vaccinated rabbits to determine anti-serum neutralization capacity . Neutralization titers were expressed as antibody concentrations sufficient to inhibit virus infection by 50% ( EC50 ) or as the serum dilution factor sufficient to inhibit virus infection by 50% ( ID50 ) . Spearman’s Rank Correlation analysis of neutralizing titers and DSC-determined Tm was performed using Prism 6 software ( GraphPad ) . To examine the contribution of potential CD4bs-directed antibodies to the serum neutralizing activity , neutralization assays were performed using the isogenic TriMut and TriMut D368R/D474A 16055 gp120 pair as Env-specific antibody-adsorbing probes as described previously [54] . The D368R mutation eliminates gp120 ( or trimer ) binding to CD4 on the TZM-bl target cells in the neutralization assay so that the proteins can be added to serum for pre-incubation and then remain in the assay during assessment of viral entry . This assay is a modified version of the standard neutralization assay described above . To perform this analysis , we purified total IgG from the serum samples obtained after the third and fourth immunization , using 2 ml of serum and 600 μl of equal parts of Sepharose A and G ( GE Healthcare Life Sciences ) equilibrated in PBS . After overnight incubation at 4°C , we washed the resin with 15 ml of PBS and eluted with 4 ml of IgG elution buffer ( Thermo Fisher Scientific ) . The eluates were neutralized with 400 μl of 1M Tris HCl pH 8 . 0 and dialyzed against PBS . Each serum IgG sample was titrated against 16055 virus in TZM-bl-based neutralization assay as described above . Before addition of pseudovirus , 100 μl of each total serum IgG sample at IC80 was pre-incubated with serial dilutions of TriMut , TriMut 368/474 , or cell culture medium ( 12 . 5 μl ) , respectively , for 1 hour at 37°C . For each purified IgG , two neutralization assays were performed . We used the unpaired two-tailed Mann Whitney test when comparing neutralization values from Group 1 animals to samples derived from either Group 2 or Group 3 subjects . This nonparametric test that does not assume Gaussian distribution of values with 6 subjects per group . | A major challenge in HIV-1 vaccine design is to generate antibodies directed toward conserved broadly neutralizing epitopes on the surface-exposed viral envelope glycoprotein ( Env ) . Most conserved epitopes are masked by self N-glycans , limiting naïve B cell recognition of the underlying protein surface following Env vaccination or during natural infection . Recently , soluble faithful mimics of the HIV Env spike have been developed , but their capacity to elicit broadly cross-reactive tier 2 ( clinical isolate ) neutralizing responses is limited . The conserved primary receptor , CD4 binding site , is a known neutralizing determinant , but is flanked by self-N-linked glycans , limiting Ab access to this site . Here , we removed up to four N-glycans surrounding the CD4 binding site without affecting trimer stability and conformation as demonstrated by multiple biophysical methods . Using these well-ordered trimers , we performed an immunogenicity experiment , demonstrating that glycan-deleted trimers elicited superior neutralizing responses compared to the fully glycosylated trimers , resulting in detectable cross-neutralization of a subset of tier 2-like viruses . | [
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"a... | 2017 | Targeted N-glycan deletion at the receptor-binding site retains HIV Env NFL trimer integrity and accelerates the elicited antibody response |
In order to assess the susceptibility of bank voles to chronic wasting disease ( CWD ) , we inoculated voles carrying isoleucine or methionine at codon 109 ( Bv109I and Bv109M , respectively ) with CWD isolates from elk , mule deer and white-tailed deer . Efficient transmission rate ( 100% ) was observed with mean survival times ranging from 156 to 281 days post inoculation . Subsequent passages in Bv109I allowed us to isolate from all CWD sources the same vole-adapted CWD strain ( Bv109ICWD ) , typified by unprecedented short incubation times of 25–28 days and survival times of ∼35 days . Neuropathological and molecular characterisation of Bv109ICWD showed that the classical features of mammalian prion diseases were all recapitulated in less than one month after intracerebral inoculation . Bv109ICWD was characterised by a mild and discrete distribution of spongiosis and relatively low levels of protease-resistant PrPSc ( PrPres ) in the same brain regions . Despite the low PrPres levels and the short time lapse available for its accumulation , end-point titration revealed that brains from terminally-ill voles contained up to 108 , 4 i . c . ID50 infectious units per gram . Bv109ICWD was efficiently replicated by protein misfolding cyclic amplification ( PMCA ) and the infectivity faithfully generated in vitro , as demonstrated by the preservation of the peculiar Bv109ICWD strain features on re-isolation in Bv109I . Overall , we provide evidence that the same CWD strain was isolated in Bv109I from the three-cervid species . Bv109ICWD showed unique characteristics of “virulence” , low PrPres accumulation and high infectivity , thus providing exceptional opportunities to improve basic knowledge of the relationship between PrPSc , neurodegeneration and infectivity .
Chronic wasting disease ( CWD ) of cervids belongs to the family of transmissible spongiform encephalopathies ( TSE ) or prion diseases , a group of fatal neurodegenerative pathologies affecting animals and humans . They are characterised by spongiform changes , gliosis and the deposition in the brain of a post-translational misfolded isoform ( PrPSc ) of the host-encoded cellular prion protein ( PrPc ) . Prion diseases also include Creutzfeldt-Jakob disease ( CJD ) of humans , scrapie of sheep and goats and bovine spongiform encephalopathy ( BSE ) of cattle . CWD is the only prion disease known to affect free-ranging wild animals . It was first described in the United States in the late 1960s [1] . Currently , CWD has been diagnosed in farmed and free-ranging cervids in several areas of North America [2] and South Korea , where it was accidentally imported from Canada [3] . The disease has been documented in Rocky Mountain elk ( Cervus elaphus nelsoni ) , mule deer ( Odocoileus hemionus ) , white-tailed deer ( Odocoileus virginianus ) and moose ( Alces alces ) [4] . Epidemiological evidence indicates that CWD spreads naturally with relative efficiency and recent trend analyses suggest that its prevalence is increasing [5] . The development of laboratory models for CWD has long been hampered by the very inefficient transmission of CWD to wild-type mice [6] . Significant progress was made by the generation of transgenic mice over-expressing cervid PrP [6] , [7] , [8] , [9] , [10] , [11] . CWD has also been transmitted , albeit less efficiently , to transgenic mice over-expressing mouse [12] or Syrian hamster PrP , as well as to different hamster species [13] . Recently , CWD was successfully transmitted to different species of North American wild rodents [14] and ferrets [15] . In recent years , with the aim of developing new animal models for prion diseases , we have studied the susceptibility of the bank vole ( Myodes glareolus ) to a wide range of human and animal prion diseases . Two lines of voles , one homozygous for methionine and the other for isoleucine at codon 109 of PrP – here designated Bv109M and Bv109I , respectively – were investigated . Bv109M was shown to be susceptible to sporadic and genetic CJD [16] , [17] , sheep scrapie [18] , [19] , mouse- and hamster-adapted scrapie strains [20] , [21] , [22] , cattle and sheep BSE [16] , [22] and atypical BSE [22] . Overall susceptibility of Bv109I was found to be comparable to that of the methionine-carrying line , although with differences depending on the specific prion disease ( unpublished data ) . In the present study , we investigated the susceptibility of bank voles to CWD . To this purpose , we inoculated Bv109M and Bv109I with various CWD sources and found that CWD replicated faster in Bv109I compared to Bv109M . We then focused on the Bv109I line , which showed unprecedented short survival times upon CWD adaptation . We describe a thorough characterization of Bv109I-adapted CWD , showing that in spite of their short survival time , high infectious titres accumulate in the brains of affected animals . Furthermore , we provide evidence that Bv109I-adapted CWD can be easily and faithfully replicated in vitro by serial automated PMCA ( saPMCA ) .
All CWD isolates transmitted with 100% attack rate and short survival times in both Bv109I and Bv109M ( Table 1 ) . In some groups survival times were not uniform . Indeed , while most of the voles inoculated were clinically affected between 150 and 350 days post-infection ( d . p . i . ) , a minor proportion of voles showed much longer survival times ( Figure S1 ) . Interestingly , Bv109I replicated CWD faster than Bv109M , with 60–100 days shorter median survival times ( Figure S1 ) . Conversely , SS7B , a scrapie isolate previously shown to replicate efficiently in Bv109M [19] , showed a 60 days longer median survival time in Bv109I . Overall , these data demonstrate that the 109 methionine/isoleucine variation influences the susceptibility of voles in a strain dependent fashion and that Bv109I is a fast model for CWD replication . We thus further investigated the transmission features of CWD in the bank vole line expressing isoleucine at codon 109 . CWD isolates transmitted in Bv109I with mean survival times ranging from 156 to 281 d . p . i . ( Table 1 ) . All isolates induced a similar clinical picture . The onset of disease was characterised by slight behavioural alterations; hypoactivity and hyporeactivity were sometimes observed along with the disappearance of the typical behaviour of hiding under the sawdust lining the cage . Dorsal kyphosis , slight ataxia of the forelimb and occasional head bobbing ( upward movements of the head ) accompanied the progression of the disease . Overall , clinical signs were faint compared with those previously observed in voles infected with classical scrapie [19] and occasionally animals were found dead because of the rapid progression to terminal stage . The three isolates from white-tailed deer gave the shortest survival times , ranging from 156 to 171 d . p . i . , while the survival times were slightly longer with isolates from elk and mule deer . As mentioned above , individual voles showing a late clinical onset were observed in CWD2 , with two outliers at 414 and 656 d . p . i . , in CWD3 ( outlier at 805 d . p . i . ) and CWD4 ( 792 d . p . i . ) ( Figure S1 ) . All infected voles showed moderate spongiform changes at brain histopathology , accompanied by gliosis and neuronal loss ( Figure 1 ) . All groups had a similar distribution of spongiform degeneration , which mainly involved the superior colliculus , the thalamus and , to a lesser extent , the cerebral cortex ( Figure 2A ) . At the cortical level , spongiosis preferentially involved the V and VI deep layers ( data not shown ) . Interestingly , spongiosis was not observed in the cerebral cortex of four voles surviving longer than 400 d . p . i . ( Figure S2 ) , although they showed spongiform degeneration in the mesencephalon and diencephalon with a distribution similar to that of the other infected voles ( data not shown ) . PrPSc was readily detected in all infected voles by IHC , PET-blot and WB . IHC showed punctate PrPSc deposition ( Figure 1 ) restricted to specific areas . The thalamus , substantia nigra , geniculate and vestibular nuclei were the most involved areas . The hippocampus was mostly spared , though some fibres of CA2 were occasionally found positive . Overall , a topographical correlation between vacuolation , neuronal loss and PrPSc deposition was observed ( Figure 1 ) . Western blot analysis showed the typical three-band PrPres pattern , which was similar in all CWD-infected voles ( Figure 3 ) . Compared with elk and deer PrPres , bank vole CWD PrPres showed a lower molecular mass of the unglycosylated band ( Figure 3A ) , similar to that reported in prairie voles infected with mule deer CWD [23] . In keeping with the discrete brain distribution of PrPSc observed by PET-blot or IHC , the overall level of PrPres detected in brain homogenates was much lower than that usually found in vole-adapted strains [16] , [19] , [22] . Direct comparison with Bv109I-adapted SS7B showed that PrPres levels were at least 30 fold lower in Bv109I-adapted CWD ( Figure S3 ) . In addition , the migration pattern was different from that previously reported in bank vole TSEs , being intermediate between scrapie and BSE ( Figure 3B ) . All the twelve control challenged voles , culled at the end of the experiment ( 810 d . p . i . ) or found dead for intercurrent disease ( two voles , at 550 and 721 d . p . i . ) , were found PrPSc negative by Western blot analysis . In order to study the adaptation of CWD prions in Bv109I and to compare more closely the biological properties of the various CWD isolates , vole-passaged CWD isolates were further propagated in Bv109I . On the second passage we observed a dramatic decrease in the incubation times , with behavioural alterations already evident at 25–28 d . p . i . . Following a short clinical phase all CWD isolates produced strikingly short mean survival times of between 34 and 44 d . p . i . ( Table 2 ) . Survival times were stable or slightly shorter on third passage , with all Bv109I-adapted CWD strains giving a survival time of ∼35 d . p . i . ( Table 2 ) . The neuropathological phenotype after the second and subsequent passages was similar to that observed on primary transmission ( Figure 2B ) , with lesion profiles showing a slight increase in cortical involvement ( Figure 2B ) . The pattern of brain PrPSc distribution by PET-blot was the same in all groups , characterised by PrPSc deposition mainly in the thalamus , substantia nigra , geniculate nuclei , vestibular nuclei and the deep layers of the cerebral cortex ( Figure 4 ) . Bv109I-adapted SS7B showed a marked neurodegeneration in all areas considered in lesion profiles , with the exception of the cerebellar cortex . PET-blot showed abundant PrPSc , distributed throughout the brain . Prominent immunolabelling was observed in the cortices , septal nucleus , hippocampus , thalamus , superior colliculi , geniculate nuclei and medulla oblongata . Overall these data suggest that the same strain , designated Bv109ICWD , was isolated from elk , mule deer and white-tailed deer . Two voles , designated CWD3outlier and CWD4outlier , which developed clinical signs after unusually long times of 805 and 792 d . p . i . at primary transmission of CWD3 and CWD4 were selected for supplementary strain typing . Second passages from CWD3outlier and CWD4outlier gave much longer survival times compared with CWD3 and CWD4 ( Table 2 ) . At subsequent passages the survival time decreased progressively and CWD3outlier and CWD4outlier were fully adapted only at the fourth passage ( Table 2 ) . Interestingly , neuropathological assessment at the second ( data not shown ) and third ( Figure S2 ) passages showed much less severe spongiform changes in the cortex and superior colliculus than Bv109ICWD at ∼35 d . p . i . . By the fourth passage , the lesion profiles converged to Bv109ICWD ( Figure S2 ) and the survival time decreased to ∼35 d . p . i . . These data suggest that the Bv109ICWD strain was eventually isolated also from CWD3outlier and CWD4outlier , although it required four subsequent passages to emerge . The deviant neuropathological profile observed in CWD3outlier and CWD4outlier at first passage was propagated for at least two vole-to-vole passages , suggesting that a second CWD strain was isolated in these outliers , which was progressively outcompeted by the extreme rapidity of the Bv109ICWD strain . The infectious titre of Bv109ICWD was determined by endpoint titration . Increasing survival times were observed with 10−2 to 10−5 dilutions , while dilutions higher than 10−5 also produced a decreasing attack rate ( Figure 5 ) . The infectious titre of Bv109ICWD was 108 , 4 i . c . ID50 U g−1 . The vast majority of diseased animals succumbed within ∼100 d . p . i . , and only 3 out of 44 voles developed the disease later on , with unequivocal clinical signs ( Figure 5 ) . Thus Bv109ICWD undergoes very fast replication kinetics , allowing high prion titres to accumulate in a very short time . To analyse the potential of Bv109ICWD to be amplified in vitro , we performed PMCA experiments using 1/100 mixtures of Bv109ICWD as seed and brain homogenates from Bv109I voles as substrate . As already shown with other vole species [24] , Bv109ICWD was efficiently amplified already at the first PMCA round ( amplified products from rounds 4 to 13 are shown in Figure S4 ) . In order to verify whether Bv109ICWD infectivity was efficiently propagated during saPMCA and to study its biological properties in comparison with the original Bv109ICWD , we performed saPMCA experiments for 15 successive PMCA rounds so that the original seed was diluted 1016-fold . This dilution was such that only the newly generated PrPSc was theoretically present in the final PMCA product . Groups of nine voles were inoculated with 10-fold dilutions of either in vitro-generated Bv109ICWD ( designated Bv109ICWDPMCA ) or the original Bv109ICWD used as seed . Bv109ICWDPMCA induced terminal disease in 42±2 d . p . i . , a survival time similar to that of the control Bv109ICWD inoculum ( 38±2 d . p . i . ) . The second passage of Bv109ICWDPMCA produced disease in 34±2 d . p . i . . The clinical phenotype and the lesion profile of animals infected with Bv109ICWDPMCA , after primary transmission and second passage , were indistinguishable from the control group ( Figure 6A ) . The type of PrPSc deposition and the pattern of PrPSc distribution in the brain , analysed by IHC ( not shown ) and PET-blot ( Figure 6B ) , were also the same in Bv109ICWDPMCA and Bv109ICWD . Overall , Bv109ICWD produced in vitro was highly infectious and faithfully maintained the properties of the original Bv109ICWD strain .
In the present study we investigated the susceptibility of Bv109I and Bv109M to seven CWD isolates from three deer species and found that these animal models are highly permissive to CWD , showing 100% attack rate and mean survival times between 156 and 281 d . p . i . . A deepened transmission and characterization study of CWD was carried out in Bv109I . The susceptibility of this model appeared comparable to that of transgenic mice expressing cervid PrP [6] , [7] , [9] , [10] , [11] . The reasons for such a high susceptibility are unclear but apparently not related to a different expression of PrPC . As a matter of fact , its expression level in Bv109I is comparable to that of mouse and hamster ( Figure S5 ) . The dramatic drop in survival time with vole-to-vole sub-passages suggests that CWD still encounters a high transmission barrier in Bv109I , implying that Bv109I permissiveness to CWD was not due to the absence of transmission barrier , as observed in transgenic mice expressing cervid PrP . Using Bv109M voles we have previously shown that this species is permissive to a variety of human and animal prion diseases . Studies aimed at investigating the molecular basis of the susceptibility of bank voles to foreign prions and their selective strain preferences suggested that two asparagine residues at positions 150 and 170 , specific to vole PrP , might play a role [18] , [22] , [25] . Interestingly , cervid PrP also has asparagine at residue 170 , suggesting that sequence identity at codon 170 might facilitate the transmission of CWD to Bv109I . This interpretation is supported by the relative ease of transmission of CWD to meadow voles [14] , which also have asparagine at positions 150 and 170 , as well as by studies in MoPrP170N , 174T transgenic mice [24] , [26] and by in vitro amplification of CWD by PMCA [27] . A striking feature of Bv109I-adapted CWD was the short incubation time of less than one month . In an earlier work , we showed that bank voles and related rodent species have peculiarly short survival times after infection with adapted prions , and presumably support equally fast prion replication kinetics , possibly due to the previously mentioned 150N–170N PrP residues [22] . This was also observed in vitro using Bv109M brain homogenates as substrate for PMCA-driven prion replication [28] . Notwithstanding this , our previous and on-going studies with Bv109M have not shown evidence of ultra-fast strains such as Bv109ICWD , and the fastest strains observed so far in voles show incubation times of ∼2 months [19 , unpublished data ) . Transmission studies of CWD to Bv109M have not yet completed but we have observed that also CWD adapts to Bv109M with survival times longer than Bv109ICWD ( 60–100 d . p . i . ) ( unpublished data ) . The short survival time of Bv109ICWD is also unprecedented when compared with those found in transgenic mouse models , in which PrP overexpression greatly fosters prion diseases [6] , [7] , [8] , [9] , [10] , [11] . Indeed , the fastest rodent models reported so far , i . e . Tga20 [29] , Tg52NSE [30] , Tg7 [31] , Tg4053 [32] and Tg338 mice [33] , express several-fold higher PrP levels compared with wild-type mice and have incubation periods at least twice as long as Bv109ICWD . Interestingly , it was recently shown that TgS3581 mice overexpressing vole PrP encoding for Isoleucine at position 109 , undergo spontaneous prion disease and that it adapts to the same model with mean survival time of 35 days [34] . These findings suggest that the presence of Isoleucine at position 109 of the vole PrP plays a specific role in determining the short survival time of Bv109ICWD In the present work we provide evidence that all the hallmarks of TSEs were recapitulated within one month in bank vole CWD . The finding that neurodegeneration and PrPSc deposition showed a discrete brain distribution , involving specific neuronal populations such as those in the medulla and thalamus , might suggest that Bv109ICWD replication primarily involves the so called clinical target areas ( CTAs ) which , once colonised by prions , trigger the clinical signs and death of the animals [35] , [36] . A recent study showed that shorter time periods were needed to initiate the clinical phase when the 127S scrapie strain primarily targeted CTAs , as in intraperitoneally-inoculated Tg338 mice , compared with intracerebrally-inoculated mice [37] . This was accompanied by comparatively low levels of PrPres and infectivity in the brain of ip-inoculated mice . In Bv109ICWD we also observed unusually low levels of PrPres , compared with those observed in most of the vole-adapted prion strains ( Figure 3 ) . However , by endpoint titration we found unexpected high prion titres in Bv109ICWD , 108 , 4 i . c . ID50 U g−1 , similar to those usually observed in standard hamster and mouse-adapted scrapie strains , whose incubation times are 3–10 times longer than Bv109ICWD . This implies that Bv109ICWD undergoes extraordinarily fast replication kinetics in Bv109I brain . Several observations suggest that prion infectivity and toxicity might be uncoupled [38] , [39] , [40] and these observations are currently incorporated in a general model of prion replication and toxicity [41] . According to this model , neurotoxicity is mediated by a lethal PrP species , PrPL , which is distinct from PrPSc , but its formation is catalysed during the autocatalytic replication of PrPSc . Neurotoxicity may require a critical PrPL concentration to be reached , which would depend on the kinetics of prion propagation . The relative levels of toxicity and infectivity are governed by the ratio of the initial rate of PrPC conversion ( which leads to the production of PrPL ) to the rate of its maturation into PrPSc . Thus fast prion replication in Bv109ICWD might have triggered the production of high levels of PrPL in short time periods , leading to rapid disease onset and animal death . The low levels of PrPSc and the fast replication kinetics observed in Bv109ICWD are consistent with this interpretation . A recent work showed that in mice inoculated with the RML scrapie strain the concentration of PrPC did not affect the overall level of prion infectious titres at terminal disease , while it was directly related to the incubation time , suggesting that the production of PrPL is directly proportional to PrPC concentrations [42] . Our observations with Bv109ICWD , i . e . the unusually short survival time and the high prion infectious titre in a model that does not overexpress PrPC , suggest that the kinetics of prion propagation and toxicity are governed by mechanisms that cannot be interpreted solely on the basis of the amount of available substrate ( PrPC ) . The unique and easily distinguishable features of Bv109ICWD prompted us to pursue its in vitro propagation by saPMCA . Bv109ICWD PrPSc was indeed easily propagated in vitro , which allowed us to produce Bv109ICWDPMCA PrPSc , theoretically devoid of any PrPSc formed in vivo . Bv109ICWDPMCA was highly infectious and faithfully reproduced the peculiar phenotype of Bv109ICWD . These findings confirm that CWD prions can be generated in vitro , as already demonstrated by others using transgenic mice expressing cervid PrP [43] , [44] , [45] , [46] and prairie voles [47] . Furthermore , given the unique characteristics of this strain , it is extremely unlikely that their faithful maintenance during saPMCA could have occurred by chance and our results represent a convincing confirmation of other studies that have already demonstrated the ability of PMCA to replicate prion strains faithfully [44] , [45] , [47] , [48] , [49] . Elk , mule deer and white-tailed deer are the species most affected by CWD . The homogeneous and peculiar phenotypes observed in Bv109I inoculated with CWD isolates from these three cervid species indicate that the same CWD strain was isolated from all species . Interestingly , Bv109ICWD was isolated not only from natural cases of disease in elk ( CWD1 , 2 and 4 ) and mule deer ( CWD3 ) , but also from white-tailed deer experimentally inoculated with CWD-affected white-tailed deer , mule deer and elk ( CWD5 , 7 and 8 , respectively ) . Along with previous findings in transgenic mice expressing cervid PrP [6] , [9] , and in keeping with the ease of indirect horizontal transmission of CWD [50] , these data suggest that the same CWD strain circulates among different cervid species and maintains its characteristics following interspecies transmission . Recently , a large transmission study with elk and mule deer isolates provided substantial evidence for two prevalent CWD prion strains and suggested that individual CWD inocula might contain mixtures of the two prion strains [11] . Interestingly , we found similar evidence in at least two of the seven inocula investigated , derived from elk and mule deer , although we were unable to stabilize two different Bv109I-adapted CWD strains . Indeed , on primary transmission of CWD2 , CWD3 and CWD4 inocula we observed voles that developed clinical signs after unusually long times , showing a slightly different neuropathological profile from that of voles with shorter survival times . A sub-passage in Bv109I of two of these outliers induced a survival time of 150–160 d . p . i . on second passage , compared with 35–45 d . p . i . observed with all other sub-passages . Such a long survival time might have depended on a low infectious titre in the brain from outlier voles , although they showed levels of PrPres similar to the other voles of their groups . However this hypothesis is excluded when these results are compared with the survival times observed in the endpoint titration experiment , which showed that even 10−5 dilution of Bv109ICWD had a mean survival time <80 d . p . i . . In addition , the slightly deviant lesion profile observed in outlier voles was preserved for 2 sub-passages , before both the survival time and the neuropathological profile converged with the fast Bv109ICWD strain . Overall , our findings strongly suggest that a second CWD strain was propagated in vole outliers , which was progressively outcompeted by the extremely rapid Bv109ICWD strain . The “evanescent” presence of a second strain might be also interpreted , according to the quasi species nature proposed for prions [51] , as the progressive elimination of the less fitting conformers occurred following interspecies transmission . Here we demonstrate the high susceptibility of Bv109I to CWD , which adds Bv109I to the portfolio of animal models useful for the study of CWD strains . The unique properties of Bv109ICWD provide exceptional opportunities to improve basic knowledge of the relationship between PrPSc , neurodegeneration and infectivity . The short survival time of Bv109ICWD , coupled with its high infectious titre , offers useful advantages for titration studies , while its unique clinico-pathological phenotype makes Bv109ICWD one of the best options for studies aimed at investigating strain fidelity in experimental conditions .
Bv109I and Bv109M were obtained from the breeding colony at the Istituto Superiore di Sanità ( ISS ) . The research protocol , approved by the Service for Biotechnology and Animal Welfare of the ISS and authorised by the Italian Ministry of Health , adhered to the guidelines contained in the Italian Legislative Decree 116/92 , which transposed the European Directive 86/609/EEC on Laboratory Animal Protection . Brain tissues from CWD affected elk ( n = 3 ) , mule deer ( n = 1 ) and white-tailed deer ( n = 3 ) were used for primary transmissions . Two isolates from elk , CWD1 and CWD2 , were provided by the United States Department of Agriculture ( Dr . A . L . Jenny ) and carried the wild type PRNP genotype . The third elk isolate , CWD4 , was heterozygous methionine/leucine at codon 132 . It was provided by the University of Wyoming ( Dr . J . E . Jewel ) as the mule deer isolate , CWD3 , which was homozygous for serine at codon 225 . The three isolates from white-tailed deer derived from an intracerebral experimental challenge [52] . All deer were heterozygous glycine/serine at codon 96 and were challenged with a white-tailed deer CWD isolate ( CWD5 , code 654 in the original paper ) , a mule deer CWD ( CWD7 , code 643 ) or a elk CWD ( CWD8 , code 677 ) . Brain sample from a healthy elk was also inoculated as negative control . An Italian scrapie isolate from Sarda sheep ( SS7B ) carrying the ARQ/ARQ genotype previously characterised into Bv109M [19] , was used for comparison . Tissues were homogenised at 10% ( wt/vol ) in phosphate buffered saline ( PBS ) and stored at −80°C . The PrPres amount estimated by Western blot was comparable in all the homogenates ( data not shown ) . Groups of eight-week-old Bv109I and Bv109M were inoculated intracerebrally with 20 µ of homogenate into the left cerebral hemisphere , under ketamine anaesthesia ( ketamine 0 . 1 µg/g ) . The animals were examined twice a week until neurological signs appeared , after which they were examined daily . Diseased animals were culled with carbon dioxide at the terminal stage of the disease , but before neurological impairment was such as to compromise their welfare , in particular , their ability to drink and feed adequately . Survival time was calculated as the interval between inoculation and culling or death . After that , the brain was cut parasagittally into two parts . The smaller portion was stored at −80°C and the larger one was fixed in formalin . The inocula for the second and third passages were prepared , as 10% wt/vol homogenates in PBS , using the brain of one of the first animals of each group that developed the disease . Supplementary second and subsequent passages from two voles that showed much longer survival times compared with the rest of their groups ( outliers ) were also performed . The specific infectivity of Bv109ICWD was assessed in Bv109I by end-point titration using tenfold dilutions of brain homogenates from the third passage of CWD1 and calculated as ID50 U g−1 according to the Spearman and Kärber method [53] . Survivors were animals that survived until the end of the experiment ( 450 d . p . i . ) with no sign of infection , as assessed by Western blot . The transmission rate was calculated as the ratio between voles confirmed positive by Western blot and the number of voles inoculated , excluding animals culled for intercurrent disease before 30 d . p . i . . Histology , immunohistochemistry ( IHC ) and PET-blot analysis were performed on formalin-fixed tissues as previously described [16] , [19] . Briefly , brains were trimmed at standard coronal levels , embedded in paraffin wax and cut at 6 µm for haematoxylin and eosin staining , immunohistochemistry and PET-blot . Sections were randomly mixed and coded for blind pathological assessment . For the construction of lesion profiles , vacuolar changes were scored in nine grey-matter areas of the brain on haematoxylin and eosin stained sections [16] , [54] , [55] . Vacuolation scores were derived from the examination of at least six voles per group . PET-blot and IHC were performed as described in Di Bari et al . [19] , [26] using the SAF84 mAb . CWD inocula ( 10% w/v homogenates in PBS ) were added to PBS/sarcosyl up to a final 2% sarcosyl ( Sigma ) concentration and incubated for 20 min at room temperature before digestion with proteinase K ( 200 µg/ml ) for 1 hour at 37°C with gentle shaking . Protease treatment was stopped with 3 mM PMSF . Brain homogenates ( 10% w/v ) from individual voles were prepared in 100 mM Tris-HCl , pH 7 . 4 , containing 2% sarcosyl , incubated for 20 min at room temperature and then digested with proteinase K ( 50 µg/ml ) for 1 hour at 37°C with gentle shaking . Protease treatment was stopped with 3 mM PMSF . Electrophoresis and Western blotting were performed as previously described [16] . Briefly , samples were denatured by adding NuPage LDS Sample Buffer ( Invitrogen , Carlsbad , California , United States ) and NuPage Sample Reducing Agent ( Invitrogen ) , and heated for 10 min at 90°C . After centrifugation at 10 , 000 g for 5 min each sample was loaded onto 12% bis-Tris polyacrylamide gels ( Invitrogen ) . After electrophoresis and Western blotting on PVDF membranes ( Immobilon-P; Millipore , Bedford , MA , USA ) , the blots were processed by SNAP i . d . Protein Detection System ( Millipore ) in accordance with the manufacturer's instructions . Vole PrPres was detected using monoclonal antibodies SAF84 ( 1 . 2 µg/ml; epitope at amino acids 163–169 of the bank vole PrP sequence ) and 12B2 ( 2 . 4 µg/ml; epitope at amino acids 89–93 of the sheep PrP sequence ) . Horseradish peroxidase-conjugated anti-mouse immunoglobulin ( Pierce Biotechnology , Rockford , Illinois , United States ) was used as secondary antibody ( 1∶13 , 000 ) . The membranes were developed using an enhanced chemiluminescence method ( SuperSignal Femto , Pierce ) . The chemiluminescence signal was detected using the VersaDoc imaging system ( Bio-Rad ) and was quantified by QuantityOne software ( Bio-Rad ) . A vole brain from the third passage of CWD1 was homogenised at 10% w/v in PBS and divided into two aliquots . The first aliquot was used as a seed for saPMCA while the second was used for bioassays . Substrates from Bv109I voles were prepared as 10% brain homogenates in conversion buffer ( PBS , 0 . 15 M NaCl and 1% Triton X ) and saPMCA was performed as previously described [28] by diluting 1∶100 the seed into Bv109I substrate followed by one round of PMCA using a Misonix 3000 Sonicator , with the following settings: 20 seconds of sonication every 30 minutes for 24 hours ( 48 cycles of incubation/sonication ) ; 200–250 watts ( potency 8 ) sonication power . For successive rounds of saPMCA , the product of the previous round was diluted 1∶10 into fresh substrate and subjected to a further PMCA round . This procedure was repeated 14 times to reach a 10−15 final dilution of the initial CWD infected brain homogenate . The detailed protocol for saPMCA has been published elsewhere [56] , [57] , [58] . After each round , PrPres was evaluated by Western blot ( Figure S4 ) . Given the risk of contamination that is intrinsic for an ultrasensitive technique as PMCA [28] , several healthy Bv109I brain homogenates were processed , as negative controls , in each round . Before each successive round , the products of the previous round were analysed by Western blot to confirm the amplification of Bv109ICWD and the negativity of controls . | Chronic wasting disease ( CWD ) is a prion disease that affects free-ranging and captive cervids and is expanding increasingly in the USA and Canada . Animal models are of key importance in the study of prion diseases but their development for CWD has long been hampered by its very inefficient transmission to wild-type mice . Significant progress was made following the generation of transgenic mice over-expressing cervid PrP . Here we show that the bank vole ( Myodes glareolus ) , a wild rodent species that we demonstrated to be susceptible to many animal and human prion diseases , is also very susceptible to CWD from elk , mule deer and white-tailed deer . Adaptation of CWD to bank vole led to the isolation of a prion strain with peculiar characteristics: unprecedented short incubation and survival times , respectively of 25–28 and ∼35 days , low PrPSc levels compared with other vole-adapted prion strains and high infectious titre . These features were all faithfully maintained upon the generation of this strain in vitro by protein misfolding cyclic amplification . The development of a model for prion diseases that led to disease in less than one month accumulating high infectious titres but low PrPSc levels , represents a significant tool for investigating the still unclear relationship between PrPSc , neurodegeneration and infectivity in prion diseases . | [
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] | 2013 | Chronic Wasting Disease in Bank Voles: Characterisation of the Shortest Incubation Time Model for Prion Diseases |
Beat-to-beat variability of repolarization duration ( BVR ) is an intrinsic characteristic of cardiac function and a better marker of proarrhythmia than repolarization prolongation alone . The ionic mechanisms underlying baseline BVR in physiological conditions , its rate dependence , and the factors contributing to increased BVR in pathologies remain incompletely understood . Here , we employed computer modeling to provide novel insights into the subcellular mechanisms of BVR under physiological conditions and during simulated drug-induced repolarization prolongation , mimicking long-QT syndromes type 1 , 2 , and 3 . We developed stochastic implementations of 13 major ionic currents and fluxes in a model of canine ventricular-myocyte electrophysiology . Combined stochastic gating of these components resulted in short- and long-term variability , consistent with experimental data from isolated canine ventricular myocytes . The model indicated that the magnitude of stochastic fluctuations is rate dependent due to the rate dependence of action-potential ( AP ) duration ( APD ) . This process ( the “active” component ) and the intrinsic nonlinear relationship between membrane current and APD ( “intrinsic component” ) contribute to the rate dependence of BVR . We identified a major role in physiological BVR for stochastic gating of the persistent Na+ current ( INa ) and rapidly activating delayed-rectifier K+ current ( IKr ) . Inhibition of IKr or augmentation of INa significantly increased BVR , whereas subsequent β-adrenergic receptor stimulation reduced it , similar to experimental findings in isolated myocytes . In contrast , β-adrenergic stimulation increased BVR in simulated long-QT syndrome type 1 . In addition to stochastic channel gating , AP morphology , APD , and beat-to-beat variations in Ca2+ were found to modulate single-cell BVR . Cell-to-cell coupling decreased BVR and this was more pronounced when a model cell with increased BVR was coupled to a model cell with normal BVR . In conclusion , our results provide new insights into the ionic mechanisms underlying BVR and suggest that BVR reflects multiple potentially proarrhythmic parameters , including increased ion-channel stochasticity , prolonged APD , and abnormal Ca2+ handling .
Beat-to-beat variability of repolarization duration ( BVR ) is an intrinsic characteristic of cardiac function that can be observed at multiple scales , from temporal variations in action-potential ( AP ) duration ( APD ) of the single cardiac myocyte to instability of the QT interval on the body-surface ECG [1]–[3] . When increased by adverse repolarization changes , it is a better marker of proarrhythmia than repolarization prolongation per se in various experimental models of torsades-de-pointes ventricular tachycardia [4]–[6] and in human cardiac pathologies [2] , [7] . Recently , we reported an important rate-dependent role for abnormal Ca2+ handling and the slowly activating delayed-rectifier K+ current ( IKs ) in the increased BVR observed during β-adrenergic stimulation in single canine ventricular myocytes [8] . However , the exact mechanisms underlying BVR and its rate dependence under physiological conditions , as well as the various factors contributing to exaggerated BVR in pathological conditions , remain incompletely understood . Computational models of cardiac myocyte electrophysiology have a rich history , dating back more than 50 years [9] . Recent models have provided detailed descriptions of various cardiac cell types in different species . They have created insight into the role of the different ion channels in rate-dependent alterations in repolarization , have helped to elucidate arrhythmogenic mechanisms in various pathological conditions , and have facilitated analysis of the integration of regulatory pathways and electrophysiological responses ( reviewed in [10]–[12] ) . However , to date most computational models are deterministic and have an APD that converges to a fixed steady state or a limit cycle ( fixed sequence of APDs; e . g . , APD alternans ) for a given pacing cycle length ( CL ) . As such , these models are unsuitable for the study of BVR . Tanskanen et al . [13] were among the first to investigate the effect of stochastic properties of local-control Ca2+ models on ventricular repolarization . They showed that a variable occurrence of arrhythmogenic early afterdepolarizations ( EADs ) could be explained by the stochastic gating of the L-type Ca2+ ( ICaL ) channel . In contrast , Sato et al . [14] described temporal repolarization variability due to the chaotic occurrence of ICaL-mediated EADs in a deterministic model of the H2O2-treated rabbit ventricular myocyte . These authors provided strong evidence that noise-induced transitions between states were insufficient to account for the large APD fluctuations observed under their experimental conditions . Instead , these transitions were intrinsically chaotic , although stochastic fluctuations could potentiate the complexity of the dynamics [15] . However , none of these studies addressed BVR under physiological conditions or under pathological conditions in the absence of EADs . Recently , the role of stochastic ion-channel gating in repolarization variability under physiological conditions has been described in computational studies by Lemay et al . [16] and Pueyo et al . [17] . These authors found that stochastic gating of selected ion channels , notably ( late ) INa and IKs , could affect global BVR , quantified as the coefficient of APD variability . A detailed investigation of the contribution of all major ionic processes to BVR in physiological and pathological conditions , including a quantitative comparison of short-term variability ( STV; which includes differences between consecutive APs ) to experimental data , has not yet been performed and was the aim of this study . We developed a stochastic version of our recently published model of the canine ventricular myocyte including β-adrenergic stimulation [18] . Stochastic formulations of 13 major ion channels and active ion transporters were included , and APD dynamics were compared to results obtained in isolated canine ventricular myocytes . We employed the model to obtain novel insights into the quantitative contribution of individual electrophysiological processes to cellular BVR under physiological conditions and the factors contributing to increased BVR during pathological conditions .
AP recordings from isolated canine ventricular myocytes showed beat-to-beat variability in APD ( Figure 1A , top panel ) consistent with previous reports [1] , [8] . In contrast , under physiological conditions , APD in the deterministic model ( an extension of the Hund-Rudy model of the canine ventricular myocyte [19] , incorporating localized β-adrenergic signaling pathways , as described by Heijman et al . [18] ) converged to a steady state without APD variability ( Figure 1A , second panel ) . In previous research , stochastic processes were simulated using either stochastic differential equations [17] , [20] , or by simulating stochastic state transitions ( channel gating ) in the Markov models of various ion channels [13] , [21] , [22] . Application of both methodologies to the Markov model of IKr resulted in APD variability ( Figure 1A , third and fourth panel ) . However , these two approaches showed different temporal dynamics ( Figure 1B ) . Poincaré plots of APDi+1 versus APDi have a circular shape under these conditions in experimental recordings and in simulations with stochastic channel gating , indicating similar magnitudes of short- ( STV ) and long-term ( LTV ) variability ( STV or LTV = average distance perpendicular to or along the line of identity , respectively; Figure 1B , inset ) . In contrast , BVR in simulations employing stochastic differential equations of gating variables was predominantly long term , resulting in a STV-to-LTV ratio that was markedly different from experimental recordings . To obtain insight into the direct contribution of the stochastic gating of ion currents/fluxes to whole-cell BVR , we performed simulations with stochastic formulations of each individual channel/pump/transporter in an otherwise deterministic model at CL of 500 , 1000 , and 2000 ms ( Figure 2A ) . The number of channels underlying each current was estimated based on experimentally obtained single-channel conductance and whole-cell conductance in the model ( see Section 2 . 5 of Text S1 ) . To investigate the sensitivity of this parameter , we simulated normal channel density as well as a 5-fold increase or decrease in channel density ( offset by a reciprocal change in single-channel conductance to maintain the same total current ) . A lower channel density ( with larger single-channel conductance ) resulted in a larger STV for all stochastic simulations . A large difference between the impacts of individual ion currents on BVR could be observed , with the largest contributions by persistent INa and IKr to STV under these conditions ( Figure 2A ) . Stochastic gating of IKs also had a substantial impact on BVR , despite its small effect on APD under basal conditions in isolated myocytes [8] , [23] , consistent with results by Pueyo et al [17] . In contrast , pumps and exchangers , which have relatively low individual throughput but high expression density [24] , contributed little to BVR . In general , BVR increased with increasing CL . When all 13 stochastic components were included , STV was larger than that obtained with any individual stochastic formulation , but the results were not additive , indicating that certain stochastic fluctuations cancel each other out . The stochastic model showed APD and BVR rate dependence similar to that observed in canine ventricular myocytes ( Figure 2B ) , indicating that stochastic channel gating ( particularly of INa and IKr channels ) is a major contributor to the baseline BVR observed in physiological conditions . In addition to a direct impact on Vm fluctuations , individual ion channels may modulate STV indirectly ( e . g . , via fluctuations in intracellular ion concentrations that affect other currents ) . To dissect the effect of channel stochasticity versus effects of maximal conductance on BVR in the fully stochastic model , we employed the linear-regression method proposed by Sobie ( [25] , [26] and in Section 3 of Text S1 ) . Although the linear regression is an approximation of a strongly non-linear system , this approach has previously been employed to study the contribution of different ionic currents to various pathophysiological processes [25] , [26] . We simulated 200 parameter sets in which the maximal conductance of each of the currents was scaled based on a Gaussian distribution with mean 1 . 0 and standard deviation ( Std ) 0 . 3 . For each parameter set , mean APD , STV , and LTV were determined at steady state during stochastic simulations at 1000-ms CL ( Figure 3 ) . The contribution of each current was determined by performing a linear regression on the parameter settings ( maximal conductances of individual currents ) and output measures ( Figure 3A ) . Consistent with the results based on the direct stochastic impact shown in Figure 2 , the linear regression analysis identified major roles for alterations in conductances of INa and IKr in modulating both APD and STV ( Figure 3B ) . In addition , this approach also identified a substantial impact of INaK and INaCa on STV . Because the stochastic gating of these currents did not result in significant BVR when simulated in an otherwise deterministic model , it follows that variations in the maximal conductance of these targets affect STV via other parameters ( e . g . , APD , intracellular ion concentrations , etc . ) , which remains to be confirmed experimentally . One potential mechanism through which individual ion channels may impact BVR is their influence on AP morphology [27] . To study this , we increased or decreased the amplitude of IK1 , IKur , ITo and/or ICaL to adjust AP morphology without affecting average APD . The three currents were simulated deterministically in all cases to prevent any direct effects of the altered current amplitudes on BVR . This protocol allowed us to compare the effect of AP morphology on the stochastic gating of the 9 remaining currents and on BVR in the absence of confounding changes in average APD . STV was 4 . 3±0 . 6 ms in the control model with deterministic IK1 , IKur , ITo , and ICaL , and APD was 252±5 . 7 ms ( Figure 4A ) . When IK1 and ITo were reduced by 70% and 60% , respectively , and IKur was increased by 275% , a triangular AP morphology was obtained with similar APD ( 249±4 . 2 ms ) but significantly lower STV ( 2 . 9±0 . 5 ms; Figure 4B ) . Interestingly , with a square AP morphology ( ICaL and ITo reduced by 75% and 20% , respectively , IKur and IK1 increased by 20% ) , variability was strongly increased ( STV = 11±1 . 9 ms; Figure 4C ) compared with control AP morphology . This pattern suggests that , in addition to APD , AP morphology can strongly affect BVR , whereby a conspicuous AP plateau is associated with increased BVR . Zaniboni et al . [1] have previously shown that the electrical coupling of two myocytes reduced their temporal repolarization variability , which was confirmed in the modeling study by Lemay et al [16] . When identical cells were coupled in our simulations , the overall temporal variability ( coefficient of variance: 100%×Std ( APD ) /mean ( APD ) ) in the model was reduced from 2 . 4% to 1 . 9% , quantitatively similar to that observed by Zaniboni et al . in guinea-pig ventricular myocytes ( 2 . 3±1 . 2% in uncoupled cells vs . 1 . 5±0 . 6% in cell pairs ) . We observed a reduction in BVR of 1 . 1 ms in cell pairs compared to uncoupled cells when two identical cells were coupled ( Figure 5A ) . Gap-junction conductance did not influence BVR over the range of values that would result in successful propagation in a one-dimensional strand [28] . Interestingly , Zaniboni et al . also reported that there was an asymmetrical redistribution of APD when a cell with a long APD was coupled to a cell with short APD , by which the long APD shortened more than the short APD prolonged . We hypothesized that this asymmetrical response may also apply to BVR and prolonged APD in one of the two cells through the injection of a constant , deterministic current for the duration of the APD ( Figure 5B ) . BVR was larger in the cell with prolonged APD , thereby increasing the average BVR . When the two cells were coupled , spatial APD dispersion was lost . Although BVR remained larger than that of the symmetrical cell pair , the decrease in average BVR compared with the uncoupled situation was more pronounced ( 1 . 9 ms; Figure 5A , inset ) . Moreover , BVR was further reduced ( but not eliminated ) when more cells were coupled together in a one-dimensional strand ( Figure 5C ) , although , as expected , the spatial dispersion of repolarization increased with increasing length of the strand . Taken together , these data suggest that cell-to-cell coupling not only reduces spatial dispersion of repolarization but may also limit excessive BVR of vulnerable regions . As such , conditions in which coupling is reduced ( e . g . , in ischemia ) may lead to increased BVR . Because of the hyperbolic relationship between the rate of repolarization and APD , sensitivity to changes in net membrane current ( Im ) may be higher in longer APs , thereby contributing to reverse rate-dependence of APD modulation and , thus , to BVR [27] , [29] . This mechanism , of which the physiological relevance is supported by recent experimental findings [30] , has been identified as “intrinsic” because it reflects a numerical property independent from channel gating [27] . Nevertheless , the impact of stochastic channel gating on net membrane current might also be rate dependent , contributing an additional source of APD variability , which we will refer to as “active” to differentiate it from the “intrinsic” component . We hypothesize that i ) both the “intrinsic” and the “active” components may underlie BVR reverse rate-dependence ( Figure 2B ) and ii ) that individual ionic conductances may contribute unequally to the “active” component . To address these hypotheses , membrane potential and net membrane current ( Vm and Im ) were recorded for 30 beats during steady-state stimulation at different cycle lengths . The mean and Std of Im and Vm over these 30 beats was measured at each time point during the action potential . In simulations with the stochastic model , Std ( Im ) was found to vary along the action-potential course , reaching a maximum during phase-3 repolarization ( Figure 6A ) . While such a Std ( Im ) profile was present at all CLs , Std ( Im ) global magnitude increased at longer CLs ( Figure 6A , B ) . The observation that Std ( Im ) is rate dependent confirms the presence of an “active” component in BVR rate dependence . According to the “intrinsic” component concept , rate-independent Std ( Im ) should still result in rate-dependent BVR [27] . Thus , to quantify the impact of the intrinsic component on BVR rate-dependency , BVR was measured at various CLs in the presence of rate-independent Std ( Im ) . To this aim , a stochastic Gaussian component was selectively added to the Vm update step of the deterministic model . The amplitude of the stochastic component ( α = 1 . 5; Section 2 of Text S1 ) was chosen such that BVR at CL = 300 ms matched that of the fully stochastic model . As shown in Figure 6C , rate dependence of STV was blunted , but not eliminated by this procedure . The remaining rate dependence of STV ( dashed line ) reflects the “intrinsic” component contribution ( i . e . , even with the same amount of Im “noise” , BVR is larger at longer CLs ) . If affected by the “intrinsic” component only , BVR magnitude should be independent of diastolic interval ( DI ) [27] . However , it was less obvious whether this should apply also to overall BVR , i . e . , including the “active” component . To answer this question , in the stochastic model APD was prolonged through injection of a constant current and the resulting STV vs . APD relationship was measured at three CLs ( Figure 6D ) . The STV/APD relationships at the three CLs largely overlapped , indicating that mean APD , rather than DI , is also the main determinant of the “active” component . To confirm this conclusion , we adapted the pacing protocol in the simulations such that the virtual cell was paced with a fixed diastolic interval . To this end , APD was determined online ( i . e . , during the simulation ) , and the next pacing instant was set to achieve a pre-specified DI . Multiple simulations were performed with different pre-specified DIs to obtain a curve similar to that for BVR rate dependence . In fact , when BVR was plotted vs . CL ( determined via CL = mean APD+DI ) , an identical BVR/CL relationship was obtained compared to normal pacing ( compare “fixed DI” and “variable DI”; Figure 6C ) , confirming that under these physiological conditions , variations in DI do not contribute to BVR rate dependence . In contrast , in the chaotic models of Sato et al . in the setting of EADs , beat-to-beat APD differences occur because of a steep APD/diastolic interval ( DI ) relationship [14] . We previously reported that BVR is increased in pharmacological models of long-QT syndrome ( LQT ) type 2 ( using the IKr-blocking drug dofetilide ) and LQT3 ( increased persistent INa due to ATXII ) and that this BVR could be reduced by β-adrenergic stimulation ( βARS ) [8] . The present finding that BVR is most sensitive to IKr and INa modulation ( Figure 2 ) is consistent with these observations . In contrast , we found that during IKs inhibition ( using HMR1556 ) , βARS significantly increased BVR , whereas HMR1556 alone had no effect on BVR [8] . Notably , all these experimental findings were reproduced by the model ( Figure 7A , B ) , indicating that it can be employed to study the factors contributing to exaggerated BVR in pharmacological representations of LQT1-3 . The contribution of both intrinsic and active mechanisms to BVR rate dependence ( Figure 6 ) suggests that the increased BVR observed in pharmacological models of LQT syndrome ( Figure 7 ) could result directly from APD changes . On the other hand , APD and BVR changes may be dissociated under these conditions . To investigate the effect of APD prolongation on BVR , we employed a deterministic current injection to reduce average APD back to baseline levels . When APD was reduced , BVR was also reduced to control values ( STV equaled 3 . 3±0 . 5 , 7 . 6±1 . 0 , and 4 . 1±0 . 6 ms in control , LQT2 , and LQT2 with reduced APD , respectively; Figure 8A ) . In contrast , removing the stochastic gating of IKr did not significantly alter BVR ( 7 . 7±1 . 1 ms ) compared to LQT2 simulations with stochastic IKr gating . These findings suggest that the increased BVR in the presence of simulated IKr blockade is not due to increased channel stochasticity , but instead reflects the intrinsic component of BVR . When STV was plotted against average APD for individual canine ventricular myocytes or individual model cells generated based on a Gaussian distribution of conductances for all 13 currents ( similar to the approach for Figure 3A ) , a non-linear relationship was obtained , which was fitted by an exponential for the purpose of parameter quantification . There was no difference in the APD dependence of STV between experiments and model , or between control and IKr-block ( LQT2 ) conditions ( Figure 8B , C ) , although the fit to LQT2 simulation data had a slightly larger offset ( parameter a ) and lower R2 due to a few isolated instances with short APD but very large STV . These data indicate that the model is able to quantitatively reproduce experimental BVR characteristics covering a range of cell-to-cell differences . Moreover , these data strongly suggest that APD prolongation is the main determinant for the increased BVR in LQT2 . Qualitatively similar results were obtained for INa augmentation with ATXII , although the simulated APs were very prone to EAD formation , preventing accurate STV comparison with experimental results . We have recently shown that spontaneous Ca2+ release ( SCR ) from the sarcoplasmic reticulum ( SR ) causes prolongation of the next APD in a pharmacological model of LQT1 ( βARS and IKs inhibition ) , contributing to increased BVR if SCR occurs irregularly [31] . This suggests that BVR is controlled by factors other than APD alone under these conditions . In agreement , we found that experimentally recorded BVR values were substantially larger for LQT1 than for control ( Figure 9A , left panel ) or LQT2 ( not shown ) conditions for any given APD . The BVR vs . APD relationship showed a correspondingly larger value for parameter “a” in LQT1 ( Figure 9A , right panel ) . In the presence of 100% IKs inhibition , 10% INaK inhibition , and simulated β-adrenergic stimulation , we observed a brief period of instability in Ca2+ handling in the deterministic model , resulting in APD variability even in the absence of stochastic gating ( Figure 9B ) . Stochastic gating significantly prolonged the window of Ca2+-handling abnormalities and caused pronounced APD variability during this period , in agreement with our recent experimental observations [31] . However , in the single-domain model , SCRs and the resulting delayed afterdepolarizations ( DADs ) had almost identical amplitudes ( Figure 9C ) , resulting in two clusters of APD values , depending on the timing of the SCR . If an SCR closely preceded a beat , APD was prolonged ( Figure 9C , bottom panel , second beat ) , whereas with a longer delay between the SCR and subsequent AP , APD was comparatively short . This resulted in a triangular Poincaré plot , which is not seen experimentally [8] , [31] . Since it is well-established that SCR is a highly localized subcellular process [21] , we hypothesized that local fluctuations in intracellular [Ca2+] could modulate BVR . To study the effect of subcellular variations in Ca2+ handling , we divided the cell into four identical domains connected via Ca2+ diffusion . The resulting model still falls in the category of “common-pool” models and does not reflect the dyadic nanostructure of “local-control” models . Nonetheless , the presence of local Ca2+ domains resulted in a wider range of SCR amplitudes and , consequently , a wider distribution of APD values compared with the stochastic model with a single domain ( Figure 9D ) . These data suggest that although stochastic gating of Ca2+-handling proteins does not contribute to baseline BVR , it plays a critical role under conditions with SR Ca2+ overload . Moreover , we provide a first indication that local domains may amplify stochastic fluctuations and contribute to APD variability .
Despite the experimental evidence of an important role for BVR as an indicator of proarrhythmic risk [2] , [32] , few computational models have incorporated this to date . Wilders and Jongsma were among the first to examine stochastic channel gating in a computational cardiac cell model for their investigation of beating-rate variability in sinoatrial node cells [33] . Subsequently , Tanskanen et al . employed a local control model of the canine ventricular myocyte to investigate the role of stochastic gating of ICaL channels in EAD formation [13] . These authors also provided a mathematical analysis indicating that increased voltage noise skewed the distribution of APD towards longer APDs , enhancing the susceptibility to EADs [34] . In contrast , Sato et al . have shown that the EADs observed in their model of the H2O2-treated rabbit ventricular myocyte were chaotic and not due to stochastic fluctuations . However , stochastic channel gating resulted in an increased variety of temporal dynamics of the chaotic model [14] . Pueyo et al . also found that stochastic channel gating favored the occurrence of alternans and EAD generation during IKr blockade [17] . However , both Sato et al . and Pueyo et al . only considered stochastic gating of IKs . Lemay et al . adapted the Luo-Rudy dynamic model of the guinea-pig ventricular myocyte to investigate the role of stochastic gating and protein turnover of a selected number of currents on APD variability and intercellular conduction delays under physiological conditions [16] . The results presented here provide a significant extension of the previously developed models by considering both stochastic gating of all major ion currents ( except background currents ) and Ca2+-handling processes . Moreover , we show that the stochastic model is quantitatively consistent with experimental measures of BVR in isolated canine ventricular myocytes and identify contributors to BVR in physiological and pathological conditions . BVR has been proposed as a more reliable proarrhythmic marker than prolongation of repolarization per se , at least for specific pathological conditions [2]–[4] . Our data indicate that BVR , determined largely by stochastic channel gating during baseline conditions , is modulated by a number of factors that may play a role in arrhythmogenesis . We find that AP morphology ( Figure 4 ) and duration ( Figure 6 ) affect BVR . In particular , we show that increased APD and a prolonged AP plateau increase BVR , whereas triangulation of the AP reduces BVR . As we showed in previous work [30] , non-linearity of the relationship between repolarization rate and APD is , per se , sufficient to account for a larger impact of current fluctuations occurring during phases with very slow repolarization ( plateau ) on APD . Nevertheless , a prolonged AP plateau may also increase the likelihood of autoregenerative reactivation of “window” currents [35] , which would boost current fluctuations . This may ultimately result in EADs , a cause of extreme temporal and spatial variability of repolarization and of ectopic impulse formation . Both Pueyo et al . [17] and Tanskanen et al . [13] have shown that stochastic fluctuations in channel gating ( of IKs and ICaL , respectively ) can indeed facilitate the development of EADs ( however , see Sato et al . [15] ) . These data suggest that BVR reflects the robustness of repolarization and , when exaggerated , the tendency towards EAD development . It should be noted that Hondeghem et al . have previously associated drug-induced AP triangulation with increased instability and proarrhythmia in the Langendorff-perfused methoxamine-sensitized rabbit heart [36] , indicating that other species-dependent factors such as the ion current profiles and the amplitude of the AP plateau may influence the effects of AP morphology on BVR . In addition to EADs induced by reactivation of ICaL during a prolonged AP plateau , abnormal Ca2+ handling has been shown to be able to induce EADs and delayed afterdepolarizations [31] , [37] . As such , the consideration of both stochastic Ca2+ handling and ion-channel gating in the model presented here is important . Previous experimental data from our group have shown that buffering of intracellular Ca2+ ( using BAPTA ) can suppress BVR during βARS and IKs blockade in single ventricular myocytes [8] . Furthermore , in a pharmacological LQT2 model in intact rabbit hearts , abnormal Ca2+ handling also preceded fluctuations in membrane potential [38] . We found no direct contribution of individual Ca2+-handling proteins to BVR under baseline conditions ( Figure 2 ) . However , alterations in Ca2+ homeostasis can have a significant impact on BVR and are at least partially mediated by stochastic gating of SR Ca2+-handling proteins ( Figure 9 ) . Moreover , in the ventricular myocyte , the strong local positive feedback characteristics of Ca2+-induced Ca2+ release may amplify stochastic fluctuations within a subsarcolemmal microdomain and modulate BVR . Thus , BVR also reflects the stability of the intracellular Ca2+-handling system and Ca2+-sensitive currents . Dispersion of repolarization has been shown to be arrhythmogenic in a variety of conditions [39] . Cell-to-cell coupling is able to suppress both temporal and spatial dispersion of repolarization ( Figure 5 ) , suggesting that BVR can indicate the degree of ( un ) coupling of the myocardium . Combined , these data suggest that BVR reflects both the intrinsic temporal variability ( stochastic channel gating and Ca2+ handling ) as well as the sensitivity of the electrical system to these fluctuations . For example , we have shown that BVR is strongly modulated by APD , AP morphology , and cell-to-cell coupling . The integration of APD and these additional parameters may contribute to the value of BVR as proarrhythmic marker . Our results highlight an important role for abnormal Ca2+ handling in BVR , consistent with experimental recordings [31] . Future experimental and computational studies may elucidate the impact of Ca2+ on BVR at the subcellular level , providing a more extensive validation of local Ca2+ release and Ca2+-wave properties . Stochastic formulations of all 13 targets were based on the well-validated characteristics of the deterministic model [18] , [19] using the methodology employed in local control models [13] , [21] , [22] . This approach allows tracking of single-channel behavior; however , a formal validation of single-channel characteristics based on dwell times , open probability distributions , etc . is beyond the scope of this study . Furthermore , since single-channel recordings are often performed in non-physiological solutions , it would be unclear whether any deviations in single-channel behavior observed in the model under these conditions would affect the stochastic properties relevant for BVR . We estimated the effective number of channels in the model based on experimentally obtained single-channel conductance . For several targets , the single-channel conductance or expression density is not well constrained . For example , to the best of our knowledge , there are no data on IKs single-channel conductance from native tissue , and experimental data from heterologous expression systems show considerable variability ( section 2 . 5 of Text S1 ) . We performed simulations over a range of channel densities to investigate the impact of this parameter ( Figure 2 ) . Because single-channel conductance has a large impact on BVR ( Figure 2 ) , the contribution of these targets may therefore be under- or overestimated . The model presented here falls in the category of “common-pool” models that do not capture the detailed nanostructure of the ventricular myocyte where L-type Ca2+ channels on the T-tubular membrane and ryanodine receptors on the sarcoplasmic reticulum interact in a local nanodomain ( dyad ) . Although we divided the model into a number of compartments and showed that this ‘local’ Ca2+ handling can modulate BVR ( Figure 9 ) , the present model cannot reproduce arrhythmogenic Ca2+ waves or other properties of “local-control” models that incorporate this dyadic structure . In contrast to experimental recordings [31] , Poincaré plots in the presence of Ca2+-handling abnormalities showed a triangular pattern in the model , an effect that may be due to the limited number of domains in the local simulations . A number of “local-control” models investigating key properties of subcellular Ca2+ handling have recently been described [22] , [40] , [41] . Integration of these “local-control” models and the model presented here could facilitate the mechanistic analysis of the role of Ca2+-handling abnormalities in BVR in subsequent studies . In addition to single-channel gating and Ca2+ , other factors may modulate BVR . These factors include signaling pathways , changes in cell volume and pH , stretch and electro-mechanical feedback , etc . and are beyond the scope of the current investigation . Moreover , most of these factors will change on a timescale of minutes , whereas BVR reflects the changes in repolarization duration on the order of seconds . Thus , although these factors can affect BVR , they are likely to do so via changes in repolarization duration , Ca2+ handling , or stochastic channel gating that have been investigated here . Finally , although the results presented here suggest that BVR reflects a combination of potentially proarrhythmic signals at the ( sub ) cellular level , its role as a marker for arrhythmogenesis can only be thoroughly investigated in a large multicellular model . The complexity of the cell model makes this computationally prohibitive for the present implementation . An alternative approach to simulate stochastic channel gating with improved computational efficiency has recently been proposed [42] . Future studies could apply this technique to study the role of BVR as a proarrhythmic marker in large-scale multicellular simulations . Our cell-pair and one-dimensional-strand simulations show that cell-to-cell coupling will reduce but not eliminate BVR . Future studies could focus on the synchronization of variability during arrhythmogenesis in a multi-scale model . We present a novel stochastic model of the canine ventricular-myocyte electrophysiology showing APD and BVR rate dependences consistent with experimental data from isolated canine ventricular myocytes under physiological conditions and in pharmacological models of LQT1-3 . The model provides new insights into the ( sub ) cellular determinants of BVR and suggests modulating roles for several processes , including APD , AP morphology , Ca2+ handling , and cell-to-cell coupling . In addition to providing an important framework to further our understanding of the role that BVR can play as a proarrhythmic marker , it also gives novel insights into the differential roles of ion channels in arrhythmogenesis .
A detailed overview of the methods employed can be found in Text S1 . A brief summary of the main aspects is given below . This investigation conformed with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health ( NIH Publication No . 85-23 , revised 1996 ) . Animal handling was in accordance with the European Directive for the Protection of Vertebrate Animals Used for Experimental and Other Scientific Purposes ( 86/609/EU ) . Our recent model of the canine ventricular-myocyte electrophysiology with β-adrenergic stimulation [18] formed the basis for the present study . The model was extended with i ) a Markov model of the IKr including block by dofetilide , based on the work by Brennan et al . [43]; ii ) a Markov model of the INa and its augmentation by ATXII; and iii ) a Markov model of the RyR . For the 13 major ion currents , ion transporters and Ca2+-handling proteins , stochastic formulations were developed . For those proteins for which modification by phosphorylation was included in the original model [18] , stochastic implementations of both the phosphorylated ( P ) and non-phosphorylated ( NP ) populations were simulated . Stochastic simulations were performed using the state-vector of the deterministic model at any given cycle length as the initial state vector . At least 250 stochastic APs were simulated in each condition to determine APD and BVR characteristics . Cell-pair experiments were simulated via a finite difference approximation of the cable equation , as previously described [19] . Both cells received an external stimulus current to eliminate the effect of depolarization differences on BVR . The model was implemented in C ( model code available as Software S1 ) , compiled with MinGW , and simulations were run on an Intel Core I7 computer with 6 GB of RAM using a piece-wise constant time-step ( 0 . 005 ms during the AP , 0 . 1 ms otherwise ) . Data were stored in binary format with a 0 . 5 ms resolution and were analyzed using the mathematical software Octave . The Mersenne-Twister random number generator was used for single-channel simulations with numerical approximations for multinomial distributions as indicated in the Section 2 of Text S1 . For experimental ( “wet” ) studies , transmembrane APs were recorded at 37°C using high-resistance ( 30–60 MΩ ) glass microelectrodes filled with 3 mol/L KCl in midmyocardial myocytes isolated from canine left-ventricular tissue , as previously described [8] . APD was quantified at 90% repolarization . BVR was quantified as short- or long-term variability of APD ( STV or LTV ) using the formulas Σ ( |APDi+1−APDi| ) /[nbeats×√2] or Σ ( |APDi+1+APDi−2×APDmean| ) /[nbeats×√2] , respectively , for 30 consecutive APs , as previously described [8] . Pooled data are expressed as mean ± SD unless otherwise specified . | Every heartbeat has an electrical recovery ( repolarization ) interval that varies in duration from beat to beat . Excessive beat-to-beat variability of repolarization duration has been shown to be a risk marker of potentially fatal heart-rhythm disorders , but the contributing subcellular mechanisms remain incompletely understood . Computational models have greatly enhanced our understanding of several basic electrophysiological mechanisms . We developed a detailed computer model of the ventricular myocyte that can simulate beat-to-beat changes in repolarization duration by taking into account stochastic changes in the opening and closing of individual ion channels responsible for all main ion currents . The model accurately reproduced experimental data from isolated myocytes under both physiological and pathological conditions . Using the model , we identified several mechanisms contributing to repolarization variability , including stochastic gating of ion channels , duration and morphology of the repolarization phase , and intracellular calcium handling , thereby providing insights into its basis as a proarrhythmic marker . Our computer model provides a detailed framework to study the dynamics of cardiac electrophysiology and arrhythmias . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"arrhythmias",
"biology",
"computational",
"biology",
"electrophysiology",
"biophysics",
"cardiovascular"
] | 2013 | Determinants of Beat-to-Beat Variability of Repolarization Duration in the Canine Ventricular Myocyte: A Computational Analysis |
Soil-transmitted helminth ( STH ) infections affect more than two out of every five schoolchildren in the poorest regions of rural China , an alarmingly high prevalence rate given the low cost and wide availability of safe and effective deworming treatment . Understanding of local knowledge , attitudes , and practices regarding STH infection in rural China has until now , been sparse , although such information is critical for prevention and control initiatives . This study aims to elucidate the structural and sociocultural factors that underlie high STH infection rates as well as explain why deworming treatment is rarely sought for children . In-depth , qualitative interviews were conducted in six rural villages in Guizhou Province; participants included schoolchildren , children’s parents and grandparents , and village doctors . Data analysis exposed three predominant reasons for high STH prevalence: ( 1 ) lack of awareness and skepticism about the high prevalence of STH infection , ( 2 ) local myths about STH infection and deworming treatment , and ( 3 ) poor quality of village health care . The findings from this study reveal reasons for why deworming treatment is not sought , and inform specific recommendations for a deworming intervention that can more effectively address underlying barriers to deworming in areas of persistently high STH infection rates . The main barrier to seeking STH treatment is not availability or cost of the drugs , but rather the lack of impetus to seek the drugs . A comprehensive nationwide deworming program in China should involve annual provision of free deworming treatment in village clinics or schools , distribution of culturally appropriate educational materials to inform children and families about STH infection , and improvement of the quality of health care delivered by village clinicians .
Soil-transmitted helminths ( STH ) are a group of parasitic intestinal worms that can infect humans through ingestion of parasite eggs or skin contact with motile larvae . Four STH species are of particular significance in public health: roundworm ( Ascaris lumbricoides ) , whipworm ( Trichuris trichiura ) , and two species of hookworm ( Necator americanus and Ancylostoma duodenale ) [1] . As of June 2013 , it was estimated that more than one billion people around the world are infected with at least one of these four species [2] . Infections with Ascaris lumbricoides , hookworm and whipworm are often asymptomatic , but can cause consequences such as anemia and other nutritional deficiencies , stunted growth , and cognitive impairment in the long run [3] . Infections with A . lumbricoides can cause abdominal pain , lactose intolerance , and decreased absorption of vitamin A and other nutrients . Severe infection with whipworm can cause inflammation at the site of attachment in the intestines and result in colitis and rectal prolapse . Infection with hookworm may lead to intestinal blood loss that results in iron-deficiency anemia [4] . Chronic and intense STH infections contribute to malnutrition and other significant consequences for growth and cognitive development , primarily in children [4 , 5] . The diverse and non-specific nature of the clinical manifestations of STH infection hinders patients and even clinicians from making a definitive diagnosis without a stool sample . Anti-helminthic drugs can effectively treat STH infections in afflicted individuals as well as be utilized in mass drug administration among populations of children living in STH-endemic areas [4] . Albendazole , the most common pharmaceutical treatment used to treat STH infection , is a broad spectrum antihelminthic with high cure rates and fecal egg count , although efficacy varies among the different STH species . A review of seven trials testing the efficacy of albendazole among infected schoolchildren in different endemic areas found cure rates of 98 . 2% , 87 . 8% , and 46 . 6% for A . lumbricoides , hookworm , and T . trichiura respectively , and fecal egg count ( FEC ) reductions of 99 . 5% , 94 . 8% , and 50 . 8% for A . lumbricoides , hookworm , and T . trichiura respectively [6] . Side effects from albendazole are minor and relatively rare , and it is safe for use in mass drug administration programs [7] . The low cost of albendazole facilitates its wide availability in many developing countries , including large developing countries such as China and India . This in turn increases the cost-effectiveness of large-scale deworming initiatives . Historically , STH infections have been a longstanding public health challenge in China . In the 1950s and 60s , the Chinese government recognized the problem and took steps to control high infection rates , integrating STH control measures into the rural health care system [8] . Unfortunately , such health measures have since been discontinued , and the problem of STH infection has re-emerged with evidence of increased prevalence over the past several decades , especially in impoverished and remote communities [9] . Recent studies conducted in China have highlighted both high STH prevalence as well as disparities in infection prevalence between rural and urban areas [10] . In 2010 , researchers at Stanford University , the Chinese Academy of Sciences , and the Chinese Center for Disease Control and Prevention found that over 40 percent of school-aged children in poor regions of rural Guizhou Province were infected with at least one of the four most common STH species [9] . These high rates of infection were confirmed by a follow-up survey in 2013 conducted by the same research team [5] . Given the evidence of high STH infection rates in China—where safe , effective , and affordable treatment is available—the goal of this qualitative research study is to understand why there is such a high prevalence of these infections . In this paper , we investigate knowledge , attitudes , and practices regarding STHs in rural China to reveal the sociocultural and structural factors underlying the persistently high infection prevalence . We also seek to understand why so few individuals in rural communities seek deworming treatment . We anticipate that the findings will be valuable for informing the design and implementation of effective deworming campaigns in China as well as broader public health efforts .
This research was approved by the Human Subjects Committee at Stanford University and by the appropriate authorities at the Chinese Centers for Disease Control and Prevention . All relevant research procedures adhered to the guidelines of both institutions . Prior to conducting the interviews , we ensured that the participants understood the information given in the written consent process , which was reviewed and approved by the ethics committee of the Institutional Review Board at Stanford University in Stanford , California ( Protocol ID 25027 ) , as well as the Institutional Review Board at Sichuan University in Chengdu , China ( Protocol ID 2013005–02 ) . All study participants received treatment for STH infection at the conclusion of the study . The study was conducted in Guizhou Province , located on the eastern part of the Yunnan-Guizhou Plateau in southwest China . Guizhou's population , estimated to be about 35 million in 2011 , is demographically diverse with ethnic minority groups making up more than 37 percent of the population [11] . Guizhou Province is the second poorest of China’s 31 provinces , with most people living in extremely remote mountainous areas that lack adequate road infrastructure . Agriculture is the main occupation of most Guizhou inhabitants , and farmers in Guizhou often live in poverty with poor access to basic health services [11] . The study area included six villages in four different townships in rural Guizhou Province ( Fig . 1 . Map of the study area and location of the six villages in Danzhai , Guizhou , China ) . Villages within Guizhou Province were chosen for this study based on inclusion in the research team's ongoing randomized controlled trial and accessibility over several days to conduct the fieldwork [5] . In total , 49 interviews were conducted: 23 elders , 23 children , and three village doctors . The elders were either parents or grandparents of the children who participated in our study . Children in each village were chosen randomly , with their ages ranging from 9 to 12 years old . Among the children , 12 were boys and 11 were girls . In the six villages that we visited , twenty households were of the Miao minority ethnic group while three were of the Shui minority ethnic group . STH infection status ( positive or negative ) was determined at the laboratory of the county CDC using the Kato-Katz smear method on stool samples collected from schoolchildren . The study team collected one stool sample per day from each child for two consecutive days; two smears were taken from each stool sample for testing . Children were considered positive for STH infection if either one of their stool samples tested positive for one or more types of STH . Village level infection rates ranged from 13 to 57 percent ( Table 1 . STH infection prevalence by village in Danzhai County , Guizhou ) . In our study sample of 23 children , 8 tested positive for infection with at least one of the four STH species ( roundworm Ascaris lumbricoides , whipworm Trichuris trichiura , and hookworm Necator americanus and Ancylostoma duodenale ) and 15 tested negative for infection . The researchers were blind to the children's infection status at the time of the interview . Qualitative methodology for this study was comprised of in-person interviews based on semi-structured questionnaires . The interview questions consisted of an extensive list of open-ended questions that prompted individuals on various themes related to STH infection and treatment . The interview guide began with obtaining initial demographic information about the family members . The questions then progressed into inquiries about knowledge , attitudes , and practices regarding STHs and deworming treatment . The interview protocol questions varied depending on whether the interviewee was a child , a parent/grandparent of a child , or a village doctor . The most extensive interviews were conducted with the parents and grandparents of the children , with several open-ended questions surrounding four topics: children's overall health and hygiene; opinions about the village doctor; knowledge about STH infection , prevalence , and health consequences of infection with STHs; and attitudes and practices regarding seeking of deworming treatment . Questions for the children focused on their knowledge , or lack of knowledge , about STHs . Questions for the village doctors aimed to assess the extent to which they were knowledgeable about STHs , whether deworming treatment was available in the clinic , and how often they prescribed deworming treatment to patients . The interviews were conducted in both Mandarin Chinese and the Guizhou dialect of Chinese . One member of the research team in the field was a native of Guizhou Province , and clarified translations to the rest of the team . The interview protocol served as the primary guide , and it was ensured that all sections were covered . Some flexibility was utilized in following up on interviewees’ statements with additional probes and questions . Participants in the study were not paid monetarily , but each household surveyed was given a small gift of a hand towel ( valued at around 3 USD ) in gratitude for their participation . Extensive field notes and transcriptions of audio-recorded interviews were closely analyzed using the constant comparative method described by Strauss and Corbin , in which codes from the data were constantly being compared to other identified codes to elucidate similarities , differences , and patterns in our collected data [12] . The coding process consisted of several readings , after which an initial process of open coding was performed on the detailed field-notes and transcripts , followed by focused coding , in which we extracted subcodes that emerged from prominent or recurring themes , trends , and ideas in the data . Interview data from parents , children , and village doctors were also supplemented with memos and field observations in the households and villages . Thus , analyses from multiple data sources were synthesized to formulate the most accurate and complete representation possible of the knowledge , attitudes , and practices regarding STH infection and deworming treatment in rural Guizhou .
Parents and grandparents are both unaware and highly skeptical of the fact that STH infection is common in children today . In response to inquiries about how children become infected with STH , the most common response was “I don't know . ” ( Table 2 . Frequency of Household Interview Responses ) A few parents seemed to have a slightly better recognition of possible transmission , responding that perhaps children become infected when they eat unclean , raw foods , or when they play outside in the fields and get their hands and feet soiled . It should be noted that these responses were still quite vague , and seemed more like guesses than informed statements of known fact . Moreover , from the interview questions regarding health and hygiene practices in the household , all children admitted to drinking water straight from the tap and going barefoot while playing both indoors and outdoors . While some of the elders recognized that these behaviors could potentially be linked to risk factors for STH infection , they were not proactive about discouraging these behaviors among their children . In addition , some parents and grandparents noted their perceived symptoms of STH infection , which included loss of appetite , yellow face , and teeth-grinding . Similar to the elders' statements about STH transmission , these responses likewise seemed to be vague guesses about physical manifestations linked to general "sickness" rather than informed statements or observations corresponding to STH infection . When asked whether they thought that their children were currently infected with STH , most parents responded with confident denial . When asked to approximate the prevalence of STH in the village by estimating how many children out of a randomly chosen group of five village children would be infected , the majority of the parents and grandparents responded that it would be unlikely that any of the children would have STH infection . However , when asked to estimate the frequency of STH infection in the villages when they were young children , the parents responded that most , if not all , of their peers back then were likely to have been infected with STH . In fact , many of them recalled having STH infections themselves . They believe that STH infections are a disease of the past because the quality of living conditions in the village has improved since their childhood years . When asked to consider their reactions to the hypothetical situation that their child was in fact , infected with STH , the parents responded without hesitation that they would be very concerned . They stated that their response would be to take their child to the doctor and that they would want their child to be treated . The responses from the parents about how they would respond if they knew that their child contrasted with the reality of the situation . The problem is that the parents and grandparents have little to no knowledge about STH infection and are highly skeptical that their children could possibly be infected . The knowledge gap serves as the primary barrier towards the seeking of deworming treatment .
The results from this qualitative study supplement our understanding of the myriad factors that contribute to high STH prevalence in rural China . This information serves to inform health policymakers about how to approach and design more effective and comprehensive deworming interventions . First , the finding that parents are skeptical of the high prevalence of STH infection among children in the villages points to the need for greater awareness and health education . Parents , grandparents , and village doctors were uninformed about STH prevention , manifestations , and treatment . The rural Chinese health care system recognized STH as a public health problem and prioritized regular deworming of children from the 1960s to the 1980s . The subsequent disappearance of STH from the national health agenda likely facilitated the notion among adult generations that STH infection has diminished or disappeared as a problem in children today . Redirecting attention towards the problem of STH infections in rural China may provide the necessary stimulus for individuals to realize and internalize the continued significance and seriousness of the problem . The practice of regularly deworming piglets , which was common among all households interviewed , portends significant implications for a public health intervention . The majority of households in rural China raise at least one or two pigs each year as a critical source of income [13] . From the interviews , it seemed that parents practiced regular deworming of their piglets for two simple reasons: first , because they receive free deworming pills every time they purchase pig feed , and second , because they were told that the pills would produce faster-growing , healthier pigs . If village doctors were to consistently provide parents with free deworming pills twice a year , they could prompt an analogous paradigm shift in which parents make two realizations to encourage deworming as a pervasive practice: first , STH infection is common in children ( as much or even more so than in piglets ) , and second , deworming leads to improved child health and growth . The problem of distant parent-child relations preventing children from opening up to their caregivers about their health symptoms is rooted deeply in sociology and therefore , more challenging to address through health policy . However , the implication of this finding is that regularly administering deworming treatment to children in school or at home ( by village doctors or trained local health personnel , like barefoot doctors ) should be preferred to an alternative approach that relies on caregivers themselves to take the initiative to seek deworming treatment . The discovery of myths pertaining to STH infection and deworming medicine ( i . e . that helminths are necessary for digestion and that deworming medication harms fertility ) presents an extra dimension to the lack of accurate knowledge about STH infection and treatment . The educational component of a comprehensive deworming intervention should actively debunk these myths in a culturally sensitive way while also presenting factual information about STH infections and the benefits of treatment . Parents and grandparents should be cautioned that their beliefs in myths about STHs and deworming treatment are unsupported by scientific and medical evidence , and may be preventing them from properly attending to their child's health . In summary , an understanding of parents’ and grandparents’ attitudes towards STH and deworming treatment indicates that the main barrier to seeking treatment is not availability or cost of the drugs , but rather the lack of impetus to seek the drugs . A successful deworming intervention can be designed to work around this barrier , firstly by providing accurate information , but also by tasking village doctors or community health workers with administering albendazole in the households , rather than relying on compliance by the elders or children themselves . Furthermore , witnessing the absence and poor reliability of village doctors in rural Guizhou highlights the need for improvement in the local health care system . The New Cooperative Medical System ( NCMS ) requires all villages to have a designated village doctor , hence the "assignment" of individuals in the community to be the village doctor in the cases where none is present [14] . Despite the NCMS requirement , we were unable to locate village doctors in three out of the six villages that we visited . Lack of access to an adequately trained village doctor and , by extension , to deworming treatment , serves as the final barrier to accessing treatment for children . Even when village doctors were present , they themselves were uninformed about STH infections and some did not carry albendazole in their clinic . Greater attention should be directed towards structurally improving the state of the rural health care system in China as well as increasing the competence of local doctors . In addition , stricter oversight—by township and county officials—over village doctors and the medications that are stocked in their clinics would improve the reliability of doctors and encourage more individuals to seek health care at the local level . The study sample was comprised of twenty-three households and three village clinicians in six villages across four townships in Guizhou Province . While we attempted to include villages that had variable characteristics in terms of population size , geographical location , density of households in the village , and proximity to the nearest township center , the results of this study may not be representative of all households across all of the different villages in rural Guizhou Province . However , the diverse sample of households provided a collection of perspectives to inform conclusions that can be reasonably applied to poor rural households in Guizhou Province . While we do have information on prevalence of STH infection in the sample areas , we do not have information on the intensity of infection in these areas , which could have enlightened us as to whether infected individuals would be likely to have experienced acute symptoms . Despite adherence to a carefully designed and meticulously reviewed interview protocol , qualitative interview data has the potential to be influenced by inherent limitations: recall bias , social desirability bias , and possibly even the intentional misinformation or withholding of information on behalf of the participants . We attempted to minimize social desirability bias and intentional misinformation as much as possible by phrasing our questions carefully . For example , we asked adults what they perceived the deworming practices of their neighbors to be , in addition to their own practices , in case families wished to protect their own practices ( no significant distinction was noted ) . Moreover , when appropriate , all interviews were all conducted in the local dialect in an attempt to build trust between the research team and the families . The high prevalence of STH infections among millions of schoolchildren is a serious public health problem in rural China today . The findings of this study fill a critical gap in the current literature regarding the complex combination of factors that contribute to high STH burden in rural China despite the availability of effective and affordable treatment . First and foremost , there is a lack of both knowledge about STH and awareness of high infection rates in the villages , which prevents parents and grandparents from suspecting the problem of STH infections in their children . In the rare case that caregivers do realize that children in the household have STH infections , myths and false local beliefs deter and discourage them from seeking deworming treatment . The final barrier lies in the inadequate village health care system , in which village doctors are absent from their clinics or are themselves misinformed about STH . This makes deworming medication difficult to obtain even when caregivers realize that their children are infected with STHs and are willing to seek treatment . The results of this study suggest that a comprehensive deworming program to reduce STH infection rates in rural China should include the following components: free deworming treatment that is provided annually either through schools or village clinics , educational materials that provide accurate and necessary knowledge about STH , an emphasis on the impacts of deworming on children's educational attainment and future financial prospects , education about STH infections through community engagement , cultural sensitivity when overturning local myths about STH infections and deworming treatment , and lastly , greater efforts towards improving the quality of the village health care system . | Soil-transmitted helminths ( STHs ) are parasitic intestinal worms that infect more than two out of every five schoolchildren in rural China , an alarmingly high prevalence given the low cost and wide availability of safe and effective deworming treatment . Understanding of local knowledge , attitudes , and practices regarding STHs in rural China has until now , been sparse , although such information is critical for prevention and control initiatives . This study elucidates the structural and sociocultural factors that explain why deworming treatment is rarely sought for schoolchildren in poor villages of rural China with persistently high intestinal worm infection rates . In-depth , qualitative interviews were conducted in six rural villages in Guizhou Province; participants included schoolchildren , children’s parents and grandparents , and village doctors . We found evidence of three predominant reasons for high STH prevalence: lack of awareness and skepticism about STHs , local myths about STHs and deworming treatment , and poor quality of village health care . The findings have significant relevance for the development of an effective deworming program in China as well as improvement of the quality of health care at the village level . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Gut Instincts: Knowledge, Attitudes, and Practices regarding Soil-Transmitted Helminths in Rural China |
Additive genetic variance ( VA ) and total genetic variance ( VG ) are core concepts in biomedical , evolutionary and production-biology genetics . What determines the large variation in reported VA/VG ratios from line-cross experiments is not well understood . Here we report how the VA/VG ratio , and thus the ratio between narrow and broad sense heritability ( h2/H2 ) , varies as a function of the regulatory architecture underlying genotype-to-phenotype ( GP ) maps . We studied five dynamic models ( of the cAMP pathway , the glycolysis , the circadian rhythms , the cell cycle , and heart cell dynamics ) . We assumed genetic variation to be reflected in model parameters and extracted phenotypes summarizing the system dynamics . Even when imposing purely linear genotype to parameter maps and no environmental variation , we observed quite low VA/VG ratios . In particular , systems with positive feedback and cyclic dynamics gave more non-monotone genotype-phenotype maps and much lower VA/VG ratios than those without . The results show that some regulatory architectures consistently maintain a transparent genotype-to-phenotype relationship , whereas other architectures generate more subtle patterns . Our approach can be used to elucidate these relationships across a whole range of biological systems in a systematic fashion .
The broad-sense heritability of a trait , , is the proportion of phenotypic variance attributable to genetic causes , while the narrow-sense heritability , is the proportion attributable to additive gene action . The total genetic variance includes the variance explained by intra-locus dominance ( ) and inter-locus interactions ( ) . The reasons for and importance of this non-additive genetic variance that distinguishes the two heritability measures has been subject to substantial controversy for more than 80 years ( e . g . , [1]–[6] ) . It was recently shown through statistical arguments that for traits with many loci at extreme allele frequencies , much of the genetic variance becomes additive with h2/H2 ( or equivalently VA/VG ) typically >0 . 5 [3] . In populations with intermediate allele frequencies , such as controlled line crosses , lower VA/VG ratios are often reported [7] , [8] . This is illustrated in Table 1 , which summarizes estimated VA/VG ratios from a collection of studies on such populations . This wide range of h2/H2 ratios reported for line crosses cannot be explained by an allele-frequency argument , and putative explanations must be based on how the regulatory architecture of the underlying biological systems shape the genotype-phenotype ( GP ) map . It is important to understand the causal underpinnings of the observed variation in h2/H2 ratios within and between biological systems for several reasons . In human quantitative genetics , where twin studies are commonly used , most heritability estimates refer to H2 [9] . In cases where h2/H2 is low , this can lead to unrealistic expectations about how much of the underlying causative variation may be located by linear QTL detection methods [6] . On the other hand , low narrow sense heritability for a given complex trait does not necessarily imply that the environment determines much of the variation . In evolutionary biology , additive variance is the foremost currency for evolutionary adaptation and evolvability . Important questions in this context are for example ( i ) to which degree is there selection on the regulatory anatomies themselves to maintain high additive variance , ( ii ) are there organizational constraints in building adaptive systems such that in some cases a low h2/H2 ratio must of necessity emerge while the proximal solution is still selected for ? Moreover , in production biology with genetically modified , sexually reproducing organisms , one would like to ensure that the modifications would be passed over to future generations in a fully predictable way . Thus , one would like to ensure that the modification becomes highly heritable in the narrow sense . As a step towards a physiologically grounded understanding of the variation of the h2/H2 relationship across biological systems or processes , we posed the question: Are there regulatory structures , or certain classes of phenotypes , more likely to generate low VA/VG ratios than others ? Addressing this question requires the linking of genetic variation to computational biology in a population context ( e . g . , [10]–[19] ) , so-called causally-cohesive genotype-phenotype ( cGP ) modeling [15] , [17] , [18] . We applied this approach to five well-validated computational biology models describing , respectively , the glycolysis metabolic pathway in budding yeast [20] , the cyclic adenosine monophosphate ( cAMP ) signaling pathway in budding yeast [21] , the cell cycle regulation of budding yeast [22] , the gene network underlying mammalian circadian rhythms [23] , and the ion channels determining the action potential in mouse heart myocytes [24] These models differ in their regulatory architecture; below , we show that they also differ in the range of VA/VG ratios that they can exhibit . In particular , positive feedback regulation and oscillatory behavior seem to dispose for low VA/VG ratios . The results suggest that our approach can be used in a generic manner to probe how the h2/H2 ratio varies as a function of regulatory anatomy .
The five cGP models were built and analyzed with the cgptoolbox ( http://github . com/jonovik/cgptoolbox ) an open-source Python package developed by the authors; further source code specific to the simulations in this paper is available on request . In the following we describe the three main parts of the workflow: ( i ) the mapping from genotypes to parameters , ( ii ) the mapping from parameters to phenotypes , i . e . solving the dynamic models and ( iii ) the setup of Monte-Carlo simulations combining the two mappings ( Figure S1 ) . For each model , we briefly describe its origins , the software used to solve it , which parameters were subject to genetic variation , what phenotypes were recorded , and criteria for omitting outlying datasets . Figures S2 , S3 , S4 , S5 , S6 shows graphical representations of the five model systems and Text S1 contains more detailed descriptions of all five models .
The five cGP models studied in this work differ in several ways , both in their function and the underlying network structure . The glycolysis and cAMP models are both pathway models with an acyclic series of reactions transforming inputs to outputs . The glycolysis model [20] is more advanced than the metabolic models in earlier genetically oriented studies ( e . g . , [3] , [31] , [32] ) as it contains many different types of enzyme kinetics as well as negative feedback regulation of some enzyme activities through product inhibition . The cAMP model [21] contains a number of negative feedback loops , but overall it also has a clear pathway structure where the glucose signal is relayed from G-proteins to cAMP to the target kinase PKA . Both these two models have in common relatively simple dynamics with solutions converging to a stable steady state ( Figure 1A and B ) . In contrast , the three other models show cyclic behavior resulting from an interplay between positive and negative feedback loops ( Figure 1 C–E ) . However , there are clear differences between these models too . The heart cell model [24] is an excitable system with feedback mechanisms including calcium-induced calcium release and several voltage-dependent ion channels . In contrast to pacemaker cells , it relies on external pacing to initiate the action potential . The circadian rhythm model [23] , [33] is a gene expression network with intertwined positive and negative transcriptional feedback loops , giving a limit cycle oscillator with sustained oscillations even in continuous darkness . The cell cycle model [22] centers around a positive feedback loop between B-type cyclins in association with cyclin dependent kinase and inhibitors of the cyclin dependent kinase , which establishes a hysteresis loop causing the cell cycle to emerge from transitions between the two alternative stable steady states . This crude classification of the five cGP models into pathway models and more complex regulatory systems is clearly reflected in the effective dimensionality of the phenotypes arising in our Monte Carlo simulations . We studied the phenotypic dimensionality for all five cGP models by Principal Component Analysis ( PCA ) for each Monte Carlo simulation ( Figure 2 ) . Across all simulated datasets , 95% of phenotypic variation of the glycolysis and cAMP models can be explained by the first 3 principal components , the cell cycle and heart cell models require the first 5 principal components , and 7 components are required for the circadian model . Since the genotype-to-parameter maps are additive for all five models , these differences in the effective dimensionality of high-level phenotypes indicate that the mappings from parameters to phenotypes are simpler for the pathway models than the other three models . This , together with results on the effect of positive feedback on statistical epistasis in gene regulatory networks [11] , suggested that the glycolysis and cAMP models might result in higher VA/VG ratios than the other three models . The results confirmed our expectations regarding high VA/VG ratios for the glycolysis and cAMP models . Furthermore , a number of distinct patterns emerged . The cAMP model shows the overall highest VA/VG ratios values ( Figure 3A and Table S6 ) , with mean and median values above 0 . 99 across all recorded phenotypes . The 0 . 05-quantile ( i . e . only 5 percent of the Monte Carlo simulations show lower values than this ) VA/VG values were above 0 . 97 for all phenotypes and no values lower than 0 . 6 were observed . In other words , an intra- and inter-locus additive model of gene action very well approximates the genotype-phenotype maps arising from this cGP model . The glycolysis model also has mean and median VA/VG values close to 1 for all phenotypes ( Figure 3B and Table S7 ) . But compared to the cAMP model , the numbers are clearly lower; the lowest recorded mean value ( phenotype BPG ) is 0 . 9 and 0 . 05-quantile values are below 0 . 7 for some phenotypes . A few VA/VG values below 0 . 5 are observed for all phenotypes . The distribution of VA/VG ratios for the cell cycle model ( Figure S7 and Table S8 ) is quite similar to that of the glycolysis model , with a lowest mean VA/VG value of 0 . 93 for time to peak for Sic1 and with 0 . 05-quantiles below 0 . 8 for some phenotypes . VA/VG values below 0 . 1 are observed for a few Monte Carlo simulations in some phenotypes . For each of the cAMP , glycolysis and cell cycle models the distributions of VA/VG ratios were quite similar across all phenotypes , and a large majority of the Monte Carlo simulations showed very high ratios . The circadian clock model differs from these three models both in terms of displaying large variation between phenotypes and in terms of having a much larger proportion of low VA/VG values ( Figure 4A and Table S9 ) . Four phenotypes stand out with VA/VG distributions that resemble a uniform distribution U ( 0 , 1 ) . These are the time from bottom to peak for the phosphorylated and unphosphorylated proteins of Per and Cry , and they have median VA/VG values ranging from 0 . 46 to 0 . 70 and 0 . 05-quantile values in the range 0 . 04 to 0 . 10 . The remaining phenotypes have somewhat higher VA/VG values , but over half of the recorded phenotypes have 0 . 05-quantiles below 0 . 6 . Median VA/VG values are below 0 . 9 for the majority of phenotypes of the action potential model . And all recorded phenotypes have a large proportion of low VA/VG ratios ( Figure 4B and Table S10 ) with 0 . 05-quantiles in the range 0 . 18-0-35 . The distributions are quite similar across action potential and calcium transient phenotypes , but the time to 90% repolarization for the action potential shows somewhat higher values than the others . All five cGP models are capable of creating VA/VG ratios close to 1 , and except for two phenotypes for the circadian model all median values of VA/VG are well above 0 . 5 . This supports the hypothesis [30] that biological systems tend to involve regulatory machinery that in general leads to considerable additive genetic variance even at intermediate allele frequencies . That is not to say that selection cannot sometimes produce regulatory solutions that tend towards low VA/VG ratios; in fact , the incidence of low VA/VG ratios varied markedly among the five models that we studied . Because the genotype-parameter maps were purely additive , all non-additive genetic variance is a result of non-linearity in the ODE models . The expected effect of introducing non-additivity in the genotype-parameter maps would be a further decrease in the VA/VG ratios . Our finding that models with complex regulation involving positive feedback loops tend to give lower VA/VG agrees with a previous study on gene regulatory networks [11] . Considering the relatively high VA/VG ratios of the cell cycle model compared to the circadian and action potential models , the following quote from Tyson and Novak's [34] discussion of why the cell-cycle is better understood as a hysteresis loop than as a limit cycle oscillator ( LCO ) , is highly informative: “Generally speaking , the period of an LCO is a complicated function of all the kinetic parameters in the differential equations . However , the period of the cell division cycle depends on only one kinetic property of the cell: its mass-doubling time . ” This seems to explain why the genotype-phenotype maps arising from the cell-cycle models are much more linear than the maps from the circadian model , which is an LCO . In a given population VA/VG is a function of allele frequencies as well as the GP map , and GP maps with strong interactions can still give high VA/VG values in populations with extreme allele frequencies [3] . In populations with intermediate allele frequencies the VA/VG values are determined mainly by the shape of the genotype-phenotype map , and the observed differences between the five cGP models in the distribution of VA/VG values motivates a search for underlying explanatory principles . The recently proposed concept of monotonicity ( or order-preservation ) of GP maps seems to be one such principle . In short , a GP map is said to be monotone if the ordering of genotypes by gene content ( the number of alleles of a given type ) is preserved in the ordering of the associated phenotypic values ( see [30] for details ) . Figure 5 depicts three extreme types of GP maps seen in our simulations . Nearly additive GP maps as shown in Figure 5A give VA/VG values very close to one . GP maps with strong magnitude epistasis , but still order-preserving , typically result in intermediate VA/VG values ( Figure 5B ) , while highly non-monotone or order-breaking GP maps ( Figure 5C ) showing strong overdominance and/or sign epistasis result in VA/VG values close to zero . Based on recent results from studies of gene regulatory networks [30] , we anticipated that the three cGP models with complex regulation involving positive feedback would result in considerably more non-monotone or order-breaking GP maps than the two pathway models . To test this , we measured the amount of order-breaking in all simulated GP maps ( see Methods ) and found a very clear pattern ( Figure 6 ) . While the datasets from the glycolysis and cAMP models contained only 1 . 1% and 1 . 3% GP maps with order-breaking for any locus , those from the cell cycle , circadian and action potential models contained 20 . 7% , 44 . 4% and 69 . 5% , respectively . Moreover , monotone GP maps gave higher VA/VG values than non-monotone GP maps for all five cGP models ( Mann-Whitney test; p-values below 1e-10 for all five models ) . However , despite the fact that the glycolysis model rarely shows order-breaking even for a single locus , it possesses much lower VA/VG values than the cAMP model . A plausible explanation is that the steady-state concentrations of metabolites can markedly increase for parameter values close to a saddle-node bifurcation point [26] . Simulation outcomes with unstable steady states were discarded , but in those cases where one of the genotypes ( i . e . parameter sets ) come close to the bifurcation point without crossing it we get genotype-phenotype maps as in Figure 5B , where one genotype ( or a small set ) gives much higher phenotypic values than the others . Such GP maps , similar to the duplicate factor model in Hill et al . [3] , are known to give low VA/VG ratios despite being monotonic . Similar GP maps giving VA/VG ratios close to zero were also found by Keightley [32] in his study of metabolic models possessing null alleles at all loci . Our main reason for restricting the sampled genetic variation of parameters to within 30% of the published baseline values was to avoid qualitative ( or topological ) changes of the dynamics . Such qualitative changes are often biologically realistic descriptions of knockouts or other large genetic changes , for example action potentials of alternating amplitude ( alternans ) [17]; loss of stable circadian oscillation [23]; and non-viable cell-cycle mutants phenotypes [22] . However , since the heritability and variance component concepts are defined for phenotypes showing continuous rather than discrete variation , we sought to avoid such qualitative changes here . We ran simulations with five polymorphic loci for the cAMP ( Figure S8A ) , glycolysis ( Figure S8B ) , cell cycle ( Figure S9 ) and action potential ( Figure S10 ) models ( the circadian model describes only three genes explicitly ) . The resulting VA/VG values were slightly lower than with three loci , but the overall shape of the distributions and the clear differences between models did not change . This indicates that our findings are of general relevance for oligogenic traits . It should be emphasized that the five studied cGP models differ in several other aspects than those highlighted here , such as the system size ( number of state variables ) and the process time scales . These features could also contribute to the observed variation in the distributions of VA/VG ratios . However , such structural differences are unavoidable when the aim is to compare experimentally validated models designed to describe specific biological systems . A complementary approach is to study generic models where system size and equation structure is fixed , while the connectivity matrix can be changed to describe a family of systems [35] . This facilitates graph-theoretic comparison of systems at the expense of some biological realism . We anticipate that the major conclusions from such studies will be similar to ours , but it may very well be that other important generic insights may also come to the fore . All the models in our study describe parts of the cellular machinery and the resulting phenotypes are thus cellular rather than organismal . We do not think this is a major shortcoming in terms of the main conclusions that emerge from our results . However , we anticipate that application of our approach on multiscale models including cellular , tissue and whole-organ phenotypes [36] will provide a much improved foundation for explaining how properties of the GP map vary across and within biological systems in terms of regulatory anatomy and associated genetic variation [37] , [38] . As our approach can be used together with any computational biology model , it has the potential to contribute substantially to a theoretical foundation capable of predicting when we are to expect low or high VA/VG or h2/H2 ratios from the principles of regulatory biology . Causally cohesive genotype-phenotype modeling thus appears to qualify as a promising approach for integrating causal models of biological networks and physiology with quantitative genetics [39]–[44] . | The broad-sense heritability of a trait is the proportion of phenotypic variance attributable to genetic causes , while the narrow-sense heritability is the proportion attributable to additive gene effects . A better understanding of what underlies variation in the ratio of the two heritability measures , or the equivalent ratio of additive variance VA to total genetic variance VG , is important for production biology , biomedicine and evolution . We find that reported VA/VG values from line crosses vary greatly and ask if biological mechanisms underlying such differences can be elucidated by linking computational biology models with genetics . To this end , we made use of models of the cAMP pathway , the glycolysis , circadian rhythms , the cell cycle and cardiocyte dynamics . We assumed additive gene action from genotypes to model parameters and studied the resulting GP maps and VA/VG ratios of system-level phenotypes . Our results show that some types of regulatory architectures consistently preserve a transparent genotype-to-phenotype relationship , whereas others generate more subtle patterns . Particularly , systems with positive feedback and cyclic dynamics resulted in more non-monotonicity in the GP map leading to lower VA/VG ratios . Our approach can be used to elucidate the VA/VG relationship across a whole range of biological systems in a systematic fashion . | [
"Abstract",
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"Methods",
"Results/Discussion"
] | [
"genetics",
"biology",
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] | 2013 | Effect of Regulatory Architecture on Broad versus Narrow Sense Heritability |
The analytical validation of sensitive , accurate and standardized Real-Time PCR methods for Trypanosoma cruzi quantification is crucial to provide a reliable laboratory tool for diagnosis of recent infections as well as for monitoring treatment efficacy . We have standardized and validated a multiplex Real-Time quantitative PCR assay ( qPCR ) based on TaqMan technology , aiming to quantify T . cruzi satellite DNA as well as an internal amplification control ( IAC ) in a single-tube reaction . IAC amplification allows rule out false negative PCR results due to inhibitory substances or loss of DNA during sample processing . The assay has a limit of detection ( LOD ) of 0 . 70 parasite equivalents/mL and a limit of quantification ( LOQ ) of 1 . 53 parasite equivalents/mL starting from non-boiled Guanidine EDTA blood spiked with T . cruzi CL-Brener stock . The method was evaluated with blood samples collected from Chagas disease patients experiencing different clinical stages and epidemiological scenarios: 1- Sixteen Venezuelan patients from an outbreak of oral transmission , 2- Sixty three Bolivian patients suffering chronic Chagas disease , 3- Thirty four Argentinean cases with chronic Chagas disease , 4- Twenty seven newborns to seropositive mothers , 5- A seronegative receptor who got infected after transplantation with a cadaveric kidney explanted from an infected subject . The performing parameters of this assay encourage its application to early assessment of T . cruzi infection in cases in which serological methods are not informative , such as recent infections by oral contamination or congenital transmission or after transplantation with organs from seropositive donors , as well as for monitoring Chagas disease patients under etiological treatment .
Chagas disease , caused by the protozoan parasite Trypanosoma cruzi ( T . cruzi ) , remains a major public health concern in 21 endemic countries of America , with an estimated prevalence of 8 million infected people [1] . The human disease occurs in two stages: an acute stage , which occurs shortly after an initial infection , and a chronic stage that develops over many years . Out of individuals at the chronic stage , 60–80% will never develop symptoms , while the remaining 20–40% will develop life-threatening heart and/or digestive disorders during their lifetime [1] , [2] . Individuals from different endemic regions are infected with distinct parasite populations , recently classified into six Discrete Typing Units ( DTUs ) , designated as T . cruzi I ( TcI ) to T . cruzi VI ( TcVI ) [3] , initially defined as “sets of stocks that are genetically more related to each other than to any other stock and that are identifiable by common genetic , molecular or immunological markers” [4] . These DTUs are differently distributed in the endemic regions and transmission cycles and probably are differently involved in the clinical manifestations and severity of the disease [5] , [6] . TcI is the major cause of Chagas disease in northern South America and Central America and prevails in wild cycles throughout the continent [6] , whereas TcII , TcV and TcVI are predominant in the southern cone [7]–[10] . Moreover , remarkable intra-DTU variability has been observed within TcI , hence five groups of genotypes ( TcIa to TcIe ) have been proposed [11]–[16] . Current chemotherapies are more effective in recent infections than in chronic disease [17] , being the serological conversion to negative the accepted criteria for cure , which usually occurs years after treatment , hampering the execution of clinical trials using novel drugs in chronically infected adult cohorts [18] . On the other hand , parasitological response to treatment is usually monitored by means of Strout , hemoculture or xenodiagnosis , which lack of sensitivity in the chronic phase [19] . In this context , the development of sensitive and accurate quantitative PCR ( qPCR ) strategies for T . cruzi quantification is crucial to provide a surrogate marker to assess treatment efficacy . A few real-time PCR strategies have been developed for detection of T . cruzi in Chagas disease patients [20]–[22] . Our group developed a SYBR-Green based qPCR strategy which used an internal amplification control ( IAC ) that was added to each blood sample prior to DNA extraction [22] . Although this meant an improvement in qPCR for Chagas disease , amplification of T . cruzi and IAC targets had to be done in separate tubes . Accordingly , we developed and standardized a multiplex qPCR strategy based on TaqMan technology , aiming to quantify both T . cruzi and IAC DNAs in a single-tube multiplex reaction . This work presents the analytical validation and evaluation of this qPCR test in blood samples from Chagas disease patients under diverse clinical and epidemiological scenarios .
The study was approved by the ethical committees of the participating institutions , namely , Comité de Bioética de la Provincia de Jujuy ( CPBJ ) and Comité de Bioética de la ANLIS “Dr Carlos G . Malbrán” , Ministerio de Salud , Argentina; Comité de Bioética de la Facultad de Medicina , Universidad Mayor de San Simón , Cochabamba , Bolivia; Comité de Bioética del Instituto de Medicina Tropical , Universidad Central de Venezuela , Caracas , Venezuela; following the principles expressed in the Declaration of Helsinki . Written informed consents were obtained from the adult patients and from parents/guardians on behalf of all newborns and children participants . Seronegative human blood samples were spiked with cultured epimastigotes of Sylvio X10 and CL-Brener stocks ( TcI and TcVI , respectively ) and mixed with one volume of Guanidine Hidrochloride 6M , EDTA 0 . 2 M buffer , pH 8 . 00 ( GE ) . A pZErO-2 recombinant plasmid containing an inserted sequence of Arabidopsis thaliana aquaporin was used as an heterologous extrinsic IAC [22] . The recombinant was gently provided by Dr Jorge Muschietti and coworkers ( INGEBI-CONICET , Argentina ) . It was used to transform Escherichia coli bacteria in the presence of kanamicine to obtain plasmidic DNA after column extraction . For PCR purposes , the recombinant plasmid was linearized using the restriction enzyme Pst1 . The assay was evaluated in different groups of patients , as follows: Group 1 ( G1 ) : Sixteen Venezuelan patients detected during the study of an outbreak of oral transmission of T . cruzi in an urban school in the Municipality of Chacao , Caracas , Venezuela [23] . All 16 patients were symptomatic , presenting facial edema , long lasting high fever and decay . Serological studies were positive on the basis of ELISA-IgM , ELISA-IgG , indirect hemagglutination test and lytic antibodies . The patients were treated with Benznidazole for one week plus three months with Nifurtimox and followed-up during two years after treatment . The qPCR assay was carried out at time of diagnosis , and 24 and 48 months after the end of treatment . Culture isolates obtained from one of these patients were genotyped as TcId ( Diaz Bello Z et al . , unpublished data ) . Five mL of peripheral blood samples were obtained for the analysis and immediately mixed with an equal volume of GE buffer , boiled during 15 min . and conserved at −20°C . Group 2 ( G2 ) : Sixty three chronic Chagas disease patients from Bolivia ( Chagas Epidemiological Network , Dr Faustino Torrico and DNDi , Dr Isabela Ribeiro ) . Ten mL of peripheral blood samples were obtained for the analysis and immediately mixed with an equal volume of GE and conserved at 4°C . Group 3 ( G3 ) : Thirty four patients with chronic Chagas disease from Argentina admitted to a clinical trial entitled TRAENA ( “Tratamiento en adultos” , Dr Adelina Riarte , unpublished data ) . Ten mL of peripheral blood samples were obtained and immediately mixed with an equal volume of GE , boiled during 15 min . and conserved at 4°C . Group 4 ( G4 ) : Twenty seven out of 74 newborns to seropositive mothers delivered at Hospital Pablo Soria , San Salvador de Jujuy , Argentina from September 2011 to March 2012 , were analyzed by qPCR . This province has been declared free of vectorial transmission [24] . Serodiagnosis of pregnant women was done by means of conventional serological methods . Newborns were tested by the microhematocrite test [25] and positive cases were treated with Benznidazole . Three out of the 74 newborns ( 4 . 0% ) were positive by the microhematocrite method . In eight newborns , 5 mL of umbilical cord blood was collected at delivery , in other 15 cases 1 mL of peripheral blood was withdrawn , and in four ones both umbilical and peripheral blood were collected . The umbilical cord was clamped , the segment was cleaned with a broad-range antiseptic product ( Povidone-iodine , Phoenix Lab; Argentina ) and 5 mL of blood was withdrawn from the end closer to the placenta . Samples were collected in tubes containing an equal volume of GE , boiled during 15 min . and stored at 4°C for DNA purification and PCR analysis . Group 5 ( G5 ) : One seronegative patient ( 42 years old , man ) that received on emergency a kidney transplant from a seropositive cadaveric donor followed up by Dr Roberta Lattes at the “ Instituto de Nefrología Buenos Aires” . Infection by T . cruzi was diagnosed by serological methods and Strout 121 days after transplantation and Benznidazole treatment was implemented during 60 days . Samples were treated with an equal volume of GE , boiled during 15 min . and stored at 4°C for DNA purification and PCR analysis . Blood samples treated with GE ( GEB ) from G1 , G2 , G3 and G5 were processed using the High Pure PCR Template Preparation kit ( Roche Diagnostics Corp . , Indiana , USA ) : Five µL of linearized IAC ( 40 pg/µL ) were added to 100 µL of binding solution in a clean tube and 300 µL of GEB ( G2 ) or 200 or 300 µL of boiled GEB ( G1 and G3/G5 , respectively ) were added and the mix was homogenized . This quantity of IAC was chosen because it renders a Ct value around 20 , which is in the middle of the linear range of IAC amplification , as reported [22] . The solution was further mixed with 40 µL of proteinase K by vortexing during 15 sec . , spinned down and incubated at 70°C for 10 min . in a dry thermo-block . After spin down , 100 µL of isopropanol were added , vortexed during 15 sec . and spinned down . Each sample was loaded into an extraction column placed into a 2 mL microtube . The content was centrifuged at 8000 rpm during 1 min . The extraction column was placed into a new collection tube . Inhibitors removing solution ( 500 µL ) was added to each column and centrifuged as described before . The column was placed into a new tube . Washing solution ( 500 µL ) was added to the column and centrifuged as described before . The column was placed into a new tube and the washing step was repeated . The column was placed into a 1 . 5 mL microtube and centrifuged at maximum speed for 10 sec . One hundred µL of pre-heated elution buffer were added to the column and centrifuged as previously described . The eluate was stored at −20°C for qPCR analysis . In order to build the standard curves for quantification of parasitic loads in G1 , G2 , G3 and G5 patients' specimens , DNA from spiked blood was prepared in the same way as reported for the clinical samples . Three hundred µL of boiled GEB samples from G4 newborns were processed using the QIAamp DNA Mini Kit , after addition of 5 µL of linearized IAC ( 40 pg/µL ) to the lysis buffer and processed as recommended by the manufacturer ( Qiagen , USA ) . DNA from spiked blood used to build the respective standard curve for quantification was extracted as described for G4 samples . On the basis of a previously reported TaqMan procedure for detection of T . cruzi satellite DNA [21] that showed high sensitivity and specificity in an international PCR study [26] , we assayed the same T . cruzi primers and probe and designed a set of primers and probe for the IAC target ( Table 1 ) . The melting temperatures of IAC Fw and IAC Rv primers are similar to those of Cruzi 1 and Cruzi 2 primers ( 61 . 2°C , 60 . 9°C , 58 . 4°C and 59 . 5°C , respectively ) using Oligo Calculator version 3 . 26 at http://www . basic . northwestern . edu/biotools/oligocalc . html . The qPCR reactions were carried out with 5 µL of re-suspended DNA , using FastStart Universal Probe Master Mix ( Roche Diagnostics GmbHCorp , Mannheim , Germany ) in a final volume of 20 µL . Optimal cycling conditions were a first step of 10 min . at 95°C followed by 40 cycles at 95°C for 15 sec . and 58°C for 1 min . The amplifications were carried out in a Rotor-Gene 6000 ( Corbett , UK ) or in an Applied Biosystems ( ABI 7500 , USA ) device . Standard curves were constructed with 1/10 and 1/2 serial dilutions of total DNA obtained from a GEB sample spiked with 105 par . eq . /mL of blood . TcI and TcVI based standard curves were used to quantify parasitic loads in G1 and in G2–G5 samples , respectively . In order to evaluate the influence of the concentrations of IAC template , primers and probe in the efficiency of T . cruzi DNA amplification in the multiplex format , DNA extracts from samples carrying 0 . 5 to 750 par . eq . /mL as well as samples without T . cruzi were amplified by both simplex qPCR ( only T . cruzi primers and probe ) and multiplex qPCR formats . In order to assess the influence of T . cruzi load on the efficiency of IAC amplification in the multiplex format , T . cruzi DNA samples obtained to build the CL-Brener standard curve were amplified and the IAC was quantified . For this , a standard curve was built with DNA obtained from 300 µL of GEB spiked with 50 to 800 pg of linear IAC on duplicate as well as the PCR assay from each DNA lysate . A negative control and two positive controls containing different concentrations of T . cruzi DNA were included in every run: namely a high-positive control and a low-positive control near the lower limit of detection , as recommended [30] . The Tukey's criterion ( boxplots ) [31] was used to detect samples with outlier Ct values of IAC ( Cts>75th percentile+1 . 5×interquartile distance of median Ct ) , which would indicate inhibition or material loss in samples from a same experiment/clinical group with n>10 . The bilateral t test was done to compare the IAC recovery between a ) boiled and not boiled spiked GEB samples , b ) umbilical and peripheral blood samples in G4 newborns , c ) peripheral blood samples from G2 and G3 chronic cases after elimination of outlier samples , and d ) samples processed using QIAamp versus Roche DNA extraction kits . Values of p<0 . 05 were considered as significative . The software InfoStat 2012 ( Infostat/Students version 2 . 0 . Infostat/FCA Group . Córdoba's National University; Ed . Brujas , Córdoba , Argentina ) was used for the analysis . Satterwait's correction was applied in cases of non-homogeneous variances .
We compared the qPCR positivity in 1 fg and 10 fg of purified DNA samples from cultured parasites of reference stock CL-Brener and in a panel of GEB samples from 18 chronic Chagas disease patients from Cochabamba , Bolivia using four different commercial Master Mixes developed for real-time PCR: namely TaqMan Fast Advanced Master Mix ( Invitrogen , USA ) , FastStart Universal Probe Master Mix ( Roche Diagnostics GmbHCorp , Mannheim , Germany ) , TaqMan Universal PCR Master Mix ( Applied Biosystems , USA ) and Multiplex PCR Kit , ( Qiagen , USA ) . Each Master Mix was challenged with different combinations of Cruzi 1 and Cruzi 2 primers ( 0 . 25 , 0 . 5 , 0 . 75 and 1 µM ) and Cruzi 3 TaqMan probe ( 50 , 100 , 200 and 400 nM ) concentrations . A first experiment using purified T . cruzi DNA , allowed discarding TaqMan Fast Advanced Master Mix ( Invitrogen ) because it was incapable of detecting 10 fg of T . cruzi DNA . The remaining 3 Master Mixes were evaluated using 5 µL of DNA lysates obtained from the mentioned panel of GEB samples , out of which the FastStart Universal Probe Master Mix ( Roche ) gave 12 PCR positive results ( 66 . 67% ) , the Multiplex PCR Kit ( Qiagen ) gave 7 PCR positive results ( 38 . 89% , also positive with Fast-Start Universal Probe Master Mix ) and the TaqMan Universal PCR Master Mix ( Applied Biosystems ) gave 3 PCR positive results ( 16 . 67% , also positive with the other Master Mixes ) . Accordingly , subsequent optimization and validation of the multiplex assay was carried out using FastStart Universal Probe Master Mix and the concentrations of primers and probes described in Table 1 . In multiplexed assays , IAC amplification must be limited to avoid competition with subsequent T . cruzi DNA amplification . Thus , we evaluated different concentrations of IAC Fw and Rv primers ( 0 . 06 , 0 . 08 , 0 . 1 , 0 . 2 and 0 . 5 µM ) and IAC TaqMan probe ( 50 , 100 , 200 and 400 nM ) to obtain a limiting IAC amplification with high efficiency . Higher analytical sensitivity was achieved working with 0 . 75 µM T . cruzi primers , 0 . 1 µM IAC primers and 50 nM of T . cruzi and IAC TaqMan probes , using the FastStart Universal Probe Master Mix ( Roche Diagnostics GmbHCorp , Mannheim , Germany ) . Similar Ct values for a panel of T . cruzi DNA concentrations were obtained when qPCR was carried out in simplex or multiplex formats , indicating that IAC template as well as IAC primers and probe did not interfere with the efficiency of parasite DNA amplification ( data not shown ) . Moreover , T . cruzi DNA samples spanning 0 . 25 par . eq . /mL to 105 par . eq . /mL did not interfere in the efficiency of IAC amplification , indicating no inhibition of the IAC in the presence of the tested parasite loads ( data not shown ) . Amplification of IAC standard curve had an efficiency of 91 . 7% ( y = −3 . 539x+19 . 831 , R2 = 0 . 994; Figure S1 ) . Besides , no significant differences in IAC amplification were obtained from 22 replicates of boiled and not boiled spiked GEB samples , giving mean Ct values of 19 . 13 ( IC95% [19 . 07–19 . 19] ) and 19 . 04 ( IC95% [18 . 92–19 . 16] ) , respectively ( p = 0 . 2204 ) . The Multiplex qPCR test was carried out on blood samples from different groups of patients , namely Venezuelan patients infected by the oral route ( G1 , n = 16 ) , chronic Chagas disease patients from Bolivia ( G2 , n = 63 ) and Argentina ( G3 , n = 34 ) and newborns to seropositive women ( G4 , n = 27 ) ; in the latter group , peripheral blood as well as cord blood samples were tested ( Table 5 and Figure 3 ) . The proportion of qPCR positive results was 87 . 5% in G1 , and 60 . 3 to 76 . 5% in G2 and G3 , respectively ( Table 5 ) . In G4 , only 3 out of the 27 newborns to seropositive mothers were qPCR positive , two cases were detected from the umbilical cord blood sample ( case 1: T . cruzi Ct: 21 . 14 , 3 . 84 log10 par . eq . /10 mL , IAC Ct: 18 . 10 and case 2: T . cruzi Ct: 20 . 27 , 4 . 07 log10 par . eq . /10 mL , IAC Ct: 18 . 05 ) whereas the third one was detected from the peripheral blood sample ( case 3 , T . cruzi Ct: 27 . 51 , 2 . 14 log10 par . eq . /10 mL , IAC Ct: 19 . 42 ) . These three cases were diagnosed as congenitally infected by means of the microhematocrite assay , thus , concordance between qPCR and microhematocrite was 100% . The parasitic loads were heterogeneous in the studied populations , being highest in G4 and lowest in G2 , in which only three out of the 38 qPCR positive samples were quantifiable ( 1 . 25 , 1 . 44 and 1 . 45 log10 par . eq . /10 mL blood ) , indicating in the majority of G2 patients very low parasitic loads , below the LOQ of the assay ( Figure 3 ) . On the other hand , the individual with highest parasitic load belonged to G1 , presenting 5 . 23 log10 par . eq . /10 mL blood , compatible with an acute infection ( Figure 3 ) . In order to validate the above mentioned T . cruzi qPCR results on each clinical group on the basis of IAC recovery , the Tukey's criterion was applied to each group of tested specimens , allowing detection of outliers ( Table 6 ) . No outliers were obtained , except for a single blood sample from G2 ( PCC 311 , IAC Ct 19 . 20 , Table 6 ) . Moreover , given that some groups of clinical specimens were processed using different DNA extraction kits , we compared the IAC recovery between samples extracted using the QIAamp DNA Mini Kit ( Qiagen ) with those using the High Pure PCR Template Preparation kit ( Roche ) ( mean IAC-PCR Cts 19 . 60 vs 18 . 35 , respectively , p<0 , 0001 ) , showing higher recovery using the latter kit . Figure 4A depicts parasitic loads obtained from peripheral blood samples collected from three patients of G1 at time of diagnosis and after etiological treatment . The tested cases presented T . cruzi loads higher than 3 log10 par . eq . /10 mL of blood at time of diagnosis , becoming undetectable one year after treatment . However , two years after treatment , the qPCR rendered positive results , though with low parasitic loads , indicating that the patients were already in a chronic form because of the treatment failure . Figure 4B shows parasitic loads from a 42 year-old seronegative man who received kidney transplantation from a seropositive cadaveric donor and became infected . Acute infection by T . cruzi was detected 93 days after transplantation by means of qPCR , however it was diagnosed by conventional parasitological ( Strout ) and serological tests only 121 days after transplantation . Upon conventional diagnosis , treatment with Benznidazole was initiated . Parasitic loads diminished and were non-detectable in the sample collected 159 days after transplantation , persisting non-detectable at least 228 days after transplantation .
In 2007 , an international collaborative study to evaluate current PCR procedures for detection of T . cruzi infection was initiated [26] . A high variability was observed among laboratories and methods that used similar DNA extraction procedures and identical primer sequences , confirming that the lack of standardization led to poor reproducibility , precluding the possibility to compare findings among different laboratories . Furthermore , some methods showed an important reduction of the analytical sensitivity when spiked blood samples were tested in comparison to purified parasite DNA , suggesting that the DNA purification step was crucial for the PCR yield . Since most procedures lacked internal amplification controls , discrimination between true and false negative results could often not be assessed . Indeed , PCR cannot be given diagnostic status , before it includes an internal amplification control [32] . Homologous extrinsic controls , as well as heterologous intrinsic and extrinsic controls have been proposed as IACs [30] . The former may give rise to competitive reactions with the target . Heterologous intrinsic controls are often referred as “housekeeping genes” and are conserved fragments of the host's genome that are present naturally in patient specimens in low copy number . These controls are amplified with a different set of primers in the same or a separate reaction vessel . Commonly used intrinsic controls include the genes encoding beta-globin , beta-actin , RNAse P , among others . Depending on the marker chosen and the specimen type , intrinsic controls can be used to establish the presence of cellular material in a clinical specimen . A concern when using intrinsic controls is that the number of human gene copies may be much higher than the target infectious organism copy number and thus have an amplification advantage and not accurately test for inhibition [30] . Furthermore , when analysing blood samples , patients with different blood cell counts will render heterogenic values of the control precluding the possibility to evaluate the yield of DNA extraction , as well as to accurately quantify the target sequence relative to the sample volume . Finally , heterologous extrinsic controls are non-host-derived controls that require primers and probes different from the target . They are added to the sample before DNA preparation and dually serve as extraction and amplification controls . In this context , the latter type of IAC has been used in our multiplex qPCR approach . In this work , to validate T . cruzi qPCR results , the Tukey's boxplot method was carried out using the Ct values of IAC products from all samples tested in every PCR run , in order to detect outlier values of IAC-PCR [32] that would indicate poor DNA yield or inhibition , leading to sub-estimate the parasitic load or to give a false negative result . Although satellite DNAs belong to the fast-evolving portion of eukaryotic genomes , it has been shown that over 100 satellite units of nine T . cruzi strains from different DTUs display almost 100% of nucleotide identity . No DTU-specific consensus motifs have been identified , inferring species-wide conservation [33] . The method was inclusive for all DTUs , though variations in analytical sensitivity were found among parasite stocks belonging to different DTUs , reflecting disparities in gene dosage of their satellite repeats [22] , [34] , [35] . Interestingly , the qPCR analytical sensitivity was variable among different TcI genotypes too [13] , [16] , indicating for the first time , heterogeneity in satellite copy numbers within this DTU . In this scenario , trueness of parasitic load measurements should be more accurate if standard curves are built using a strain belonging to the same DTU/genotype of the patient under follow-up . However , this may be unfeasible in clinical practice , because direct typing of parasite DTUs or genotypes from clinical samples is difficult , in particular in chronic Chagas disease patients [9] , [10] , [13] , [36] . Nevertheless , since it has been observed that bloodstream parasite genotypes are persistent during chronic infection [37] or reactivation [36] , any parasite stock could be useful as a standard as long as it is included through the whole monitoring of a certain patient or cohort . T . rangeli and T . cruzi are found in the same mammalian hosts , sharing triatomine vectors and a significant portion of their antigenic coat , hence T . rangeli infections and/or mixed infections by both species may confound the diagnosis . However , T . cruzi harbors satellite sequences at a much higher dosage than T . rangeli [38] . Moreover , leishmanial infections may lead to serological cross-reaction with T . cruzi . The qPCR test was also selective for T . cruzi DNA; it did not amplify DNAs from Leishmania sp . and amplified T . rangeli DNA only at high concentrations ( Table 3 ) . Analysis of GEB samples from different groups of individuals , allowed identification of different degrees of qPCR positivity as well as parasitic loads . Among the 16 orally infected cases from G1 , 14 were positive in the qPCR test ( 87 . 5% ) and baseline parasitic loads ranged between 1 . 69 and 5 . 23 log10 par . eq . /10 mL blood , which is compatible with acute infections . Quantitative PCR monitoring is reported for three cases ( Figure 4A ) . This analysis allowed detection of treatment failure two years after the conclusion of treatment . However , at that time parasitic loads were lower than at baseline analysis , which is compatible with the evolution of the infection to the chronic phase . These patients have received a second treatment and are currently under follow-up ( Dr Belkisyole Alarcón de Noya , unpublished data ) . In chronic Chagas disease cases parasitic loads were low , especially in G2 , which was conformed by adult patients from Bolivia . Indeed , many of them gave detectable but non-quantifiable qPCR results ( Figure 3 ) . The T . cruzi qPCR result of the sample giving an outlier IAC-PCR value ( Table 6 ) was positive ( Ct: 32 . 77 , 0 . 69 log10 par . eq . /10 mL blood ) yet below the LOQ . Then , if a more accurate parasitic load is needed , the DNA extraction and amplification of the same GEB sample should be repeated and the result re-analyzed . Samples from G3 presented higher degree of PCR positivity and parasitic loads than G2 . One difference between both groups is that G3 GEB specimens were boiled before DNA extraction . In fact , many PCR methods using GEB samples incorporated a boiling step before DNA extraction [26] . This was originally designed to enhance sensitivity of procedures based on minicircle DNA amplification [39] . Indeed , incubating samples during 15 minutes favoured fragmentation of minicircle concatemers and distribution of individual minicircles throughout all sample volume , allowing processing of small aliquots ( 100 µL ) with satisfactory sensitivity [39] . In this context , experiments to determine the LOD of the multiplex qPCR assay were carried out from both boiled and non-boiled spiked samples , obtaining slightly higher sensitivity using boiled GEB ( 0 . 46 vs 0 . 70 par . eq . /mL , respectively; p = 0 . 044 ) . So , we can not discard that higher PCR positivity and higher parasitic loads found in G3 chronic cases were partially influenced by the boiling step . However , as the boiling procedure might enhance the risk of cross-contamination among samples , leading to false positive results , we decided to continue the analytical validation of the qPCR using non-boiled spiked samples . Finally , the lower qPCR positivity and parasitic burden of G2 specimens could also be an intrinsic feature of the study population , such as the host genetic background and immunologic status which in turn may play a role in control of parasitic replication . Another factor could be related to the strains involved , though in both countries TcV appears to be the predominant DTU [10] , [20] , [40] . Among G4 newborns to seropositive mothers , we detected three positive cases , both by qPCR and microhematocrite , which allowed early diagnosis of congenital infection and subsequent treatment with Benznidazole . Thus , clinical sensitivity of qPCR respect to microhematocrite was 100% . The final diagnosis of cases with negative findings by microhematocrite and qPCR will be assessed by means of serological tests at 9 months of age , allowing determination of the qPCR sensitivity respect to final diagnosis . Interestingly , mothers of G4 infected newborns were also qPCR positive ( unpublished data ) , in agreement with the reported correlation between maternal parasitemia and risk of vertical transmission [37] , [41] . Cord blood has proven useful for early detection of congenital T . cruzi infection , with the advantage of being a non-invasive specimen without volume restrictions [42] , [43] . The IAC recovery from G4 peripheral and umbilical cord blood samples showed no significant differences ( p = 0 , 0589 ) . Bern and coworkers observed that qPCR carried out from cord blood samples increased sensitivity for early diagnosis of congenital infection in comparison with conventional parasitological examination [43] . However , risk of contamination with parasite DNA from maternal blood may exist; accordingly the cord must be washed prior to sampling . Standard operative procedures for umbilical cord blood collection are still needed . The qPCR method was also useful for earlier diagnosis of post-transplant infection in a seronegative receptor of a cadaveric organ explanted from an infected subject . This may allow prompt treatment before the appearance of clinical signs and symptoms of acute disease . In this work , we have presented multiplex TaqMan qPCR-based results using blood specimens treated with GE , following the criteria used in an international collaborative study [26] . However , in a recent work , TaqMan qPCR strategies targeted to the satellite sequence as well as to minicircle DNA were also satisfactory when tested in fresh-EDTA blood samples and in buffy-coat preparations [44] . Further evaluation of our multiplex qPCR test in different type of biological specimens and conservation conditions will allow its validation for different clinical , experimental and eco-epidemiological settings . When compared to SYBR Green qPCR strategies [22] , the multiplex qPCR assay presents the advantages that it permits simultaneous detection of target DNA and the internal control , allowing identification of reduction in parasitic load or negative findings due to inhibitors or DNA loss; moreover , the TaqMan strategy decreases the likelihood of obtaining false positive results , due to the specificity of TaqMan probes and the multiplex format is less expensive and cumbersome , since only one PCR reaction per sample is needed . It is expected that the use of this qPCR strategy in clinical trials will demonstrate the potential of parasitic loads as surrogate markers of treatment efficacy . Demonstration of cure is up-to-date based on persistent seronegative results after treatment implementation , which in chronic Chagas disease usually takes many years to occur . Especially in these patients , fluctuancy of parasitic loads along lifetime determines that undetectable bloodstream qPCR results can not be taken as indicative of cure . On the contrary , persistence of positive qPCR findings is indeed indicative of treatment failure . In addition , this methodology can offer early diagnosis of infection in cases in which serological methods are not informative , such as transmission by the oral , congenital , transfusional routes or after transplantation with organs from seropositive donors or in events of Chagas disease reactivation due to immunosuppression . | Chagas disease , caused by the parasite Trypanosoma cruzi , is endemic in several Latin American countries and still represents a major neglected tropical threat . It is transmitted to humans by blood-sucking triatomine bugs , congenital transmission , blood transfusion , organ transplantation and by consuming food and juice contaminated with the parasite . Tools for accurate diagnosis and surrogate markers of parasitological response to treatment remain key needs in the field . This study focused on the evaluation of a novel quantitative PCR assay for the diagnosis and follow-up of patients with Chagas disease , on the basis of international guidelines for analytical validation of molecular diagnostic methods . The method allows the simultaneous amplification of parasite satellite DNA sequence and a heterologous internal amplification control that permits rule out false negative results due to inhibitory substances or loss of DNA during sample processing . It was evaluated in peripheral blood samples from acute and chronic patients as well as in umbilical cord blood samples from newborns to seropositive mothers . The performing characteristics of this assay position it as a promising candidate for application to clinical trials and kit developments . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"biology"
] | 2013 | Analytical Performance of a Multiplex Real-Time PCR Assay Using TaqMan Probes for Quantification of Trypanosoma cruzi Satellite DNA in Blood Samples |
Miltefosine ( MF ) is the first oral compound used in the chemotherapy against leishmaniasis . Since the mechanism of action of this drug and the targets of MF in Leishmania are unclear , we generated in a step-by-step manner Leishmania major promastigote mutants highly resistant to MF . Two of the mutants were submitted to a short-read whole genome sequencing for identifying potential genes associated with MF resistance . Analysis of the genome assemblies revealed several independent point mutations in a P-type ATPase involved in phospholipid translocation . Mutations in two other proteins—pyridoxal kinase and α-adaptin like protein—were also observed in independent mutants . The role of these proteins in the MF resistance was evaluated by gene transfection and gene disruption and both the P-type ATPase and pyridoxal kinase were implicated in MF susceptibility . The study also highlighted that resistance can be highly heterogeneous at the population level with individual clones derived from this population differing both in terms of genotypes but also susceptibility phenotypes . Whole genome sequencing was used to pinpoint known and new resistance markers associated with MF resistance in the protozoan parasite Leishmania . The study also demonstrated the polyclonal nature of a resistant population with individual cells with varying susceptibilities and genotypes .
Leishmania is a protozoan parasite responsible for a spectrum of diseases collectively known as leishmaniasis in tropical and subtropical areas of the world [1] . There is no effective vaccine for the prevention of this parasitic disease and its control relies on chemotherapy . The arsenal of available drugs is limited with most compounds being compromised by toxicity , cost , or resistance [2] . The alkyl-lysophospholipid analogue miltefosine ( MF ) , a drug initially developed as an antitumoral compound , was the first effective oral drug against Leishmania . It has been successfully used in the last decade for the treatment of visceral leishmaniasis [3]–[5] . The exact mode of action of MF is not well understood , but it was shown to induce changes in the biosynthesis of phospholipids and the metabolism of alkyl-lipids in Leishmania [6] , in addition to induce an apoptotic cell death [7] , [8] . The uptake of MF and other alkyl-glycerophospholipids in Leishmania requires a P-type ATPase , named miltefosine transporter ( MT ) , which is responsible for the translocation of phospholipids from the exoplasmic to the cytoplasmic leaflet of the plasma membrane of the parasite [9] . Based on findings in yeast [10] , [11] , the uptake of MF in L . donovani was further shown to require a protein named LdRos3 , the β-subunit of the MT [12] . Differences in susceptibility to alkyl-lysophospholipids between Leishmania species [13] , [14] has recently been associated with the low expression of this MF translocation machinery in species displaying a lack of intrinsic MF susceptibility [15] . Resistance to MF in Leishmania [12] and other organisms [16] , [17] involved a decreased accumulation of the drug due to reduced uptake or increased efflux . In L . donovani , a drastic reduction ( 40-fold decrease ) in the ability to internalize the drug was shown to be accompanied by a decreased content of unsaturated phospholipids in the plasma membrane of resistant parasites [18] , [19] . The acquisition of inactivating mutations or the deletion of the MT gene in L . donovani [9] , [20] was shown to drastically increase MF resistance both in vitro and in vivo . In addition , the overexpression of ABCG4 and ABCG6 , two ABC proteins localized to the flagellar pocket and plasma membrane of the parasite , was associated with an increased resistance to several alkyl-lysophospholipid analogues in Leishmania [21]–[23] . Parasites overexpressing ABCG4 or ABCG6 had a reduced accumulation of fluorescent phospholipids and [14C]-MF , which suggested an enhanced outward translocation of the drug across the plasma membrane . A L . tropica mutant resistant to daunomycin harboring an amplification of the ABC protein ABCB4 ( MDR1 ) was also shown to display cross-resistance to MF [24] , although the absence of similar amplicons in MF-resistant L . donovani suggested that the amplification of ABCB4 ( MDR1 ) is not a frequent mechanism of resistance to MF [25] . Finally , functional cloning experiments identified a hypothetical gene in L . infantum whose increased expression conferred resistance to MF and antimony [26] . The central role of MF in the control of leishmaniasis warranted further studies on its mode of action and the mechanisms involved in resistance . Indeed , the ease with which MF-resistant parasites are selected in vitro suggests that clinical failures resulting from MF-resistant isolates are likely . In this study , we sought to determine the mutational events involved in MF resistance on a whole genomic scale by sequencing the entire genome of two L . major mutants independently selected for high level MF resistance in vitro .
L . major Friedlin and L . infantum ( MHOM/MA/67/ITMAP-263 ) wild-type promastigotes were grown at 25°C in SDM-79 medium supplemented with 10% heat inactivated fetal bovine serum and 10 µg/ml hemin . The L . major Friedlin MF80 . 1 , MF80 . 2 , MF80 . 3 and MF80 . 5 mutants were selected from a cloned parental population using a stepwise selection until they were resistant to 80–100 µM MF . MF was purchased from Cayman Chemical ( Ann Harbor , USA ) . To obtain clones derivied from the mutants 5 . 3 , 10 . 3 , 15 . 3 40 . 3 and 80 . 3 , parasites were spread on SDM-agar ( 1% Noble Agar , Nunc ) plates in absence of MF . Partial revertants were obtained by culturing the resistant lines in the absence of MF for 30 passages . The L . infantum MF200 . 3 and MF200 . 5 mutants selected for MF resistance had been generated previously [27] . Growth curves were obtained by measuring absorbance at 600 nm as previously described [28] . Gene transfection was performed by electroporation as reported previously [29] . Statistical significance was determined by Student's t-test . Significance was considered as P<0 . 05 . Total DNA was isolated using DNAzol reagent ( Invitrogen ) as recommended by the manufacturer . For quantitative Southern blots , the genomic DNA was digested with appropriate restriction enzymes and migrated in 0 . 8% agarose gels . Southern blots , hybridizations and washes were performed following standard protocols [30] . Probes were obtained by amplifying the upstream or downstream regions of the gene studied . The pSP72αHYGα-MT construct was generated by the amplification of the MF transporter gene from L . major Friedlin ( LmjF13 . 1350 ) with primers containing the restriction enzymes XbaI and HindIII ( Table S1 ) and cloned in the respective plasmid . All the constructs were checked by sequencing with internal primers ( Table S1 ) . Similarly , the genes pyridoxal kinase ( LmjF30 . 1250 ) and α-adaptin like protein ( LmjF07 . 0050 ) were cloned in pSP72αNEOα or pSP72αHYGα[31] at XbaI and HindIII sites . The pyridoxal kinase gene ( LmjF30 . 1250 ) inactivation cassette was generated by overlap extension PCR [32] , [33] using the primer A-PK-KO and primer B-PK-NEO-KO to amplify a region of 550-bp upstream of the gene; primer E-PK-NEO-KO and primer F-PK-KO to amplify a DNA fragment of 600-bp downstream of the gene; and primers C-PK-NEO-KO and primer D-PK-NEO-KO , to amplify the NEO gene . The fragment generated was cloned in the pGEM-T-easy vector leading to the pGEM-T-KO-NEO-PK . The LmjF30 . 1250 inactivation cassette was isolated from pGEM-KO-NEO-PK by an EcoRI digestion and transfected in L . major Friedlin . A second inactivation cassette containing a HYG marker was generated using the A and F primers described above along with primers B-PK-HYG-KO , D-PK-HYG-KO and E-PK-HYG-KO ( Table S1 ) . The fragment generated was cloned in the pGEM-T-easy vector leading to the second construct pGEM-T-KO-HYG-PK to inactivate the PK gene . The integration of the inactivation cassette at the LmjF30 . 1250 locus was confirmed by PCR and Southern blots . A rescue vector containing the wild-type gene was generated by PCR amplification using the primers PKF and PKR , cloned in pGEM-T easy vector , sequenced and then subcloned in pSP72αZEOα generating the plasmid pSP72αZEOα-PK . All the primers used are listed in Table S1 . Genomic DNAs were prepared from mid-log phase cultures of L . major Friedlin mutants MF80 . 3 and MF80 . 5 and L . major Friedlin wild-type parasites and sequenced using Illumina Genome AnalyzerIIx short 76-nucleotide single-end reads . We had a genome coverage over 50-fold for the two independent mutants and the wild-type parasite . This strategy allowed us to identify point mutations and small indels ( ≤3 bp ) when comparing with the known genome sequence of L . major Friedlin [34] . Sequence reads from each clone were aligned to the L . major Friedlin reference sequence available ( TriTrypDB version 3 . 3 ) [35] using the software bwa ( bwa aln , version 0 . 5 . 9 ) with default parameters [36] . The maximum number of mismatches was 4 , the seed length was 32 and 2 mismatches were allowed within the seed . The detection of single nucleotide polymorphisms ( SNPs ) was performed using samtools ( version 0 . 1 . 18 ) , bcftools ( distributed with samtools ) and vcfutils . pl ( distributed with samtools ) [37] , with a minimum of three reads to call a potential variation prior to further analysis . The sequence data for L . major Friedlin wild-type , MF80 . 3 and MF80 . 5 are available at the EMBL European Nucleotide Archive ( http://www . ebi . ac . uk/ena ) ( accession number ERP000917; samples ERS056015 , ERS056016 and ERS056017 corresponding to L . major Friedlin wild-type; MF80 . 3 and MF80 . 5 respectively ) . Several python ( version 2 . 4 . 3 ) scripts and bash ( version 3 . 2 ) scripts were created to further analyse the data . The quality assessment software samstat ( v1 . 08 ) was used to generate quality reports [38] . All the putative point mutations detected by whole genome sequencing were verified by PCR amplification and conventional DNA sequencing . The primers were designed using Primer3 program [39] .
Four independent L . major Friedlin MF-resistant mutants ( MF80 . 1 , 80 . 2 , 80 . 3 and 80 . 5 ) were generated by a stepwise fashion until they reached a MF EC50 of at least 80 µM ( Fig . 1 ) . The MF80 . 3 and MF80 . 5 lines displayed the highest levels of resistance , with at least 15 fold increase in resistance compared to the wild-type ( WT ) parent . The phenotype of MF resistance appeared relatively stable , as culturing the parasites in absence of MF for 30 passages led to only a small albeit significant decrease in resistance in both MF80 . 3 and MF80 . 5 ( Fig . 1 , lanes 6 , 7 ) but also in MF80 . 1 and MF80 . 2 ( data not shown ) . Because of the small decrease of resistance , we tested whether MF resistance in our mutants was accompanied by gene amplification , a frequent mechanism of drug resistance in Leishmania [40] , [41] . However , neither alkaline lysis nor clamped homogeneous electrical field electrophoresis revealed the presence of circular or linear amplicons in any of MF resistant mutants ( results not shown ) . The genomes of one clone derived from the wild-type L . major , MF80 . 3 and MF80 . 5 mutants were resequenced using the single-end reads Illumina platform . We obtained a total of 30 , 083 , 681 , 28 , 369 , 175 and 28 , 259 , 044 reads leading to an average genome coverage of 52-fold for the wild-type L . major Friedlin strain and MF80 . 3 and MF80 . 5 respectively . In all cases , close to 80% of the reads were aligned to the L . major Friedlin genome . The unmapped reads ( 25 . 1% , 20 . 2% and 18 . 5% respectively for L . major wild-type , MF80 . 3 and MF80 . 5 ) had low mean base quality according to samstat ( version 0 . 08 ) [38] with slightly less than half mapping to mitochondrial DNA . Read depth coverage can be used to predict copy number variations being either amplifications or deletions . We investigated copy number of chromosomal regions by read depth coverage for each chromosome of the resistant mutants which were compared to the wild-type sensitive L . major Friedlin . This analysis did not reveal changes in read depth between sensitive and resistant isolates that would constitute signatures of copy variation , hence confirming the absence of gene amplification in these mutants but also indicating no obvious gene/locus deletion or changes in ploidy ( data not show ) . While no change in copy number was related to resistance , we found that some chromosomes were polyploid in both L . major Friedlin wild-type cells and in the two MF-resistant clones . Chromosome 1 , 5 , 11 , 20 and 23 were in three copies while chromosome 31 was at least in six copies in the wild-type cells but also in the mutants ( Fig . S1A–C ) . While no changes in copy number were observed , the resequencing of the L . major Friedlin wild-type strain and of the mutants differed from the published sequence [34] . Indeed , 20 homozygous ( Table S2 ) and 42 heterozygous SNPs ( Table S3 ) were observed . The polymorphic sites identified here in the coding sequences were highlighted by resequencing the wild-type Friedlin strain , the two MF-resistant strains and also other L . major Friedlin resistant mutants that we are currently analysing in the lab . A majority of polymorphisms ( 68% ) changed the amino acids of the encoded proteins ( Table S2 and S3 ) . Thus , the majority of putative mutations found by this next-generation sequencing approach corresponded to natural polymorphisms present in the parental clones and that were not selected for during the drug pressure . However , whole genome sequencing of the two miltefosine resistant mutant clones also revealed genuine mutations ( Table 1 ) . Indeed , a three nucleotide deletion was seen in in the MT gene of MF80 . 3 ( LmjF13 . 1530 ) and a transition mutation leading to a mutated G852D protein version was observed in mutant MF80 . 5 ( Table 1 ) . These mutations highlighted by next generation sequencing were confirmed by PCR amplification and standard DNA sequencing and the same validation approach was used for all other mutations that will be discussed afterwards . Interestingly , another mutation was in a common gene for both mutants . It corresponded to the LmjF30 . 1250 gene that code for a putative pyridoxal kinase ( PK ) . The MF80 . 3 mutant acquired a mutation in both alleles that led to a T216M amino acid substitution while one wild-type allele and one allelic version leading to a G269D protein were observed in the MF80 . 5 line ( Table 1 ) . Other mutations were specific to either MF80 . 3 or MF80 . 5 . Three heterozygous mutations in LmjF15 . 0800 and LmjF . 27 . 1250 , both coding for hypothetical proteins , and in LmjF36 . 5880 coding for a small GTPase were found in MF80 . 3 ( Table 1 ) . The sequences of the MF80 . 5 clone allowed the detection of a homozygous mutation in LmjF07 . 0050 coding for an α-adaptin like protein and an heterozygous mutation in the hypothetical protein coding gene LmjF21 . 0835 ( Table 1 ) . Since few genuine point mutations were highlighted by whole genome sequencing , we next amplified and sequenced these specific mutated genes in additional L . major and L . infantum MF resistant mutants that were available . This targeted approach revealed that only the MT and PK genes were mutated in the four independent L . major resistant mutants ( Table 2 ) , and that MT was also mutated in the two L . infantum mutants investigated ( Table 2 ) . The mutations were at different sites in the L . infantum or L . major mutants ( Table 2 ) , but these mutations occurred at conserved sites within the MT protein ( Fig . S2 ) . The targeted sequencing of the PK gene in the other L . major mutants revealed that they were also heterozygous for mutations leading to G269D or T216M amino acid substitutions , respectively ( Table 2 ) . However , the PK gene was not mutated in the two L . infantum MF resistant mutants analysed ( Table 2 ) . The LmjF07 . 0050 α-adaptin gene was the only other mutation that was not strain-specific since the gene was also mutated in the two independent L . infantum MF resistant mutants ( Table 2 and Fig . S3 ) . The four remaining mutations highlighted by next-generation sequencing were unique to either MF80 . 3 or MF80 . 5 ( Table 2 ) . Surprisingly , we did not observe point mutations in the Ros3 β-subunit of MT , a known MF-resistance gene [12] , in any of the miltefosine resistant mutants analyzed ( Table 2 ) . We were intrigued by the diversity of mutations in the MT gene between the different mutants ( Table 2 ) suggesting that mutations in MT could be polyclonal . We investigated the MT status in clones ( and population ) isolated at intermediate passages leading to the generation of MF80 . 3 . Most interestingly , no mutations in MT were found in any of the resistant populations leading to MF80 . 3 ( Fig . 2 ) , suggesting indeed that resistance can be polyclonal , and since no clones are dominant , mutations can be missed when sequencing the population . In none of the 10 clones investigated derived from MF5 . 3 , MF10 . 3 , or MF15 . 3 could we detect any mutations in MT . However in the 10 clones derived from MF40 . 3 where the MT gene was sequenced , all clones had a mutated MT and 5 different genotypes were observed . Three clones had a three nucleotide deletion leading to the deletion of M547 , two had a deletion of M546 , one clone had a D567V mutation and 4 clones had a L235R mutation ( 3 homozygous and one heterozygous ) ( Fig . 2 ) . The three clones derived from MF80 . 3 had the deletion of M547 ( Fig . 2 ) . The situation was different in the lineage leading to MF80 . 5 . Indeed , the homozygous G852D mutation found in the MT of the sequenced clone ( Table 1 ) was also detected in the MF80 . 5 population but also in the MF60 . 5 population and the mutation was heterozygous at the earlier passages of MF20 . 5 and MF40 . 5 ( Table 3 ) . Similarly to the lineage leading to MF80 . 3 , mutations in the MT gene were observed only at the highest drug concentration . In contrast to MT , mutations in the PK gene LmjF30 . 1250 appeared during the first selection of MF selection in both MF80 . 3 and MF80 . 5 mutants ( Fig . 2 and Table 3 ) . While the mutation remained heterozygous in the MF80 . 5 lineage ( Table 3 ) , the number of mutated alleles in the PK gene in the MF80 . 3 lineage , with the exception of MF40 . 3 ( see below ) , correlated with resistance levels ( Fig . 2 ) . The role in resistance of the MT mutations highlighted in one clone of MF80 . 3 and MF80 . 5 was further studied by gene transfection experiments . Episomal transfection of the wild-type copy of MT sensitized wild-type cells to MF by 3-fold whereas transfections of the G852D or M547- versions cloned in the same vector did not change the phenotype ( Fig . 3A , bars 2–4 ) . Transfection of the wild-type gene copy of MT in MF80 . 3 and MF80 . 5 sensitized the mutants to MF although not to wild-type levels ( Fig . 3B and C , bar 2 ) . Significantly , transfection of the mutated versions of MT neither sensitized the mutant cells nor the wild-type cells , showing that these mutations alter the function of MT ( Fig . 3 , bars 3 , 4 ) . The role of the PK and α-adaptin-like proteins that were mutated in more than one mutant ( Table 1 ) , in MF resistance was also assessed by gene transfection experiments . In contrast to the MT gene , however , the overexpression of WT versions of the PK or α-adaptin-like genes failed to alter the MF resistance levels of L . major WT ( Fig . 3A , bars 5 and 6 ) , MF80 . 3 ( Fig . 3B , bars 5 and 6 ) and MF80 . 5 mutants ( Fig . 3C , bars 5 and 6 ) . Transfection of mutated versions of the PK and α-adaptin-like genes also failed to alter the MF susceptibility status of the wild-type L . major Friedlin strain ( data not shown ) . The PK mutations were acquired during the first step of selection in both lines , prior to the selection of mutations in the MT ( Fig . 2 and Table 3 ) . To further investigate the role of PK in MF resistance , we investigated clones at different level of selection giving rise to MF80 . 3 . The genotype of PK varied with clones being either heterozygous ( WT/T216M ) or T216M homozygous mutants ( Fig . 2 ) . The level of resistance to MF and the number of mutated PK alleles was not straightforward but a wild-type PK allele has never been found in MF resistant cells ( Fig . 2 , and Table 3 ) . Only heterozygous mutations were found in clones derived from MF5 . 3 but in MF10 . 3 we found a mixture of clones with either homozygous or heterozygous mutations in PK , while all clones derived from MF15 . 3 had homozygous T216M mutation in PK ( Fig . 2 ) . In the MF40 . 3 clone , we observed cells that were surprisingly heterozygous for PK but these cells have a mutation in MT , a mutation not present in the MF15 . 3 derived clones ( Fig . 2 ) and possibly the establishment of mutations in MT is facilitated in cells still having some wild-type PK activity . When the MF40 . 3 population was selected for higher resistance to yield MF80 . 3 , all clones exhibited the same PK homozygous mutation ( Fig . 2 ) . We also investigated the susceptibility levels of selected clones for the earlier passages . While , as expected , the population of MF5 . 3 , MF10 . 3 and MF15 . 3 became more and more resistant to MF ( Fig . 4 ) we found variations in the EC50 between the individual clones derived from the population . Indeed , some clones were as resistant as the population and others had susceptibilities close to wild-type levels ( Fig . 4 ) . For example , clones 4 and 10 derived from MF5 . 3 had the same genotype ( for MT and PK ) but clone 4 had an EC50 close to wild-type while clone 10 was resistant to MF ( Fig . 4 ) . Similarly , the cloning of MF10 . 3 led to parasite with either homozygous ( clone 3 ) or heterozygous ( clone 7 ) mutations in PK and clone 3 was found to be more resistant ( Fig . 4 ) . However , clones 1 and 2 derived from MF15 . 3 had the same genotype ( for MT and PK ) but their resistance to MF varied ( Fig . 4 ) . While no susceptibility phenotype was observed in transfecting PK in MF80 . 3 ( Fig . 3B ) , the MF15 . 3 mutant is homozygous for the T216M PK substitution and still has a wild-type version of MT ( Fig . 2 ) . The transfection of the wild-type PK-containing plasmid in MF15 . 3 resulted in the reversion of MF resistance ( Fig . 5 ) . In contrast , the transfection of theT216M version had no effect on MF susceptibility ( Fig . 5 ) . We next investigated whether deletion of PK was possible and tested how this could modulate MF susceptibility . Two replacement cassettes containing the neomycin phosphotransferase ( NEO ) or hygromycin phosphotransferase ( HYG ) genes were generated for replacing PK ( Fig . 6 ) . Since Leishmania is diploid , the sequential integration of the NEO or HYG inactivation cassette should lead to a 1 . 5 and 1 . 8 kb DNA fragment , respectively , in addition to the remaining WT PK fragment of 1 . 2 kb when the DNA of transformants is digested with PstI and hybridized to a 3′UTR PK probe corresponding to a 600 bp fragment upstream the PK gene stop codon ( Fig . 6A ) . The generation of NEO single disruptant led to the expected genotype ( Fig . 6B , lane 2 ) . Introduction of the HYG construct into a PK/NEO parasite indicated that while both NEO and HYG alleles integrated properly , there was always a remaining wild-type allele ( Fig . 6B , lane 3 ) , a phenomenon often observed when attempting to inactivate essential genes [42]–[45] . Parasites with a single allele of PK inactivated with NEO were more sensitive to MF than the control-transfected parasites ( Fig . 6C ) and this phenotype could be partially rescued by the addition of an episomal construct encoding a functional copy of PK ( Fig . 6C ) .
We sequenced two L . major MF-resistant mutants in order to identify mutations putatively associated with MF resistance . Our strategy was based on sequencing two independent resistant strains and to assess the role in resistance of recurrent mutations . Leishmania often amplifies regions of its genome in response to drug selection [40] , [41] , but this does not appear to be the case for the MF resistant strains as determined by read depth sequence analysis ( Fig . S1A–C ) and also further confirmed by comparative genomic hybridization experiments ( unpublished observations ) . The absence of amplicons in our MF-resistant L . major is consistent with the previous characterization of MF resistant strains of L . donovani [25] . However , the analysis revealed the presence of point mutations in protein coding genes for both L . major MF-resistant mutants ( Table 1 ) . Three mutations were found in more than one mutant: the miltefosine transporter MT LmjF13 . 1530 , the pyridoxal kinase PK LmjF30 . 1250 , and the α-adaptin like protein LmjF07 . 0050 ( Table 2 ) . The MT protein is a P-type ATPase that mediates the inward translocation of MF and glycerophospholipid analogues in Leishmania [9] , [12] . The acquisition of inactivating mutations in the MT gene was shown to be a major determinant of resistance to MF in L . donovani and it appears that several distinct mutations in MT can confer resistance in both promastigote and amastigotes parasites [9] , [20] . The mutant Li MF200 . 5 with the MT gene mutated , was indeed shown to be MF resistant using an intramacrophagic essay ( results not shown ) . Although several distinct MT point mutations have also been identified in our panel of L . major MF-resistant mutants ( Table 2 ) , they all differed from those previously reported in L . donovani . These mutations were located at conserved residues within the P-type ATPase protein ( Fig . S2 ) . The number of mutated alleles in MT correlated with the level of resistance to MF with the most resistant strains being homozygous mutants . Mutations in MT only occur at later step of drug selection ( Fig . 2 , Table 3 ) . Prior to MT mutations , the only other recurring mutation in low-level resistant cells was the acquisition of the T216M or G269D amino acid substitutions in the PK gene . Similarly as for MT , the level of MF resistance correlated with the number of mutated PK alleles in the MF 80 . 3 lineage ( Fig . 2 ) . As explained in the result section , it is possible that mutations in MT happens in a minority population of cells with heterozygous PK alleles and this could explain how cells appear to be mostly homozygous for a mutant allele in PK ( 15 . 3 ) , and then becomes heterozygous ( 40 . 3 ) . . Pyridoxal-5′-phosphate ( PLP ) is the active form of vitamin B6 and is an essential cofactor of several enzymes , predominantly involved in the transformation of amino acids [46] , [47] . Leishmania cannot synthesize PLP de novo and rely on the scavenging of vitamin B6 precursors like pyridoxal , pyridoxine and pyridoxamine [48] . PK was shown to catalyze the ATP-dependent phosphorylation of these precursors and to play an essential role in the formation of the biologically active PLP [46] . The role of PK in MF susceptibility warrants further studies and may be due to the many roles of PLP in cell physiology . Point mutations in PK are found in the four L . major MF-resistant mutants ( Table 2 ) and transfection of a wild-type gene in MF15 . 3 restored susceptibility ( Fig . 5 ) . Mutations in PK were not observed in MF resistant L . infantum ( Table 2 ) , possibly suggesting additional species-specific differences . For example , the expression of the MF translocation machinery was found to vary between species and this altered their MF susceptibilities [15] . Mutations in PK were in conserved regions between Leishmania and other species but not in active sites ( Fig . S4 ) , suggesting that the mutated PK possibly retains activity . These mutations may modulate activities necessary to mitigate the effect of MF . Reintroduction of wild-type alleles eliminates this selective advantage . Our inability to generate a PK null mutant may suggest that this gene is essential and cells lacking one allele were shown to be more susceptible showing that PK is not likely to be a target of MF and that other activities of PK or some enzymatic reactions requiring PLP are essential to deal with MF stresses . MF was shown to induce cellular events similar to those resulting from alterations in the metabolism of PLP . Indeed , MF was shown to induce altered content of phosphatidylcholine ( PC ) and phosphatidylethanolamine ( PE ) in MF-sensitive but not MF-resistant L . donovani parasites [19] , [49] . Interestingly , vitamin B6 deficiency in rat liver cells was shown to lead to a drastic reduction in the methylation of PE to PC [50] . MF was also shown to induce the accumulation of reactive oxygen species ( ROS ) in a dose-dependent manner in Leishmania and this could be reverted by reduced thiols [27] . The major reduced thiol in Leishmania is the glutathione-spermidine conjugate trypanothione . The rate limiting step of spermidine biosynthesis is catalyzed by the PLP-dependent enzyme ornithine decarboxylase [51] . Moreover , vitamin B6 has recently been associated with resistance to oxidative stresses and to be itself an efficient scavenger of ROS [52] . Mutations in PK may thus help in sustaining MF pressure by maintaining the PLP levels , the homeostasis of phospholipids , or the redox state of the parasite . Obviously , further experiments will be required to confirm these hypotheses . Other mutations were identified in the mutants MF80 . 3 and MF80 . 5 but these were specific to a mutant with the exception of the α-adaptin protein where mutations were also observed in two L . infantum mutants ( Table 2 ) . However , these mutations occurred only in the latest step of selection and transfection of the α-adaptin gene did not change MF susceptibility ( Fig . 3 ) . While most of the other mutated genes code for hypothetical proteins and are found in single mutants they may be worth further investigating in additional studies . This study highlighted a number of concepts of general interest in the genesis of drug resistance with also possible practical implications . Resistance appears to be highly heterogeneous at the level of the population with individual clones within the population differing both in terms of genotype ( Fig . 2 ) but also phenotype ( Fig . 4 ) . This may be similar to the recent description of bacterial charity where in a resistant bacterial population there are clones with variable susceptibility , various clones helping others depending on the selective pressure [53] . This heterogeneity both in genotype and phenotype in resistant populations may explain the difficulties when studying field isolates . Analysis of drug resistance in several clones may be more revealing than population analysis . Indeed , the sequencing of the MF40 . 3 ( or MF80 . 3 ) population failed to highlight mutations in MT but the sequencing of several clones showed mutations associated with resistance ( Fig . 2 ) . This study also showed the sequential order of appearance of mutations , with mutations during the selection of resistance in PK arising always before mutations in MT and heterozygous mutation being more frequent in the lower resistant cells and homozygous mutations prevailing in more resistant isolates . Whole genome sequencing is becoming a useful technique to study resistance in bacteria [54]–[58] and presently applied to the larger parasite genomes [59] , [60] . This study pinpointed known and new resistance markers and also allowed the demonstration of the polyclonal nature of a resistant population with individual cells with varying susceptibility and genotypes . In further studies we hope to study more deeply the clonal variety within a resistant population and how these contribute to either resistance or fitness , as recently highlighted for a population of resistant bacteria [53] . | Leishmania spp . are parasitic protozoa responsible for a spectrum of diseases known as leishmaniasis . There are few drugs available for the treatment of these diseases , and miltefosine is the first oral drug used in treatment of visceral leishmaniasis , a form of the disease that can be lethal if not treated . In this study , we seek to understand the mechanism of action and identify targets of the drug by generating promastigote mutants highly resistant to miltefosine . Two independent mutants were submitted to short read whole genome sequencing . Genome analysis of these mutants has permitted us to identify point mutations in three genes ( P-type ATPase , pyridoxal kinase and α-adaptin like protein ) that were also present in other independent miltefosine resistant mutants . Some of the new genes identified here could be useful as potential markers for miltefosine resistance in Leishmania . Moreover , our approach has permitted us to highlight that resistance can be highly heterogeneous at the population level with individual clones derived from this population differing both in terms of genotypes but also susceptibility phenotypes . This may have practical applications while studying resistance . | [
"Abstract",
"Introduction",
"Methods",
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"Discussion"
] | [
"genomics",
"virology",
"biology",
"computational",
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] | 2012 | Multiple Mutations in Heterogeneous Miltefosine-Resistant Leishmania major Population as Determined by Whole Genome Sequencing |
Vocal folds are used as sound sources in various species , but it is unknown how vocal fold morphologies are optimized for different acoustic objectives . Here we identify two main variables affecting range of vocal fold vibration frequency , namely vocal fold elongation and tissue fiber stress . A simple vibrating string model is used to predict fundamental frequency ranges across species of different vocal fold sizes . While average fundamental frequency is predominantly determined by vocal fold length ( larynx size ) , range of fundamental frequency is facilitated by ( 1 ) laryngeal muscles that control elongation and by ( 2 ) nonlinearity in tissue fiber tension . One adaptation that would increase fundamental frequency range is greater freedom in joint rotation or gliding of two cartilages ( thyroid and cricoid ) , so that vocal fold length change is maximized . Alternatively , tissue layers can develop to bear a disproportionate fiber tension ( i . e . , a ligament with high density collagen fibers ) , increasing the fundamental frequency range and thereby vocal versatility . The range of fundamental frequency across species is thus not simply one-dimensional , but can be conceptualized as the dependent variable in a multi-dimensional morphospace . In humans , this could allow for variations that could be clinically important for voice therapy and vocal fold repair . Alternative solutions could also have importance in vocal training for singing and other highly-skilled vocalizations .
A biological trait is usually the result of a trade-off between different selective forces and constraints [1] . Vocal behavior is no exception , and one important set of constraints is related to the mechanism of sound production . In order to understand the design of vocal organs ( larynx and syrinx in vertebrates ) , investigators have often focused on size as the primary determining factor of fundamental frequency and acoustic power produced by a sound source . In fact , a number of size-dependent factors are responsible for the observation that species of larger body sizes tend to produce lower frequencies[2] , [3] , yet some observations cannot be explained by vocal fold size alone . First , the relation between fundamental frequency ( fo ) and body size appears uncoupled within some species [4] , [5] , [6] . Considering that vocal fold size remains closely linked to body size , other mechanisms must facilitate the fo variations . Second , vocal fold morphology in the mammalian larynx [7] , [8] and labial morphology in the avian syrinx [9] vary greatly within and among species . Mechanical properties , a direct consequence of morphological design , also show a large variation and contribute to vocal differences within and between species [10] , [11] , [12] , [8] . Third , the exceptionally large fo range that some species rely on to generate large vocal versatility cannot be explained by size [11] . Here we present predictions from vibrating string theory that offer an explanation for why a larger than expected range of fo can be achievable in large and small species . If all species had the same tissue construct and the same ability to strain the vocal folds , then a vibrating string model would predict a larger fo range ( in Hz ) for smaller animals , as will be shown . However , if the range is expressed in high/low ratios , or octaves , the range is normalized across species . It will be shown that , additionally , there is a large variation in this high/low ratio prediction because material properties are not the same and the ability to strain vocal fold tissues is also not the same . The mechanism for achievement of a large fo range in animals stands in stark contrast to the design of man-made musical string instruments , which utilize multiple strings to cover a wide pitch range . Violins have four strings , classical guitars have six , and pianos have eighty-eight , which are either single , doubled , or tripled . With these multiple strings , violins and guitars can produce on the order of 4–5 octaves of pitch range and a piano can produce a little over 7 octaves . Vocalizations in mammals [13] , [14] , [15] , [16] , [17] , [18] , [19] are generated by airflow-induced vibrations of vocal folds or labia , respectively . Humans , other mammals , and birds can produce 3 octaves , and in some cases 4–5 octaves , with a single pair of vocal folds in the larynx or labia in the syrinx . Vocal folds are basically the equivalent of one double string . What are the properties of these folds or labia that produce such versatile biological “strings” ? We show here that geometry plays a role , but the dominant factor is the molecular structure of laminated tissue that can generate orders of magnitude variation in fiber tension . The morphology of vibrating vocal fold tissue in the larynx is sufficiently complex that voice scientists and clinicians have debated for decades whether “vocal fold” or “vocal cord” is the best descriptor . Prior to Hirano’s [7] pioneering work , the term vocal cord was most prevalent , but it was understood that only the vocal ligament , a portion of the entire tissue construct , was cord-like . In human speech , the ligament is not under much tension , making the entire system fold-like in the sense that the superior portion folds over the inferior portion in vibration . Simple mechanical models have been of the mass-spring type to represent folding tissue , [20] but a vibrating string model was also introduced [21] , [22] . The conceptualization of a fiber-gel construct , not claimed here to be novel , embraces both the fold and the string construct ( Fig 1 ) . The ground substance is a viscoelastic continuum in the form of a homogenous , isotropic gel , similar to the vitreous humor in the eye . With the inclusion of directional fibers in multiple layers ( collagen and elastin in the lamina propria and muscle fibers in the thyroarytenoid muscle ) , the construct develops into an adult human vocal fold . The development is gradual , however , and is likely influenced by vocal demand . At birth , the vocal fold consists of a single layer of ground substance ( gel ) with sparse fibers randomly oriented [23] . Through childhood and puberty , the gel develops into multiple morphological layers of tissue [26] . The superficial layer of the vocal fold lamina propria remains mostly ground substance ( gel-like ) , whereas the intermediate and deep layers develop into elastin and collagen fibers aligned in a ventral-dorsal direction [24] , [25] , [26] . The fibers originate and insert on cartilages in the larynx ( not shown ) that can be moved by laryngeal muscles . The moving boundaries apply variable tension to the fibers .
In a string fixed at both ends and under tension , the fundamental frequency of the dominant mode of vibration is fo=12Lμ′ρ , ( 1 ) where L is the length of the string , μ′ is the combined shear and tensile stress for vibrational displacement transverse to the string , and ρ is the tissue density . Density is a constant in soft tissue ( about 1 . 04 g/cm3 ) , which leaves control of fo for any fibrous layer to L and μ′ . In man-made string instruments , length is either held constant ( e . g . , piano ) or varied with finger position ( violin or guitar ) . In vocal folds , length can only be varied by moving boundary cartilages , which means that individual layers cannot be lengthened or shortened independently . Thus , with one common elongation , fiber stress μ′ becomes the critical variable for fo control between layers . Based on Eq ( 1 ) , the total variation in fo can be written as Δfo=∂fo∂LΔL+∂fo∂μ′Δμ′ ( 2 ) which after partial differentiation yields the expression Δfo=fo[−ΔLL+12Δμ′μ′] ( 3 ) Here we see that an absolute frequency range Δfo ( in Hz ) varies directly with fo . If the terms in brackets were equal across species , smaller species with higher mean fo would have larger changes in fo . The above expression also shows that a positive change in fiber stress Δμ′/μ′ must overcome the negative change in strain ΔL/L if a positive change Δ fo is to occur . Non-muscular tissue layers , known as the lamina propria in the vocal folds , can experience an increase in μ′ only with an increase in length . The length-tension curve must be highly nonlinear for a large fo range . The degree of nonlinearity is related directly to the desired fo range . Stress-strain curves of the vocal ligament are typically exponential [28] , [10] , [12] , of the form μ′=AeB ( L−Lo ) /Lo , ( 4 ) where A and B are empirically-determined constants , L is an arbitrary length , and Lo is a reference length . According to Eq 1 , two fundamental frequencies are related as fo2fo1= ( L2L1 ) −1e12B ( L2−L1 ) /Lo . ( 5 ) Note that for B = 0 ( constant fiber stress at all lengths ) , the fundamental frequency ratio is inversely related to vocal fold length ratio . This is the general size principle . The larger the animal , the longer the vocal folds and the lower the frequency if stress is kept constant . The reference length Lo is generally taken as the in situ cadaveric length for measurement purposes . From this reference length , the length for phonation can be increased and decreased on the order of ± 50% , but typically more like ± 30% , as will be shown later . Fig 2 shows two contrasting cases of how the same fo range can be produced . In Fig 2 ( A ) the stress-strain curve is steep , with a large B value , and the elongation is small . In Fig 2 ( B ) the stress-strain curve is shallow , with a small B value , but the elongation is large . Anatomically and physiologically , the trade-off is between range of motion between cartilages versus fiber tension in the vocal folds . Fig 3 shows a plot of Eq 5 , with an assumption that L1 = 0 . 7Lo . The L2/L1 ratio is plotted on the horizontal axis and the B value is the parameter . We will show that a value of 2 for L2/L1 appears to be a typical limit in humans ( a length change from L1 = 0 . 7Lo to L2 = 1 . 4Lo in the phonation range ) . The value of B is determined by the density of collagen fibers that can be packed into the ligament layer of tissue . According to Fig 3 , a four octave range ( fo2/fo1 = 16 ) requires an exponent value of B = 10 if the L2/L1 ratio is 2 , as the intersection of the middle vertical dotted line and the upper red solid line shows in the diagram . If L2/L1 is restricted to 1 . 5 , only a 2 octave range is obtained with B = 10 ( left-most dotted line ) . However , a 4 octave range would be achievable with B = 7 ( lower red line ) if L2/L1 were 2 . 5 ( right-most vertical dotted line ) . Thus , frequency range hinges on two variables , ability to change vocal fold length and nonlinearity of the dominant fiber stress-strain curve . Some data will now be given from various species . Vocal fold length change with fo has been quantified in several investigations . [29] used stereo videoscopy to measure the membranous vocal fold length during phonation in 4 female and 3 male human subjects . For the males , L1 averaged 0 . 77 cm and L2 averaged 1 . 3 cm , such that L2/L1 was 1 . 7 . For the females , L1 averaged 0 . 71 cm and L2 averaged 1 . 1 cm , such that L2/L1 was 1 . 5 . The fundamental frequency ranged on the order of 100–500 Hz for the males and 130–800 Hz for the females . Thus , a 2 ½ octave fo range was achieved with the L2/L1 ratios of 1 . 7 for males and the L2/L1 ratio of 1 . 5 for females . Fig 1 would predict values of B in the 8–9 range if the string model applies to the combination of fibrous tissue layers ( ligament and muscle ) . Later reports of measurements of B will confirm this range of values . In so-called “falsetto” register , the vocal ligament ( intermediate and deep layers of the lamina propria ) dominates in fo control [14] . In this falsetto region , Nishizawa et al . measured an approximate 1-octave fo range with an L2/L1 ratio of no more than 1 . 2 . According to Fig 3 , this would require a value of B of about 10–12 . Min et al . [28] measured a value of 9 . 7 in human ligaments ( 8 specimens from three males and two females , left and right averaged ) . Chan et al . [10] measured values of 9 . 4 for males and 7 . 6 for females . A more recent study by Cho et al . [30] on vocal fold length change in humans used an ultrasonic imaging technique to follow anterior and posterior landmarks on the vocal folds . Results showed that L1 = 1 . 47 cm and L2 = 2 . 0 cm for males for low and high pitch , with L2/L1 = 1 . 4 . For females , L1 = 1 . 14 and L2 = 1 . 65 also yielded an L2/L1 ratio of 1 . 4 . This is a little less than the ratios of 1 . 7 for males and 1 . 5 for females reported by [29] . The small discrepancy is probably related to a smaller fo range in the Cho et al . study , but unfortunately the fo ranges were not reported . Measurements for length change versus fundamental frequency are also available from studies using excised larynges [30] , [31] , [32] . For example , excised domestic dog ( Canis familiaris ) larynges were vibrated on a laboratory bench with an artificial air supply [31] . Self-sustained vocal fold oscillation was achievable from L1 = 0 . 5 cm to L2 = 1 . 2 cm , but these lengths were produced mechanically rather than by muscle control . The corresponding fo range was 50 to 230 Hz , somewhat greater than 2 octaves . For this large L2/L1 ratio of 2 . 4 , the value of B from Fig 3 would be predicted to be about 6 . 0 . Some dogs do not have a vocal ligament , but measurements on canine mucosa produced a value of B = 4 . 4 and measurements on canine thyroarytenoid muscle fibers yielded a value of B = 6 . 5 [27] . Given that the thyroarytenoid muscle is the fibrous layer in vibration , it would dominate the fo range . The predicted and measured value of B are therefore in agreement . Table 1 shows measured stress-strain relations for various mammalian species , [8] , [10–12] , [33–42] . In some cases , the frequency ranges are shown . Note that the rhesus monkey has an approximate four-octave range ( 100–1800 Hz ) . With values of B = 16 . 2 for males and 12 . 9 for females , this range is achievable with a modest L2/L1 ratio of about 1 . 6 for males and 1 . 7 for females according to Fig 3 . Fig 4 ( A ) shows measurements of vocal fold cadaveric length Lo as a function of body mass M for fourteen species in Table 1 ( mouse to giraffe , in some cases both male and female ) . Note the general increase in Lo with size , plotted logarithmically with the regression line L0=3 . 28M0 . 4r2=0 . 96p<0 . 001 ( 6 ) The regression is a very tight fit over a length range of 1–40 mm and a body mass range of . 05–1000 kg , reinforcing the earlier claim that vocal fold length and body mass are tightly related . Fig 4 ( B ) shows high and low values of fo as a function of body mass ( size ) . There is a general decrease of fo1 and fo2 with mass , expressed by the following regression lines fo2=63196M-0 . 386Hzr2=0 . 70p<0 . 01 ( 7 ) fo1=2135 . 7M-0 . 305Hzr2=0 . 85p<0 . 001 ( 8 ) It is clear , however , that much greater variability is associated with these frequency trends , suggesting that factors other than body mass play a role in fundamental frequency prediction . Combining Fig 4 ( A ) and 4 ( B ) by eliminating the mass variable from the regression equations , an inverse relation between fo1 and fo2 with cadaveric length Lo is obtained , as shown in Fig 4 ( C ) . This inverse relation is in agreement with the B = 0 curve in Fig 3 , showing only the length dependence on fo . Note that the range of fo , if expressed logarithmically as a ratio fo2/fo1 rather than a difference fo2 –fo1 , is essentially a constant . This is a strong validation of the simple vibrating string model ( Eq 5 ) . Taking the ratio of Eq ( 7 ) to Eq ( 8 ) yields the number 12 . 0 , which constitutes about 3 . 5 octaves as an average across species . Empirical data for exponent B versus Lo are shown in Fig 4 ( D ) . Omitting the one outlier ( the rhesus monkey ) , a mild trend is quantified by the relation B=6 . 285+0 . 0468Lor2=0 . 15p<0 . 3rhesus monkey excluded ( 9 ) However , when the outlier is included , the trend disappears . Thus , with the sparsity of data available across species , it is not possible to assert whether or not there is an increase in B with longer vocal folds . What is important to note , however , is the large variation in B across species . Since B is an exponent , a range of 3–15 leads to orders-of-magnitude variations in frequency range . With this empirical relation between B and Lo , a better fo range prediction can be made with Eq 5 . If we continue to assume that L1 = 0 . 7 Lo , as in humans , then Fig 5 shows a contour plot of the fo2/fo1 range achievable in octaves . The two morphological variables are B on the vertical axis and L2/L1 on the horizontal axis . The figure shows that a greater fo range is attainable with either greater B or greater L2/L1 . The empirical B values allow some species to be identified on the figure . The greater the B value , the smaller L2/L1 needs to be to achieve a large fo range . Conversely , the larger L2/L1 is , the smaller B needs to be to achieve a large fo range . For example , the male rhesus monkey requires only an L2/L1 ratio of 1 . 6 for a 4-octave range . Humans , lions , and tigers require an L2/L1 ratio of about 2 . 2 for a 4-octave range . For animals that scream or roar , a larger B value may be a protective requirement for greater vibrational amplitude and vocal fold collision .
A simple theory of the range of fundamental frequency fo achievable in various species has been proposed . Laryngeal size , and specifically vocal fold length , is a good predictor of mean fo , but a poor predictor of fo range . When vocal fold tissues become layered and tissue fibers assume a ventral-dorsal direction , the layer with the densest and stiffest fiber composition produces string-like vibration and determines the fo range . This can be a vocal ligament or a layer of muscle fibers . The stress-strain curve of the fibrous layer must be highly nonlinear to overcome the natural tendency for fo to decrease with increased length . For an exponential stress increase with a factor eBɛ , where ε is the strain ( fractional length change ) and B is a stiffness constant , a range of values 5 < B < 15 can produce a 4–5 octave fo range with greater or lesser length change . If the laryngeal framework mechanics allows a large length change , on the order of ± 50% from the resting length , B values on the order of 5–10 can produce the 4–5 octave range . If the larynx is restricted in its range of motion such that only a ± 20% length change is possible , a value of B on the order of 10–15 is necessary to obtain a 4–5 octave range . A laryngeal adaptation for greater length change is greater rotation or gliding between cartilages that anchor the ends of the vocal folds . Alternatively , a tissue layer that can bear a greater tension ( i . e . , a ligament with high density collagen fibers ) can also increase the fundamental frequency range and thereby allow vocal versatility . As a consequence , fundamental frequency can become uncoupled from size . Two large frequency ranges produced by two species can overlap even if the two have dramatically different body sizes . The proposed framework for fundamental frequency range regulation has three important implications . First , voice production is an example of “many-to-one” mapping , which occurs when the functional property of interest depends on more than one underlying morphologic parameter [43] . In the cases of voice fundamental frequency , the parameters include laryngeal framework mechanics and all variables affecting the B value , i . e . the number and depth of vocal fold tissue layers , vocal fold boundary geometry , and tissue fiber stress . Consequently there are surfaces in a morphospace that represent functionally neutral variations , which means that morphological diversity between vocal folds of different species is not necessarily indicative of functional diversity . The evolution of vibrating tissue design in laryngeal or syringeal sound sources may lead to different morphologies that function similarly . For example , multilayered characteristics have been described in vocal folds of different mammals[8] , [12] , [44] as well as in alligators [45] and even within the oscillating tissue masses ( “labia” ) in the avian vocal organ , the syrinx [9] . Laryngeal design across mammals is morphologically distinct in each species but fundamental frequency remains overlapping . Findings in excised mammalian larynges [32] or the excised avian syrinx [19] suggest that multiple activation patterns of intrinsic muscles of the larynx and syrinx , respectively , produce a redundant output , i . e . they can facilitate similar vocal frequencies . In a complex laryngeal or syringeal cartilaginous framework , different muscle activations generate different tension settings of the oscillating tissue , yet in combination with the appropriate driving pressure , the soft tissue can vibrate at identical rates [46] . Our findings have a second , more practical implication as it pertains to the treatment of human voice disorders . The observation that multiple vocal fold morphologies can serve the same function , i . e . produce the same fundamental frequency , can be informative for surgical treatment of impaired vocal folds . Surgery to remove vocal fold lesions often results in irreparable loss of normal vibratory mucosa [47] . Restoration of normal human vocal fold morphology may not be feasible in many cases because the deficits are large . Our proposal that fundamental frequency range can be regulated through two distinct mechanisms , and its broader implication that multiple vocal fold morphologies can achieve the same vocal output , suggest vocal function may be restored with alternative strategies . Examples of alternative morphologies already exist in laryngeal surgery in which non-laryngeal tissue is used to restore voice production [48] , [49] , [50] . However , the concept of alternative morphologies as viable solutions has not been considered systematically in vocal fold repair and deserves further exploration . Computer simulation of voice production can provide the means for intelligent exploration of the vocal fold morphospace to search for viable alternatives . Simulations based on finite-element and finite-difference approaches have been reported over the past two decades [51] , [52] , [53] , [54] . A single simulation produces one set of acoustic output variables given a defined input vocal fold morphology at a fixed subglottal pressure . A meaningful comparison between two different vocal fold morphologies should entail a range of possible acoustic outputs , given a clinically relevant range of subglottal pressures as well as a range of physiologic variations in the vocal fold morphologies . Such a comparison would entail thousands of simulation runs to fully cover the range of inputs . One approach to reduce the computational cost and to increase the efficiency of morphospace exploration is to combine a finite element model ( FEM ) voice simulation with multiobjective optimization [55] . This approach has been applied to vocal fold surgery simulation , in which the functional viabilities of two alternative vocal fold morphologies were demonstrated in silico [56] . Finally , the current findings relate well to vocal development and vocal training . If the density of collagen fibers in the vocal ligament is increased by exercise ( frequent stretching ) , a speaker or a singer can increase the fundamental frequency range even if the laryngeal framework cannot be altered much due to tight spaces between cartilages . On the other hand , laryngeal massage and framework exercise could widen the spaces , allowing greater fo range with existing molecular constructs . It appears that the development of a theory for fundamental frequency range regulation based on comparative data across species in nature is paramount to understanding possible intervention strategies for improving human communication . | Mammals , birds , and reptiles vocalize ( make sounds with vocal cords ) . Various species , and individuals within the species , are identified by pitch , loudness , and other acoustic features they can build into a repertoire of rhythmic and melodic patterns . Range of pitch , or more precisely fundamental frequency , is required to produce a variety of patterns . Whereas the average fundamental frequency is predictable by body size , the range of fundamental frequency depends on two different factors . The first is a freedom of movement factor–how much vocal cord length change can be produced by muscles that rotate or glide cartilages in the larynx . The second is a molecular composition factor–how much collagen density can be produced in the vocal cord ligament . Development and evolution has not been uniform with regard to these factors , suggesting that alternative choices are available for growth , training , and repair . | [
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"biological",
"tiss... | 2016 | Predicting Achievable Fundamental Frequency Ranges in Vocalization Across Species |
The immunomodulatory properties of lipophosphoglycans ( LPG ) from New World species of Leishmania have been assessed in Leishmania infantum and Leishmania braziliensis , the causative agents of visceral and cutaneous leishmaniasis , respectively . This glycoconjugate is highly polymorphic among species with variation in sugars that branch off the conserved Gal ( β1 , 4 ) Man ( α1 ) -PO4 backbone of repeat units . Here , the immunomodulatory activity of LPGs from Leishmania amazonensis , the causative agent of diffuse cutaneous leishmaniasis , was evaluated in two strains from Brazil . One strain ( PH8 ) was originally isolated from the sand fly and the other ( Josefa ) was isolated from a human case . The ability of purified LPGs from both strains was investigated during in vitro interaction with peritoneal murine macrophages and CHO cells and in vivo infection with Lutzomyia migonei . In peritoneal murine macrophages , the LPGs from both strains activated TLR4 . Both LPGs equally activate MAPKs and the NF-κB inhibitor p-IκBα , but were not able to translocate NF-κB . In vivo experiments with sand flies showed that both stains were able to sustain infection in L . migonei . A preliminary biochemical analysis indicates intraspecies variation in the LPG sugar moieties . However , they did not result in different activation profiles of the innate immune system . Also those polymorphisms did not affect infectivity to the sand fly .
The major cell surface glycoconjugate of Leishmania is the lipophosphoglycan ( LPG ) , implicated in a wide range of functions , both in vertebrate and invertebrate hosts [7] . In the invertebrate host , LPG variations are important for Leishmania specificity to the sand fly [8] , where attachment of the parasite to a midgut receptor is a crucial event [9] . In the vertebrate host , the main functions of this virulence factor during the earlier steps of infection include: protect the parasite from complement-mediated lysis , attachment and entry into macrophages [10] , able to inhibit phagolysosomal fusion [11] , modulation of nitric oxide ( NO ) production [12] and inhibition of protein kinase C ( PKC ) [13] . Interestingly , although L . major LPG mutants ( lpg1- ) were highly susceptible to complement mediated lysis , they were able to invade macrophages reinforcing the role of other molecules and the host defenses during the interaction [11] . Many functions have been attributed to L . amazonensis LPG including induction of neutrophil extracellular traps ( NETs ) [14] , induction of protein kinase R ( PKR ) [15] , triggering and killing of the parasite via Leukotriene B4 ( LTB4 ) [16] . Although L . amazonensis LPG is important in many steps of host infection , its role during the interaction with macrophages and sand flies remains unknown . LPG structures have been described for several dermotropic and viscerotropic Leishmania [17–26] . LPGs have a conserved glycan core region of Gal ( α1 , 6 ) Gal ( α1 , 3 ) Galf ( β1 , 3 ) [Glc ( α1 ) -PO4]Man ( α1 , 3 ) Man ( α1 , 4 ) -GlcN ( α1 ) linked to a 1-O-alkyl-2-lyso-phosphatidylinositol anchor . The salient feature of LPG is another conserved domain consisting of the Gal ( β1 , 4 ) Man ( α1 ) -PO4 backbone of repeat units ( n = ~15–30 ) . The distinguishing feature of LPGs that is responsible for the polymorphisms among Leishmania spp . is variable sugar composition and sequence of branching sugars attached to the repeat units and cap structure [27] . For example , the LPG of Leishmania major ( Friedlin ) has β-1 , 3 galactosyl side-chains , often terminated with arabinose , whereas the LPGs of Leishmania donovani ( Mongi ) and L . infantum ( PP75 and BH46 strains ) possess β-glucoses in their repeat units [17 , 20 , 24] . However , there is no available information on the degree of variability in the LPG structure for L . amazonensis . The L . major LPG was identified as potent agonist of Toll-like receptor 2 ( TLR2 ) in human natural killer ( NK ) cells and murine macrophages , triggering the production of TNF-α and IFN-γ through MyD88 [28 , 29] . Recently , the LPGs of two New World species ( L . infantum and Leishmania braziliensis ) differentially activated TLR2 . In this case , L . braziliensis LPG was more pro-inflammatory being able to induce the translocation of NF-κB to the nucleus [30] . As a part of a wider project on the glycobiology of New World species of Leishmania , we evaluated the role of L . amazonensis LPGs ( PH8 and Josefa strains ) during the interaction with host cells and the sand fly L . migonei . The present study might help to improve our understanding on the immune modulation mediated by glycoconjugates of L . amazonensis , the etiological agent of diffuse cutaneous leishmaniasis ( DCL ) .
The animals were kept in the Animal Facility of the Centro de Pesquisas René Rachou/FIOCRUZ . All animals were handled in strict accordance with animal practice as defined by Internal Ethics Committee in Animal Experimentation ( CEUA ) of Fundação Oswaldo Cruz ( FIOCRUZ ) , Belo Horizonte , Minas Gerais ( MG ) , Brazil ( Protocol P-82/11-4 ) . This protocol followed the guidelines of CONCEA/MCT , the maximum ethics committee of Brazil . Knockout mice handling protocol was approved by the National Commission of Biosafety ( CTNBio ) ( protocol #01200 . 006193/2001-16 ) . World Health Organization Reference strains of L . amazonensis ( IFLA/BR/1967/PH8 and MHOM/BR/75/Josefa ) were used . The PH8 strain was originally isolated from the sand fly L . flaviscutellata from Pará State , Brazil , and the Josefa strain was isolated from a human case from Bahia State , Brazil . Promastigotes were cultured in M199 medium supplemented with 10% fetal bovine serum ( FBS ) , penicillin 100 units/mL , streptomycin 50 μg/mL , 12 . 5 mM glutamine , 0 . 1 M adenine , 0 . 0005% hemin , and 40 mM Hepes , pH 7 . 4 at 26°C until late log phase [21] . Parasites were seeded in triplicate ( 1 x 105 cells/mL ) , and growth curves of PH8 and Josefa strains were determined daily using a Neubauer improved haemocytometer until cells reached a stationary phase . Both strains exhibited a similar division profile reaching stationary phase after 7 days of culture . For this reason the 6th day was chosen for harvesting parasites for LPG extraction and molecular typing ( S1A Fig ) . For molecular typing , genomic DNA was extracted from log-phase Leishmania using the phenol/chloroform method ( 1:1 ) for amplification of the HSP70 fragment prior to digestion with HaeIII as previously described [31] . Positive controls included DNA from L . braziliensis ( MHOM/BR/75/M2903 ) , L . infantum ( MHOM/BR/74/PP75 ) , Leishmania guyanensis ( MHOM/BR/75/M4147 ) and L . amazonensis ( IFLA/BR/67/PH8 ) . After PCR-RFLP both L . amazonensis strains were confirmed ( S1B Fig ) . For optimal LPG extraction , late log phase cells were harvested and washed twice with PBS prior to extraction of LPGs ( Fig 1 ) . The LPG extraction was performed as described elsewhere with solvent E ( H2O/ethanol/diethylether/pyridine/NH4OH; 15:15:5:1:0 . 017 ) after a sequential organic solvent extraction [32] . For purification , the solvent E extract was dried under N2 evaporation , resuspended in 2 mL of 0 . 1 M acetic acid/0 . 1 M NaCl , and applied onto a column with 2 mL of phenyl-Sepharose , equilibrated in the same buffer . The column was washed with 6 mL of 0 . 1 M acetic acid/0 . 1 M NaCl , then 1 mL of 0 . 1 M acetic acid and finally 1 mL of endotoxin free water . The LPGs were eluted with 4 mL of solvent E then dried under N2 evaporation . LPG concentrations were determined as described elsewhere [33] . Prior to use on in vitro cells cultures , LPGs were diluted in RPMI . All solutions were prepared in sterile , LPS-free distilled water ( Sanobiol , Campinas , Brazil ) . All extractions and purifications procedures are depicted in Fig 1 . Purified LPGs ( 5 μg ) were subjected to dot-blot , blocked ( 1 h ) in 5% milk in PBS and probed for 1 h with monoclonal antibody ( mAb ) CA7AE ( 1:1000 ) , that recognizes the unsubstituted Gal ( β1 , 4 ) Man repeat units [34]; mAb LT22 ( 1:1000 ) that recognizes β-glucose side chains and WIC 79 . 3 ( 1:1000 ) that recognizes β-galactose side chains [21 , 35] . After three washes in PBS ( 5 min ) , the membrane was incubated for 1 h with anti-mouse IgG conjugated with peroxidase ( 1:5 , 000 ) and the reaction was visualized using luminol . Thioglycollate-elicited macrophages were extracted from C57BL/6 and C57BL/6 knockouts TLR2 ( -/- ) and TLR4 ( -/- ) by peritoneal washing with ice cold RPMI and enriched by plastic adherence ( 1 h , 37°C , 5% CO2 ) . Cells ( 3 x 105 cells/well ) were washed with fresh RPMI then culture in RPMI , 2 mM glutamine , 50 U/mL of penicillin and 50 μg/mL streptomycin supplemented with 10% FBS in 96-well culture plates ( 37°C , 5% CO2 ) . Cells were primed with interferon-gamma ( IFN-γ ) ( 3 IU/mL ) for 18 h prior to incubation with LPGs from both strains ( 10 μg/mL ) , live stationary Leishmania parasites ( MOI 10:1 ) and lipopolysaccharide ( LPS: 100 ng/mL ) [30 , 36] . For CBA multiplex cytokine detection , cells were plated , primed as describe above and incubated with LPGs and live stationary promastigotes ( MOI 10:1 ) for 48 h . LPS was added as a positive control and medium as negative control . Supernatants were collected and IL-1β , IL-6 , IL-10 , IL-12p40 and TNF-α were determined using BD CBA Mouse Cytokine assay kits according to the manufacturer’s specifications ( BD Biosciences , CA , USA ) . Flow cytometry measurements were performed on a FACSCalibur flow cytometry ( BD Bioscience , Mountain View , CA , USA ) . Cell-QuestTM software package provided by the manufacturer was used for data acquisition and the FlowJo software 7 . 6 . 4 ( Tree Star Inc . , Ashland , OR , USA ) was used for data analysis . A total 1 , 500 events were acquired for each preparation . Results are representative of six experiments in duplicate . Nitrite concentrations were determinate by Griess reaction ( Griess Reagent System , 2009 ) . For MAPKs , peritoneal murine macrophages were obtained as described above . They were applied on 24 wells tissue culture plates ( 106 cells/well ) for 18 h prior to assay . The cells were washed with warm RPMI and incubated with LPG from both species for different times ( 5 , 15 , 30 , 45 and 60 min ) or with medium ( negative control ) or E . coli extracts ( 100 ng/mL , only 45 minutes ) as positive control . p-p38 , p-JNK , p-IκBα and total p38 were assayed as previously described [25] . p-IκBα antibody was provided by Dr . L . P . de Sousa . NF-κB translocation using CHO reporter lines ( a kind gift by M . A . Campos ) was determined as described elsewhere [30] . CHO reporter cells were plated ( 1 x 105 cells/well ) in 24-well tissue culture dishes and the LPG ( 0 . 02 and 0 . 2 μg/mL ) from both strains was added in a total volume of 0 . 25 mL medium/well . The cells were examined by flow cytometry ( BD Biosciences , CA , USA ) and the analyses were performed using CellQuestTM software . Lutzomyia migonei ( Baturite strain ) sand flies were kept under laboratory conditions and were fed on 30% sucrose solution for 3–4 days prior to experiments . The insects were artificially fed using a chick skin membrane in a glass-feeder device . The chick skin membrane was provided by the Animal Facility of Centro de Pesquisas René Rachou/FIOCRUZ under the Protocol LW 30/10 . Heparinized mouse blood ( drawn intracardially from Balb/C ) , with penicillin ( 100 U/mL ) and streptomycin ( 100 μg/mL ) ( 37°C ) containing 2 x 107/mL logarithmic phase promastigotes ( PH8 and Josefa strains ) offered for 5 h under dark conditions [5] . Blood engorged flies were separated and maintained at 26°C with 30% sucrose . Engorged sand flies had their midguts dissected on days 2 and 4 post feeding . The midguts were homogenized in 30 μl of PBS and the number of viable promastigotes determined by counting under a Neubauer improved haemocytometer [24] . For nitrite , cytokine measurements and in vivo sand fly experiments , the Shapiro Wilk test was conducted to test the null hypothesis that data were sampled from a Gaussian distribution [37] . For the non-parametric distribution , it was performed the Mann-Whitney test . Data were analyzed using GraphPad Prism 5 . 0 software ( Graph Prism Inc . , San Diego , Ca ) . P < 0 . 05 was considered significant .
The purified LPGs from L . amazonensis PH8 and Josefa strains were differentially recognized by the mAbs CA7AE and LT22 ( S2 Fig ) . LPG from PH8 strain was recognized by CA7AE and LT22 as well as the positive control represented by L . infantum ( BH46 ) . However , a different recognition profile was observed for the Josefa strain since its LPG was weakly recognized by LT22 but not by CA7AE , indicating the presence of side-chains branching-off the repeat units . Because CA7AE recognizes Gal ( β1 , 4 ) Man unsubstituted repeat units in LPG [34] , these results indicate that at least some of the repeat units are indeed unsubstituted in the LPG of PH8 strain . On the other hand , the presence of side-chains suggestive of glucoses , due to LT22 reactivity , was detected in the LPGs of PH8 and Josefa strains . However , LT22 also recognized the galactose-branched repeat units of L . major ( strains FV1 and LV39 ) indicating cross-reactivity of the antibodies , thus suggesting the presence of either glucose or galactose as side chains ( S2 Fig ) . These data suggested an intraspecific polymorphism in the LPGs of L . amazonensis strains . We investigated whether LPGs purified from different strains could have an impact on the parasite’s interaction with host cells , the ability to elicit NO and cytokine production by murine macrophages . LPGs from both strains were incubated with murine peritoneal macrophages from C57BL/6 and respective knockouts for TLR2 ( -/- ) and TLR4 ( -/- ) . We did not detect any production of the cytokines IL-1β , IL-10 and IL-12 ( S3A–S3C Fig ) . Both LPGs and respective parasites were able to activate through TLR4 , resulting in NO , TNF-α and IL-6 production ( Fig 2A–2C ) ( P < 0 . 05 ) . As expected , LPS ( positive control ) activated TLR4 in the TLR2 ( -/- ) ( Fig 2A–2C ) . No difference in MAPKs phosphorylation ( p38 and JNK ) and p-IκBα was observed after incubation with LPGs from both strains . In peritoneal murine macrophages this activation was mainly via TLR4 ( Fig 3A and 3B ) . We also evaluated if the LPGs from these strains were able to translocate NF-κB in CHO cells . No activation of NF-κB was detected in those cells ( Fig 4 ) . In vivo midgut infections of the sand flies were determined on days 2 and 4 post feeding , in order to evaluate the number of parasites after the blood meal digestion , as well as , after its excretion on day 3 , where non-attached parasites are lost . Although a higher parasite density was detected for PH8 strain on day 2 ( P < 0 . 05 ) , no statistical differences in the parasite densities from both L . amazonensis strains were observed on day 4 , and both strains were able to colonize L . migonei midgut ( P > 0 . 05 , Fig 5 ) .
Leishmania amazonensis , etiologic agent of the cutaneous and anergic diffuse leishmaniasis , is characterized by disseminated non-ulcerative skin lesions and constantly proportion of negative delayed hypersensitivity skin-test ( DTH ) , resulting in a high resistance of this disease to any type of chemotherapy [1 , 38 , 39] . In the Old and New World , parasite glycoconjugates have being implicated in a variety of events during parasite-host interactions [40 , 41] . More recently , the role of LPG and GIPLs in the L . braziliensis and L . infantum was determined , suggesting that two distinct LPGs were able to differentially modulate macrophage functions [30 , 41] . Regarding L . mexicana complex , from where L . amazonensis is a member , a recently study has demonstrated the inflammatory role of LPG [42] . This glycoconjugate naturally exposed to the host immune system could contribute to the maintenance of infection by interfering with the assembly immune response , like modulation of cytokine production and non-activation of effectors cells . In the present work , we investigated whether LPGs from two L . amazonensis strains would account for differences in the interaction with macrophages and L . migonei . LPG polymorphisms are common in the composition of branching sugars attached to the conserved repeat units of its backbone . While in the Old World species , a wide spectrum of sugar composition and structure is commonly observed , in New World species only glucose residues in the side chains of Leishmania were documented to date [17 , 21 , 23 , 24 , 43] . Our preliminary characterization of the repeat units using specific antibodies suggested the existence of intraspecies polymorphism in L . amazonensis LPGs with differences in the side-chains and in the level of glycosylation . The LPG of PH8 strain strongly reacted with CA7AE , that recognizes the basic backbone of the repeat units is Gal ( β ) Man-PO4 [21 , 34] . However , Josefa LPG did not reacted with this antibody , thus suggesting the existence of sugars as side-chains in the repeat units . This feature is commonly found in the LPG of L . major reference strain FV1 , which does not react with CA7AE [17] . In order to evaluate the quality of the sugars branching-off the repeat units , LT22 and WIC . 79 . 3 antibodies were used to detect the presence of glucose and galactose , respectively [21 , 35] . Based on L . major LPGs used as controls , they were either recognized by those antibodies , suggesting cross-reactivity . Moreover , those data reinforced the presence of either glucoses or galactoses as side-chains in L . amazonensis LPGs . A fully detailed biochemical analysis must await the results of further investigations . Understanding variations and the LPG structures are crucial for the comprehension of the mechanisms of how parasites survive under extremely adverse conditions . Although the role of LPG in the interaction with the vertebrate host immune system has been studied , it is still unclear how its polymorphism affects the parasite survival . L . amazonensis LPG induces release of NETs and LTB4 production by neutrophils , thus contributing to diminish parasite burden in the Leishmania inoculation site [14 , 16] . Additionally , L . mexicana LPG induce TNF-α and IL-10 in monocytes , modulates IL-12 production and diminishes NF-κB nuclear translocation [44] . Here we show that LPGs from both L . amazonensis strains stimulates NO and cytokine production ( TNF-α and IL-6 ) by peritoneal murine macrophages via TLR4 . A similar cytokine production was also observed for other species such as L . braziliensis LPG , another important dermotropic species . However , this activation was primarily via TLR2 [30] . The NO production by macrophages play a central role in determining intracellular killing of Leishmania [45] and the intact structure of LPG appears to be important for this activation [12 , 29] . In many models , NO synthesis is dependent on a combination of IFN-γ and TNF-α via TLR-dependent mechanisms as an important leishmanicidal effector complex to macrophages [46] . In conclusion , the preliminary variations in the sugar motifs of LPG , did not result in any difference in macrophage activation/signaling thus suggesting the role of conserved motifs such as the lipid anchor [29] . Previous studies have demonstrated that different macrophage receptors mediate the uptake and phagocytosis of Leishmania . The early recognition of pathogens by cells capable of synthesizing cytokines is crucial for the adequate control of intracellular pathogens . Gene knockout studies in mice have suggested that TLR signaling is essential for the immune response against Leishmania parasites . Moreover , Leishmania LPGs and GIPLs are agonists of TLR2 and TLR4 [28–30 , 41 , 42] . Glyconjugates can modulate the host immune response and their activity seems to be structure dependent . The L . braziliensis LPG exerts a pro-inflammatory interaction with TLR2 , inducing the production of NO and cytokines ( IL-1β , TNF-α and IL-6 ) . On the other hand , the L . infantum LPG was shown to be immunosuppressive and did not induce NO , cytokines and NF-κB translocation [30] . Our results indicate that LPG from both L . amazonensis strains induce the production of NO and cytokines in IFN-γ-primed macrophages via TLR4 . However in other members of the L . mexicana complex , L . mexicana LPG activates either TLR2 or TLR4 leading to ERK and p38 MAPK phosphorylation and production of cytokines in human macrophages [42] . Thus , although it has been shown that LPG of Leishmania activates TLRs and that the engagement of these receptors is important for the infection , the complete intracellular processes that are involved in this activation remain unknown . Here we bring some light into the effects of LPG on MAPK and NF-κB signaling , a kinase and transcription factor known for their crucial role in immune defense against pathogens [44 , 47–49] . According to previous reports , infection by L . amazonensis altered phosphorylation of ERK1/2 in response to LPS in murine macrophages [50] and also activates a transcriptional repressor of the NF-κB [48 , 51] . Consistent with those observations , here LPGs from both L . amazonensis strains also activated p-IκBα , a NF-κB translocation inhibitor , via TLR4 . Since no further NF-κB translocation was detected in the CHO cells , a possible mechanism that has been suggested favors its inhibition by p50/p50 NF-κB homodimer [55] . Moreover , L . donovani and L . major infection caused inactivation of ERK1/2 and p38 , respectively , which was accompanied by the inhibition of transcription factors also modulation of cytokine production [52 , 53] . In contrast to GIPLs ( with fail to activate MAPKs ) [41] , our data show that LPG from both L . amazonensis strains is equally activating MAPKs ( p38 and JNK ) and p-IκBα in peritoneal murine macrophages via TLR4 ( Fig 3 ) . On the other hand , these LPGs do not activate the NF-κB translocation . These and our results strongly suggest that Leishmania species have distinct mechanism of modulating the signaling pathways during immunopathological events . The role of LPG during the interaction with the invertebrate host is a very controversial subject and it has been extensively investigated using in vitro and in vivo models [8 , 21 , 24 , 54 , 55] . Although the in vitro system has limitations [56] , this model provided important evidence for parasite attachment in the sand fly midgut using many restricted and specific vector as classified elsewhere [57 , 58] . For example , successful binding to the midgut was reported using the Old World pairs L . major/Phlebotomus papatasi [8 , 54] , L . major/Phlebotomus duboscqi [59] and L . tropica/Phlebotomus sergenti [60] . Perhaps , due its similarity to L . major LPG , who also possesses terminal β-galactosyl residues , L . turanica LPG may also be important for development in P . papatasi [61 , 62] . Moreover , the role of LPG has been questioned in permissive vectors such as Lutzomyia longipalpis and Phlebotomus perniciosus , where LPG mutants of L . mexicana and L . major were able to sustain infection in those vectors [63] . Recently , an alternative mechanism was suggested that flagellar protein FLAG1/SMP1 has been also implicated as an attachment binding candidate for specific and restricted vectors . In this work , a competitive binding assays using an antibody against FLAG1/SMP1 inhibited interaction using the pair L . major and P . papatasi . However , no effect was observed for permissive L . longipalpis [64] . The significance of LPG modifications was investigated during in vivo interaction of L . amazonensis with L . migonei . Although L . amazonensis is naturally transmitted by L . flaviscutellata , the absence of a colony led us to use an alternative sand fly , which had been previously shown to successfully harbor this parasite and L . braziliensis [5] . Since this species , although suspected , is not yet considered a natural proven vector of L . amazonensis , a high parasite doses was artificially offered to the sand flies . In spite of a loss after the 3rd day , parasite multiplication inside the alimentary tract of the L . migonei was successful for both L . amazonensis strains . To survive , the parasites need avoid a number of barriers including the lethal effects of digestive enzymes in the early blood-fed midgut and the excretion with the digested blood meal [5 , 7 , 65 , 66] . The strong correlation between the excretion of blood meal and the sudden loss of promastigotes suggests that the inability of Leishmania strains to persist in an inappropriate sand fly is related to their failure to remain anchored to the gut wall via specific attachment sites [22 , 67] . Nevertheless , L . migonei was able to sustain infection with both of the L . amazonensis strains tested , regardless of the type of LPG . It seems likely that L . migonei together with L . longipalpis might be considered a permissive vector as previously suggested [57 , 58 , 68] . However , the fully development of those two L . amazonensis strains should be further investigated . Some studies have determined that polymorphisms in the phosphoglycan domains of LPG might be crucial for Leishmania promastigotes to attach to the midgut and to maintain vector infection after blood meal excretion [9] . Additional support is based on the altered behavior of LPG deficient L . donovani and L . major mutant promastigotes ( lpg- ) who showed diminished capacity to maintain infection within the sand fly midgut [54 , 69] . Furthermore , it was recently presented the occurrence of intraspecies polymorphism in L . infantum LPG . Also , the biological role of the three LPG types ( I , II and III ) was studied during the interaction with the vector L . longipalpis [24] . Consistent with our results , all strains could successfully sustain infection in this vector , indicating that LPG polymorphisms did not affect this process . In spite of having a strong evidence for the existence of a midgut receptor for LPG , there is no current information in L . migonei . Indeed , the only known receptor was described for L . major , a galectin receptor found in the midgur of P . papatasi binding to LPG β-galactose residues [9 , 70] . The existence of midgut glycoproteins bearing terminal N-acetylgalactosamine in sand fly was also suggested as a putative parasite ligand [71] . Here we describe for the first time the immunomodulary properties of two LPGs isolated from different hosts . Those LPGs were equally able to trigger NO and cytokine ( TNF-α and IL-6 ) production via TLR4 . The preliminary differences in carbohydrate structure did not seem to affect the interaction of these strains with macrophages and the sand fly vector . | Leishmania amazonensis , a member of the Leishmania mexicana complex , is the causative agent of localized cutaneous leishmaniasis ( LCL ) and anergic diffuse cutaneous leishmaniasis ( ADCL ) [1 , 2] . It is widely distributed throughout the Amazon basin , where it infects a wide range of terrestrial rodents and , less frequently , marsupials . Its main vector is Lutzomyia flaviscutellata ( Diptera: Psychodidae ) widely distributed in South America and a recent study has predicted its expansion towards South of Brazil [3] . Moreover , Lutzomyia migonei ( França , 1920 ) can also harbor the infection of this species [4 , 5] . Although its transmission to man is very uncommon , L . amazonensis triggers an incurable and disseminated form of cutaneous leishmaniasis [2 , 6] . However , most of the mechanisms involved in L . amazonensis pathogenesis are still unknown , especially those related to surface molecules . Glycoconjugates have been extensively characterized as important for the establishment of infection as they protect the parasite from the early action of the host immune system and therefore acting as invasive/evasive strategies . Consequently , we here present the role of lipophosphoglycan ( LPG ) of L . amazonensis in the interaction with vertebrate and invertebrate hosts . | [
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"protozo... | 2016 | Lipophosphoglycans from Leishmania amazonensis Strains Display Immunomodulatory Properties via TLR4 and Do Not Affect Sand Fly Infection |
Despite ongoing efforts to control transmission , rabies prevention remains a challenge in many developing countries , especially in rural areas of China where re-emerging rabies is under-reported due to a lack of sustained animal surveillance . By taking advantage of detailed genomic and epidemiological data for the re-emerging rabies outbreak in Yunnan Province , China , collected between 1999 and 2015 , we reconstruct the demographic and dispersal history of domestic dog rabies virus ( RABV ) as well as the dynamics of dog-to-dog and dog-to-human transmission . Phylogeographic analyses reveal a lower diffusion coefficient than previously estimated for dog RABV dissemination in northern Africa . Furthermore , epidemiological analyses reveal transmission rates between dogs , as well as between dogs and humans , lower than estimates for Africa . Finally , we show that reconstructed epidemic history of RABV among dogs and the dynamics of rabid dogs are consistent with the recorded human rabies cases . This work illustrates the benefits of combining phylogeographic and epidemic modelling approaches for uncovering the spatiotemporal dynamics of zoonotic diseases , with both approaches providing estimates of key epidemiological parameters .
Rabies remains a significant threat to public health in the 21st century [1] , causing around 60 , 000 human fatalities worldwide each year [2] . Rabies control in the developing world is currently hindered by a lack of timely and accurate data about rabies cases in both humans and animals [3] . It is thought that the number of human deaths due to rabies virus ( RABV ) infections is underestimated , and that the dynamics of the virus in dog populations is poorly understood . These uncertainties inevitably hamper improvements in disease control strategies and the evaluation of control measures . China is second only to India [4] in terms of the national number of human rabies cases , and in recent years the prevalence of rabies has increased in some areas of China [5] . More than 90% of human rabies cases in China occur in rural regions [6] where the proportion of vaccinated dogs is very low [7] . Additionally , China has a growing population of dogs , currently estimated at 80–200 million animals [6] , and the breeding , management , and vaccination of dogs in the country is uncontrolled [8] . A better quantitative understanding of rabies epidemiology in dogs is needed to help predict future vaccine demand in China and other developing countries . Dogs are the primary reservoir and vector of human rabies throughout most of Africa and Asia [9] and are responsible for more than 99% of human rabies cases [1] . Therefore , understanding the dispersal dynamics of rabies in dogs is essential for quantifying dog-to-human transmissions and for disease prevention . Previous studies have documented “traveling waves” of rabies among wildlife populations [10–16] and uncovered the genetic signature of spatial expansion in RABV genomes [17–21] . Recent studies [22–24] have attempted to combine epidemiological and genomic data to study the transmission dynamics of RABV . However , rabies spread in rural areas of China , like Yunnan Province , remains poorly documented despite an increasing incidence of human rabies . Yunnan Province in Southwest China first reported human rabies in 1956 and eliminated the disease by integrated dog management and control measures ( including dog registration and the use of dog enclosures , dog vaccination , and culling of rabid/suspected rabid dogs and stray dogs ) in the late 1990s . However , a re-emergence of rabies in this province was reported in 1999 . In this study , we combine phylogeographic approaches and mathematical modelling to examine the dispersal dynamics of RABV in Yunnan , a rural province of China . We analyse publicly available and newly-generated RABV gene sequences from domestic dogs , cattle , and humans sampled between 2006 and 2015 , together with comprehensive rabies epidemiological data dating back to 1999 , when the first re-emerging human rabies case in Yunnan was reported ( Fig 1A ) . Specifically , we aim to use both types of data to reconstruct the demographic and dispersal history of RABV spread in Yunnan , as well as to estimate key epidemiological parameters that can be compared to other previously-studied RABV outbreaks . Finally , we use phylogeographic inference to investigate which environmental factors may have impacted the spatial dispersal dynamics of RABV in Yunnan .
Analyses of case records date the first documented rabies case in Yunnan Province back to 1956 . These data indicate that an epidemic wave of rabies occurred in the 1980s and that , from the mid-1990s onwards , human rabies cases were reported only sporadically in the region . However , starting in 1999 , a new RABV epidemic emerged . More than 900 human rabies cases were reported during 1999–2015 , with the highest incidence of 0 . 3 cases per 100 , 000 people occurring in 2010 ( Fig 1A ) . Human rabies in Yunnan was first reported in 1999 in the Southeast prefecture of Wenshan , which borders Vietnam and the Chinese province of Guangxi and spread across the province in the next decade [25] . We first used the discrete diffusion model implemented in BEAST 1 . 8 to undertake a continental-scale phylogeographic analysis of a broad data set of Asian RABV sequences ( S1 Table; see also the Materials and Methods for details ) . Genetic histories of the RABV N and G genes reveal two individual lineages ( YN-A1 and YN-A2 , Fig 2 ) that represent the majority of sampled RABV infections in the post-1999 Yunnan epidemic . We then used a GLM ( generalised linear model ) implementation , also available in BEAST 1 . 8 , to measure the correlation between viral effective population size and RABV cases counts in Yunnan . This analysis estimated a GLM coefficient of 0 . 021 for the association between case counts and the trajectory of log effective population size of clade YN-A1 ( which is the main RABV clade identified in Yunnan ) . The credible interval associated with this GLM coefficient estimate excludes zero ( 95% HPD: [0 . 005 , 0 . 036] ) , and therefore indicates a significant association . The time to most recent common ancestor ( tMRCA ) estimates of the YN-A2 clade in the N and G gene largely overlap ( N gene: 2007 . 8 , 95% HPD: 2006 . 1–2009 . 2; G gene: 2008 . 3 , 95% HPD: 2007 . 0–2009 . 4 ) . The G gene estimate of the tMRCA of the YN-A1 lineage predates the N gene estimate ( N gene: 1999 . 2 , 95% HPD: 1996 . 6–2008 . 2; G gene: 1994 . 7 , 95% HPD: 1992 . 5–1996 . 6 ) . We next performed separate continuous phylogeographic reconstructions [26] for the YN-A1 and YN-A2 clades that were identified above in the discrete phylogeographic analysis . As shown in Fig 3 , the inferred RABV diffusion histories estimated from the N and G gene alignments are consistent with each other . The YN-A1 clade underwent a larger spatial expansion and is more widely distributed within dog populations , while the YN-A2 clade appears to be geographically more limited . We compared the amino acid and nucleotide sites that vary between the YN-A1 and YN-A2 clades . There were no amino acid sites changes between these clades , suggesting that the differences in lineage distributions are not associated with pathobiological features , but rather over-representation of local area induced by stochastic founder events or by potential sampling bias ( see Fig 1B ) . Statistics of spatial dispersal estimated from continuous phylogeographic analyses are reported in Table 1 . Estimation of diffusion coefficients [27] allows different outbreaks to be directly and quantitatively compared [19] . Specifically , we found that the diffusion coefficient of RABV in Yunnan ( D = 1733 km2/year; 95% HPD = 1082–2928 ) is on average substantially lower than that previously estimated for RABV among North African dogs ( D = 2874 km2/year; 95% HPD = 1900–5420 ) [28] . Several environmental variables were tested as potential factors that could affect RABV lineage dispersal velocity . As detailed in Materials and Methods , we estimated the correlation between phylogenetic branch durations and environmental distances computed on different rasters ( S1 Fig ) . These correlations were then compared to the correlation between branch durations and distance computed on a “null” raster ( i . e . an empty raster with no spatial heterogeneity whose cell values are set uniformly to “1” ) . Finally , the difference between these two correlations was assessed using a randomisation procedure . The rationale of this approach was to investigate which environmental factors can explain dispersal velocity heterogeneity better than simple geographic distances [28–30] . Complete results are reported in S2 Table , and S2 Fig reports results for the environmental factors that are most likely to have impacted virus lineage dispersal velocity . Only the forest coverage variable , tested as a potential conductance factor , was associated with a Bayes factor > 20 . Although significant , the correlation is weak: using the “forest coverage” raster to compute environmental distances only increases the correlation with branch durations by 3% , relative to the uniform null raster . This means that forest coverage ( treated as a conductance factor ) would be a minor contributor to the observed heterogeneity in virus lineage dispersal velocity . In addition to the analyses investigating the impact of environmental factors on dispersal velocity , we also investigated the impact of these factors on the dispersal tendency , i . e . we investigated if lineage translocation events tended to terminate in particular environmental conditions . Specifically , we compared environmental values at the beginning ( oldest node ) and end ( youngest node ) of each branch . Differences between the environmental values at each end of phylogeny branches tested using a randomisation procedure ( see Materials and Methods ) . This analysis also indicated a possible effect of the “forest coverage” variable ( Table 2 ) : RABV lineages tended to spread towards areas associated with lower forest coverage ( BF > 20 when considering forest coverage to be a potential negative driver of dispersal ) . We also note the relative importance of croplands ( positive driver , BF = 17 . 2 ) and inaccessibility ( negative driver , BF = 12 . 5 ) factors , meaning that the virus lineages were also more likely to spread towards croplands and accessible areas . We used mathematical transmission models and data from human and dog surveillance of RABV outbreaks in Yunnan Province to quantify rabies spread in a rural area of China . Dog demography and human rabies data were collected by the Yunnan Institute of Endemic Disease Control and Prevention . Our model-based simulation of dog-to-human RABV transmission captures the rapid increase in human rabies at the end of the year 2000 , reaching a peak around 2010 , before rapidly declining from 2011 onwards ( Fig 4 ) . Moreover , we find that the simulated density of rabid dogs is highly correlated with reconstruction of viral effective population size through time . Table 3 shows the fitted parameters and values used in the model . Trace plots as well as Gelman and Rubin diagnostic indicate convergence of the MCMC chains [31] ( S3 Fig ) . The estimated carrying capacity ( K ) is 13 . 59 dogs/km2 [95% credible interval ( CI ) : 7 . 80–19 . 37 dogs/km2] . The dog-to-dog transmission rate ( βd ) is 6 . 62/year , [95% CI: 5 . 60–7 . 64/year] . The dog-to-human transmission rate ( βdh ) is 0 . 0004/year , [95% CI: 0 . 0002–0 . 0006/year] . The average basic reproductive number ( R0 ) of the most recent wave of infection is estimated at 1 . 05 during the early stages of the outbreak . High values for the transmission parameters and initial conditions are positively correlated with a high peak of human rabies infections ( S4 Table ) , and the simulations are sensitive to these parameters . Our precise field measures are crucial for reliable model simulations , in particular the dog-to-human transmission rate and dog population size .
Human rabies is one of the biggest public health risks facing China . While current rabies surveillance mostly reflects the RABV prevalence in humans , very little is known about its prevalence in dogs . In most of mainland China , diagnosis of dog rabies is currently effectively impossible in rural areas [8] , because dogs are not leashed , can move freely and have a low vaccination coverage [6] . Because of this , rural dog populations are at increased risk of exposure and are crucial to rabies prevention and control efforts as they are the main reservoir for zoonoses [36] . Analysis of high-resolution epidemiological and genomic data provides an opportunity to explore the dynamics of re-emerging rabies dispersal and RABV transmission . Continuous phylogeographic analyses yield a lower diffusion coefficient than that previously estimated for dog RABV in northern Africa [19 , 28] . Previous analysis of the northern Africa data set also revealed the importance of human-related environmental factors ( human population density , accessibility to nearest major cities ) as explanatory factors for the heterogeneity in RABV lineage dispersal velocity in Africa [28] . The higher diffusion coefficient of the northern African RABV data set was thus suspected to be related to human-based connectivity and/or mobility . Consequently , the lower diffusion coefficient estimated for the Yunnan data set could , in turn , indicate that human-related movements are less important contributors to the spatial dissemination of RABV in Yunnan , although we note that this is only one possible interpretation . Here , we also investigated the potential impact of several environmental factors and found a significant but weak effect of forest coverage acting ( directly or indirectly ) as a factor favouring RABV lineage dispersal velocity in Yunnan . One potential interpretation of this result is that in sparsely populated forest areas a few rapid ( and potentially human-related ) movements of infected dogs may have occurred . Such lineage dispersal movements could have happened via the road network crossing forest areas . However , even if identified as a significant conductance factor , the forest coverage in itself is not necessarily the causal factor and could instead be correlated with the true causal factor that is not included in the present analysis . The potential importance of forest coverage was further indicated by comparing environmental conditions at branch termination locations . This analysis mainly revealed that viral lineages did not tend to spread towards forest areas . However , this result could also reflect , to some extent , the impact of a potential sampling bias arising if there were a lower sampling probability in less densely human-populated regions , such as forest areas . Furthermore , even under the assumption that viral lineages would be less likely to spread towards forest areas , our analysis based on dispersal velocity revealed that these areas do not act as barriers decreasing lineages dispersal velocity among infected areas . Our dynamic model provides estimates of epidemiological parameters for dog rabies in Yunnan . The simulated dog population indicated a low level of RABV transmission , different from the oscillations observed in N’Djamena , Africa [11] . We also obtained an estimate of the transmission rate among dogs ( βd ) , and from dogs to humans ( βdh ) ; key epidemiological parameters for assessment of rabies outbreaks and incidence among dogs . The estimate ( βd ) of 6 . 62/year was higher than the 4 . 20/year estimated in N’Djamena , Chad [34] , and lower than 14 . 68/year estimated in Machakos District , Kenya [37 , 38] . The transmission rate from dogs to humans ( βdh ) was estimated to be 0 . 0004/year , which is consistent with our field investigation of dog bites ( 0 . 0004/year ) in the study area [39] , but much lower than the 0 . 0107/year estimated in N’Djamena [34] . In addition , the estimated carrying capacity ( 13 . 59 dogs/km2 , 95% CI: 7 . 80–19 . 37 dogs/km2 ) was also lower than 33 . 6 dogs/km2 estimated in N’Djamena , Chad , but was similar to the value estimated in Tamil Nadu , India ( 12 . 69 dogs/km2 , estimated using livestock census data ) [40] . We suspect these may result from socio-economic differences , e . g . human population density [41] . Despite a national rabies control and prevention program that was implemented in China in 1985 , which resulted in a drastic reduction in human rabies cases in the 1990s , a new RABV epidemic started in China in 1999 ( Fig 1 ) . Phylogeographic reconstructions confirm the East-to-West invasion history of RABV in Yunnan , which was previously suggested by epidemiological records [25] . Furthermore , we find a time lag between the peaks of viral effective population size and the corresponding peaks of rabid dog numbers ( Fig 4 ) . This may be due to the substantial proportion of undetected dog rabies infections; epidemiological surveillance of dog rabies in the region is far from exhaustive . This study illustrates how RABV dispersion dynamics can be analysed by two complementary approaches based on genetic and epidemiological data . Overall , our work further highlights that domestic dogs play a key role in transmission and expansion of rabies in rural areas of China . The data reveal a low level of RABV transmission among dogs , and from dogs to humans , with the basic reproductive number estimated at 1 . 05 during the early stages of the outbreak . Our results indicate that interventions in the dog population would be effective in reducing transmission to humans , in particular because they have the potential to subvert the self-sustaining capacity of epidemics in dogs . A better understanding of RABV spread in terms of spatial and temporal dynamics is necessary to help inform the prevention and control of human rabies in the vast rural areas of China .
It was determined by the Yunnan Institute of Endemic Diseases Control and Prevention , that the collection of data from rabies cases was part of continuing public health surveillance of a notifiable infectious disease and was exempt from institutional review board assessment . Experimental procedures were performed in compliance with guidelines established by the Chinese Center For Disease Control And Prevention and have been approved by ethics committee of Yunnan Institute of Endemic Diseases Control and Prevention . From 2008 to 2015 , we collected 1392 brain tissue samples . These samples were obtained from 252 dogs suspected of having rabies , 1129 apparently healthy domestic dogs , 2 cows , and 9 human patients within 24 hours of death in 14 prefectures and 43 counties of Yunnan Province ( Fig 1B ) . In addition , 18 saliva samples and 1 cerebrospinal fluid sample were obtained from surviving patients . All brain specimens were tested using direct immunofluorescence assay ( DFA ) and RABV nucleoprotein monoclonal antibody ( Rabies DFA Reagent; Chemicon , Temecula , CA , USA ) . Total RNA was extracted from the original brain samples with the Trizol reagent ( Invitrogen , USA ) according to the manufacturer’s instructions [25] . PCR products were purified by using a QIAquick PCR Purification Kit ( QIAGEN , Germany ) . Complete nucleoprotein gene ( N gene; 1 cow , 84 dog and 6 human isolates ) and glycoprotein gene ( G gene; 2 cow , 110 dog and 8 human isolates ) sequences were obtained by using previously described primers [21 , 42] . Newly generated RABV sequence data were submitted to GenBank ( accession numbers KP072009–KP072030 , KP202418- KP202448 , KT932670-KT932698 and KX096992-KX097000 for N gene , and JF819597-JF819602 , JQ040570-JQ040581 , JX276383-JX276404 , KP072031-KP072052 , KP202402-KP202417 , KT861554-KT861586 and KX096983–KX096991 for G gene ) . An overview of the newly sequenced isolates is provided in S1 Table . Human rabies case records ( Fig 1A ) in Yunnan from 1999 to 2015 were obtained from the Yunnan Center for Disease Control and Prevention ( CDC ) and the Chinese CDC . In China , human rabies is a class B notifiable infectious disease and all human cases must be reported to the Chinese CDC . Rabies cases were confirmed according to diagnostic criteria ( WS281–2008 ) from the Ministry of Health of the People’s Republic of China . To identify Yunnan-specific RABV circulation , nucleotide sequences of the N and G genes of all available RABV sequences sampled from non-flying mammals in Asia were downloaded from NCBI GenBank and aligned [43] together with our newly generated sequences . The resulting data sets included 543 N sequences ( the entire N gene coding sequence , 1350 nt long ) and 491 G sequences ( the entire G gene coding sequence , 1575 nt long ) . RABV clades specific to Yunnan were identified using the discrete trait analysis model implemented in BEAST 1 . 8 [44 , 45] . For the molecular clock phylogeographic analyses , we specified a general time-reversible GTR+I nucleotide substitution model [46–48] , a skygrid coalescent model [49] and a relaxed uncorrelated lognormal ( UCLN ) molecular clock model across branches [50] . The data sets lack a clear temporal signal [51] ( S1 Fig ) . Therefore , we specified informative prior distributions on the gene-specific mean clock rate parameter . These prior distributions were based on previously published estimated substitution rates [52]: 1 . 88×10−4 substitutions/site/year ( 95% highest posterior density , HPD: [1 . 37×10−4 , 2 . 41×10−4] ) for the N gene and 2 . 13×10−4 substitutions/site/year ( 95% HPD: [1 . 56×10−4 , 2 . 73×10−4] ) for the G gene . The MCMC chain was run for 250 million states and mixing and convergence were inspected using Tracer [http://tree . bio . ed . ac . uk/software/tracer/] . TreeAnnotator 1 . 8 [44] was used to infer maximum clade credibility ( MCC ) summary trees . We used the GLM ( generalised linear model ) extension of the skygrid coalescent model [53] , implemented in BEAST 1 . 8 , to simultaneously infer viral effective population sizes and measure the association between estimated effective population size and cases counts . For this analysis , we focused only on sequences associated with the largest Yunnan clade identified by the discrete phylogeographic analysis ( clade YN-A1 , see the Results section ) . Furthermore , we considered the N and G genes as two independent markers ( see Appendix S1 in S1 Text for the detailed procedure ) . The history of virus lineage dispersal in Yunnan was recovered from geo-referenced phylogenies , which were estimated using the continuous phylogeographic method [26] implemented in BEAST 1 . 8 . A separate continuous phylogeographic analysis was performed for each gene and each Yunnan clade identified by the preliminary discrete phylogeographic analyses; however , the nucleotide substitution and molecular clock models were linked across the clades to avoid over-parameterisation . For these models , we used the same models as described above , together with a relaxed random walk model for inference of the continuous spatial locations . This relaxed random walk model assumed a log-normal probability distribution among phylogeny branches of diffusion rate scalars . The spatiotemporal information contained in inferred phylogenetic trees was extracted with the R package “seraphim” [29 , 30] . For the present study , we extracted spatiotemporal information from a subset of 1 , 000 trees sampled at regular intervals from the posterior distribution of trees ( after burn-in had been removed ) . This was done for each gene and for each Yunnan clade . After this extraction step , each phylogeny branch is represented as a distinct movement vector [27] . “seraphim” was also used to estimate statistics of spatial dispersal based on these extracted movement vectors . We estimated the mean branch velocity , the weighted dispersal velocity , the diffusion coefficient ( as originally defined in Pybus et al . [27] ) and the weighted diffusion coefficient ( as defined by Trovão et al . [54] ) . Further details regarding these statistics can be found in the “seraphim” package [30] . We next sought to investigate the impact of environmental factors on lineage dispersal velocity . These analyses followed a similar structure to those used in previous studies [28 , 29 , 55] . All scripts for these analyses are available in the R package “seraphim” [30] . Here , we investigated the impact of the following environmental variables ( S1 Fig ) : elevation , annual mean temperature , annual precipitation , key land cover variables ( e . g . “grasslands” , “savannas” , “forests” , “croplands” , “urban areas”; land cover categorised according to the International Geosphere Biosphere Program , IGBP ) , human population density , human footprint , major roads , and inaccessibility ( quantified as the time it takes to travel to the nearest major city of >50 , 000 inhabitants ) . The sources of the data in the original raster files are listed in S3 Table . All factors were tested as potential conductance factors ( i . e . factors facilitating movement ) and as potential resistance factors ( i . e . factors impeding movement ) . Correlations between phylogenetic branch durations and environmentally-scaled distances were quantified as a statistic Q , which represents the difference between two coefficients of determination ( R2 ) : ( i ) the R2 obtained when branch durations are regressed against environmentally-scaled distances , and ( ii ) the R2 obtained when branch durations are regressed against distances computed on a “null” raster , i . e . a raster with a value of “1” assigned to every cell . An environmental factor was only considered as potentially explanatory if both its distribution of regression coefficients and its associated distribution of Q values were positive [56] . In a positive distribution of estimated Q values ( i . e . with at least 90% of positive values ) , statistical support was then evaluated against a null distribution generated by a randomisation procedure and formalised using a Bayes factor ( BF ) value [28] ( see Appendix S2 in S1 Text for the full procedure ) . Due to computational limits , this analysis was based on 100 trees subsampled from each post-burn-in posterior distribution obtained by continuous phylogeographic inference . Addition to the analyses based on lineage dispersal velocity , we also used a new analytical procedure to investigate the impact of several environmental factors on dispersal tendency . In order words , this procedure aims at testing if virus lineages tend to disperse towards particular environmental conditions . In this framework , environmental conditions are compared between the locations of the two nodes connected by a phylogeny branch . For each branch and environmental factor , we computed the difference in raster cell values between the start ( oldest node ) location and at the end ( youngest node ) location . These differences were then averaged within each sampled tree and evaluated against a null distribution generated by the same randomisation procedure used for the analyses of the impact on dispersal velocity ( see Appendix S3 in S1 Text for the detailed procedure ) . In the dynamic modelling part of this study , we extended the discrete susceptible-exposed-infectious ( SEI ) model for dog rabies [10 , 11 , 34] . Our discrete-time model framework was developed by dividing a closed dog population into four rabies classes , susceptible ( Sd ) , exposed ( Ed ) , and infectious ( Id ) , and vaccinated ( Vd ) . We then extended the model for dog rabies to include dog-to-human RABV transmission . A similar modelling framework has been applied to quantitatively assess the dynamics of RABV transmission [34] . Sh is the number of people susceptible to disease , Ph is the annual vaccinated individuals in Yunnan ( based on medical records from Chinese Center For Disease Control and Prevention ) , Eh and Vh represents the number of exposed humans and immunized humans during the time period , respectively . Sd , t+1=Sd , t+bdNd , t+λdVd , t−ddSd , t−γNd , tSd , t−βdSd , tId , t−veSd , t [1] Ed , t+1=Ed , t+βdSd , tId , t−ddEd , t−γNd , tEd , t−σEd , t−veEd , t [2] Id , t+1=Id , t+σEd , t−ddId , t−γNd , tId , t−αdId , t [3] Vd , t+1=Vd , t+ve ( Sd , t+Ed , t ) −ddVd , t−γNd , tVd , t−λdVd , t [4] Sh , t+1=Sh , t ( 1−βdhId , t ) +bh ( Sh , t+Eh , t+Vh , t ) −dhSh , t+λhVh , t−Ph , t [5] Eh , t+1=βdhSh , tId , t−σhEh , t−dhEh , t [6] Ih , t+1=σhEh , t−dhIh , t−αhIh , t [7] Vh , t+1=Vh , t ( 1−λh ) +Ph , t−dhVh , t [8] where Nd is the total dog population ( Sd + Ed + Id + Vd ) . Dog population growth is considered to be density-dependent , with birth ( bd ) and death ( dd ) rates . The density-dependent mortality parameter ( γ ) is a function of the dog population growth rate per year ( bd—dd ) and the carrying capacity density of the dog population in km-2 ( K ) is defined by γ = ( bd—dd ) / K . Carrying capacity is defined as the maximum number or density of individuals that can be sustainably supported by a given environment . In rural areas of China , many domestic dogs are free-roaming and there has been no population control for most of the time . We can then assume that dog populations ( including owned and stray dogs ) are regulated through represented by carrying capacity determined by some unknown combination of environmental and human geographic factors . βd and βdh are the dog-to-dog and dog-to-human transmission rate , respectively . σd is the latent-to-infectious rate per year and α is the rabies-induced mortality . At the beginning ( t = 0 ) , the basic reproductive number ( R0 ) is σβdS0/ ( ( σ+b ) ( α+b ) ) . Informative prior distributions are assumed for death rate for rabid dog/human , latent period , incubation period , as well as dog-to-human transmission rate ( βdh ) , which is assessed from our field investigation of dog bites [39] . We assumed dog vaccination rate ( v ) of 0 . 07 and dog vaccination success rate ( e ) of 0 . 60 in Yunnan by previous surveillance [32] . λ is loss of vaccination immunity rate . Culling was simulated assuming the elimination of 1% of the total dog population based on empirical estimation , due to the temporal irregular and changing intensity of potential culling . The susceptible reconstruction provided initial estimates of the initial susceptible dog population size , Sd , 0 , for which we chose a uniform prior of 5–15 dogs/km2 , according to our surveillance [32] . Initial numbers for Ed , 0 and Id , 0 were unknown and fitted in the model with a wide prior 0–1 dogs/km2 . We fitted the dynamic model using the Bayesian state space framework using Metropolis-Hastings Markov chain Monte Carlo algorithms implemented in the MATLAB ( vR2009b ) toolbox DRAM ( Delayed Rejection Adaptive Metropolis ) [57 , 58] . The chain was initiated using published estimates of dog demography and RABV transmission rates [34 , 38 , 59–61] with an uninformative prior that varied from 0 to infinity , and then was run for 5 million iterations and sampled every 5000th step , after a burn-in of 500 , 000 iterations . Sensitivity analysis was performed using latin hypercube sampling ( LHS ) and partial rank correlation coefficient ( PRCC ) techniques [62] . Two criteria were retained as outputs for the analysis: the intensity of the peak of the human rabies infections and the date of the peak . Uniform distributions were used for all parameters and the same ranges as for the prior distributions were used . A total of 1 , 000 parameter sets were sampled with LHS and PRCC was estimated using the MATLAB code provided by Marino et al . [63] . | Although dogs are known to be the primary reservoir and vector of human rabies in African and Asian countries , the spatial epidemiology of rabies virus ( RABV ) spread in developing regions is still unclear . Using 17 years of genomic and epidemiological data , we reconstruct the recent dispersal history of RABV in domestic dogs in Yunnan , a rural province of China , and estimate RABV transmission rate between dogs and from dogs to humans . Using a phylogeographic approach , we also evaluated the potential impact of several environmental factors on the mode and tempo of virus lineage dispersal . Our findings have implications for rabies prevention and control in Asian countries . | [
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"geographic... | 2018 | Transmission dynamics of re-emerging rabies in domestic dogs of rural China |
Neurofibromatosis type 1 ( NF1 ) is an autosomal dominant , monogenic disorder of dysregulated neurocutaneous tissue growth . Pleiotropy , variable expressivity and few NF1 genotype-phenotype correlates limit clinical prognostication in NF1 . Phenotype complexity in NF1 is hypothesized to derive in part from genetic modifiers unlinked to the NF1 locus . In this study , we hypothesized that normal variation in germline gene expression confers risk for certain phenotypes in NF1 . In a set of 79 individuals with NF1 , we examined the association between gene expression in lymphoblastoid cell lines with NF1-associated phenotypes and sequenced select genes with significant phenotype/expression correlations . In a discovery cohort of 89 self-reported European-Americans with NF1 we examined the association between germline sequence variants of these genes with café-au-lait macule ( CALM ) count , a tractable , tumor-like phenotype in NF1 . Two correlated , common SNPs ( rs4660761 and rs7161 ) between DPH2 and ATP6V0B were significantly associated with the CALM count . Analysis with tiled regression also identified SNP rs4660761 as significantly associated with CALM count . SNP rs1800934 and 12 rare variants in the mismatch repair gene MSH6 were also associated with CALM count . Both SNPs rs7161 and rs4660761 ( DPH2 and ATP6V0B ) were highly significant in a mega-analysis in a combined cohort of 180 self-reported European-Americans; SNP rs1800934 ( MSH6 ) was near-significant in a meta-analysis assuming dominant effect of the minor allele . SNP rs4660761 is predicted to regulate ATP6V0B , a gene associated with melanosome biology . Individuals with homozygous mutations in MSH6 can develop an NF1-like phenotype , including multiple CALMs . Through a multi-platform approach , we identified variants that influence NF1 CALM count .
Neurofibromatosis type 1 ( NF1 ) is a common , monogenic disorder of dysregulated tissue growth that is caused by mutations in the tumor suppressor gene NF1 ( chromosome 17q11 . 2 ) . Neurofibromas , soft fleshy tumors , are the hallmark lesion of NF1; affected individuals may have dozens to thousands of neurofibromas . Other clinical features include multiple café-au-lait macules ( CALM ) on the skin , axillary and groin freckling , benign tumor-like lesions of the iris ( Lisch nodules ) , scoliosis , enlarged head circumference and learning disabilities . Individuals with NF1 are also susceptible to variety of other benign and malignant tumors [1] . Although the allele responsible for NF1 is inherited in an autosomal dominant pattern , the NF1 phenotype is complex because of variable expressivity , pleiotropy and limited NF1 genotype-phenotype correlates [2] , [3] . The inability to predict the severity of phenotype in NF1 has important clinical consequences and essentially precludes prognostication regarding disease severity even among family members who share an identical NF1 mutation . “Simple” monogenic disorders like NF1 are often more complicated than expected , and thus comprise a potential model for studying complex traits [4] , [5] , a term usually reserved for disorders like diabetes , which cluster in families but typically are not due to single-gene Mendelian inheritance . The phenotypic complexity of NF1 likely is multifactorial , including epigenetic phenomena , stochastic events and heritable elements such as genetic modifiers [6] . There is experimental and clinical evidence that genetic modifiers explain a major fraction of phenotypic variation in NF1 . In mice , specific loci responsible for susceptibility to astrocytoma/glioblastoma in male mice ( Arlm1 ) [7] , resistance to spinal cord astrocytoma in mice ( Scram1 ) [8] , and murine peripheral nerve sheath tumors ( Nstr1 and Nstr2 ) [9] have been identified [9] , [10] . In one study in humans , correlation between CALM count and cutaneous neurofibroma burden was highest among monozygotic twins and decreased successively among first- and second-degree relatives . Furthermore , four of the five binary traits studied ( presence/absence of plexiform neurofibromas , optic pathway gliomas , scoliosis , epilepsy and need for remedial education ) also showed significant familial clustering [11] . Szudek et al . observed similar patterns of intra-familial phenotype correlation that suggested a role for genetic factors [12] . An analysis of NF1 phenotype presence and severity in a large French cohort found patterns of familial correlations that indicated a strong genetic component , with no apparent influence of the normal ( non-mutated ) germline NF1 allele [13] . Only a few genes and loci influencing the NF1 phenotype have been found to date . In a pedigree with both NF1 and congenital megacolon , only members with both the paternally derived GDNF R93W allele and maternally inherited NF1 mutation had megacolon [14] . In a study of neurofibroma burden in NF1 , evidence of a higher rate of DNA mismatch repair ( MMR ) gene MSH2 ( but not other MMR genes MLH1 , MSH6 or PMS2 ) promoter methylation was observed in NF1 cases compared with controls . Among NF1 patients with higher tumor count , statistically significant enhanced methylation of two ( of six ) CpG islands in MSH2 was observed in 79 NF1 patients , versus 39 controls [15] . Beyond the MMR pathway , the noncoding RNA gene ANRIL is transcribed in the antisense orientation to CDKN2A and CDKN2B genes and influences their expression . ANRIL was deleted in six of 22 plexiform neurofibromas , as detected by genome-wide array comparative genomic hybridization . Using a family-based association test , a single SNP ( rs2151280 ) in ANRIL was significantly associated with the number of plexiform neurofibromas in a cohort of 740 NF1 patients [16] , but not in a cohort of 29 individuals with a microdeletion of NF1 [17] . To identify genetic modifiers in NF1 , we recruited and quantitatively phenotyped two cohorts of individuals with NF1 . We used the principles of the genetics of gene expression to develop a screen for candidate genes [18]–[20] . We performed the test of association between transcript abundance ( as determined by microarray ) and variation in human NF1 quantitative phenotypic severity by simple linear regression to identify candidate loci that may modify quantitative traits in NF1 . Also known as “genetical genomics” [21] or expression quantitative trait loci ( eQTL ) mapping , this approach has been successful in elucidating mechanism and causal genes in animal models [22]–[24] and human disease [25]–[30] . Large effect size and widespread prevalence ( especially cis-acting variation ) in the genome makes eQTL mapping an appealing approach , especially in small studies [31] . Thus , we hypothesized that normal variation in germline gene expression confers risk for certain clinical phenotypes in an individual haploinsufficient for NF1 . Select variants were then genotyped in a validation cohort . We studied gene expression in lymphoblastoid cell lines ( LCLs ) . The use of phenotype-specific tissues ( e . g . , melanocytes or Schwann cells ) in a large study is impractical and we used LCLs as a surrogate tissue . There are no studies comparing the degree of expression overlap between LCLs ( Epstein-Barr virus-transformed lymphocytes ) with melanocytes or Schwann cells . LCLs share 30% of eQTLs with skin and fat; other studies estimate cis-eQTL overlap between blood and fat to be ∼50% [31] . The selection of which phenotype to study is a key consideration in modifier studies . In NF1 , many phenotypic features ( e . g . , neurofibroma burden ) are time-dependent and thus comparisons between groups must take age into account . Although we measured a variety of phenotypic features , in this study we focused on CALM count since it is easily quantified and the complement of CALM is typically stable after early childhood . CALM count shows significant familial aggregation and a pattern of familial correlation that suggest a strong genetic component independent from the influence of the germline NF1 mutation [13] . Finally , CALM are “tumor-like” in that they follow the Knudsen two-hit hypothesis: melanocytes in these lesions acquire a second somatic mutation in NF1 [32] . Thus , genes that modify CALM count may also plausibly modify tumor burden .
Table 1 summarizes the demographic and phenotypic data from the datasets collected in the study . The 99 NF1 individual ( “DISC” ) set included 70 of the 79 individuals used for expression regression ( “EXPR” ) plus an additional 29 participants . We sought to identify genetic modifiers of NF1 by test of association by simple linear regression between variation in quantitative phenotype severity and the expression level of each transcript ( among ∼10 , 000 transcripts expressed in the LCLs ) . After filtering for the false discovery rate ( FDR ) <0 . 30 , range of expression level >2 , or >6 , and biological significance , we identified candidate transcript-phenotype pairs for 80 genes ( Table S1: “Set of 80” ) . We chose 21 genes for verification by measuring their expression with quantitative real-time PCR , using the original set of RNA samples ( Table S1: “Set of 21” ) . We chose genes either by the significance of their association with NF1 phenotypes in the original screen or their biological plausibility . Seven of the 21 transcripts ( 33% ) remained significantly associated with phenotype severity ( nominal p values<0 . 05 ) ( Table S1: “Verified 7”; Table S2 and Figure S1A–H ) . The verified genes included MED21 and MSH6 ( CALM ) ; NMT2 and TMEM109 ( Lisch nodules ) ; FHL2 , RAB11FIP1 and PREB ( height ) . We focused on candidate genes influencing the CALM phenotype only , given its clinical tractability and tumor-like biology . Thus , we identified the coding and limited intronic nucleotide sequence of the following genes in germline DNA: MSH6 , MSH2 , MLH1 , MED21 and DPH2 . We sequenced MSH6 and MED21 genes because of their highly significant association with CALM count in both microarray and qPCR experiments , and because germline mutations in MSH6 have been associated with development of café-au-lait macules in non-NF1 patients . We included MSH2 and MLH1 because their protein products are known to associate with MSH6 in functional MMR complexes . Moreover , germline mutations in MSH2 and MLH1 have been linked to an NF1-like clinical phenotype with multiple CALM [33]–[35] . Despite of the fact that DPH2 qPCR did not confirm association of the gene with CALM phenotype , the gene was included in the sequencing phase of the analysis because of its biological function ( see Discussion ) . By sequencing these five genes in the DISC sample set and performing simple linear ( Table 2 and Table S3 ) and tiled regression ( Table 3 ) analyses using additive , dominant and models with untransformed and log-transformed CALM count , we identified thirteen variants in the genomic regions of MSH6 and two near DPH2 and ATP6V0B that were significantly associated with CALM count . Significance levels were set at 0 . 05 for linear and tiled regressions . Each model was evaluated with hotspot-based tile regions . For untransformed CALM , the best-fitted model representing the independent SNVs in TRAP is:where “rs4660761” represents the number of minor alleles in SNP rs4660761 . We did not identify variants in MED21 or MSH2 that were significantly associated with CALM count . None of the SNPs in MSH6 ( rs1800934 ) and near DPH2 and ATP6V0B ( rs7161 and rs4660761 ) significant in DISC were significantly associated with CALM count by simple linear regression in REP1 and REP2 ( Table 2 ) at the level of 0 . 05 . In the meta-analysis , SNP rs7161 ( DPH2 ) was significant assuming dominant effect of the minor allele using untransformed CALM and SNP rs4660761 ( DPH2 ) and SNP rs1800934 ( MSH6 ) were marginally significant . In the mega-analysis , SNP rs7161 and SNP rs4660761 ( near DPH2 and ATP6V0B ) were significant , but not SNP rs1800934 ( MSH6 ) ( Table 2 ) . Based on Roadmap and ENCODE data , SNP rs4660761 [A/G] is located in an active promoter region and an unmethylated CpG island ( CGI ) upstream of the gene ATP6V0B in normal penile foreskin melanocytes , fibroblasts and keratinocytes ( Figure 1 ) . The variant G allele of SNP rs4660761 also creates a CpG dinucleotide within the CGI . The DNA region containing SNP rs4660761 maps to DNase I sites and interacts with a number of proteins in ENCODE cell lines including POL2 , and the variant has the potential to alter the DNA binding motifs of BRCA1 , YY1 and ZBTB33 proteins ( Table S4 ) . SNP rs7161 , which is in high correlation with SNP rs4660761 ( Pearson correlation coefficient , ρ = 0 . 89 ) , is located in the 3′ UTR region of DPH2 or 5′ of ATP6V0B . SNP rs7161 is reported to locate to an enhancer region with weak H3K4me1 and strong H3K27ac marks in penile foreskin melanocytes using the HaploReg tool ( http://www . broadinstitute . org/mammals/haploreg/detail_v2 . php ? query=&id=rs7161 ) . However , we found no evidence for this enhancer region using Roadmap ChromHMM Primary Core Marks and data from normal melanocytes on the UCSC browser ( Figure 1 ) . In K562 and HeLa cells , the DNA region containing SNP rs7161 is strongly enriched for POL2 binding and can also form a chromatin loop structure with the promoter region of the upstream gene IPO13 ( Table S4 ) .
In this study we identified sequence variants that influence CALM count in self-reported European-Americans with NF1 . To find genetic modifiers in NF1 subjects we hypothesized that in cells bearing a mutation in the NF1 gene , normal and genetically determined germline variation in expression level of a potential genetic modifier ( other than NF1 gene itself ) will either exacerbate or ameliorate NF1 phenotypes in a quantitative and linear way . We developed a genome-wide screen that regressed transcript expression level against quantitative phenotype to identify transcript-phenotype pairs . We focused primarily on transcripts associated with CALM count , an easily-measured , highly-heritable [13] phenotype; CALM are tumor-like in that they arise from biallelic inactivation of NF1 . Identification of MSH6 in the screen also prompted us to sequence MSH2 and MLH1 , whose protein products are known to associate with MSH6 . Sequencing the DPH2 locus led to the identification of two SNPs ( rs7161 and rs4660761 ) that were statistically significantly associated , in a variety of models ( Tables 2 and 3 ) , with CALM count in NF1 in the discovery ( DISC ) cohort . In the mega-analysis of all three cohorts ( DISC , REP1 , REP2 ) , both DPH2 SNPs in all models were an order of magnitude more significant than in the DISC cohort alone . In addition , the DPH2 SNP rs4660761 was significant by TRAP analysis in an additive model . Sequencing MSH6 led to the identification of one SNP ( rs1800934 ) that was statistically significantly associated with CALM count in NF1 in the DISC cohort ( assuming dominant effect of the minor allele ) and trended to significance in the REP2 cohort . Mega-analysis of all three cohorts for MSH6 SNP rs1800934 was not significant , although it trended to significance in the meta-analysis . A group of twelve rare ( mean MAF = 0 . 015 ) MSH6 SNPs collapsed in hotspot-based regions identified in the DISC cohort were significant in a model coded by the proportion of the minor allele . Given their rarity we did not attempt to validate them in the REP1 or REP2 sets . The two validated DPH2 SNPs , rs4660761 and rs7161 , are non-coding and reside in the ∼1 . 5 kb region between the 3′-UTR of DPH2 and 5′-end of ATP6V0B . Genetic variation in DPH2 and ATP6V0B have not previously been associated in any GWAS study with any known human phenotype [36] . The DNA region containing SNP rs4660761 appears to be in the active promoter of the gene ATP6V0B in normal melanocytes , keratinocytes and fibroblasts . The region is further enriched for POL2 in K562 cells and the variant G allele of SNP rs4660761 forms the consensus DNA sequence of the binding motif of the transcriptional regulator ZBTB33 . A positive relationship between ZBTB33 binding , the absence of DNA methylation , the presence of active promoter marks and gene expression in K562 and GM12878 cells has been reported [37] . Collectively , these data suggest an important function for SNP rs4660761 in the transcriptional regulation of ATP6V0B . The SNP rs7161 is upstream of SNP rs4660761 , and while these two SNPs are in high correlation ( Pearson's correlation of 0 . 836 ( in DISC ) and 0 . 817 ( DISC+REP1+REP2 ) ) in our population , SNP rs7161 does not appear to be in a regulatory region in melanocytes , fibroblasts or keratinocytes . We observed higher mRNA levels of ATP6V0B in melanocytes compared to fibroblasts , keratinocytes and PBMC cells using Roadmap RNA-sequence data . However , data from neXtProtein suggests that ATP6V0B is only expressed at the protein level in melanocytes ( http://www . nextprot . org/db/entry/NX_Q99437/expression ) . ATP6V0B is a subunit of the V0 membrane integral domain ( or proton-conducting pore ) of the vaculolar ATP multi-protein complex ( V-ATPase ) [38] . V-ATPases are known for their role in H+ transport in which they are important for intracellular and extracellular acidification events , protein transport and membrane fusion [39] , [40] . Importantly , V-ATPase function is essential for melanosome biogenesis [41] . In fact , melanosomes are acidic organelles where low luminal pH is an essential environment for their function and the required acidic pH is produced by a V-ATPase . Interestingly , the hyperpigmentation in CALM is characterized by increased melanin in melanocytes and basal keratinocytes [42] . In mammals , mature melanosomes are transported from melanocytes to keratinocytes [43] . Furthermore , mutations in V-ATPase subunits produce pigment dilution phenotypes in Drosophila , zebrafish , mice and humans [44] , [45] . Since V-ATPase function has been shown to be essential for melanosome biogenesis , we hypothesize that the pigmented phenotype of CALM may be a consequence of increased expression of ATP6V0B and an increase in the number of mature melanosomes produced in melanocytes ( or heightened pigmentation ) and/or transported to surrounding keratinocytes . However , the potential effect of the variant G allele of SNP rs4660761 on the expression of ATP6V06 in melanocytes is not known and thus testing these hypotheses and the tissue-specific nature of ATP6V06 function remain interesting biological questions for the future . The gene DPH2 is involved with diphthamide synthesis , which is a post-translational modification of histidine residue 715 on elongation factor 2 ( eEF2 ) , a housekeeping protein involved in elongation of translation [46] . This modification is exceptional in that it occurs only on eEF2 [47] . Yeast strains lacking Dph2 are prone to increased frequency of ( −1 ) frameshifting by the ribosome during translation . In mice , inactivation of one copy of Dph1 or Dph3 , two of the five genes involved with murine diphthamide modification , increases incidence of tumor development , while inactivation of both copies of either gene is embryonically lethal . Human DPH1 ( also known as OVCA1 , ovarian cancer-associated gene 1 ) inhibits the proliferation of epithelial ovarian cancer cells [48] . These observations imply that Dph genes and diphthamide modification of eEF2 may affect accuracy of protein synthesis in the cell , the rate of tumor incidence and other developmental processes . We found variation in MSH6 associated with CALM count , although these SNPs did not validate as convincingly as those in DPH2 . However , MSH6 deserves special note . It is a member of the DNA mismatch repair ( MMR ) family of genes , which ensures fidelity of DNA replication . Hereditary nonpolyposis colorectal cancer ( Lynch syndrome ) is caused by heterozygous germline mutations in MMR genes ( including MSH6 ) [49] . Individuals with homozygous or compound heterozygous mutations in MSH6 develop an NF1-like phenotype with multiple CALM as well as central nervous system , hematologic and gastrointestinal malignancies [50]–[55] , perhaps secondary to post-zygotic mutations in NF1 [56] . Zebrafish models of MMR deficiency also feature neurofibromas and other NF1-associated tumors [57] . This study's strengths include thorough , prospective , quantitative phenotyping of a cohort of individuals who all met diagnostic criteria for NF1 . We used rigorous statistical analysis of two additional cohorts to validate findings from the discovery cohort . We acknowledge several limitations . We used LCLs as the source of tissue for our expression studies . As a proxy , LCLs are easy to obtain and culture , but there is limited overlap in blood expression profiles and other tissues [25] . We did not determine the germline mutation of NF1 in each participant in the DISC and REP1 cohorts , given the limited genotype-phenotype correlation in the disorder . However , there were no NF1 microdeletions in the DISC and REP1 cohorts [58] , nor did we detect the 3-basepair in-frame deletion ( NM_000267 . 3:c . 2970_2972delAAT ) of exon 22 ( legacy exon 17 ) , an NF1 genotype know to affect neurofibroma number [3] , [58] . In the REP2 cohort there were three individuals with an NF1 microdeletion , although this is not known to affect CALM count . NF1 mosaicism is frequently invoked to explain milder disease presentations , but it is difficult to prove or disprove its existence in an individual . In the DISC cohort , 77 ( 58% ) individuals presented de novo NF1 , and were more likely to be mosaics or of unknown inheritance . NF1 mosaicism is approximately 10 times less common than the prevalence of germline NF1 mutations itself [59] . We conservatively estimate that 10% of the de novo/unknown inheritance group ( approximately 8 individuals ) in our study of 132 individuals ( 6% ) may be NF1 mosaic . This modest percentage is unlikely to influence our study results . Identifying common genetic modifiers of monogenic disorders is akin to the detecting common genetic variation influencing traditional complex traits [60]: both are difficult to study , prone to small effect sizes and dependent on the selection of the proper phenotype [61] . Efforts to identify genetic modifiers of tumor burden or severity in the NF1 mouse model yielded alleles with modest effects but required sizable , complex breeding schemes [62] . The SNPs we identified were associated with CALM count , which is among the most heritable of all NF1 features [11] , [13] . Tractability of phenotype is also important; CALM count is relatively easy to measure and is established by early childhood , although the lesions may fade with age . Our work is proof that genetic modifiers of the NF1 phenotype can be identified . Efforts to identify variants influencing time-dependent phenotypes ( e . g . dermal neurofibroma burden ) will require careful phenotyping and large , collaborative efforts .
The DISC and REP1 cohorts were comprised of adults meeting the consensus criteria for the diagnosis of NF1 [63] , [64] who were willing to travel to the NIH Clinical Center in Bethesda , Maryland and who had both living biological parents willing to donate a blood sample . The parents did not need to be affected with NF1 . Exclusion criteria for probands included: 1 ) any past or present history of radiation therapy , chemotherapy or biologic agents that might be expected to alter the natural history of neurofibroma growth , 2 ) any history of surgery to remove multiple neurofibromas or spinal neurofibromas , 3 ) cognitive delay that would preclude sedation to obtain an MRI , 4 ) presence or suspected presence of surgical hardware ( e . g . , Harrington rods ) or metallic objects that would preclude MRI imaging and 5 ) inability or unwillingness to tolerate an extended ( one hour or more ) MRI protocol . Study participants were recruited via a variety of means ( e . g . , Google advertising , letters to NF1 clinics ) from throughout the United States . Travel and lodging costs were covered by the protocol . Lymphoblastoid cell lines ( LCLs ) from the first 79 participants ( “EXPR” ) were used in the gene expression screen to identify putative modifiers . For tests of association of variants in putative modifiers identified in the EXPR screen , 99 participants were used as a discovery cohort ( DISC ) where 70 samples of the EXPR cohort were included in the DISC sample . An additional independent 33 and 81 participants were used as validation cohorts ( REP1 and REP2 , respectively ) . This study was approved by the National Human Genome Research Institute and National Cancer Institute institutional review boards and all participants provided written , informed consent . We sought to quantify the NF1 phenotype in a comprehensive two-day visit to the NIH Clinical Center . A single observer ( DRS ) performed a history and physical exam ( with measurements ) , Wood's lamp exam , slit-lamp exam , and collected photographs of the skin . NF1-specific abnormalities were noted ( e . g . , presence/absence of intertriginous freckling , bony abnormalities , dysmorphic features ) and a clinical assessment of the probability of mosaic NF1 was made . Whole-body cutaneous neurofibroma burden ( lesions projecting above the skin ) was estimated within a set of ranges ( 0 , 1–10 , 11–50 , 51–100 , 101–500 , 500+ ) . In addition , a paper frame with a 100 cm2 cut-out at the center was placed on the mid-back , abdomen and left thigh of each participant and a photograph was taken . Within the 100 cm2 , all protruding cutaneous neurofibromas greater than 2 mm were counted , marked with water-soluble ink and re-photographed . The number of cherry hemangiomas , an under-recognized feature associated with NF1 [65] , [66] , was also counted within each frame at the three different sites . The number , size and distribution of CALM and other dermatologic abnormalities were counted , measured and mapped with a Wood's lamp and ruler in a darkened room . The CALM count was defined as the total number of café-au-late spots greater than 5 mm in any dimension . A slit-lamp exam was used to enumerate and photograph Lisch nodules in the eye , as previously described [58] . From the physical exam we measured height , weight and head circumference . Growth charts specific for the NF1 population ( recruited at an Italian center ) were used to determine centile rankings of height and weight [67] . Centile charts for adult head circumference ( adjusting for gender and height ) were also used for NF1-affected individuals [68] . We obtained demographic and self-reported ethnicity data , a pedigree and associated data ( parity , presence of consanguinity , age of parents at birth ) , subject and parental heights , an MRI of the spine and clinical photographs , and referred participants to the dental clinic at the NIH Clinical Center for a cephalogram , panograph , and intra-oral photography . All participants received genetic counseling . Blood samples for DNA extraction , RNA extraction ( PaxGene tubes ) and for LCL production were drawn on morning of the second day of the evaluation . Patients with NF1 were enrolled in the “Neurofibromatosis Type 1 Natural History Study” ( NCT00924196 ) , approved by the NCI Institutional Review Board . Patients or their guardians were provided written informed consent . Eligibility criteria included a clinical diagnosis of NF1 or presence of an NF1 germline mutation . A detailed skin evaluation at the time of enrollment by a single observer ( AMB ) was used . The number , size and distribution of CALM >5 mm in any dimension were recorded . They were measured with a ruler and documented on a standard form utilized on the natural history study . All LCLs were established from peripheral white blood cells at the Lombardi Comprehensive Cancer Center , Georgetown University , using standard procedures . Cells were stored in liquid nitrogen until needed for an experiment . To minimize batch effects , 79 cell lines were thawed on the same day and seeded at initial density of 500 , 000 cells per mL in 12-well plates . The cultures were maintained in an incubator at 37°C with 5% CO2 in RPMI 1640 medium supplemented with 2 mM L-glutamine , 100 Units/mL penicillin , 100 mg/mL streptomycin and 15% heat-inactivated fetal bovine serum . The cells were fed every other day and harvested on the same day after 10 days of culturing . The cell densities in the fastest and slowest growing cultures were 1 . 9 and 1 . 1 million cells/mL on the day of harvesting , respectively . The majority of LCLs exhibited similar growth rates and were at density of 1 . 3 to 1 . 7 million cells/mL at the time of harvesting . For harvesting , the cells were transferred into 15 mL tubes , spun at 400 g for 5 min at room temperature , washed once with PBS ( no Ca++ or Mg++ ) , spun again , and the pellets were lysed in 1 mL of Trizol reagent . The lysates were stored at −80°C prior to RNA extraction . All reagents were from Life Technologies ( Grand Island , NY , USA ) . For RNA isolation , Trizol cell lysates were mixed with chloroform ( 1/5 of lysate volume ) , vortexed for one minute and centrifuged in a table-top centrifuge at 13 , 000 rpm for 15 min at 4°C . The aqueous phase containing RNA was mixed with an equal volume of 70% ethanol and immediately loaded onto RNeasy mini columns ( Qiagen , Valencia , CA , USA ) , with subsequent steps performed as per the manufacturer's protocol . The RNA quality was estimated on a 2100 Bioanalyzer , RNA 6000 Nano Chips ( Agilent , Santa Clara , CA , USA ) . Samples with RNA integrity number ( RIN ) of 8 . 0 and above were used for further analysis . For microarray analysis of RNA , all reagents , consumables , lab-ware , instruments , and software were obtained from Illumina , Inc ( San Diego , CA , USA ) unless otherwise indicated . RNA amplification/labeling , microarray hybridization , and microarray washing/staining and scanning procedures were done according to the Illumina protocols without modifications . Amplified biotinylated cRNA ( 1 . 5 µg ) was hybridized to HumanRef-8_v2 Sentrix BeadChips . Samples were hybridized to microarrays at 55°C for 16–17 hours . Microarrays were washed to remove non-specifically bound cRNA , stained with 1 mg/mL Streptavidin-Cy3 ( GE Healthcare , Piscataway , NJ , USA ) , dried , and scanned in an Illumina BeadStation 500 scanner . Image acquisition and initial image analysis were done with Illumina BeadScan and BeadStudio applications . Raw expression data were quintile normalized , background subtracted , floored to remove negative values and transformed by calculating logarithm , base 2 , for each value ( for better approximation to a normal distribution ) . Simple linear regression analyses between specific NF1 quantitative phenotypes ( height , head circumference , total number of CALM count , cutaneous neurofibroma burden , Lisch nodule count and cherry hemangioma count ) and expression values obtained for each individual in the EXPR set ( Table 1 ) were performed for each of the 22 , 177 transcripts on the microarray . The FDR calculation procedure was applied to correct for multiple testing [69] . All phenotype-transcript regression pairs with an FDR below 0 . 3 were considered significant . The output was further filtered by subtracting phenotype-transcript pairs with expression level of transcripts below 6 ( mean log2 ) , expression range ( difference between maximum and minimum expression ) below 2 and considering biological significance of the candidate genes . In some cases , genes with an expression level below 6 and an expression range below 2 were still considered for validation because of their biological importance . Filtered transcripts with putative phenotype/expression correlates ( Table S1: “Set of 80” ) were investigated for outliers by generating scatter plots of quantitative phenotype vs . transcript expression values . Twenty-one select transcripts ( Table S1: “Set of 21” ) plus an endogenous control ( GAPDH ) were interrogated by qPCR in all 79 samples that were analyzed on microarrays on 384-well microfluidic cards ( Applied Biosystems , Carlsbad , CA , USA ) . The microfluidic cards were processed and analyzed per the manufacturer's instructions without modifications . Relative expression of each gene was calculated using the standard “double delta Ct” method , per the manufacturer's protocol . Simple linear regression analysis of the qPCR expression values and corresponding quantitative phenotypes was performed as described above . For a given transcript , correlation of qPCR expression with phenotype with a nominal p value less than 0 . 05 was considered significant . Coding and limited evolutionarily conserved non-coding sequences of MSH6 , MSH2 , MLH1 , MED21 and DPH2 were sequenced from germline DNA using the dideoxynucleotide chain termination method ( Sanger ) . The genes MSH6 and MED21 were sequenced because of prior validation by qPCR . We included MSH2 and MLH1 because the protein products of these genes are known to associate with MSH6 in functional MMR complexes . Despite not being verified by qPCR , DPH2 was included because of its biological significance . The concentration of genomic DNA ( gDNA ) used in sequencing was determined using a DyNA Quant 200 fluorometer ( Hoefer , Holliston , MA USA ) and dsDNA-specific Hoechst Dye 22358 according to the manufacturer's protocol . The gDNA sample was then tested for functionality in PCR reactions with positive and negative control primers: Pos_For: TGTAAAACGACGGCCAGTATCCCACTGTTAGGAGAACTGC Pos_Rev: CAGGAAACAGCTATGACCGGTCAGGAAAGGGACACAGATA Negative control primers are the forward and reverse sequencing primers to lac-Z of M13: M13_For: TGTAAAACGACGGCCAGT M13_Rev: CAGGAAACAGCTATGACC To each gDNA sample , a trace amount of a plasmid with a unique non-human insert was added to serve as a biological barcode; the identifying inserts were amplified and checked using the universal sequencing primers above . The gDNAs were diluted to a working concentration of 2 . 5 ng/µL . To amplify gDNA , primers were obtained from Eurofins MWG Operon ( Huntsville , AL , USA ) in individual tubes and reconstituted to 100 µM in 10 mM TRIS , pH 8 . 0 , 0 . 1 mM EDTA . The primers pairs were tested at a concentration of 0 . 16 µM each in 10 µL PCR reactions containing KAPA 2G Fast HS ReadyMix PCR Kit ( KAPA Biosystems , Woburn , MA , USA ) and 5 ng of control human DNA ( Coriell Institute , Camden , NJ , USA ) . Cycling conditions: 1 ) activate enzyme at 95°C for 3 min , 2 ) 40 cycles at 95°C for 10 sec , 60°C for 10 sec and then 72°C for 30 sec and , 3 ) hold at 10°C . A 5 µL aliquot of the PCR reaction was examined by agarose gel to assess multiple or missing bands . The PCR products were then diluted to 0 . 4 ng/µL and sequenced in 6 µL reactions using M13 universal forward and reverse primers and BDT version 3 . 1 ( Applied Biosystems ) using standard ABI protocols . The reactions were then analyzed on 3730 DNA Sequencers ( Applied Biosystems ) . The sequence traces were individually inspected for quality . Primer pairs that did not lead to high-quality traces were retested using one additional control DNA . Primers failing both rounds were redesigned . PCR amplification of amplimers was performed in 10 µL reactions in 384-well plates , as described above . Prior to sequencing , the PCR products were diluted to 0 . 4 ng/µL . Sequencing was performed on an Applied Biosystem 3730 Sequencer using BigDye Terminator version 3 . 1 . Three µL of diluted PCR products were used in sequencing reaction volumes of 6 µL . Sequencing primer sequences are as above . Reaction cleanup was accomplished through alcohol precipitation . Reaction precipitates are dissolved in 10 µL water immediately before sequencing . All genomic coordinates reference the hg18 ( March 2006 ) build . A targeted , multiplex PCR primer panel was designed using the custom Ion Ampliseq Designer v3 . 0 ( Thermo Fisher Scientific , Life Technologies , Carlsbad , CA , USA ) . The primer panel covered 11 kb of sequence that includes the specific variants of interest in the MSH6 and DPH2 loci . Each site was 100% covered in the design . Average amplicon size was 225 bp . Sample DNA was amplified using this custom Ampliseq primer pool , and libraries were prepared following the manufacturer's Ion Ampliseq Library Preparation protocol ( Life Technologies , Carlsbad , CA , USA ) . Individual samples were barcoded , pooled , templated , and sequenced on the Ion Torrent PGM Sequencer using the Ion PGM Template OT2 200 and Ion PGM Sequencing 200v2 kits per manufacturer's instructions . Mean read length after sequencing was 159 bp . To confirm the association of SNVs in the DISC sample set with CALM count , we genotyped the variants in germline DNA in an independent set of 33 samples ( REP1 ) and an additional independent set of 81 samples ( REP2 ) . Since none of the significant SNVs in the DISC set were significantly associated with CALM count by simple linear regression at the level of 0 . 05 based on 29 and 62 European-American samples in REP1 and REP2 , respectively ( Table 2 ) , we performed additional analyses . In the meta-analysis , p-values of these three ( DISC , REP1 , REP2 ) datasets were combined using Liptak's method [73] by weighting each p-value by its square root of the sample size ( Table 2 ) . In the mega-analysis , three ( DISC , REP1 , REP2 ) datasets including 180 samples were combined and simple linear regression was performed on each SNV by adjusting for age , sex and each dataset ( Table 2 ) . Tiled regression was not performed in the replication study since the method requires genotyping all variants , not just markers of interest . We did not attempt to validate rare variants due to limited size of the additional set . | Neurofibromatosis type 1 ( NF1 ) is a relatively common genetic disease that increases the chance to develop a variety of benign and malignant tumors . People with NF1 also typically feature a large number of birthmarks called café-au-lait macules . It is difficult to predict severity or specific problems in NF1 . We sought to identify genes ( other than NF1 , the gene that causes the disease ) that influence severity in NF1 . We determined the number of café-au-lait macules in two groups of people with NF1 . We measured the gene expression of about 10 , 000 genes in the cultured white blood cells from one group of people . We then sequenced a group of genes whose expression level was increased in people with higher numbers of café-au-lait macules . In the first group , we found common variants in genes MSH6 and near DPH2 and ATP6V0B that were significantly associated with the number of café-au-lait macules . Some of these variants were close to significant in the second group of people . The two variants near DPH2 and ATP6V0B were very significant when analysed in both groups combined . Our work is among the first to identify genetic variants that influence the severity of NF1 . | [
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"tr... | 2014 | Genetic Modifiers of Neurofibromatosis Type 1-Associated Café-au-Lait Macule Count Identified Using Multi-platform Analysis |
The guanylate-binding proteins ( GBPs ) belong to the dynamin superfamily of GTPases and function in cell-autonomous defense against intracellular pathogens . IpaH9 . 8 , an E3 ligase from the pathogenic bacterium Shigella flexneri , ubiquitinates a subset of GBPs and leads to their proteasomal degradation . Here we report the structure of a C-terminally truncated GBP1 in complex with the IpaH9 . 8 Leucine-rich repeat ( LRR ) domain . IpaH9 . 8LRR engages the GTPase domain of GBP1 , and differences in the Switch II and α3 helix regions render some GBPs such as GBP3 and GBP7 resistant to IpaH9 . 8 . Comparisons with other IpaH structures uncover interaction hot spots in their LRR domains . The C-terminal region of GBP1 undergoes a large rotation compared to previously determined structures . We further show that the C-terminal farnesylation modification also plays a role in regulating GBP1 conformation . Our results suggest a general mechanism by which the IpaH proteins target their cellular substrates and shed light on the structural dynamics of the GBPs .
The guanylate-binding proteins ( GBPs ) play critical roles in cell-autonomous immunity against a diverse range of bacterial , viral , and protozoan pathogens . The charter member of this family is GBP1 , which was identified as a protein that is strongly induced by the interferons and can specifically bind to the guanylate affinity column [1 , 2] . There are seven GBPs in human ( GBP1-7 ) , which share 52%-88% sequence identity between each other [3] . GBP1 , GBP2 , and GBP5 contain C-terminal CaaX box sequences that allow them to be prenylated in cells . GBP1 is farnesylated , which is important for its localization to membrane structures such as the Golgi apparatus [4 , 5] . The farnesylation modification , together with a nearby triple-arginine motif , is also required for the localization of GBP1 to cytosolic bacteria [6 , 7] . Once on the bacterial surface , GBP1 is able to recruit other GBPs via heterodimerization and oligomerization [7 , 8] . A unique property of GBP1 is its ability to hydrolyze GTP first to GDP and then to GMP in a processive manner [9 , 10] . In contrast , GBP2 only converts ~10% GTP to GMP , whereas GBP5 hydrolyzes GTP only to GDP [11 , 12] . The physiological significance of the unusual enzyme activity of GBP1 , as well as the biochemical differences between different GBPs , remains unclear . Mechanistically , the GBPs belong to the dynamin superfamily of GTPases , which often mediate membrane fission or fusion [13 , 14] . By analogy , the GBPs could also function in the membrane remodeling processes . For example , they may contribute to the lysis of pathogen-containing vacuoles . Other reported functions of the GBPs include promoting autophagy , initiating inflammasome assembly , and inhibiting bacterial motility ( for recent reviews , see [15–20] ) . However , our understanding towards the functions of these important proteins is still in its infancy . The GBPs have complex structural dynamics . Crystal structures have been determined for the full-length GBP1 in the monomer state and the isolated GTPase domain of GBP1 in the dimer state [10 , 21 , 22] . GBP1 contains an N-terminal large GTPase ( LG ) domain and a C-terminal helical region , which can be further divided into a middle domain ( MD ) that contains the α7-α11 helices and a GTPase effector domain ( GED ) that consists of the α12-α13 helices . The GED folds back and interacts with LG and MD , which is important to maintain GBP1 at the resting state [21 , 23] . Binding of GTP induces the release of GED from the rest of the protein , resulting in an extended conformation that was previously interpreted as a “dimer” based on size-exclusion chromatography analyses [24] . Unlike the isolated LG domain that readily dimerizes under several guanine nucleotide conditions , full-length GBP1 only forms a stable dimer in the presence of GDP-AlFx that mimics the catalytic transition state [10 , 24] . Due to the extended conformation of the GED domain , the dimer of the full-length protein has a large hydrodynamic radius and was long regarded as a “tetramer” . Dimerized full-length GBP1 can cause the tethering of unilamellar vesicles in vitro , and this activity depends on the C-terminal farnesylation modification [25] . Furthermore , the farnesylated GBP1 can form a transient ring-like oligomer that is reminiscent of dynamin and related proteins such as the Mx ( Myxovirus resistance ) proteins [25] . Whether these properties are related to the cellular functions of GBP1 remains to be investigated . Pathogens often antagonize key cellular proteins to evade host defense . Due to the important functions of the GBPs in innate immunity , it is not a surprise that some pathogens have evolved strategies to counter their activity . The IpaH family of proteins are unique E3 ubiquitin ligases that are only found in bacteria , especially pathogenic bacteria such as Shigella and Salmonella [26] . They all contain an N-terminal Leucine-rich repeat ( LRR ) domain and a C-terminal novel E3 ligase ( NEL ) domain . Although the NEL domain is structurally unrelated to the HECT family of E3 ligases , it also catalyzes the ubiquitination reaction by forming a ubiquitin-thioester intermediate via an invariant Cys in the CxD motif [27 , 28] . IpaH9 . 8 from Shigella flexneri , an intracellular bacterium that causes bacillary dysentery , is one of the most extensively studied member of the IpaH family . In fact , it is one of the first IpaH proteins that is demonstrated to be an E3 ligase [26] . Recent studies have discovered that IpaH9 . 8 ubiquitinates and degrades a subset of GBPs , which is important for S . flexneri to suppress host defense and replicate in the cells [6–8] . To delineate how the GBPs are targeted by IpaH9 . 8 and gain further insights into GBP-mediated immunity , we have first determined the crystal structure of IpaH9 . 8LRR in complex with GBP1LG-MD , which explains the specific recognition of select GBPs by IpaH9 . 8 . Mutating the GBP1-binding residues in IpaH9 . 8 diminish its ability to degrade the GBPs . By comparing with other IpaH protein structures , we have identified interaction hot spots in the LRR domains of this unique family of bacterial ubiquitin ligases . A large rotation of GBP1MD is observed in our structure , revealing that the elastic α7 helix plays an important role in regulating the structural dynamics of GBP1 . Finally , we determined the structure of farnesylated full-length GBP1 and show that the farnesylation modification is involved in restraining GBP1 conformation .
The IpaH proteins are modular enzymes that all contain a LRR domain and a NEL domain . The NEL domains are highly conserved , and therefore the substrate specificity is largely dictated by the variable LRR domains . Indeed , IpaH9 . 8LRR binds to GBP1 [6] . Swapping the LRR domains of IpaH4 and IpaH7 . 8 to IpaH9 . 8LRR enables the chimera IpaHs to degrade the GBPs ( Fig 1a ) . To elucidate the molecular basis of how IpaH9 . 8LRR recognizes GBP1 , we sought to determine their complex structure . We first crystallized full-length GBP1 in complex with IpaH9 . 8LRR . However , the crystal diffracted to only ~10 Å and could not be improved despite extensive attempts . We subsequently crystallized the LG-MD region of GBP1 ( GBP1LG-MD ) in complex with IpaH9 . 8LRR and determined the structure at 3 . 6 Å ( Table 1 , Fig 1b ) . The moderate resolution is likely caused by a high solvent content of the crystal ( 73 . 4% ) . Nevertheless , the electron density map generated from the molecular replacement solution is of high quality and allows unambiguous model building ( S1 Fig ) . The LG domain of GBP1 features a canonical globular GTPase fold that highly resembles GBP1LG in the full-length GBP1 structure [21 , 22] . Superimposing it to the full-length structure generates a root mean square deviation ( rmsd ) of 1 . 0 Å for 257 Cα atoms . The MD domain features two three-helix bundles that spiral around the common α9 helix and also resembles the corresponding region in the full-length structure . Superimposing the MD domain from our structure to the corresponding region in full-length GBP1 yields a rmsd of 1 . 9 Å for 169 Cα atoms . However , the arrangement of the LG and MD in our structure is different from that in the full-length structure , and a large swing of the MD is observed ( S2a Fig ) . IpaH9 . 8LRR is very similar to the previously determined IpaH9 . 8LRR alone structure [29] , and contains eight LRR motifs ( LRR1-LRR8 ) that are organized into a slightly curved solenoid . In the complex structure , it engages GBP1LG using the concave surface of the solenoid ( Fig 1b ) . Three regions in GBP1LG are involved in interacting with IpaH9 . 8LRR: the P-loop , the switch II region , and the α3 helix ( Fig 1b ) . These regions are located on the opposite side of the GED domain in the full-length GBP1 structure , so the GED domain , which is not present in our structure , would not interfere with the binding ( S2a Fig ) . On the other hand , these regions are involved in forming the dimer interface in the LG dimer structure [10] , and therefore binding of IpaH9 . 8 would lead to the disruption of the GBP1LG dimer ( S2b Fig ) . This is consistent with our previous observation that IpaH9 . 8 disrupts the GBP1 “tetramer” in the presence of GDP-AlFx [6] . In the structure , seven out of the eight LRR modules in IpaH9 . 8LRR contribute residues to interact with GBP1 ( S1b Fig , Fig 2 ) . In LRR1 , Arg629 . 8 ( superscripts 9 . 8 and G indicate residues in IpaH9 . 8 and GBP1 , respectively ) forms bidentate interactions with Glu105G in the Switch II region of GBP1 . Asp649 . 8 forms a hydrogen bond with Tyr47G , and Arg659 . 8 interacts with Tyr47G via a cation-π interaction . Asn679 . 8 forms a hydrogen bond with Gln137G . In LRR2 , Asn839 . 8 forms a hydrogen bond with Glu105G . Tyr869 . 8 forms a hydrogen bond with the main chain carbonyl group of Gly102G , at the same time forms van der waals interactions with Tyr47G . Gln889 . 8 appears to stabilize the position of Lys1089 . 8 in LRR3 , which in turn forms a salt bridge with Asp140G . Other residues in LRR3 that interact with GBP1 include Tyr1039 . 8 , which packs against the aliphatic region of Glu105G . His1269 . 8 from LRR4 interacts with Tyr143G via cation-π and van der waals interactions . In LRR5 , Asn1439 . 8 forms a hydrogen bond with Asn109G , and Tyr1469 . 8 hydrogen bonds with Glu147G . Arg1669 . 8 from LRR6 forms a salt bridge with Glu147G . Arg1909 . 8 from LRR7 may form a hydrogen bond with His150G . The residues involved in binding GBP1 are unique to IpaH9 . 8 ( S3 Fig ) , explaining the fact that only IpaH9 . 8 , but not other IpaH proteins , specifically degrades the GBPs [6 , 8] . The seven human GBPs are highly homologous to each other . However , only a subset of GBPs such as GBP1 , GBP2 , GBP4 , and GBP6 are efficiently targeted and degraded by IpaH9 . 8 [6 , 8] . GBP3 and GBP7 are particularly resistant ( Fig 3a ) . Careful examination reveals subtle differences in their Switch II and α3 helix regions . For example , GBP3 contains a Lys ( Lys105 ) in its Switch II that aligns with Glu105 in GBP1 ( S4 Fig ) , which lies at the center of GBP1LG-MD/IpaH9 . 8LRR interface and makes critical interactions with several IpaH9 . 8 residues ( Fig 2 ) . Mutation of this residue to Glu allows the GBP3 mutant ( GBP3-M ) to be efficiently degraded by IpaH9 . 8 ( Fig 3a ) . GBP3-M also binds strongly to IpaH9 . 8-C337A , an IpaH9 . 8 mutant that has abolished E3 ligase activity ( Fig 3b ) . The α3 helix of GBP5 is slightly different when compared with GBP1 ( S4 Fig ) . Gly137 , Leu141 , and His143 replace GBP1 residues Gln137 , Gln141 , and Tyr143 , respectively . These differences likely reduce the interaction between GBP5 and IpaH9 . 8 , and make GBP5 a suboptimal substrate that requires higher amounts of IpaH9 . 8 for degradation ( Fig 3a ) . A double mutant of GBP5 , G137Q/L141Q ( GBP5-M ) , is degraded more efficiently by IpaH9 . 8 ( Fig 3a ) . Several residues in the Switch II and α3 helix region of GBP7 are different compared to GBP1 , including Met104 that replaces Val104 in GBP1 and His143 like in GBP5 ( S4 Fig ) . The bulkier Met104 may hinder the binding of IpaH9 . 8 . Furthermore , molecular dynamics simulation study suggests that the α3 helix region of GBP7 prefers to adopt a loop rather than a helical conformation ( S5 Fig ) , caused partly by the presence of Ser111 , instead of an Asn in other GBPs , at the end of its Switch II ( S4 Fig ) . Ser111 appears to stabilize a hydrogen bond interaction between Ser113 and Glu147 , which causes the α3 helix to unfold . Swapping the GBP7 Switch II-α3 region ( residues 104–151 ) to the corresponding segment in GBP1 renders the GBP7 mutant ( GBP7-M ) susceptible to IpaH9 . 8-mediated degradation ( Fig 3a ) . GBP7-M also shows a stronger interaction with IpaH9 . 8-C337A ( Fig 3b ) . To further verify our structure , we mutated several IpaH residues that are involved in binding to GBP1 , including Tyr86 , Gln88 , His126 , Tyr146 , and Arg190 . When these mutations are generated in combination with C337A , the resulting mutants IpaH9 . 8-Y86A/Q88A/C337A , IpaH9 . 8-H126A/R190A/C337A , and IpaH9 . 8-Y146A/R190A/C337A all display greatly reduced interaction with GBP1 , as shown by the co-immunoprecipitation experiments ( Fig 4a ) . Mutating Tyr86 and Gln88 together generates the strongest effect . Similarly , IpaH9 . 8-Y86A/Q88A/C337A also failed to interact with other GBPs , including GBP2 , GBP4 , and GBP6 ( Fig 4a ) . To validate the physiological relevance of these GBP-binding residues , we performed cell imaging experiments as we previously described [6] . We made mutations to IpaH9 . 8 that are fused with 10 tandem repeats of the SUperNova tags ( SunTags ) [30] . We then expressed these IpaH9 . 8 mutants in the S . flexneri ΔipaH9 . 8 strain and used these bacteria to infect HeLa cells stably expressing RFP-GBP1 and scFv-GCN4-GFP . GCN4 is a single chain antibody that specifically recognizes the SunTag . In uninfected cells , GCN4-GFP display a dispersed pattern in the cell ( Fig 4b ) . When infected with S . flexneri expressing wild-type IpaH9 . 8-10xSunTag , the GFP signals are enriched in the cytoplasm due to the delivery of IpaH9 . 8 by the bacteria , and the RFP signal is largely diminished due to the degradation of GBP1 ( Fig 4b ) . In contrast , RFP-GBP1 is not efficiently degraded by the bacterial strains expressing IpaH9 . 8-Y86A/Q88A , IpaH9 . 8-H126A/Y146A , or IpaH9 . 8-Y146A/R190A . In these cells , the RFP signal is most bright around the bacteria , due to the localization of GBP1 to the bacterial surface ( Fig 4b ) . Together , these results demonstrate that an intact GBP-binding surface in IpaH9 . 8LRR is critical for the function of IpaH9 . 8 in vivo . The IpaH proteins have diverse substrates in the host [31] . In particular , two IpaH proteins from Salmonella , SspH1 and Slrp , use their LRR domains to target the host PKN1 kinase and Trx1 thioredoxin , respectively [32 , 33] . Crystal structures have been determined for SspH1LRR in complex with a coiled-coil region of the PKN1 kinase [34] , and Slrp in complex with Trx1 [35] . Comparing these structures with the GBP1LG-MD/IpaH9 . 8LRR complex reveals both differences and common features ( Fig 5 ) . Like IpaH9 . 8 , SspH1 binds its target using the concave surface of its LRR domain . While the N-terminal region of IpaH9 . 8LRR mediates the majority of the interactions with GBP1 , the contact site for PKN1 is more focused on the C-terminal half of SspH1LRR ( Fig 5a and 5b ) . Nonetheless , the edge of the concave surface that are pointed by the LRR strands are important for the binding in both structures . In IpaH9 . 8LRR , Asn67 from LRR1 , Gln88 from LRR2 , Lys108 from LRR3 , His126 from LRR4 , Tyr146 from LRR5 , Arg166 from LRR6 , and Arg190 from LRR7 form a continuous surface patch that are critical for GBP1 binding ( Fig 5a ) . In SspH1LRR , a similar edge is formed by Leu247 from LRR3 , Asn266 from LRR4 , Asn286 from LRR5 , Asn326 from LRR7 , His346 from LRR8 , Asp368 from LRR9 , and His392 from LRR10 ( Fig 5b ) . When SspH1LRR is compared with IpaH9 . 8LRR , SspH1 residues Leu247 , Asn266 , Asn286 , and Asn326 align exactly with IpaH9 . 8 residues Lys108 , His126 , Tyr146 , and Arg190 , respectively ( S3 Fig ) . In the crystal structure of Slrp/Trx1 , Slrp interacts with Trx1 using two interfaces [35] . The so-called type I binding site highly resembles the GBP1 binding site in IpaH9 . 8LRR ( Fig 5a and 5c ) . This site is formed by the first six LRR modules of SlrpLRR , and also involves the concave surface . Trx1 binding residues Arg184 , Lys186 , Ile187 , Ile205 , Asn208 , Tyr226 , Gln231 , Ile250 , and His271 all align with IpaH9 . 8 residues Arg62 , Asp64 , Arg65 , Asn83 , Tyr86 , Tyr103 , Lys108 , His126 , and Tyr146 ( S3 Fig ) . Although the physiological significance of the type I binding site in Slrp remains to be explored , these analyses suggest that the IpaH family proteins could generally bind their target proteins using the LRR concave surfaces . In particular , residues located at positions corresponding to Lys108 in IpaH9 . 8-LRR3 , His126 in IpaH9 . 8-LRR4 , and Tyr146 in IpaH9 . 8-LRR5 are important for binding in all three complexes ( Fig 5 , S3 Fig ) , suggesting that these three positions could function as “hot spots” to mediate the interaction between the IpaH proteins and their cellular targets . The dynamin superfamily proteins are considered mechanochemical enzymes that convert the energy from GTP binding and hydrolysis to mechanical force . The conformational dynamics of GBP1 is likely at the heart of its function but remains poorly understood . In the previously determined structures , the GED folds back and locks the conformation of GBP1 ( Fig 6a ) . However , biophysical studies suggest that the GED domain is unleashed during the GTPase reaction cycle and the C-terminal region of GBP1 undergoes large degree of conformational change . In our structure , since the GED domain is not present , the MD domain is free to adopt a relaxed conformation . The α7 helix , which is forced to bend in the apo structure due to the interaction between the GED and the LG-MD domains , springs back to the straightened state ( Fig 6b ) . Starting from a highly conserved Gln321 ( S4 Fig ) , the C-terminal half of the α7 helix rotates ~13° , and this conformational change is transmitted toward the rest of the protein , causing an ~20° en bloc rotation of the α8-α11 helices ( Fig 6c , S2a Fig ) . Due to the unfavorable geometry of the α7 helix in the “GED on” state , this conformational change likely also occurs in the full-length protein when the GED domain is set free during GBP1 function . The conformational change seen above prompted us to further investigate the conformation dynamics of GBP1 . GBP1 is farnesylated at Cys589 , and this modification is important for its localization to the Golgi apparatus and recruitment by various pathogens [4–7] . Despite this modification , GBP1 is primarily a cytosolic protein until the cells are infected by pathogens [4 , 5] , suggesting that the farnesyl group is probably not exposed at the resting state . The farnesylation modification changes the behavior of GBP1 on hydrophobic chromatography column and reduced its ability to hydrolyze GTP to GMP , suggesting that it impacts the conformation of GBP1 [36] . To assess how the farnesyl group affects GBP1 structure , we followed a previously described protocol [36] and prepared farnesylated GBP1 ( GBP1F ) by co-expressing GBP1 with the farnesyltransferase in E . coli . Successful modification is confirmed by mass spectrometry analyses of the purified protein ( S6a Fig ) . We subsequently determined the crystal structure of GBP1F ( Table 1 ) . Interpretable electron density is present for the farnesyl group , as well as the entire C-terminal tail of GBP1 ( S6b Fig ) . The farnesyl group is accommodated in a pocket formed by His378 , Gln381 , Lys382 , Ala385 from the α9 helix and Tyr524 , His527 , Leu528 , Leu531 from the α12 helix ( Fig 7a ) . These interactions pull the α12 helix towards the α9 helix , and cause the GED domain to become more tightly fastened to the rest of the protein . In this conformation , the α7 helix remains bent; while the N-terminal half the α12 helix , as well as the majority of the MD domain , undergoes a ~10° rotation when compared to the previously determined full-length GBP1 structure ( Fig 7b ) .
Despite the fact that GBP1 was identified more than 30 years ago as one of the most prominent proteins that are induced by the interferons , its precise function remains elusive . Recent studies suggest that GBP1 inhibits intracellular bacterial replication by translocating to the bacterial surface , hindering their actin-dependent motility , and blocking their cell-to-cell spread [6–8] . Clearly , GBP1 plays an important role in cell-autonomous immunity , and poses a major threat to cytosolic bacteria such as S . flexneri . In the arms race between the bacteria and the host , S . flexneri has acquired the ability to eliminate a subgroup of GBPs through the action of its virulence E3 ligase IpaH9 . 8 . To provide insight into the interaction between IpaH9 . 8 and the GBPs , we have solved the crystal structure of the LRR domain of IpaH9 . 8 in complex with a major fragment of GBP1 . Our results show that the residues involved in interacting with GBP1 are unique to IpaH9 . 8 , elucidating how IpaH9 . 8 , but not other IpaH family proteins , can specifically target the GBPs . Due to the differences in the Switch II and α3 helix regions , GBP3 , GBP5 , and GBP7 are not efficiently degraded by IpaH9 . 8 . Mutating relevant residues in these GBPs makes the mutant proteins more susceptible to IapH9 . 8-mediated degradation . By comparing our structure with other IpaH proteins in complex with their target molecules , we further reveal interaction hot spots in the LRR domain of this unique family of bacterial effectors . These results provide a deeper understanding on the pathogenesis of S . flexneri , and may facilitate the investigation of other IpaH proteins in the future . Our results also shed light on the structural dynamics of GBP1 . Previously , GBP1 without the farnesyl moiety has been crystallized in the apo state and in complex with GMP-PNP , a nonhydrolyzable analog of GTP [21 , 22] . However , the two structures are largely similar and have not provided sufficient insights into the conformational change of GBP1 . Through the examination of the GBP1LG-MD and GBPF structures determined in this study , we uncovered two new conformations of GBP1 . In a way , the GBP1F structure likely reflects GBP1 at its most tense state . By creating additional interactions between the GED domain and the MD domain , the farnesyl group appears to function as the second tier of bolt to lock the GED domain to the rest of the protein . A bending of the α7 helix is forced in this conformation . In contrast , the GBP1LG-MD structure likely reflects GBP1 at its most relaxed state . We envision that when the structural restraints imposed by the GED domain and the farnesyl group are relieved upon GBP1 activation , the α7 helix would become straight , and this would cause the C-terminal region to rotate like seen here in the GBP1LG-MD structure . How the GED domain and the farnesyl moiety are arranged in the active state , and how these conformational changes are translated to the function of GBP1 , remain important questions to be addressed . In this regard , it is worth noting that , GBP5ta , a splicing variant of GBP5 that is associated with the T-cell lymphoma tissues , naturally lacks the GED domain [37] . GBP3ΔC , a splicing variant of GBP3 that does not have the α13 helix , has also been reported [38] . The functional significance of these GBP variants are unclear , but they would be more prone to adopt a relaxed conformation compared to full-length GBP5 and GBP3 .
Primers used in this study are listed in Supplementary Table 1 . IpaH9 . 8LRR ( residues 22–252 ) [6] and GBP1LG-MD ( residues 1–479 ) were expressed as His6-SUMO fusion proteins in E . coli BL21 ( DE3 ) . The bacterial cultures were grown at 37 °C in the Luria-Bertani ( LB ) medium to an OD 600 of 0 . 6–0 . 8 before induced with 0 . 5 mM isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at 18 °C for overnight . The cells were collected by centrifugation and were resuspended in a lysis buffer containing 50 mM Tris-HCl , pH 8 . 0 , 500 mM NaCl , 10 mM imidazole , 5 mM β-mercaptoethanol , and 1 mM phenylmethylsulfonyl fluoride ( PMSF ) . The cells were then disrupted by sonication , and the insoluble debris was removed by centrifugation . The supernatant was applied to a Ni-NTA column ( GE Healthcare ) . The column was then washed extensively with a wash buffer containing 50 mM Tris-HCl , pH 8 . 0 , 500 mM NaCl , 30 mM imidazole , and 5 mM β-mercaptoethanol , and eluted with an elution buffer containing 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 250 mM imidazole , and 5 mM β-mercaptoethanol . Next , the eluted proteins were digested with the ULP1 protease to cleave the N-terminal His6-SUMO fusion tag . The protein samples were then passed through another Ni-NTA column to remove the His6-SUMO fusion tag and the ULP1 protease . Untagged IpaH9 . 8LRR and GBP1LG-MD were further purified by gel filtration chromatography using a Superdex 200 column ( GE Healthcare ) , and eluted in the final buffer containing 25 mM Tris-HCl , pH 8 . 0 , 20 mM NaCl , and 2 mM Dithiothreitol ( DTT ) . To obtain the farnesylated GBP1 ( GBP1F ) , full-length GBP1 was cloned into a vector that is kanamycin resistant and expresses GBP1 as a His6-SUMO fusion protein . The two subunits of the farnesyltransferase ( FTase α and β , respectively ) were cloned into the pACYCDuet-1 ( Novagen ) vector that is chloramphenicol resistant . His6-SUMO-GBP1 was then co-expressed with the FTase α/β in E . coli BL21 ( DE3 ) . The bacterial cultures were supplemented with both kanamycin ( 50 μg/ml ) and chloramphenicol ( 25 μg/ml ) , and were induced with 0 . 5 mM IPTG at an OD 600 of 0 . 8 . The cells were then cultured at 20°C for 18h and were collected by centrifugation . The GBP1F was then purified similarly as described above for the GBP1LG-MD protein . To obtain the IpaH9 . 8LRR/GBP1LG-MD complex , purified IpaH9 . 8LRR and GBP1LG-MD were incubated overnight on ice using a 1 . 5:1 molar ratio . The mixtures were then passed through a Superdex 200 column and eluted using the final buffer described above . The protein complex was concentrated to 18 mg/ml for crystallization . Crystals were grown at 20°C using the sitting drop vapor diffusion method . The crystallization solution contains 1 . 6 M sodium/potassium phosphate , pH 6 . 5 . Crystals grew to full size in several days and were transferred to a cryo solution containing 1 . 6 M sodium/potassium phosphate , pH 6 . 5 , and 38% sucrose before flash-cooled in liquid nitrogen . GBP1F was crystallized using the sitting drop vapor diffusion method at a concentration of 15 mg/ml . Crystals appeared overnight in 20 mM citric acid , 80 mM Bis-Tris propane , pH 8 . 8 , and 16% ( w/v ) Polyethylene glycol 3 , 350 . For data collection , the crystals were transferred to a solution containing 20 mM citric acid , 80 mM Bis-Tris propane , pH 8 . 8 , 16% Polyethylene glycol 3 , 350 , and 20% ethylene glycol before flash-cooled in liquid nitrogen . The diffraction data were collected at Shanghai Synchrotron Radiation Facility ( SSRF ) beamline BL17U . The diffraction data were indexed , integrated , and scaled using HKL2000 ( HKL Research ) . The structure was determined by the molecular replacement method using the published structure of IpaH9 . 8LRR ( PDB ID:5B0N ) and GBP1 ( PDB ID:1DG3 ) as the search models . The structure modeling was performed in Coot [39] and refined using Phenix [40] . Structural validation was performed with MolProbity [41] . Composite omit map was generated with Phenix [42] . The structure models of GBP6 and GBP7 were obtained by homology modeling using MODELLER [43] with GBP1 structure as the template . The molecular dynamics simulations were carried out using the GROMACS 5 . 1 . 2 package ( http://www . gromacs . org ) [44] . HEK293T and HeLa cells , originally obtained from ATCC , were grown in a humidified incubator with 5% CO2 at 37 °C in Dulbecco's modified Eagle's medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) and 100 μg/ml penicillin/streptomycin ( GIBCO ) . All cell lines were tested to be free of mycoplasma by the standard PCR method . The mammalian expression plasmids have been previously described [6] . Mutations were introduced into plasmids by a PCR-based method . For the immunoprecipitation experiments , a catalytically dead mutant of IpaH9 . 8 ( IpaH9 . 8-C337A ) was used , since wild-type IpaH9 . 8 would lead to quick degradation of co-expressed GBPs . HEK293T cells were grown in 10 cm dishes to 70%-80% confluency . They were then co-transfected with 5 μg IpaH9 . 8-C337A and 10 μg indicated GBP plasmids using Polyethylenimine ( PEI ) . The cells were harvested 18–24 hours later , washed with the phosphate-buffered saline ( PBS ) buffer , and lysed in a buffer containing 25 mM Tris-HCl , pH 8 . 0 , 2 mM MgCl2 , 1 mM GTP , 1 mM PMSF , and 0 . 5% Triton X-100 . The cell lysates were cleared by centrifugation , and then incubated with the Flag M2 beads ( Sigma , A2220 ) for 2 hours . The beads were spun down and then washed three times with the wash buffer ( 25 mM Tris-HCl , pH 8 . 0 , 2 mM MgCl2 , 1 mM GTP , and 0 . 2% Triton X-100 ) . The immunoprecipitated proteins were eluted from the beads using the 3x Flag peptides ( NJPeptide , NJP50002 ) and analyzed by SDS-PAGE and western blotting . Purified GBP1 protein interacts strongly with purified IpaH9 . 8 under all nucleotide conditions ( apo , GMP , GDP , GppNHp , and GDP-AlFx ) [6] . Also , no nucleotide is required for the formation of the IpaH9 . 8LRR/GBP1LG-MD complex . However , we observed more consistent binding between GBP1 and IpaH9 . 8 co-expressed in cells when we included GTP in the lysis buffer . The reason for this is not entirely clear . We noticed that GBP1 tends to form puncta/aggregates when overexpressed in HEK293T cells , and we hypothesized that GTP may help to solubilize these aggregates . For the degradation experiments , HEK293T cells were grown to 70%-80% confluency in 6-well plates , and were transfected with indicated plasmids using PEI . 18–24 h after transfection , the cells were harvested , washed , and then lysed in a lysis buffer containing 25 mM Tris-HCl , pH 8 . 0 , and 0 . 5% Triton X-100 . The cell lysates were cleared by centrifugation and then analyzed by western blot using antibodies for HA ( Sigma , H3663 ) , c-Myc ( HuaxingBio , HX1802 ) , Flag ( Sigma , F3165 ) , and β-tubulin ( TransGen , HC101 ) . The IpaH9 . 8 gene with indicated mutations were cloned into the pME6032-10x SunTag plasmid as previously described [6] . S . flexneri ΔipaH9 . 8 2a strains were then transformed with these plasmids , and single colonies were picked up for each individual plasmid . The bacterial strains were cultured overnight at 37°C in the LB broth , before diluted 1:100 in fresh LB broth , and grown to an OD 600 of 0 . 8 in the presence of IPTG . The HeLa cell line stably expressing RFP-GBP1 and scFv-GCN4-GFP was described previously [6] . The cells were seeded onto glass coverslips in 24-well plates and cultured for 16 h before infection . The infection ( MOI , 50 ) was facilitated by centrifugation at 800 g for 5 min at room temperature , and cultured for another hour at 37°C in a 5% CO2 incubator . Cells were washed three times with PBS . Fresh DMEM containing 100 μg/ml gentamycin was then added to kill the extracellular bacteria . Two hours later , infected cells were washed three times with PBS , fixed with 4% paraformaldehyde for 30 min at room temperature , and then place in the mounting medium ( ZSGB-BIO , ZLI-9556 ) for imaging . Cell images were recorded using the Zeiss LSM 510 Meta confocal microscope and processed with the LSM software package . | Shigella flexneri is a Gram-negative bacteria that causes diarrhea in humans and leads to a million deaths every year . Once inside the cell , S . flexneri injects the host cell cytoplasm with effector proteins to suppress host defense . The guanylate-binding proteins ( GBPs ) have potent antimicrobial functions against a number of pathogens including S . flexneri . For successful infection , S . flexneri relies on an effector protein known as IpaH9 . 8 , a unique ubiquitin E3 ligase to target a subset of GBPs for proteasomal degradation . How these GBPs are specifically recognized by IpaH9 . 8 was unclear . Here , using a combination of structural and biochemical approaches , we reveal the molecular basis of GBP-IpaH9 . 8 interaction , and show that subtle differences in the seven human GBPs can significantly impact the targeting specificity of IpaH9 . 8 . We also show that the GBPs have considerable structural flexibility , which is likely important for their function . Our results provide further insights into S . flexneri pathogenesis , and laid the groundwork for future biophysical and biochemical studies to investigate the functional mechanism of GBPs . | [
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"crystallograp... | 2019 | Structural mechanism for guanylate-binding proteins (GBPs) targeting by the Shigella E3 ligase IpaH9.8 |
Prion strains are characterized by strain-specific differences in neuropathology but can also differ in incubation period , clinical disease , host-range and tissue tropism . The hyper ( HY ) and drowsy ( DY ) strains of hamster-adapted transmissible mink encephalopathy ( TME ) differ in tissue tropism and susceptibility to infection by extraneural routes of infection . Notably , DY TME is not detected in the secondary lymphoreticular system ( LRS ) tissues of infected hosts regardless of the route of inoculation . We found that similar to the lymphotropic strain HY TME , DY TME crosses mucosal epithelia , enters draining lymphatic vessels in underlying laminae propriae , and is transported to LRS tissues . Since DY TME causes disease once it enters the peripheral nervous system , the restriction in DY TME pathogenesis is due to its inability to establish infection in LRS tissues , not a failure of transport . To determine if LRS tissues can support DY TME formation , we performed protein misfolding cyclic amplification using DY PrPSc as the seed and spleen homogenate as the source of PrPC . We found that the spleen environment can support DY PrPSc formation , although at lower rates compared to lymphotropic strains , suggesting that the failure of DY TME to establish infection in the spleen is not due to the absence of a strain-specific conversion cofactor . Finally , we provide evidence that DY PrPSc is more susceptible to degradation when compared to PrPSc from other lymphotrophic strains . We hypothesize that the relative rates of PrPSc formation and clearance can influence prion tropism .
Prion diseases are infectious neurodegenerative diseases that affect animals including humans . Prion diseases of humans include Creutzfeldt-Jakob disease ( CJD ) , Gerstmann-Straussler-Scheinker syndrome , fatal familial insomnia and kuru . Prion diseases of animals include scrapie of sheep and goats , bovine spongiform encephalopathy , TME , and chronic wasting disease ( CWD ) of cervids . All prion diseases are fatal and effective therapeutic treatments are not available . The infectious agent of these diseases is PrPSc , a self-propagating isoform of the normal host prion protein , designated PrPC [1–3] . In the absence of PrPC , the formation of new PrPSc is extinguished and preexisting PrPSc is cleared by an unknown mechanism [4–6] . Distinct strains of prions are characterized by differences in the distribution of spongiform degeneration in the central nervous system ( CNS ) [7 , 8] . The mechanism ( s ) by which PrPSc encodes strain diversity is unknown . Strain-specific conformations of PrPSc were first suggested by the observation of strain-specific Western blot profiles of PrPSc from murine adapted prion strains [9] . Further evidence was provided by the HY and DY strains of hamster-adapted TME . These two strains have distinct electrophoretic migration properties and conformational stability of PrPSc and , importantly , HY and DY PrPSc has strain-specific α-helical and β-sheet content [10–12] . Prion strains can have distinct PrPSc fibril structure and aggregate size suggesting strain-specific tertiary and quaternary structures [13 , 14] . It is unclear , however , if strain specific conformations of PrPSc are maintained by PrPSc alone or require a strain-specific cofactor . For example it is known that lipid molecules can influence the strain properties of in vitro amplified prion strains [15–17] . It has been hypothesized that the differences in the distribution of strain-specific prion cofactors in the host can influence which cells will support prion formation ( i . e . tropism ) . Prion strains are characterized by differences in tropism . The distribution of PrPSc in spleen and lymph nodes differs between sheep naturally infected with either the classical or the atypical strains of scrapie [18–20] . Similarly , in humans , a more widespread distribution of PrPSc in LRS tissues in variant CJD is observed as compared to classical CJD [21–23] . In natural prion disease , however , there are factors not controlled for that could explain the differences in PrPSc distribution unrelated to tropism ( e . g . route of infection ) [24–28] . In experimental prion disease where external variables are held constant , a more compelling example of strain-specific tissue tropism is observed . Hamsters infected with HY TME have detectable infectivity and/or PrPSc in the CNS , LRS , skeletal muscle , nasal secretions and blood [29–34] . In contrast , prion infectivity and/or PrPSc is restricted to the CNS of DY TME-infected hamsters [35–37] . Within a tissue or cell type , strain-specific differences in PrPSc distribution can occur . For example , hamsters or transgenic mice expressing hamster PrPC infected with either the Sc237 or 139H strains of hamster-adapted scrapie are characterized by regional differences in the localization of PrPSc in the CNS [38 , 39]; and immunohistochemical detection of PrPSc can illuminate strain-specific differences in the cellular localization of PrPSc [40–43] . The mechanism ( s ) underlying prion tropism is unclear . In this study , we investigated the entry , transport , contributions of conversion cofactors and the rates of prion formation and clearance to prion tropism .
Inoculation of hamsters with HY TME resulted in animals developing clinical signs of HY TME infection regardless of the route of infection ( Table 1 ) . Consistent with previous reports [35 , 37] , inoculation of hamsters with DY TME in the central or peripheral nervous system ( PNS ) caused disease , while extraneural inoculation failed to cause disease at extended time points post infection ( p . i . ) ( Table 1 ) . It is possible that the failure of DY TME to cause disease by extraneural routes of inoculation was due to low effective titer . To explore this possibility , hamsters were extranasally ( e . n . ) inoculated with DY TME to determine if a more efficient route of inoculation could cause disease . None of the DY TME ( n = 6 ) or mock-inoculated ( n = 3 ) hamsters developed clinical signs of DY TME at 650 days p . i . when the experiment was terminated ( Table 1 ) . Western blot analysis of 250 μg equivalents of proteinase K digested brain material from these animals failed to detect PrPSc ( Fig 1 , panel A ) . The olfactory bulb , trigeminal ganglia , cervical lymph nodes and Peyer’s patches were collected from two DY TME e . n . infected and one mock e . n . infected hamster . Each of the tissues was homogenized and the entire homogenate was intracerebrally ( i . c . ) inoculated into 4 hamsters to determine if the DY TME agent was present . The recipient hamsters failed to develop clinical signs of DY TME at 400 days p . i . when the experiment was terminated ( Table 2 ) . Western blot analysis of 125 μg equivalents of proteinase K digested 5% w/v brain material from these animals failed to detect PrPSc ( S1 Fig ) . As a positive control , hamsters were i . c . inoculated with the DY TME agent . All ( n = 10 ) of the animals developed clinical signs of progressive lethargy consistent with DY TME agent infection at 169±3 days p . i . . Western blot analysis of 125 μg equivalents of proteinase K digested brain material from these animals detected PrPSc with DY TME migration properties confirming the clinical diagnosis ( Fig 1 , panel A ) . Repeated inoculations are known to increase the efficiency of prion infection [44 , 45] . To investigate if repeated inoculations of DY TME could cause disease groups ( n = 5 ) of hamsters were inoculated with DY TME by either the e . n . or per os route once per week for 10 weeks total . These hamsters failed to develop clinical signs of DY TME at 650 days p . i . following the first infection when the experiment was terminated ( Table 2 ) . Western blot analysis of 125 μg equivalents of proteinase K digested 5% w/v brain material from these animals failed to detect PrPSc ( Fig 1 , panel B ) . Overall , DY TME fails to cause disease by extraneural routes of inoculation . Prion strains that cause disease via e . n . infection rapidly cross the respiratory and olfactory epithelia that line the surfaces of the nasal cavity ( NC ) [46–48] . We performed IHC on the NC of DY TME and mock-infected hamsters at 10 minutes post infection to determine if the inability of the DY TME agent to cause disease following e . n . infection was due to an inability to cross the nasal epithelia . We found DY TME-infected brain homogenate and PrPSc in the luminal airspace of the NC of all 3 animals ( Fig 2 , panels A and B ) and there were examples of DY TME-infected brain homogenate traversing the epithelia of the NC in all 3 animals ( Fig 2 , panel A ) . The inoculum was located between intact cells consistent with intercellular transport ( Fig 2 , panel A ) . In all infected animals , DY TME-infected brain homogenate was also detected in the lumen of lymphatic vessels located in the lamina propria of all 3 animals ( Fig 2 , panel B ) . A similar pattern of brain homogenate distribution was observed in both ( n = 2 ) of the mock-infected hamsters , which is consistent with previous results [47] . To investigate if DY PrPSc transport to secondary LRS tissues is inhibited , hamsters ( n = 3 per group ) were i . p . inoculated with either uninfected , HY TME or DY TME-infected brain homogenate . PrPSc IHC performed on spleen from animals at 2 hours p . i . failed to detect PrPSc in the mock inoculated group , while PrPSc was detected in animals inoculated with either HY or DY TME ( Fig 3 , panel A ) . PrPSc IHC of spleen from HY TME inoculated animals in the clinical phase of disease ( 104 d . p . i . ) contained PrPSc immunoreactivity ( Fig 3 , panel A ) . Hamsters inoculated with DY TME and age matched mock infected controls did not contain detectable PrPSc immunoreactivity at 600 days p . i . ( Fig 3 , panel A ) . Peritoneal lavage cells ( P . Cells ) , spleen , mesenteric lymph node ( MLN ) and medial iliac lymph node ( MILN ) from hamsters ( n = 3 per group ) i . p . inoculated with uninfected ( Mock ) , HY TME , or DY TME-infected brain homogenate ( Fig 3 , panel B ) at 2 hours p . i . was analyzed for the presence of PrPSc by Western blot . In the mock-inoculated negative control group , all tissues examined failed to detect PrPSc ( Fig 3 , panel A , lanes 1–4 ) . In the positive control HY TME infected group , PrPSc was detected in P . cells , spleen and MILN , but was not detected in MLN ( Fig 3 , panel B , lanes 5–8 ) . In the DY TME infected animals , PrPSc was detected in all lymphatic tissues examined ( Fig 3 , panel B , lanes 9–12 ) . Overall , DY PrPSc is transported to secondary LRS tissues following i . p . inoculation . PMCA supports 139H , HY and DY TME PrPSc formation and agent replication when BH is the template for conversion [41] . To investigate if PMCA supports prion formation using spleen homogenate as the template for conversion , PMCA reactions ( n = 4 per group ) were spiked with BH from either HY TME-infected or 139H-infected animals as a positive control , mock-infected BH as a negative control or DY TME-infected BH or enriched PK digested DY PrP-res ( Fig 4; S2 Fig ) . All samples were digested with PK prior to Western blot analysis . A significant ( p<0 . 05 ) fold increase in PrPSc abundance ( Fig 4 , panel A , lanes 4 and 6 ) was observed in the HY TME ( 3 . 16±0 . 14 ) and 139H ( 3 . 02±0 . 41 ) seeded groups compared to the starting material following one round of PMCA ( Fig 4 , panel A , lanes 3 and 5 ) indicating that LRS tissue can support PrPSc formation similar to what has been observed in vivo [27 , 49] . A significant ( p<0 . 05 ) 1 . 33±0 . 09 and 1 . 29±0 . 11 fold increase in PrPSc abundance was observed in PMCA reactions seeded with DY TME brain homogenate ( Fig 4 , panel A , lane 8 ) or detergent enriched PK digested DY PrP-res ( Fig 4 , panel A , lane 10 ) respectively , compared to the starting reactions ( Fig 4 , panel A , lanes 7 and 9 ) . PrPSc was not detected in the negative control PMCA reactions ( Fig 4 , panel A , lane 2 ) . The migration of the spleen PMCA generated and DY PrPSc was 1–2 kDa faster compared to HY and 139H similar to what is observed in brain-derived PrPSc ( Fig 4 , panel A ) . The fold PrPSc abundance of HY and 139H seeded reactions was similar ( p>0 . 05 ) and were significantly different ( p<0 . 05 ) compared to DY TME brain homogenate or PrP-res seeded reactions ( Fig 4 , panel B ) . The fold PrPSc abundance of DY TME brain homogenate and DY PrP-res seeded reactions did not significantly ( p>0 . 05 ) differ ( Fig 4 , panel B ) . Overall , spleen contained all of the necessary components for HY , 139H and DY PrPSc formation and the levels of PrPSc formation for the lymphotropic strains HY TME and 139H were greater compared to the non-lymphotropic strain , DY TME . This experiment was repeated at least seven times with similar results . To investigate the relative strain-specific susceptibility of PrPSc to proteolytic degradation , 2 . 5% w/v BH collected from animals at the terminal stage of HY TME , DY TME , or 139H infection ( n = 3 per group ) were subject to incubation with increasing concentrations of PK . BH from uninfected hamsters ( n = 3 ) was used as a negative control . The amount of PrP in the 100 μg/ml PK group was normalized to 100 percent ( Fig 5 , panel B ) . The percentage of HY , 139H and DY PrPSc in the 200 μg/ml PK group was 127±30 , 56±6 and 37±4 respectively ( Fig 5 , panel B ) and the percentage of HY , 139H and DY PrPSc in the 400 μg/ml PK group was 110±12 , 37±7 and 6±2 respectively ( Fig 5 , panel B ) . The abundance of HY PrPSc was similar at all PK concentrations tested ( p>0 . 05 ) . The abundance of 139H PrPSc with 200 and 400 μg/ml of PK was similar ( p>0 . 05 ) but significantly ( p<0 . 05 ) different than 139H PrPSc treatment with 100 μg/ml of PK . The abundance of DY PrPSc was decreased significantly ( p<0 . 05 ) with each PK concentration tested . The PMCA conversion coefficient ( PMCA-CC ) of HY PrPSc and 139H PrPSc was similar at all PK concentration tested . The PMCA-CC for DY PrPSc was similar for the 100 and 200 μg/ml PK groups but was reduced 10 fold in the 400 μg/ml PK treatment group . PrPSc and PMCA seeding activity were not detected in the mock treated groups ( Fig 5 ) . Overall , DY PrPSc was more susceptible to proteolytic degradation with a corresponding decrease in PMCA-CC compared to lymphotropic strains .
Extraneural inoculation of DY TME infected BH failed to cause disease even via highly efficient extraneural routes of infection and following repeated inoculations . Previous studies have shown that a single inoculation of DY TME fails to cause disease by the per os , i . p . , intravenous or intramuscular routes of infection ( Fig 1; Table 1; [27 , 35 , 36] . These extraneural routes of inoculation are , in general , orders of magnitude less efficient at causing disease compared to i . c . inoculation [25 , 50–52] . The i . c . LD50 of DY TME is 100 fold lower compared to HY TME , therefore , DY TME may fail to cause disease due to a low effective titer [32] . In an attempt to increase the effective titer of DY TME , we used e . n . inoculation and repeated inoculations of prions [44 , 45 , 50 , 53–55] . We found that e . n . inoculation of DY TME failed to cause disease within the lifespan of the host consistent with a previous report [35] . Importantly , bioassay of selected LRS , PNS and CNS tissues collected from these animals failed to detect DY TME agent , indicating that a subclinical infection had not been established consistent with previous findings following i . p . inoculation of DY TME ( Fig 1; Table 2; [56] ) . Repeated inoculations of DY TME by either the per os or e . n . routes failed to cause clinical signs of DY TME infection within 650 days of the first inoculation . Taken together this transmission data suggests that the failure of DY TME to cause disease or establish infection was not due to effective titer , however , we can not exclude the possibility that increasing the sample size for each group would identify a rare positive transmission event . Overall , these studies suggest that other factor ( s ) control this well characterized example of an exclusively neurotropic prion strain . DY TME can be found in LRS tissues within hours of inoculation . Previous studies have failed to detect DY PrPSc or infectivity in spleen and lymph nodes at later ( e . g . months ) time points p . i . [36] . We hypothesized that DY TME inoculum fails to reach spleen and lymph nodes resulting in an inability to cause disease . However , we found that DY PrPSc crossed the nasal mucosal epithelium and was detected in the lumen of lymphatics at 1 hour p . i . ( Fig 2 ) . This data indicates that DY TME can cross epithelia and enter the circulation adding to the growing body of literature indicating that entry of PrPSc into or between cells occurs independent of the prion strain [47] . Additionally , DY PrPSc was detected in secondary LRS tissues following i . p . inoculation at 2 hours p . i . ( Fig 3 ) . This data indicates that cellular transport of prions from the point of inoculation to the spleen did not degrade DY PrPSc . We interpret this to be detection of inoculum PrPSc , however , we can not exclude the possibility that newly formed PrPSc is contributing to the results . The localization of HY and DY PrPSc in germinal centers of spleen was similar ( Fig 3 ) suggesting similar targeting . We hypothesize that strain specific differences in PrPSc , such as sialylation , does not affect the initial targeting , which is consistent with previous work [57 , 58] , however , the contribution of sialylation to newly formed PrPSc in the spleen is not known [59] . We can not exclude the possibility that strain-specific requirements for cellular entry explain the failure to establish infection , however , prion uptake studies in cell culture are not consistent this hypothesis [60 , 61] . Detailed analysis of the early events in HY and DY TME pathogenesis failed to identify strain specific differences in transport to secondary LRS tissues . These observations are inconsistent with the hypothesis that strain-specific differences in prion transport result in a failure of DY TME to establish infection in spleen and lymph nodes . Furthermore , this data indicates that the restriction in DY pathogenesis must be occurring in secondary LRS tissues ( Fig 6 ) . The spleen supports DY PrPSc formation . Cellular conversion cofactors can change strain properties of in vitro generated PrPSc suggesting that tropism could be influenced by the distribution of host strain-specific conversion cofactors [15–17] . This hypothesis predicts that the failure of DY TME to establish infection in the LRS is caused by a lack of DY PrPSc specific host cofactor ( s ) required for prion conversion . We used PMCA to directly test this hypothesis and found that DY PrPSc formation was supported in spleen homogenate ( Fig 4 ) . We found that both DY TME brain homogenate and detergent enriched PK-digested DY PrP-res supported PrPSc formation indicating that residual PrPC or conversion cofactors contained in the DY brain homogenate seed did not contribute to the formation of DY PrPSc ( Fig 4 , S2 Fig ) . Additionally , the observation that the conversion activity of DY TME brain homogenate and DY PrP-res are similar suggests that sensitive forms of DY PrPSc do not significantly contribute to the observed results ( Fig 4 , S2 Fig ) . This indicates that all of the factors , including PrPC that supports conversion by DY PrPSc , required to amplify DY PrPSc are contained within the spleen [62] . These findings are consistent with in vitro cell-free conversion experiments demonstrating that RK13 and baculovirus derived PrPC can support DY PrPSc formation [63 , 64] . These data suggest that all of the DY strain-specific information is contained within the structure of PrPSc [10 , 65] . We can not exclude the possibility that cell types in the spleen that amplify PrPSc ( e . g . follicular dendritic cells ) lack DY strain-specific conversion cofactor ( s ) and therefore do not support DY PrPSc formation in vivo . In this scenario the DY strain specific co-factor would have to be present in other cells in the spleen that provide the conversion cofactor in trans during PMCA . Strain-specific rates of PrPSc accumulation ( i . e . that balance between formation and clearance ) occur in vivo [28 , 66] . This has been further investigated with PMCA that supports PrPSc formation but not clearance [67] . Using PMCA with brain homogenate as the template for conversion , prions strains with short incubation periods generally have more efficient PrPSc formation compared to PrPSc from long incubation period strains [41 , 68] . Using brain as the source of PrPC , HY TME amplifies more efficiently compared to 139H and DY TME which have the same , lower , amplification efficiency [41] . Interestingly , in this study , we found that using spleen homogenate as the template for PMCA conversion , HY TME and 139H have a similar rate of PrPSc formation that is greater than the amplification efficiency of DY TME ( Fig 4 , panel B ) . This data indicates that DY TME , while it is able to form new PrPSc , does so less efficiently in the spleen compared to these two lymphotropic strains . This data also indicates that both the prion strain and host tissue can influence the efficiency of PrPSc formation . In brain , 139H and DY TME has the same PMCA conversion efficiency , can establish infection in the brain and have similar profiles of PrPSc deposition in neurons [41] . In spleen , 139H has a more efficient PMCA conversion efficiency compared to DY TME and 139H can establish infection in the spleen while DY TME does not ( Fig 4 ) . Assuming that all prion strains have similar rates of clearance , this data would suggest that the failure of DY TME to establish infection in the spleen is that the rate of formation does not exceed the rate of clearance [69] . We found that the susceptibility of brain derived DY PrPSc to degradation is greater than that of the lymphotropic strains 139H and HY TME ( Fig 5 ) further suggesting that the balance between replication and clearance favors clearance of DY TME . A limitation of this observation is that brain derived PrPSc may have a different relative conversion efficiency and PK sensitivity compared to PrPSc produced in the spleen . Additionally , strain and cell differences in uptake and PrPSc disaggregation are not taken into account [60 , 61 , 70] . Clearly more work is needed to understand the relationship between the strain-specific features of PrPSc and host cell interactions . Prion tropism is quite unlike viral tropism , which is largely influenced by the distribution of viral receptors . Our data indicates that two prion strains can similarly cross epithelia , enter the circulation , and are rapidly transported to secondary LRS tissues ( Fig 6 ) . This lack of specificity in prion transport implies that prions are widely distributed throughout the host following inoculation . Since PrPC expression is widespread throughout the host , mechanism ( s ) must account for the limited distribution of prions . The strain-specific distribution of conversion cofactors could account for this observation . Additionally , the results presented here lead us to hypothesize that the balance between PrPSc formation and clearance can also influence the distribution of prions in the host .
All procedures involving animals were approved by the Creighton University Institutional Animal Care and Use Committee ( protocol numbers 811 and 880 ) and comply with the Guide for the Care and use of Laboratory Animals . The HY and DY strains of hamster-adapted TME and the 139H strain of hamster-adapted scrapie were used in this study [41] . The titer for each biologically cloned TME strain was determined by end-point dilution of brain homogenate ( BH ) from terminally-ill animals . The titer of HY TME and DY TME agents were 109 . 3 i . c . LD50 and 107 . 7 i . c . LD50 per gram of brain , respectively [71] . Male Syrian hamsters ( Harlan-Sprague-Dawley , Indianapolis , IN ) were used . Animals were inoculated a single time by the intracerebral ( 25μl of a 1% w/v BH ) , intraperitoneal ( 100μl of a 10% w/v BH ) , per os ( 100μl of a 10% w/v BH ) , extranasal ( 20μl of a 10% w/v BH ) , intranerve ( 2μl of a 1% w/v BH ) or intravenous route ( 2μl of a 1% w/v BH ) as described previously [37 , 72] . For the multiple inoculation studies , hamsters were inoculated once per week per os ( 100μl of a 1% w/v BH ) or extranasally ( 20μl of a 1% w/v BH ) for a total of 10 weeks . Hamsters were observed three times per week for the onset of clinical signs and the incubation period was calculated as the number of days between inoculation and the onset of clinical disease . Prion-infected and age matched mock-infected hamsters were anesthetized with isoflurane and transcardially perfused with 50ml of 0 . 01 M Dulbecco’s phosphate buffered saline ( DPBS ) followed by 75ml of McLean’s paraformaldehyde-lysine-periodate ( PLP ) fixative for the experiments that utilized immunohistochemistry [73] . The skull was immediately removed and immersed in PLP for 5–7 hours and decalcified ( Richard Allan , Kalamazoo , MI ) for 14 days at room temperature prior to paraffin processing . For protein misfolding cyclic amplification experiments , uninfected hamsters were anesthetized with isoflurane and transcardially perfused with 75 ml of ice-cold phosphate buffer saline containing 5 mM EDTA , pH 7 . 4 . Brain and spleen were immediately collected , frozen on dry ice and stored at -80°C . For Western blot analysis , animals were euthanized with CO2 followed by thoracotomy . Brain tissue was immediately collected , frozen on dry ice and stored at -80°C . PrPSc was enriched by the method of Wenborn with the addition of PK [74] . Briefly , brain homogenate were digested with 10mg/ml of Pronase E followed by digestion with 50U/ml of Benzonase in 2% w/v sarkosyl ( NLS ) . Sodium phosphotungstic acid ( NaPTA ) was added to 4% w/v and the samples were incubated at 37°C for 30 minutes . The samples were adjusted to 35%w/v iodixanol and 0 . 3% w/v NaPTA and centrifuged at 16 , 000xg for 90 minutes . The clarified supernatant was collected and filtered with a 0 . 45μm microcentrifuge filtration unit . The filtrate was mixed with an equal volume of 2% w/v NLS and 0 . 3% w/v NaPTA , incubated for 10 min and centrifuged at 16 , 000xg for 90 minutes . The pellet was resuspended in wash buffer ( 17 . 5% w/v iodixanol and 0 . 1% w/v NLS ) and was digested with PK at 0 . 1mg/ml for 60 min at 37°C that was terminated using 10mM pefabloc . The final washing of the samples was performed by the addition of wash buffer followed by centrifugation at 16 , 000xg for 30 minutes . This process was repeated two times and the final pellet was resuspended in 0 . 1% w/v NLS . Western blot analysis was used to determine the abundance of PrPSc and the volume was adjusted to give the same abundance of PrPSc as in a 10% w/v brain homogenate . The enriched PrP-res was analyzed by Sypro Ruby staining to determine purity and PMCA to determine converting activity as previously described [75] . Immunohistochemistry was performed as previously described [76] . Briefly , 7μm tissue sections were deparaffinized and incubated in 95% formic acid ( Sigma-Aldrich , St . Louis , MO ) for 20 minutes at room temperature . Endogenous peroxidase activity was blocked using 0 . 3% H2O2 in methanol for 20 minutes at room temperature . Non-specific staining was blocked with 10% normal horse serum ( Vector Laboratories , Burlingame , CA ) in tris-buffered saline ( TBS ) for 30 minutes at room temperature . The sections were incubated with anti-glial fibrillary acidic protein ( GFAP; 1:16 , 000; Dako; Carpinteria , CA ) at 4°C overnight . The sections were incubated in either a biotinylated horse anti-mouse or anti-rabbit immunoglobulin G conjugate and subsequently incubated in ABC solution ( Elite kit; Vector Laboratories , Burlingame , CA ) . Sections were developed using 0 . 05% w/v 3 , 3’-diaminobenzidine ( Sigma-Aldrich , St . Louis , MO ) in TBS containing 0 . 0015% H2O2 and counterstained with hematoxylin ( Richard Allen Scientific , Kalamazoo , MI ) . Light microscopy was performed using a Nikon i80 microscope ( Nikon , Melville , NY ) and images were captured and processed using Adobe Photoshop CS6 ( San Jose , CA ) using identical parameters . Experiments using PMCA were performed as previously described [75] . Briefly , HY TME , DY TME or 139H-infected brains were homogenized to 10% w/v in DPBS ( Mediatech , Herndon , VA ) or enriched PrPSc was used as a PMCA seed . Uninfected brain and spleen tissues were homogenized to 10% w/v and 20% w/v in conversion buffer respectively , and used as a PMCA substrate . PMCA seeds were diluted in PMCA substrate at a 1:100 ratio e . g . 1 μl of seed was diluted into 99 μl of substrate . PMCA was performed with a Misonix 4000 sonicator ( Farmingdale , NY ) . For PMCA with uninfected brain as substrate the sonicator output set to level 75 and an average power output of 160 watts during each sonication cycle . For PMCA with uninfected spleen as substrate , the sonicator output was set to level 87 with an average power output of 220 watts during each sonication cycle . One round of PMCA consist of 144 cycles; one cycle comprises of five-second sonication followed by a ten-minute incubation at 37°C . All PMCA sample groups had an n = 3 and all experiments were replicated a minimum of three times . Western blot detection of PrPSc from brain homogenate was performed as described previously [76] . Briefly , brain homogenate ( 5% w/v ) in PMCA conversion buffer is digested with proteinase K ( PK ) at a final concentration of 100 , 200 , or 400 μg/ml ( Roche Diagnostics Corporation , Indianapolis , IN ) at 37°C for 1 or 24 hours . The samples were either enriched for PrPSc as described previously [77] or an equal amount of sample buffer containing 4% ( v/v ) 2-mercapto ethanol and 8% ( w/v ) SDS was added and the mixture was incubated at 100°C for 10 minutes . Prion protein was detected with the anti-PrP antibody 3F4 ( final concentration of 0 . 1 μg/ml; Chemicon; Billerica , MA ) and HRP-conjugated donkey anti-mouse secondary antibody ( Jackson ImmunoResearch; West Grove , PA ) . The Western blot was developed with Pierce Supersignal West Femto Maximum Sensitivity Substrate according to manufacturer instructions ( Pierce , Rockford , IL ) , imaged on a Kodak 4000R Imaging Station ( Kodak , Rochester , NY ) and analyzed using Kodak Molecular Imaging Software v . 5 . 0 . 1 . 27 ( New Haven , CT ) . Statistical analysis was performed using Prism 6 . 0 for Mac ( GraphPad Software Inc . , La Jolla , CA ) . | Strain specific distribution of prions throughout the infected host are observed in both naturally occurring and experimentally induced prion diseases . The distribution of prions in the host can influence prion shedding and transmission ( e . g . iatrogenic prion transmission ) . The mechanism ( s ) responsible for strain tropism are unknown . Here we show that entry and transport of prions to lymphoid tissue are not influenced by the prion strain . However , we show that lymphotropic prion strains have a higher rate of PrPSc formation and a lower rate of prion degradation compared to a non-lymphotropic prion strain . We hypothesize that the relative rates of PrPSc formation and clearance is one of potentially several mechanisms that can determine prion strain tropism . | [
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"... | 2017 | PrPSc formation and clearance as determinants of prion tropism |
Lung volume reduction surgery ( LVRS ) and bronchoscopic lung volume reduction ( bLVR ) are palliative treatments aimed at reducing hyperinflation in advanced emphysema . Previous work has evaluated functional improvements and survival advantage for these techniques , although their effects on the micromechanical environment in the lung have yet to be determined . Here , we introduce a computational model to simulate a force-based destruction of elastic networks representing emphysema progression , which we use to track the response to lung volume reduction via LVRS and bLVR . We find that ( 1 ) LVRS efficacy can be predicted based on pre-surgical network structure; ( 2 ) macroscopic functional improvements following bLVR are related to microscopic changes in mechanical force heterogeneity; and ( 3 ) both techniques improve aspects of survival and quality of life influenced by lung compliance , albeit while accelerating disease progression . Our model predictions yield unique insights into the microscopic origins underlying emphysema progression before and after lung volume reduction .
Emphysema , a subtype of chronic obstructive pulmonary disease ( COPD ) , is a progressively destructive lung tissue disease characterized by abnormal and permanent enlargement of airspaces distal to the terminal bronchioles . This largely preventable , yet presently incurable disease is associated with high morbidity and mortality , presenting a substantial burden on resource utilization [1 , 2] . While pharmacological treatment can have limited benefits for patients , especially in advanced stages of disease , surgical and bronchoscopic treatments aimed at reducing hyperinflated lung volumes have been shown to ameliorate symptoms of dyspnea , improve quality of life , and in certain cases provide a survival advantage [3–6] . Proposed mechanisms by which lung volume reduction results in functional improvements primarily involve a reduction in hyperinflation leading to restored chest wall and diaphragm mechanics , and an increase in elastic lung recoil ( hence a decrease in lung compliance ) and radial traction on airways leading to improved expiratory flow rates and lung emptying [7 , 8] . In lung volume reduction surgery ( LVRS ) , emphysematous tissue is removed from the upper lung by wedge excision typically via median sternotomy or video-assisted thoracoscopic surgery . Although LVRS has proven efficacy in patients with predominantly upper-lobe emphysema and low baseline exercise capacity [9] , many still do not meet the strict indications for this procedure . Moreover , widespread implementation of LVRS is hindered by high costs , a limited number of highly experienced centers , and significant post-procedural morbidity and mortality [8] . Recently , a growing number of non-surgical techniques have been developed with the goal of providing less invasive alternatives [10] . Among the more extensively investigated of these bronchoscopic lung volume reduction ( bLVR ) treatments are ( 1 ) nitinol coils , which return to a predetermined shape after placement in target airways while retracting the surrounding diseased lung parenchyma [5 , 11 , 12]; ( 2 ) one-way valves , which facilitate lobar collapse by blocking regional ventilation but permitting emptying of affected areas [6 , 13 , 14]; and ( 3 ) biomaterial-based lung sealants , which function to block small airways and prevent collateral ventilation pathways inducing absorption atelectasis in the delivered region [15–17] . While recent evidence appears to be trending in support of bLVR and ongoing studies are expected to further define optimal implementations for such approaches [8] , the immediate and long-term effects in the lung have yet to be fully understood . Previous work has focused primarily on the benefits in clinical function and survival outcomes for these treatments and thus , provides no insight into the micromechanical origins leading to such improvements . The aim of this study was to investigate the structure-function relationships before and after lung volume reduction using a computational elastic network model [18–22] . Here , we simulate emphysema progression by removal of elastic elements in the network , and then track changes in network compliance and structure following LVRS and bLVR interventions . Our simulations shed light on the factors contributing to immediate and long-term treatment efficacy as well as predicted outcomes spanning several years . We show that macroscopic improvements in compliance following lung volume reduction are correlated with the microscopic distribution of mechanical forces in the local lung environment . Furthermore , our model predicts similar survival and quality of life benefits for both interventions , indicating how bLVR may be implemented as an effective , less invasive treatment for advanced emphysema .
The elastic behavior of lung tissue was modeled using a two-dimensional ( 2D ) computational network of linearly elastic elements arranged in a hexagonal lattice under the influence of gravity . Disease progression was driven by elimination of network elements carrying a high force , representing in vivo tissue failure in regions of high local mechanical stress . Fig 1A shows a representative simulation beginning with healthy tissue followed by gradual deterioration . As tissue failure progressed ( i . e . , high force elements were removed ) , smaller airspaces were observed to coalesce into larger neighboring airspaces representing the process of airspace enlargement characteristic of emphysema progression . Fig 1B illustrates the two lung volume reduction techniques initiated in parallel from the same network configuration . LVRS was simulated by removing the upper portion of the network , while bLVR was simulated by reducing specific affected regions of the network . To characterize functional changes within the network at each configuration , the network compliance , C was calculated as the inverse of the 2D bulk modulus . To characterize structural changes , network heterogeneity was quantified as the coefficient of variation of individual airspace sizes , CVarea . Prior to lung volume reduction , values of C and CVarea increased from baseline consistent with the loss of elastic recoil and enlarged airspaces observed in emphysema ( Fig 1C ) . In the representative network shown , both the non-specific LVRS and the region-specific bLVR yielded comparable immediate and long-term improvements in network elasticity as characterized by similar reductions and recoveries in C , respectively . In contrast , upper network resection with LVRS was less effective in other networks as characterized by differences in reduction and recovery in C ( S1 Fig ) . Thus , variability in initial conditions among the N = 14 large networks , representing inter-subject variability , simulated disease distributions with treatment-specific responses to lung volume reduction . To distinguish networks demonstrating a benefit from LVRS , we defined the predictive index β characterizing network heterogeneity below the line of LVRS resection . Here , β was calculated as the coefficient of variation of airspace sizes below the line of LVRS resection . Less heterogeneity in this lower region ( i . e . , smaller β ) corresponded to relatively spared tissue due to predominately upper lung emphysema . More heterogeneity in this region ( i . e . , larger β ) corresponded to emphysematous tissue destruction extending into the lower lung regions . Based on this index , an arbitrary threshold of β = 3 . 5 was defined to divide the networks into LVRS responder ( N = 8 ) and marginal-responder ( N = 6 ) groups . As shown in Fig 2A , the immediate drop in C following LVRS was inversely related to β ( R2 = 0 . 600 ) , with larger changes observed for responders compared to marginal-responders ( 36 . 4±8 . 6% vs . 20 . 1±4 . 4%; p = 0 . 001 ) . Representative networks for both groups are shown in the supplement ( S2 Fig ) . In addition , β was inversely related to the overall network heterogeneity quantified by CVarea ( S2 Fig ) . That is to say , smaller values of β implied heterogeneous network structure amenable to LVRS , whereas larger values of β implied homogeneous disease patterns less effectively treated by resection of the upper network . To better characterize the mechanical differences between networks with different β values , we compared the distribution of forces carried by individual network elements before and after intervention . As shown in Fig 2B , LVRS skewed the distribution to the right ( i . e . , toward larger forces ) for responders , whereas it did not significantly alter the skewness relative to before intervention for marginal-responders . Interestingly , bLVR skewed the distribution of forces to the right for all networks and reduction conditions . As shown in Fig 2C , plotting values of C after bLVR against the coefficient of variation of forces ( CVforce ) revealed that a simple power law function fitted the data well: C∼CVforce−0 . 7 ( R2 = 0 . 816 ) . This observation demonstrates that macroscopic functional changes immediately after bLVR are linked to the underlying microscopic force redistribution . Taken together , these findings suggest that the introduction of high-force elements plays a critical role in lung volume reduction efficacy and is dependent on pre-treatment structure for LVRS but not bLVR . In addition to the immediate response following lung volume reduction , we also evaluated the long-term response for both LVRS and bLVR . Disease progression was modeled as consecutive stages of increasing network deterioration characterized by the cumulative number of broken elements at each stage . Fig 3 shows the average changes in C and CVarea before and after intervention . Prior to lung volume reduction , average values of C and CVarea increased with worsening disease severity consistent with a softening network and expanding emphysematous regions . Comparing networks classified as either responders or marginal-responders , statistically significant differences between groups were detected for C and CVarea just before intervention , suggesting that responders were slightly less elastic with more heterogeneous disease progression . Following LVRS , average values of C and CVarea initially returned to near baseline levels . Responders demonstrated smaller increases in C and CVarea at advanced disease stages indicating more sustainable functional and structural improvements compared with marginal-responders . This highlights the improved long-term treatment efficacy in networks with smaller vs . larger β , given similar stages of tissue deterioration prior to intervention . Following bLVR , no differences were detected between networks classified as either responders or marginal-responders . Instead , long-term response was related to bLVR reduction size . Networks with affected regions reduced to 20% of their original size yielded smaller increases in C at each stage of disease progression compared with those reduced to 40% , while average values of CVarea were not statistically different between groups . Based on the long-term simulations in this computational model , we compared the predicted outcomes for LVRS and bLVR as influenced by lung compliance . We found that all treatments actually accelerated network deterioration , more than doubling the rate of increase in C prior to intervention ( Fig 4A ) . Despite the accelerated rate of increase , treatments restoring C to near baseline levels lengthened the expected survival estimated as the number of broken elements required to reach a 60% increase from baseline . LVRS and bLVR reduction to 20% yielded increases of 1 . 51±0 . 13 and 1 . 51±0 . 11 times longer than without treatment , respectively ( Fig 4B ) . We also calculated a combined index related to the area enclosed by the threshold and the curve defined by values of C ( schematic shown in Fig 5 ) . By incorporating both the rate of increase and the number of broken elements , this index termed “Relative Benefit” represented a measure for quality of life . LVRS and bLVR reduction to 20% yielded similar increases of 11 . 7±3 . 7 and 10 . 6±3 . 2 relative to without treatment , respectively , while bLVR reduction to 40% yielded a considerably smaller increase of 4 . 7±1 . 8 ( Fig 4C ) . These model predictions indicate that bLVR can yield similar outcomes as LVRS when affected regions are appropriately reduced in size .
Lung volume reduction represents the primary therapeutic strategy for advanced emphysema . LVRS is a well-established surgical treatment , but is limited by strict indications and significant post-procedural complications . Several non-surgical bLVR approaches are on the rise providing less invasive alternatives with the potential for considerably lower post-procedural morbidity and mortality . While previous work has evaluated improvements in clinical function and survival advantage provided by these techniques , little is known about the corresponding micromechanical mechanisms responsible for improvement in survival and quality of life . In this study , we constructed 2D elastic networks to simulate lung volume reduction with LVRS and bLVR in a force-based model of emphysema progression . Our main findings include: ( 1 ) analysis of network structure using a simple measure of disease heterogeneity prior to lung volume reduction can predict LVRS efficacy; ( 2 ) macroscopic functional improvements following bLVR correspond to microscopic changes in force heterogeneity; and ( 3 ) lung volume reduction improves aspects of the predicted survival and quality of life influenced by contributions of lung compliance , albeit while accelerating disease progression . Mechanical forces have long been suggested to play a role in emphysema progression [23] . Previous work [24] provided early evidence demonstrating alveolar wall rupture in elastase-treated tissue slices as a direct result of local mechanical forces . Subsequently , it was shown that increases in lung compliance paralleled changes in airspace heterogeneity associated with force-induced failure of the extracellular matrix ( ECM ) [25] . Inflammatory processes , concerted action of proteases , and ECM remodeling also likely contribute to emphysema progression [26–29]; however , their role has been proposed more broadly within self-propagating dynamic loops of enzymatically initiated but mechanically driven tissue destruction [19] . Previous network model simulations have demonstrated that emphysematous tissue breakdown cannot be reproduced by a purely chemical process , such that the inclusion of local forces are critical in developing observed emphysema patterns [18 , 19] . Alternative models based on uniform softening or random cutting have also been found to poorly characterize these progressive changes [25] . Thus , we consider the network model described here to provide a suitable description of emphysema progression with the capacity for studying the structure-function relations following lung volume reduction . It is known that patients with predominately upper-lung emphysema have more favorable outcomes following LVRS [3 , 9] . In this study , greater improvements in C after LVRS were observed in those with less affected lower network regions ( Fig 2A ) as resection of the upper diseased airspaces allowed for the remaining tissue to restore function . These networks also displayed smaller increases in C , especially at advanced disease stages ( Fig 3A ) , as well as considerably smaller decreases in total network stress , σ , that may reflect transpulmonary pressure in the lung ( S3 Fig ) . This is consistent with previous experimental data demonstrating that improvements after LVRS are correlated with increased ratios between upper to lower zone emphysema ( determined by computed tomography , CT ) , but are not well predicted by pre-surgical measurements of static lung compliance or elastic recoil [30 , 31] . The observed improvements in C may also be related to differences in force distribution between LVRS groups ( Fig 2B ) . One possible explanation is that emphysematous areas remaining after LVRS act as “shock-absorbers” contributing to a softer overall tissue , whereas networks with relatively low structural heterogeneity better facilitate force propagation and a stiffer overall tissue . Moreover , we found that a power law distribution could be used to characterize the distribution of forces before and after intervention ( S4 Fig ) . The tail of the distribution varied with the specific intervention and may indicate the emergence of complex network behavior [32] , which would be a unique case when increased lung heterogeneity potentially contributes positively to treatment outcome . Nonetheless , these findings suggest a mechanism that may explain how functional changes evolve based on intrinsic structural differences prior to LVRS . Motivated by the benefits observed with LVRS , however , non-surgical bronchoscopic alternatives have been the focus of recent investigations , where patient outcomes have improved as bLVR techniques have become more proficient [8 , 10] . A recent study using endobronchial valves [14] reported that improvements in measured FEV1 were correlated with effective collapse of the affected lobe , a finding confirmed to be enhanced by fissure completeness and absence of interlobar collateral ventilation [6] . This is conceptually similar to our model predictions that bLVR reduction size is inversely related to immediate and long-term improvements in C ( Fig 2 and Fig 3 ) . Comparing bLVR for multiple reduction sizes revealed that macroscopic functional improvements in C were linked to underlying microscopic changes in force heterogeneity . Although radiographic evidence indicates near complete reduction is currently not achievable [8 , 12 , 15] , our findings highlight important structure-function interactions between network reorganization after bLVR and its effect on the local mechanical environment in the lung ( observations which would otherwise be impossible to detect via imaging or functional studies ) . This unexpected relationship demonstrates that bLVR can be an effective treatment for advanced emphysema , but also suggests a mechanism by which elevated forces in close proximity to reduced areas may promote local tissue destruction . By simulating LVRS and bLVR in parallel from the same configuration , we were able to directly contrast outcomes and disease progression after treatment . In general , lung volume reduction led to more rapid tissue failure as a result of increased mechanical forces on elastic elements . This is consistent with clinical reports of accelerated deterioration of lung function ( relative to pre-surgery ) observed in patients following LVRS [33 , 34] . Despite elevated rates of tissue failure , LVRS and bLVR are predicted to lengthen survival and improve quality of life by restoring lung function to levels closer to healthy tissue ( Fig 4 ) . Statistically comparable outcomes were observed for LVRS and bLVR reduction to 20% , suggesting that bLVR is capable of similar treatment efficacy as current surgical standards . The modality-specific reduction in bLVR efficacy highlighted by Ingenito et al . [10] might also be explained by the less proficient treatment modeled by bLVR reduction to 40% . Nonetheless , these computational findings support bLVR application across an even broader treatment population , as suggested by Deslee et al . [35] . This is of particular interest given the potential for substantially less invasive bronchoscopic techniques to extend treatment options to those who do not qualify for LVRS . Changes in relative lung volumes are also correlated with treatment efficacy . Fessler et al . [7] have shown that decreases in residual volume ( RV ) relative to total lung capacity ( TLC ) contribute to improvements in FEV1 after LVRS . Our results support these findings illustrating that LVRS in heterogeneous networks and bLVR applied to affected regions represent treatments that come closest to the removal of pure RV and allow for expansion of more normal regions . Interestingly , related studies evaluating the success of bilateral lung transplant somewhat counterintuitively observed significantly better outcomes in cases with donor lungs larger than the recipient thorax ( as estimated by the donor-recipient predicted TLC ratio ) [36 , 37] . It was proposed , however , that a decrease in lung compliance post-transplant was likely a contributing factor for survival and performance , which would agree with the benefits of lung volume reduction modeled here . The importance of these anatomic considerations also suggests the potential for coupling this computational approach with CT imaging in the future . The non-invasiveness of such an analysis would be uniquely suited to infer patient outcomes prior to treatment and aid clinical decision-making . There are several limitations of the network model that must be considered when associating computational findings with clinical outcomes . ( 1 ) Our model considers the lung tissue to be a collection of interconnected acinar compartments; however , recent work has highlighted the involvement of the small airways in COPD development . Narrowing and loss of terminal bronchioles are believed to increase small airway resistance in COPD patients [38] , and may even precede emphysema development [39] . Hiorns et al . [40] have further demonstrated the dynamic and spatially heterogeneous nature of these airway-parenchyma tethering interactions in precision cut lung slices . Although interactions at this scale are not included here , the stiffer airways would likely be associated with local parenchymal destruction . The hexagonal network units might alternatively reflect the mechanics of secondary pulmonary lobules , approximating the coalescence of destroyed lobules and enlarged lesions characteristic of emphysema , as described by Hogg et al . [41] . ( 2 ) The number of broken elements may not have a direct temporal correlation with progression in vivo even though tissue destruction is clearly associated with more developed disease severity . Moreover , emphysema progression is modeled by breaking a specified number of elements at each stage of disease . An alternative approach would be to define a global threshold , as investigated for ventilator-induced lung damage [42] , above which elements are considered to fail . Both approaches yield similar disease patterns , but could influence the apparent rate of tissue failure differently . Nonetheless , observed inter- and intra-subject variability in vivo , along with only few data from follow-up studies , validate the general interpretations of our results presented here . ( 3 ) Ventilatory dependencies associated with incomplete lung fissures are not captured by our network models; however , the bLVR simulations presented here are markedly similar to the mechanical action of nitinol coils and biomaterial-based lung sealants believed to function independently of collateral ventilation pathways [12 , 15] . ( 4 ) Chest wall mechanics , irregular lung boundaries , nonlinear dynamics , 3D interactions , enzyme kinetics , and ECM remodeling were also not included in this study , but could improve the interpretation of factors contributing to disease progression . Future work expanding bLVR in a true multiscale model of emphysema in 3D [43] that incorporates airway-parenchymal interactions , inflammation , and enzyme kinetics may provide additional insights and enhance the potential for clinical application . Despite these limitations our network model has been shown to generate disease patterns with strong correlation to those observed using CT imaging [18] by including contributions of mechanical forces that likely drive emphysema progression [19] . While these computational simulations represent a simplified view of emphysema progression , this model provides new perspective into the structure-function relations underlying the progressive nature of emphysema before and after lung volume reduction . Immediate and long-term responses to these interventions appear to be intimately linked to changes in microscopic force heterogeneity within the lung , which could explain known structural limitations for surgical approaches and emphasize pertinent implications in disease progression for bronchoscopic approaches . Furthermore , our findings suggest that effective bronchoscopic reduction of affected lung tissue can achieve similar if not better functional improvements , survival advantages , and quality of life benefits as currently established surgical techniques . These insights have the potential to inform more rationalized design of lung volume reduction techniques and patient-specific treatment strategies .
We constructed N = 14 networks to model the elastic behavior of the lung parenchyma with different initial conditions simulating inter-subject variability . Networks were progressively degraded by eliminating elements carrying the highest forces and then finding the network configuration with minimal elastic energy for five sequential iterations . LVRS and bLVR were then applied to the same network configuration and the treated networks were subsequently degraded as before . Structural and functional parameters were tracked for each network configuration to characterize changes at each stage of disease progression . Finally , predicted survival and quality of life outcomes were compared for both treatments . The 2D network model used in this study has been described previously [18–22] . Briefly , elastic elements inter-connected via pin joints were allowed to rotate freely while nodes bordering the perimeter of the network were kept fixed to ensure the network was initially pre-stressed and hexagonal units , representing individual acini , were not collapsed . For each configuration , the total elastic energy , Etot was calculated as the sum of the energies for individual elastic elements , Ei: Etot=∑iEi=12kiΔli2 where ki are the linear spring constants and Δli are the individual element displacements from their resting length . Each network consisted of 6 , 987 elastic elements and 2 , 310 hexagonal cells ( 85x56 nodes ) . Uniform distributions ( Mean ± SD ) of spring constants ( 1 . 0 ± 0 . 4 ) and resting lengths ( 0 . 5 ± 0 . 1 ) were assigned to the elastic elements to introduce a degree of initial heterogeneity . The minimum energy corresponding to the equilibrium configuration of the network was obtained using the equation above with a variant of the simulated annealing technique [44 , 45] . Here , the position of each node was displaced by a small amount proportional to and in the direction of the local resulting force on the node . If the change in total energy compared to the previous position was negative ( ΔE < 0 ) the new configuration representing a lower energy state of the system was accepted . Alternatively , for ΔE ≥ 0 , the new configuration could be accepted with probability P = exp ( −ΔE/T ) where T was a control parameter that was sequentially reduced until a pre-defined convergence criterion was reached . Gravity dependence in the network was simulated by applying additional downward forces at each node with magnitude proportional to the number of dependent nodes below . This relatively weak influence represented the net effect of gravity over long time-scales proposed to enhance tissue destruction in the upper lung [23] . In the absence of this term , emphysema would be expected to progress with equal probability in any region of the network . Emphysema was initiated in the network model by randomly breaking ~4% of all the elastic elements . Tissue failure was then simulated using a force-based destruction approach . Elastic elements were sorted by their corresponding force , and the top 0 . 7% of elements were broken with probability P = 0 . 40 . Individual elements were not considered to experience fatigue behavior . The modified network was solved to yield a new configuration and distribution of forces , which corresponded to a later disease stage with different elastic elements at risk for failure . This discretized approach generated a disease progression driven by the spatial distribution of forces while the probabilistic elimination of elements introduced a degree of stochasticity to each network , limiting the deterministic nature of each simulation . These steps were repeated for a total of five iterations simulating progressively more developed disease severity . LVRS and bLVR were applied in parallel to reduce affected emphysematous regions . To simulate lung resection in LVRS , the upper 30% of the network was removed and affected regions intersected by the threshold were stretched to form a continuous , fixed horizontal upper border . To simulate reduction of enlarged airspaces in bLVR , nodes encompassed by a selected perimeter , corresponding to the region to be reduced , were moved toward their geometric center of mass . Regions including a fixed border were asymmetrically reduced in size parallel to the axis of the border . For each network , affected regions were selected and then reduced to 1 , 20 , or 40% of their original size . Mechanical stress σ was calculated for each network configuration by numerically differentiating the total energy , Etot of the system at the equilibrium configuration and after stretching the network by a small bi-axial strain , ε = ±0 . 01 . Here , the equilibrium configuration was assumed to correspond to FRC , representing a static measurement of lung function . Networks were then stretched with a sinusoid of amplitude ε = ±0 . 04 around the equilibrium configuration , such that the 2D bulk modulus was defined as the slope of the corresponding stress-strain curve . The compliance C was calculated as the inverse of the estimated network bulk modulus at each stage of disease progression . To facilitate comparisons with baseline , σ and C are reported as percent changes from the initial network configuration prior to emphysema destruction . Network structure was quantified by considering the sizes of individual airspace units . Each network configuration was converted to a binary image and the number of pixels enclosed by connected spring elements represented the individual airspace area . Overall structural heterogeneity was then assessed as the coefficient of variation of airspace sizes , CVarea . For network configurations directly before intervention , we also considered the coefficient of variation for airspaces below the line of LVRS resection . This predictive index , referred to as β , subsequently characterized disease heterogeneity in the network not resected by LVRS . Note that β was calculated as a single predictive index before treatment , whereas CVarea was calculated for each stage of disease progression to track changes in overall network structure . The rate of tissue failure was estimated before and after intervention as the increase in C over four stages of disease progression . The number of broken springs required to reach a 60% increase in C was calculated for each network as an estimate of survival . However , since network deterioration prior to treatment was typically less than this threshold a second order polynomial was fitted to values of C to estimate survival in the absence of any lung volume reduction . The relative benefit of treatment was then calculated as shown in the schematic ( Fig 5 ) . The area between the survival threshold and the compliance curve represents a composite index for quality of life , incorporating both the rate and sub-threshold duration of disease progression . Larger values of this area correspond to lower values of C over a longer period of time and hence represent better quality of life . To compare the benefits provided by lung volume reduction , data are reported as normalized by the estimated values in the absence of any treatment . Network simulations were completed using custom-developed software , which has been utilized previously to generate and analyze networks in conjunction with other experimental studies [18–22] . Network manipulations involving LVRS and bLVR were implemented cooperatively with this program using original scripts developed in MATLAB ( MATLAB r2013a , MathWorks , Natick , MA ) . Two-way repeated measure analysis of variance ( ANOVA ) was used to compare network values of C , CVarea , and σ between treatment groups at each stage of disease progression , as well as the skewness of force distributions for each treatment group . One-way ANOVA was used to compare estimates of disease progression rate , survival , and relative benefit . Post-hoc Holm-Sidak and Tukey tests were used to determine differences between groups . The average change in C after LVRS for responder and marginal-responders were compared using a t-test . For all comparisons , p<0 . 05 was considered significant . Statistical analyses were performed using SigmaPlot ( SigmaPlot v12 . 3 , Systat Software , Inc . , San Jose , CA ) and MATLAB . | Surgical and , more recently , bronchoscopic lung volume reduction is the only available treatments for patients with advanced stage emphysema . Several large-scale , clinical studies have outlined appropriate selection criteria based on patient outcomes; however , the underlying mechanisms determining disease progression and response to these treatments are not well-understood . To answer this question , we have developed a network model of the lung to compare immediate and long-term response to each treatment . This approach allows us to directly study macroscopic changes in function related to microscopic changes in the local structural and mechanical environment . In addition , it facilitates direct comparisons between surgical and bronchoscopic lung volume reduction given identical initial conditions , which is not feasible in a clinical study . We propose here a mechanism suggesting that lung volume reduction efficacy is intimately linked to changes in microscopic force heterogeneity within the lung . Such an understanding of the mechanisms driving emphysema has the potential to greatly improve current therapies for this condition through more rationalized , patient-specific treatment strategies . | [
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... | 2017 | Predicting Structure-Function Relations and Survival following Surgical and Bronchoscopic Lung Volume Reduction Treatment of Emphysema |
Dengue virus is a mosquito-transmitted virus that can cause self-limiting dengue fever , severe life-threatening dengue hemorrhagic fever and dengue shock syndrome . The existence of four serotypes of dengue virus has complicated the development of an effective and safe dengue vaccine . Recently , a clinical phase 2b trial of Sanofi Pasteur's CYD tetravalent dengue vaccine revealed that the vaccine did not confer full protection against dengue-2 virus . New approaches to dengue vaccine development are urgently needed . Our approach represents a promising method of dengue vaccine development and may even complement the deficiencies of the CYD tetravalent dengue vaccine . Two important components of a vaccine , the immunogen and immunopotentiator , were combined into a single construct to generate a new generation of vaccines . We selected dengue-2 envelope protein domain III ( D2ED III ) as the immunogen and expressed this protein in lipidated form in Escherichia coli , yielding an immunogen with intrinsic immunopotentiation activity . The formulation containing lipidated D2ED III ( LD2ED III ) in the absence of exogenous adjuvant elicited higher D2ED III-specific antibody responses than those obtained from its nonlipidated counterpart , D2ED III , and dengue-2 virus . In addition , the avidity and neutralizing capacity of the antibodies induced by LD2ED III were higher than those elicited by D2ED III and dengue-2 virus . Importantly , we showed that after lipidation , the subunit candidate LD2ED III exhibited increased immunogenicity while reducing the potential risk of antibody-dependent enhancement of infection in mice . Our study suggests that the lipidated subunit vaccine approach could be applied to other serotypes of dengue virus and other pathogens .
Dengue viruses belong to the Flavivirus genus of the Flaviviridae family and include four antigenically different serotypes of dengue virus [1] . Dengue virus is a growing threat to public health , not only in terms of geographical distribution but also with respect to infection cases . Dengue occurs in as many as 128 countries throughout tropical and subtropical areas [2] . Vaccination has been proposed as a cost-effective strategy to combat the threat of infectious disease . Unfortunately , an approved dengue vaccine is not presently available , despite tremendous efforts in previous decades . Several vaccine candidates are proceeding in clinical trials [3] . The most advanced candidate is Sanofi Pasteur's recombinant live , attenuated tetravalent dengue-yellow fever chimeric virus vaccine . These vaccine candidates are based on the backbone of 17D yellow fever vaccine strain , each expressing the pre-membrane and envelope genes of one of the four dengue virus serotypes [4] . Recently , the results of a phase 2b trial of this tetravalent dengue vaccine in Thai schoolchildren of 4–11 years of age were reported [5] . The overall efficacy of the vaccine was 30 . 2% . One or more doses of the vaccine reduced the incidence of dengue-3 and dengue-4 febrile diseases by 80–90% , with a smaller reduction in diseases caused by dengue-1 . However , there was no efficacy against dengue-2 . Thus , there is an urgent need to complement the deficiency of the recombinant live , attenuated tetravalent dengue-yellow fever chimeric virus vaccine . In most cases , dengue viral infection causes dengue fever , which is a self-limiting illness . However , infection with dengue virus can also develop into severe dengue hemorrhagic fever ( DHF ) or dengue shock syndrome ( DSS ) [6] , [7] . The mechanisms of DHF and DSS are still not fully understood . The pathogenesis of DHF and DSS may be due to antibody-dependent enhancement ( ADE ) . ADE is mediated by nonneutralizing antibodies or subneutralizing antibody concentrations bound to the dengue virion , which enhances viral entrance into target cells via the Fc receptor ( FcR ) [8] . ADE is also mediated by dual-specific antibodies , which bind to both dengue virus particles and target cells lacking FcR expression [9] . In addition to ADE , dengue viral proteins induced antibodies cross-react with plasminogen , endothelial cells , and platelets have been proposed to play an important role in the pathogenesis of DHF and DSS [10]–[12] . The complex pathogenesis of DHF and DSS represents a barrier that complicates dengue vaccine development . Dengue envelope protein is the major structural protein that mediates dengue virus infection . The envelope protein domain III ( ED III ) is responsible for viral attachment by binding to the cellular receptor [13] , [14] . ED III has been proposed as a suitable target for dengue vaccine development [15] . The immunogenicities of purified recombinant envelope protein or ED III have been evaluated in mice and nonhuman primates [16]–[19] . However , these purified recombinant proteins are poorly immunogenic . Adjuvants are often required in vaccine formulations to augment the immune response to antigens . However , aluminum-containing adjuvants , which are the most widely used in human vaccines , may not be suitable for use in dengue subunit vaccines to induce robust immune responses . Antigens and immunostimulators are two major components of modern subunit vaccines . We [20] and others [21]–[23] have demonstrated that both bacterial-derived lipoproteins and synthetic lipopeptides can activate antigen-presenting cells via the Toll-like receptor signaling pathway and augment humoral and cellular responses . Based on these findings , we have developed technology to express recombinant lipoprotein in high yields for the development of subunit vaccines with high immunogenicity [24] . In the present study , we prepared recombinant lipidated dengue-2 ED III ( LD2ED III ) and evaluated its immunogenicity . We demonstrated that exogenous adjuvant is not required for the induction of a robust immune response to LD2ED III . These results provide important information for future clinical studies of ED III-based subunit vaccines .
Animal studies were conducted in strict accordance with the recommendations of Taiwan's Animal Protection Act . The protocols were approved by the Animal Committee of the National Health Research Institutes ( Protocol No: NHRI-IACUC-098014 ) and performed according to their guidelines . The amino acid sequence of D2ED III was described previously [25] . Briefly , the sequence of D2ED III was obtained by aligning 13 amino acid sequences from different isolates of type II dengue ( accession numbers P07564 , P14339 , P30026 , P27914 , Q9WDA6 , P12823 , P14338 , P14337 , P14340 , P18356 , P29984 , P29990 , and P29991 ) . Based on the amino acid sequence of D2ED III , the DNA sequence of D2ED III using Escherichia coli codon usage was determined and fully synthesized by a biotechnology company ( Purigo Biotechnology Co . , Taipei , Taiwan ) . The synthesized DNA was then amplified by PCR . To generate an expression plasmid for D2ED III production , the following primers were used: forward primer , 5′-GGAATTCCATATGaaaggcatgagctatgC-3′ ( NdeI site , underlined ) ; reverse primer , 5′-CCGCTCGAGgctgctgcctt-3′ ( XhoI site , underlined ) . The PCR product was then cloned into the NdeI and XhoI sites of the expression vector pET-22b ( + ) ( Novagen , Madison , WI ) to produce the plasmid pD2DE III . As a result , the C-terminus of the recombinant protein contained a hexahistidine tag ( His-tag ) . To express protein , E . coli BL21 ( DE3 ) ( Invitrogen , Carlsbad , CA ) was transformed with pD2DE III . The transformed cells were cultured at 37°C overnight , and protein expression was induced by adding 1 mM isopropylthiogalactoside ( IPTG ) , followed by incubation for 3 hours at 37°C . To clone and express LD2ED III , the D1 domain and the lipid signal peptide of the lipoprotein Ag473 [24] were cloned into the NdeI and BamHI sites of the expression vector pET-22b ( + ) ( Novagen , Madison , WI ) to obtain the plasmid pLipo . The D2ED III gene was cloned into the BamHI and XhoI sites of the pLipo plasmid to produce the plasmid pLD2DE III . As a result , the C-terminus of the recombinant protein contained a His-tag . E . coli C43 ( DE3 ) ( Lucigen , Middleton , WI ) was transformed with pLD2DE III to express lipidated protein . The transformed cells were cultured at 37°C overnight . One ml of the overnight culture was scaled up to 600 ml in a 2 l-shake flask and incubated at 37°C for 4 h before induction . Protein expression was induced ( OD600 = 0 . 8 ) by adding 1 mM IPTG , followed by incubation at 20°C for 20 h . D2ED III was purified by disrupting the harvested cells in a French press ( Constant Systems , Daventry , UK ) at 27 Kpsi in homogenization buffer [20 mM Tris ( pH 8 . 0 ) , 50 mM sucrose , 500 mM NaCl and 10% glycerol] . The cell lysate was clarified by centrifugation ( 80 , 000× g for 40 min ) . Most of the D2ED III was present in inclusion bodies . D2ED III was then solubilized with extraction buffer [50 mM NaH2PO4/5 mM EDTA/200 mM NaCl/0 . 5 M urea/1% Triton X-100 ( pH 6 . 0 ) ] . The extracted fraction was loaded onto immobilized metal affinity chromatography ( IMAC ) columns ( QIAgen , Hilden , Germany ) to purify D2ED III . The eluent from the IMAC column was further purified by passage through an anion exchange column ( DEAE Sepharose fast flow; GE ) after dialysis against DEAE buffer [50 mM NaH2PO4/1 M urea ( pH 5 . 8 ) ] . An E membrane ( Pall Co . , USA ) was used to remove endotoxin . The endotoxin levels of the purified D2ED III were determined by the Limulus amebocyte lysate ( LAL ) assay ( Associates of Cape Cod , Inc . , Cape Cod , MA ) , and the resulting endotoxin levels were less than 0 . 06 EU/mg . After dialysis against 10 mM sodium acetate/3 mg/ml sucrose/4 mM glycine , the D2ED III was lyophilized and stored at −20°C . The fractions from each step were analyzed by SDS-PAGE and immunoblotted with anti-His-tag antibodies . The disruption and purification steps in the production of LD2ED III were similar to those used for D2ED III . However , LD2ED III was not subjected to anion exchange chromatography . After IMAC purification of LD2ED III , Endotoxin Removing Gel ( Pierce , Rockford , IL , USA ) was used to remove lipopolysaccharide ( LPS ) . The LPS levels of the purified LD2ED III were determined by LAL assay , and the resulting LPS levels were less than 0 . 06 EU/mg . After the LD2ED III was dialyzed against 10 mM sodium acetate/3 mg/ml sucrose , the LD2ED III was lyophilized and stored at −20°C . The fractions from each step were analyzed by SDS-PAGE and immunoblotted with anti-His-tag antibodies . LD2ED III was digested with trypsin ( Sigma , St . Louis , MO ) . After digestion , the reaction mixture was further purified with a ZipTip ( Millipore , Massachusetts ) . A 1-µl aliquot of the ZipTip-polished tryptic fragments was mixed with 1 ml of a saturated solution of α-cyano-4-hydroxycinnamic acid in acetonitrile/0 . 1% trifluoroacetic acid ( 1∶3 vol∶vol ) . One microliter of the mixture was placed on the target plate of a MALDI micro MX mass spectrometer ( Waters , Manchester , UK ) for analysis . Dengue-1/Hawaii , dengue-2/PL046 , dengue-3/H-087 , and dengue-4/H241 were used for this study . The virus was laboratory-adapted virus and kindly provided by Yi-Ling Lin of the Institute of Biomedical Sciences , Academia Sinica , Taiwan [26] , [27] . The virus was propagated in C6/36 cells , and viral titers were determined by focus-forming assays with BHK-21 cells . Briefly , a monolayer of BHK-21 cells in 24-well plates was inoculated with supernatant obtained from C6/36 cultured medium infected with dengue virus . Supernatants were diluted by 10-fold serial dilution ( starting at 1∶10 ) . Viral adsorption was allowed to proceed for 3 h at 37°C . An overlay medium containing 2% fetal bovine serum and 0 . 8% methylcellulose in DMEM was added at the conclusion of adsorption . The infected monolayer was incubated at 37°C . After 72 h of infection , the overlay medium was removed from the wells , and the BHK cells were washed with cold PBS . The cells were fixed for 15 min in 3 . 7% formaldehyde/PBS . After washing with PBS , the cells were permeabilized with 0 . 1% Nonidet P-40/PBS for 15 min and blocked with 3% BSA/PBS for 30 min . Infected cells were detected with a monoclonal anti-dengue antibody ( American Type Culture Collection , No . HB-114 ) . After washing with PBS , antibody-labeled cells were detected with a secondary antibody conjugated to HRP . The labeling was visualized with 3 , 3′ , 5 , 5′-tetramethylbenzidine ( TMB ) . The focus-forming units ( FFUs ) were counted , and the viral titers were determined by times dilution factor . Five BALB/c mice ( 6–8 weeks of age ) were immunized subcutaneously with recombinant D2ED III or LD2ED III . The lyophilized D2ED III and LD2ED III were reconstituted with PBS . Each mouse was received 10 µg/0 . 2 mL per dose . Mice were given 2 immunizations at a 2-week interval with the same regimen . This immunization protocol was used throughout the present study . Mice were inoculated intraperitoneally with live dengue-2 virus ( 1×107 FFUs ) on the same schedule for comparison with the D2ED III and LD2ED III vaccine candidates . Blood was collected from each mouse at different time points as indicated . Sera were prepared and stored at −20°C until use . The levels of anti-D2ED III IgG in the serum samples were determined by titrating the samples . Sera were diluted by 3-fold serial dilution ( starting at 1∶33 ) . Briefly , purified D2ED III was coated onto 96-well plates . Bound IgG was detected with HRP-conjugated goat anti-mouse IgG Fc . After the addition of TMB , the absorbance was measured with an ELISA reader at 450 nm . ELISA end-point titers were defined as the serum dilution that produced an OD value of 0 . 5 . The serum dilution was obtained from the titration curve by interpolation , unless the OD value was less than 0 . 5 at the starting dilution ( 1∶33 ) . Antibody avidity was determined on the basis of D2ED III-specific IgG dissociation induced by the chaotropic agent ammonium thiocyanate . Briefly , purified D2ED III was coated onto 96-well plates . After blocking with 1% bovine serum albumin ( BSA ) /PBS , serum at a dilution of either 1∶100 or 1∶300 was incubated at room temperature for 1 h . The plates were washed and incubated with 0–3 . 5 M ammonium thiocyanate in 0 . 5 M increments at room temperature for 15 min . The bound IgG was detected with HRP-conjugated goat anti-mouse IgG . After the addition of TMB , the absorbance at 450 nm was measured with an ELISA reader . The avidity index was calculated as the concentration of ammonium thiocyanate that resulted in a 50% decrease in the initial absorbance [28] , [29] . Sera were diluted via 2-fold serial dilutions ( starting at 1∶8 ) , and the sera were heat-inactivated prior to testing . A monolayer of BHK-21 cells in 24-well plates was inoculated with dengue-2 virus that had been pre-mixed at 4°C overnight with preimmunization or postimmunization sera to a final volume of 0 . 5 ml . The virus titer prior to pre-mixing was approximately 20–40 FFU per well . The FFUs were obtained as described previously , and the neutralizing antibody titer FRNT50 was calculated as the reciprocal of the highest dilution that produced a 50% reduction in FFU compared with control samples containing the virus alone . For calculation purposes , the neutralizing antibody titer was designated as 4 when the neutralizing antibody titer was less than 8 . Antibody-mediated enhancement of dengue virus infectivity was determined by flow cytometry in K562 cells . Sera were diluted via 4-fold serial dilutions ( starting at 1∶8 ) , and the sera were heat-inactivated prior to testing . Serially diluted sera and virus were mixed and incubated to form immune complexes for 1 h at 37°C . K562 cells were mixed with immune complexes ( MOI = 0 . 1 ) and then incubated for 1 . 5 h at 37°C . After washing , the cells were resuspended in fresh medium and incubated for 3 days at 37°C . Infections with and without virus were performed in parallel as controls . Cells were stained for intracellular with monoclonal anti-dengue antibodies ( American Type Culture Collection , No . HB-114 for dengue-1 , dengue-2 , and dengue-4; HB-49 for dengue-3 ) . Antibody-labeled cells were detected with a secondary antibody conjugated to FITC . The data were acquired with CellQuest Pro software on a BD FACSCalibur flow cytometer and were analyzed with FACS 3 software . The fold enhancement was defined as the percentage of infected cells in the presence of sera divided by the percentage of infected cells in the absence of sera . Statistical analyses were performed with the ANOVA Bonferroni post test using GraphPad Prism version 5 . 02 ( GraphPad Software , Inc . ) . Differences with a p value of less than 0 . 05 were considered statistically significant .
The D2ED III gene was cloned into the expression vector pET-22b ( + ) to produce the plasmids , pD2ED III and pLD2ED III . They were used for the production of recombinant antigens , D2ED III and lipidated D2ED III ( LD2ED III ) , respectively . Both antigens contained an additional hexahistidine sequence ( His-tag ) at their C-termini and were expressed under the control of the T7 promoter ( Figure 1A ) . The purification of D2ED III and LD2ED III were monitored and analyzed by SDS-PAGE and immunoblotting ( Figure 1B ) . After removing LPS , the residual LPS in D2ED III and LD2ED III were less than 0 . 06 EU/mg . The yields of D2ED III and LD2ED III were 40 mg/l and 8 mg/l , respectively . The immunogenicity and efficacy of endotoxin-free D2ED III and LD2ED III were comparatively analyzed in animal models . We then measured the exact mass of trypsin-digested N-terminal fragments of LD2ED III . Three major peaks with m/z values of 1452 , 1466 , and 1480 were identified ( Figure 1C ) . These peaks have been previously identified as a lipidation signature in other lipidated proteins [24] . We confirmed that the peaks of LD2ED III were associated with lipidated cysteine residues and verified that LD2ED III contained an N-acetyl-S-diacyl-glyceryl-cysteine at its N-terminus [30] . The immunogenicity of the purified D2ED III and LD2ED III was tested in mice . Groups of BALB/c mice were immunized with D2ED III or LD2ED III ( 10 µg per dose ) two times with a two-week interval between immunizations . Animals infected with live dengue-2 virus ( 1×107 FFU ) on the same schedule served as controls . Serum samples were collected from the immunized mice at different time points , as indicated in Figure 2 . Antibody responses against D2ED III were only detected in D2ED III- or dengue-2 virus-immunized mice after two vaccinations , 4 weeks post-priming ( Figure 2A ) . By contrast , the recombinant LD2ED III was highly immunogenic and induced stronger antibody responses than D2ED III ( p<0 . 05 by the ANOVA Bonferroni post test ) . Mice immunized with LD2ED III quickly elicited antibody titers 2 weeks post-priming . Antibody titers were further elevated following a booster immunization and were maintained for at least 20 weeks after the initial priming ( Figure 2A ) . The antibody avidity profiles of serum samples collected from different groups at weeks 2 and 6 were examined . The avidity index was calculated as the concentration of ammonium thiocyanate that resulted in a 50% decrease in the initial absorbance [28] , [29] . As shown in Figure 2B , the avidity index of mice immunized with LD2ED III was 0 . 41±0 . 06 at 2 weeks after priming . However , mice immunized a single time with D2ED III or dengue-2 virus did not generate significant antibody responses for avidity analysis . After booster immunization , the avidity indexes of mice immunized with D2ED III and dengue-2 virus were 0 . 50±0 . 11 and 0 . 49±0 . 10 at week 6 , respectively . Remarkably , the avidity index of mice immunized with LD2ED III increased to 0 . 82±0 . 15 by week 6 and was significantly higher than the avidity index of mice immunized with D2ED III or dengue-2 virus . Next , we evaluated the neutralizing capacity of the antibodies induced by vaccination . As shown in Figure 3 , mice immunized with D2ED III could not stimulate significant neutralizing antibody responses ( FRNT50 = 5 ) at 2 weeks after priming . Even after booster immunization , the neutralizing antibody titers were 9 and 16 at 4 and 20 weeks after priming , respectively . Dengue-2 virus-infected mice generated low neutralizing antibody titers ( FRNT50 = 11 ) at 2 weeks after primary infection . After secondary infection , neutralizing antibody titers increased and reached 37 and 223 at 4 and 20 weeks after primary infection , respectively . Notably , mice immunized with LD2ED III generated significant neutralizing antibody responses ( FRNT50 = 84 ) at 2 weeks after priming . After booster immunization , the neutralizing antibody titers were further elevated to 588 and 1176 at 4 and 20 weeks after priming , respectively . These results suggest that mice immunized with LD2ED III without exogenous adjuvant elicit quick and durable neutralizing antibody responses . Antibody-dependent enhancement of infection is a significant concern in the development of vaccines against dengue virus . Therefore , we measured the capacities of vaccine candidates to mediate antibody-dependent enhancement of infection . We employed K562 cells , which have been widely used for the measurement of dengue virus antibody-dependent enhancement of infection . The results are shown in Figure 4 . Serum samples obtained from mice immunized with dengue-2 virus possessed tremendous antibody-dependent enhancement capacities for heterotypic viral infection in K562 cells . The peak fold enhancement values were 442 . 5±389 . 1 , 28 . 0±19 . 1 , and 93 . 1±86 . 4 for dengue-1 , dengue-3 , and dengue-4 , respectively , at the dilution 1/8–1/128 . By contrast , antibodies generated from D2ED III-immunized mice did not promote antibody-dependent enhancement of heterotypic viral infection in K562 cells . Serum samples obtained from mice immunized with LD2ED III displayed minor antibody-dependent enhancement of heterotypic viral infection in K562 cells at the lowest dilution tested ( 1/8 ) . The fold enhancement values were 10 . 1±7 . 1 , 4 . 0±2 . 2 , and 16 . 5±16 . 7 for dengue-1 , dengue-3 , and dengue-4 , respectively , which were notably lower than the values for the serum samples obtained from mice immunized with dengue-2 virus . Compared with heterotypic viral infection in K562 cells , only low antibody-dependent enhancement capacities were observed for homotypic viral infection in K562 cells . The peak fold enhancement values were 3 . 1±0 . 7 , 3 . 4±0 . 8 , and 2 . 5±0 . 5 for serum samples obtained from mice immunized with dengue-2 virus , D2ED III , and LD2ED III , respectively . These results suggest that antibodies elicited by LD2ED III have less capacity for antibody-dependent enhancement than antibodies elicited by dengue-2 viral infection .
The development of novel subunit vaccines relies on a limited number of individual components , namely antigens of the specific pathogen under study . Importantly , the selected antigens must elicit protective immunity against the pathogen . To augment the rational design of subunit vaccines , we expressed LD2ED III as a dengue vaccine candidate using an E . coli-based expression system ( Figure 1 ) . D2ED III of LD2ED III served as the antigenic component , and the lipid moiety of LD2ED III provided a danger signal that activated the immune system to induce an appropriate adaptive immune response . In the present study , we demonstrated that LD2ED III alone , without exogenous adjuvant , elicited higher D2ED III-specific antibody responses than D2ED III or dengue-2 virus ( Figure 2A ) . In addition , the avidity ( Figure 2B ) and neutralizing capacity ( Figure 3 ) of the antibodies induced by LD2ED III were higher than those elicited by D2ED III or dengue-2 virus . The above properties would be beneficial to a host during dengue virus infection and suggest that LD2ED III could be a potential dengue vaccine candidate . The role of antibodies in controlling dengue virus infection is complex . Antibodies are thought to mediate both neutralization and enhancement of dengue virus infection [31] , [32] . Antibody-dependent enhancement is the leading theory to explain the higher risk of DHF associated with heterologous serotype viral infections [8] . A reduction in the enhancement capacity of antibodies induced by vaccine candidates should increase the safety of dengue vaccines . Anti-ED III antibody titers in D2ED III-immune sera were comparable with those in dengue-2 virus immune sera ( Figure 2A ) . Surprisingly , there was a remarkable capacity for ADE in dengue-2 virus-immune sera but little ADE capacity in D2ED III-immune sera ( Figure 4 ) . Anti-ED III antibody titers in LD2ED III-immune sera were also significantly higher than those in dengue-2 virus-immune sera , and the ADE mediated by LD2ED III-immune sera in K562 cells was lower than that mediated by live virus-stimulated antibodies ( Figure 4 ) . D2ED III and LD2ED III only induced anti-ED III antibody responses . However , dengue-2 virus induced antibodies against ED III and other viral antigens . These results suggest that anti-dengue virion antibodies other than ED III are the major antibodies that mediate ADE . Although the exact mechanism of ADE is still not fully understood , it is believed that antibodies against envelope proteins either neutralize or enhance the viral infection , depending on the concentration and affinity of the antibodies [33] , [34] . As shown in Figure 4D , enhancement of dengue-2 with sera from mice vaccinated using infectious dengue-2 was observed . The peak fold enhancement was 3 . 1±0 . 7 at the dilution 1/512 - 1/2048 . Neutralization was observed with antisera at the dilutions 1/8 - 1/32 . Notably , a heterotypic enhancing response occurs at a wide range of serum concentrations ( Figure 4A–C ) . These are general phenomena that a homotypic enhancing response are usually restricted to higher serum dilutions due to high neutralizing capacities , while a heterotypic enhancing response occurs at a wide range of serum dilutions because of little heterotypic neutralizing capacities . Similar profiles were observed in sera obtained from LD2ED III immunized mice for homotypic enhancement , the peak fold enhancement was 2 . 5±0 . 5 at the dilution 1/2048 - 1/8192 and neutralization was observed with antisera at the dilutions 1/8 - 1/128 ( Figure 4D ) . Most importantly , sera obtained from LD2ED III immunized mice have less enhancement capacities for heterotypic virus than sera from mice vaccinated using infectious dengue-2 ( Figure 4A–C ) . In contrast , D2ED III immunized mice did not elicit significant neutralizing antibodies . The enhancement infection was observed at the sera dilutions 1/8 - 1/32 ( Figure 4D ) . These results suggest that LD2ED III is a good vaccine candidate with low risk of antibody-dependent enhancement . Recently , Mady et al . demonstrated that the delivery of dengue virus to the cell surface at a location other than Fc receptors by a bispecific antibody can also increase viral infectivity [35] . Furthermore , Huang et al . observed that anti-prM antibodies were cross-reactive with heat shock protein 60 , which enhanced dengue virion binding and infection of cells lacking Fc receptors [9] . LD2ED III induced high-affinity antibody responses ( Figure 2B ) while providing only the ED III antigen . These properties could partly explain the notably lower ADE capacity of LD2ED III compared to dengue-2 virus . Some conserved motifs located in the dengue envelope protein domain II and non-structural protein-1 have been shown to induce autoantibodies . Cross-reaction of dengue viral protein-induced antibodies with host antigens can trigger cell damage or induce harmful effects , which may facilitate DHF/DSS development [9]–[12] . Indeed , such autoantibodies were detected in DHF/DSS patients [10] , [36] . All dengue viral antigens are absent from the LD2ED III candidate with the exception of ED III , which may not induce cross-reactive antibodies . Taken together , these results suggest that LD2ED III is a safe vaccine candidate in terms of its reduced ADE capacity and autoantibody induction of LD2ED III . The results of the current study suggest that the use of lipidated ED III from the four serotypes of dengue virus may have potential for the development of tetravalent dengue vaccines . Alternatively , the strategy of priming with live attenuated dengue vaccines followed by boosting with a lipidated ED III vaccine candidate may enhance ED III-specific immune responses to elicit safe and effective immunity against dengue virus infection . In conclusion , LD2ED III is an effective dengue vaccine candidate for inducing long-lasting neutralizing antibody responses with a low risk of detrimental effects . Future work should examine the suitability of this candidate for clinical use . | Vaccines are considered a cost-effective way to control infectious diseases . To rationally design vaccines , antigens and , frequently , adjuvants must be selected to trigger appropriate immune responses against a specific pathogen . We selected dengue-2 envelope protein domain III as a dengue vaccine candidate and expressed this candidate in the lipidated form in an Escherichia coli-based system . Dengue envelope protein domain III mediates binding of the dengue virus to the host cellular receptor . The lipid moiety of the bacterial-derived lipoprotein can activate the innate immune system to elicit an appropriate adaptive immune response . We demonstrated that lipidated dengue-2 envelope protein domain III is more immunogenic than nonlipidated dengue-2 envelope protein domain III . Most importantly , the lipidated dengue-2 envelope protein domain III alone triggered a durable neutralizing antibody response with a low risk of severe side effects . Lipidated subunit vaccines are non-replicating and thus may be less susceptible to replication interference than live attenuated vaccines . Our study suggests that the lipidated subunit vaccine approach could be applied to other serotypes of dengue virus as well as other pathogens . | [
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] | [] | 2013 | Lipidated Dengue-2 Envelope Protein Domain III Independently Stimulates Long-Lasting Neutralizing Antibodies and Reduces the Risk of Antibody-Dependent Enhancement |
Adult height is a classic polygenic trait of high heritability ( h2 ∼0 . 8 ) . More than 180 single nucleotide polymorphisms ( SNPs ) , identified mostly in populations of European descent , are associated with height . These variants convey modest effects and explain ∼10% of the variance in height . Discovery efforts in other populations , while limited , have revealed loci for height not previously implicated in individuals of European ancestry . Here , we performed a meta-analysis of genome-wide association ( GWA ) results for adult height in 20 , 427 individuals of African ancestry with replication in up to 16 , 436 African Americans . We found two novel height loci ( Xp22-rs12393627 , P = 3 . 4×10−12 and 2p14-rs4315565 , P = 1 . 2×10−8 ) . As a group , height associations discovered in European-ancestry samples replicate in individuals of African ancestry ( P = 1 . 7×10−4 for overall replication ) . Fine-mapping of the European height loci in African-ancestry individuals showed an enrichment of SNPs that are associated with expression of nearby genes when compared to the index European height SNPs ( P<0 . 01 ) . Our results highlight the utility of genetic studies in non-European populations to understand the etiology of complex human diseases and traits .
Adult height is a classic polygenic trait of high heritability ( h2∼0 . 8 ) [1] , [2] . A recent large meta-analysis of genome-wide association ( GWA ) results for height , which included data from >180 , 000 individuals of European descent , identified 180 loci that associate with variation in height [3] . The most significantly associated variants at these loci explain approximately 10% of the variance , consistent with the hypothesis put forward in 1918 by Fisher on the “cumulative Mendelian factors” , which suggested that the segregation of a large number of genetic variants , each of small effect , is sufficient to explain the variation in height observed in humans [4] . In parallel to the work in European-ancestry populations , GWA studies for adult height in other ethnic groups , including Koreans , Japanese , Africans , and African Americans have also been performed [5]–[10] . The GWA scans in East Asians replicated several of the height loci already identified in individuals of European descent , and also found evidence for new height loci not previously implicated in individuals of European ancestry [6] , [7] . The studies in Africans and African Americans were modest in size and , although they replicated nominally some of the associations previously found in European populations , were not well-powered to find new population-specific height loci [8] , [9] . To search for novel loci for height in populations of African ancestry , and to explore systematically the replication of previously validated height loci , we combined GWA results for height from nine studies totaling 20 , 427 individuals of African descent . We identified two novel height loci and observed significant evidence for the replication of European height signals in African-derived populations . In fine-mapping of the European height loci we also identified variants that better define the association in individuals of African ancestry and control local gene expression in cis ( cis-eQTLs ) , suggesting that they are likely to be better surrogates of the biologically functional alleles .
The meta-analysis included results from nine studies: four population-based African-American studies ( ARIC ( N = 2 , 740 ) , CARDIA ( N = 699 ) , JHS ( N = 2 , 119 ) , and MESA ( N = 1 , 646 ) ) , one family-based African-American study ( CFS ( N = 386 ) ) , African-American GWA study consortia of breast ( AABC ( N = 5 , 380 ) ) and prostate cancer ( AAPC ( N = 5 , 526 ) ) and two case-control studies of obesity ( Maywood ( N = 743 ) ) and hypertension ( Nigeria ( N = 1 , 188 ) ) ( Materials and Methods , Text S1 and Table S1 ) . We tested associations between 3 , 310 , 998 genotyped or imputed SNPs and sex- , age- , and disease status-adjusted height Z-scores under an additive genetic model , correcting for global admixture using principal components ( PCs ) as covariates , and modeling family structure when appropriate ( Text S1 ) . Height results for each study were scaled using genomic control , and then combined using the inverse-variance meta-analytic method ( Text S1 ) . The quantile-quantile ( QQ ) plot suggested little departure from the null expectation , except at the right end tail of the distribution ( Figure 1 ) . The associations that deviate most strongly from the null correspond to loci previously associated with height in European populations , providing a strong validation of our approach ( Table 1 ) . The overall inflation factor in the meta-analysis was λGC = 1 . 064 and results were again scaled using genomic control , a slightly conservative approach [11] . Two genomic loci ( LCORL on chromosome 4 and PPARD on chromosome 6 ) , previously implicated in height in European populations [3] , reached genome-wide significance in the discovery meta-analysis ( P<5×10−8; Table 1 , Figure S1 and Table S2 ) . We prioritized 153 SNPs with P<1×10−5 from our meta-analysis for in silico replication in up to 16 , 436 African Americans from five additional studies ( Text S1 ) . After combining the data in a joint analysis , 40 SNPs from 11 different chromosomal regions reached genome-wide significance ( Table 1 and Table S2 ) , including two SNPs not previously implicated in the regulation of height: rs12393627 on the X-chromosome and rs4315565 on chromosome 2 ( Table 1 ) . rs12393627 is located 3 . 2 kb upstream of the arylsulfatase E ( ARSE ) gene on chromosome Xp22 ( Figure 2a ) . Mutations in the ARSE gene cause X-linked brachytelephalangic chondrodysplasia punctata ( CDPX1; OMIM #302950 ) , a congenital disorder of bone and cartilage development also characterized by short stature [12] . The co-localization of human growth syndrome genes with SNPs associated with adult height has been reported in European-ancestry samples [3] , [13] , [14] . rs12393627 reached a P = 1 . 4×10−6 in the initial meta-analysis ( N = 8 , 333; the SNP was not on the genotyping arrays and/or could not be imputed for AABC , AAPC , Maywood , and Nigeria ) , and was strongly replicated for association with height in 13 , 153 African Americans ( replication P = 2 . 6×10−7; combined P = 5 . 7×10−12 ) ( Table 1 ) . When considering the number of independent markers in a 1 Mb window we found no secondary independent signals in the region conditioning on genotype at rs12393627 . We also found no significant evidence of heterogeneity at rs12393627 between men and women ( P = 0 . 26 ) . The derived A-allele ( i . e . non-ancestral allele based on the chimp genome ) at rs12393627 is monomorphic in the HapMap CEU individuals and has a frequency of 54% in the HapMap YRI participants . We also investigated the association of rs12393627 with height in 3 , 487 Japanese Americans and 2 , 979 Latinos from the Multiethnic Cohort ( MEC ) ( Text S1 ) . Whereas the marker was monomorphic in Japanese Americans , the association between height and rs12393627 was replicated in Latinos with a comparable effect size ( A-allele frequency = 97% , standardized effect size = −0 . 177±0 . 088 , P = 0 . 044 ) . The frequency of this allele is consistent with previous estimates of ∼5–10% African ancestry among Latinos in the MEC [15] . Measures of local ancestry ( the number of European-derived chromosomes ( 0 , 1 , or 2 ) in each individual ) were not available for the X-chromosome , but since the marker is polymorphic only in African-derived populations ( according to HapMap phase 3 data [16] ) , the height association signal defined by rs12393627 on Xp22 is likely to be specific to these populations . SNP rs4315565 on 2p14 ( discovery P = 1 . 5×10−7; combined P = 1 . 2×10−8 ) is located in intron 3 of the anthrax toxin receptor 1 ( ANTXR1 ) gene , and 189 kb upstream of the bone morphogenetic protein 10 ( BMP10 ) gene ( Figure 2b ) , a member of the TGF-β signaling pathway . This pathway is important in normal skeletal growth [17] and implicated in previous GWA studies of height [3] . We observed no evidence of heterogeneity by sex ( P = 0 . 34 ) and no independent signals when conditioning on rs4315565 within a 1 Mb window . The allele frequency of rs4315565 differs strongly between the HapMap CEU and YRI samples: the derived A-allele , which is associated with decreased height , has a frequency of 85% in CEU and 2% in YRI , respectively ( Fst = 0 . 701 ) . This allele frequency difference is consistent with recent weak positive selection acting in individuals of European ancestry ( iHS = −1 . 668 ) [18] , and could indicate an association with local ancestry . In a conditional analysis where we controlled for global ancestry using PCs as covariates , we did observe a significant association between height and local ancestry at the ANTXR1 locus , with an increase in the number of European chromosomes associated with a decrease in height ( P = 1 . 6×10−6; N = 18 , 495 samples available for this analysis ) [19] . Still controlling for global ancestry with PCs , genotypes at rs4315565 could account for the association between local ancestry and height ( P = 0 . 22 for local ancestry conditional on rs4315565 ) , while the association of rs4315565 with height diminished but remained significant in the same model ( P = 4 . 6×10−8 and P = 0 . 0044 , before and after conditioning on local ancestry; N = 18 , 495 ) . To investigate the relationship between rs4135565 and local ancestry further , we considered the background on which the rs4135565 variants were present in different individuals . In analyses stratified by the number of African/European chromosomes in the region , rs4315565 was nominally associated with height in African Americans that are homozygous ( P = 0 . 038 ) or heterozygous ( P = 0 . 043 ) for African chromosomes ( with effect size stronger in African chromosome homozygotes ) ( Table 2 ) . In 1 , 188 Nigerians from the discovery phase , a similar trend between height and rs4315565 was observed ( P = 0 . 075 ) . rs4315565 was not significantly associated with height in African Americans that are homozygous for European chromosomes at the locus ( P = 0 . 91 ) , although the sample size of this sub-group is small ( N = 943 ) ( Table 2 ) . More strikingly , this variant is not associated with height in populations of European ancestry in the GIANT Consortium ( N = 133 , 653 , P = 0 . 66 ) [3] . Together , these results suggest that 2p14 harbors at least one novel height-associated variant that is strongly associated with African ancestry and is correlated with rs4315565 in African- but not European-derived chromosomes . Our results also indicate that rs4315565 is a better marker of the functional variant ( s ) than is local ancestry or any other SNPs represented in HapMap . We then considered the previously known height loci . Of the 180 SNPs previously reported by the GIANT Consortium to be associated with height in populations of European ancestry , the effect estimates for 38 SNPs were in the same direction as the initial report and nominally associated ( P<0 . 05 ) with height in the African-derived height meta-analysis . This number is however a lower-bound estimate of the number of known European height loci that replicate in individuals of African ancestry because it does not take into account different LD relationships in European and African chromosomes: since any of the SNPs in LD in European-ancestry individuals with the GIANT height SNPs could be causal , this entire set of SNPs need to be evaluated , both in terms of statistical significance and direction of effect , for replication in the African height meta-analysis . To address this issue , we utilized a rigorous framework , described in the Materials and Methods section and graphically summarized in Figure S2 , to test systematically for replication at the previously known European height loci in the African meta-analysis . We started with 161 of the 180 height SNPs identified by the GIANT Consortium ( 19 SNPs could not be tested because linkage disequilibrium ( LD ) information in HapMap was not available ) [3] , and generated 5 , 819 sets of 161 SNPs matched on minor allele frequency using the HapMap2+3 CEU dataset . We then counted the number of SNPs ( also considering LD proxies ) in the African height meta-analysis with directionally consistent ( one-tailed ) P≤0 . 05 for the set of 161 height-associated SNPs and the simulated sets . We found one simulation with a count of nominal associations equal to or higher than what we observed for the 161 height-associated SNPs ( P = 1 . 7×10−4; 171 nominal associations for the GIANT height SNPs ( and their proxies ) ; median number of nominal associations to height in the matched sets of SNPs = 28 ( range = 8–172 ) ) . Therefore , we found strong overall evidence of replication in our large meta-analysis of 20 , 427 individuals of African ancestry for SNPs previously associated with adult height in individuals of European ancestry , indicating a substantial shared genetic basis for height in populations separated since the out-of-Africa event . The replication procedure described above also allowed us to identify , for each of the 161 European height loci that we assessed using data from our African meta-analysis , the best candidate height index SNP ( Table 3 and Table S3 ) . For instance in population of European ancestry at the LCORL locus on chromosome 4 , the GIANT height SNP ( rs6449353 ) and the SNP identified by fine-mapping in the African height meta-analysis ( rs7663818 ) are both strongly associated with height ( P<1×10−25 ) and in strong LD ( r2>0 . 8 ) with each other ( Figure 3a ) . However , in African-derived populations , LD is weaker between the two SNPs ( r2<0 . 6 ) and the association with height is stronger for rs7663818 ( P = 2 . 9×10−7 ) than for rs6449353 ( P = 0 . 0025 ) ( Figure 3b ) . When we consider SNPs in strong LD ( r2>0 . 8 ) with rs7663818 in HapMap CEU and YRI populations , they define genomic intervals of 250 kb and 80 kb , respectively ( light blue boxes in Figure 3 ) . Finally , in lymphoblastoid cell lines derived from YRI individuals ( Materials and Methods ) , rs7663818 , but not rs6449353 , is associated with LCORL gene expression levels ( LCORL eQTL P = 0 . 0026 and P = 0 . 13 for rs7663818 and rs6449353 , respectively ) . Thus , the LCORL locus illustrates a clear example of the utility of fine-mapping association signals in other ethnic groups , both in terms of narrowing the genomic interval of interest and highlighting potential functional variants ( cis-eQTL ) . For 40 loci , the index SNPs from our fine-mapping list was nominally associated with height ( P<0 . 05 ) in the African height meta-analysis , whereas the corresponding index European height SNPs was not . To test whether this result reflects an enrichment of surrogates for functional variants identified by fine-mapping , we designed an experiment using allelic gene expression phenotypes in the HapMap YRI cell lines as functional readouts . We hypothesized that if our trans-ethnic fine-mapping strategy was successful , a larger fraction of variants in the list of fine-mapped height SNPs should be associated with phenotypes ( in this case gene expression ) than of variants in the list of European index height SNPs . In other words , the list of SNPs from our fine-mapping experiment should contain more cis-eQTLs than the GIANT list of height SNPs in cell lines derived from Africans . We retrieved allelic expression mapping datasets from the HapMap YRI cell lines ( Materials and Methods ) and observed that 4 . 7% of the GIANT index height SNPs and 8 . 6% of the best candidate height SNPs obtained by trans-ethnic fine-mapping , were both nominally associated with height ( P<0 . 05 ) in our meta-analysis and with allelic expression phenotypes ( P<0 . 01 ) . When we used simulations to assess the significance of these results , we found no simulated set with a cis-eQTL enrichment equal or above that observed in the data ( P<0 . 01 , obtained from 100 simulations ( Text S1 ) ) . Therefore , fine-mapping European height loci in African-ancestry individuals generated a list of markers more likely to control gene expression , potentially improving mechanistic insights into the biology of height . Although we did not see an enrichment when compared to the list of GIANT index height SNPs , we also found that 17 missense SNPs are in strong LD ( r2≥0 . 8 based on HapMap phase II YRI ) with the fine-mapped height SNPs ( Table S4 ) . In conclusion , our study shows the benefit of performing large-scale genetic studies in non-European populations to discover new biology ( we identified two novel height loci ) , and to gain functional insights at the loci previously found in European-derived individuals ( in this case , by enrichment of cis-eQTL signals ) . The strong replication of most of the European height loci in African-ancestry populations suggest that many of the published association signals with common variants from GWA studies – for height and perhaps other complex diseases and traits – are relevant across different populations and caused by shared genetic factors that predate the out-of-Africa event .
All participants gave informed written consent . The project has been approved by the local ethics committees and/or institutional review boards . Five discovery studies/consortia ( AABC , AAPC , CARe , Maywood , and Nigeria ) and five replication studies ( GeneSTAR , HANDLS , Health ABC , WHI , and MEC ) contributed height association results to this project . There were eight population-based cohorts ( ARIC ( N = 2 , 740 ) , CARDIA ( N = 699 ) , JHS ( N = 2119 ) , MESA ( N = 1 , 646 ) , HANDLS ( N = 993 ) , HABC ( N = 1 , 139 ) , WHI ( N = 8 , 149 ) and MEC ( N = 11 , 569 ) ) , two family-based cohorts ( CFS ( N = 386 ) and GeneSTAR ( N = 1 , 148 ) ) two case-control studies ( Maywood ( obesity , N = 743 ) and Nigeria ( hypertension , N = 1 , 188 ) ) , and two cancer consortia comprised of case-control studies that were population-based or nested within prospective cohorts ( AABC ( breast cancer , N = 5380 ) , AAPC ( prostate cancer , N = 5 , 526 ) . All cohorts with genome-wide genotyping data available were genotyped on the Affymetrix 6 . 0 array , except AABC , AAPC , HANDLS , HABC and GeneSTAR , that were genotyped on the Illumina 1M-duo or 1Mv1_c chip . The studies , including genotyping and quality control steps , are described in detail in Text S1 . The statistics ( height and age ) are summarized in Table S1 . Genotype imputation was performed as previously described [20] and is summarized in Text S1 . Height measures were corrected for sex , age , disease status , and other appropriate covariates ( e . g . recruitment centers ) , and were normalized into Z-scores ( Text S1 ) . Association analysis was performed using linear regression for studies of unrelated individuals and a linear mixed effect model for family-based studies , testing an additive model and including the 4–10 first principal components . Results were combined using the inverse variance meta-analysis method . Local ancestry was estimated using the HAPMIX software using default parameters [19] . Conditional analyses were performed by including SNP genotypes or local ancestry estimates in the linear models . The list of European height loci from the largest study to date was used as a source of known European loci for fine-mapping [3] . The procedure is graphically summarized in Figure S2 . Of the 180 SNPs from this list , 19 were filtered for lack of available LD data ( we combined data from HapMap2 haplotype release 22 ( Aug 2007 ) , HapMap3 haplotype release 2 ( Jul 2009 ) , and HapMap2+3 LD data release 27 ( Apr 2009 ) ; conflicting data , as is the case for these 19 SNPs , were excluded ) . LD estimates ( r2 ) from CEU HapMap 2+3 were used to generate the set of common SNPs ( proxies ) tagging the remaining putative loci ( r2≥0 . 8 ) . These sets were then binned using YRI HapMap 2+3 LD as follows: the whole list of proxies was randomized , to remove any bias towards significance in the representative P-values; the first SNP was removed and set as an “index” SNP; then all SNPs not yet binned were filtered based on LD ( r2≥0 . 3 ) with the index SNP . This procedure was repeated until all SNPs were binned . The metric for replication of a European signal was the number of SNP bins nominally significant ( P≤0 . 05 ) , and replication of the entire list of known SNPs was the number of significant bins across all loci . Each SNP bin was represented by the index SNP used to generate it . Because the SNPs are in LD with known European signals , there is a strong prediction as to which index SNP allele should be increasing height: it should be the allele in LD with the height-increasing allele in Europeans . Therefore , all index SNP P-values were made one-tailed ( set to P/2 or 1-P/2 ) based on the hypothesis that the height-increasing allele should be the one predicted by the European SNP , based on the phased HapMap CEU data . The LD thresholds used for proxy determination in European ancestry and binning in African ancestry were arbitrary and likely do not fully encompass the LD structure of the populations in this meta-analysis . To control for artifacts introduced by these thresholds and the HapMap data , 5 , 819 sets of 161 SNPs , matched to the European known loci on HapMap 2+3 CEU minor allele frequency , were generated . Since the European SNP list contains independent loci , each simulated list was designed to contain relatively independent SNPs ( CEU r2≥0 . 2 ) ; changing this threshold did not alter the results . The same procedure of proxy generation and SNP binning ( see Figure S2 for a graphical description of the binning strategy ) was performed on each of the 5 , 819 sets to generate a null distribution of significant bins . To generate the list of “best” SNP for each locus ( fine-mapped list ) , the binning procedure was repeated for the known SNPs , except each iteration selected an index SNP from the list of remaining SNPs , sorted on P-value , not randomized . Note that the best SNPs at each locus are not perfectly concordant between Table 1 and Table 3 because our fine-mapping approach did not consider the in silico replication data and required that the SNPs are available in the HapMap phased haplotypes . We note that our fine-mapping approach focuses on SNPs with low P-values and is thus more likely to identify markers with fewer missing genotypes , that is markers for which we have more statistical power . To assess whether European SNPs replicated for height ( at nominal P<0 . 05 ) in African-ancestry populations would also be more likely to show links to functional variation in samples of African ancestry , we applied a sensitive technique for mapping cis-regulatory allelic expression SNPs [21] in lymphoblastoid cell lines ( LCLs ) derived from 56 unrelated Yoruba HapMap participants . A detailed description of the protocols and statistical methods used is available in the Text S1 . | Adult height is an ideal phenotype to improve our understanding of the genetic architecture of complex diseases and traits: it is easily measured and usually available in large cohorts , relatively stable , and mostly influenced by genetics ( narrow-sense heritability of height h2∼0 . 8 ) . Genome-wide association ( GWA ) studies in individuals of European ancestry have identified >180 single nucleotide polymorphisms ( SNPs ) associated with height . In the current study , we continued to use height as a model polygenic trait and explored the genetic influence in populations of African ancestry through a meta-analysis of GWA height results from 20 , 809 individuals of African descent . We identified two novel height loci not previously found in Europeans . We also replicated the European height signals , suggesting that many of the genetic variants that are associated with height are shared between individuals of European and African descent . Finally , in fine-mapping the European height loci in African-ancestry individuals , we found SNPs more likely to be associated with the expression of nearby genes than the SNPs originally found in Europeans . Thus , our results support the utility of performing genetic studies in non-European populations to gain insights into complex human diseases and traits . | [
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"epidemi... | 2011 | Identification, Replication, and Fine-Mapping of Loci Associated with Adult Height in Individuals of African Ancestry |
Taenia solium is a cestode parasite that causes cysticercosis in both humans and pigs . A serological survey was undertaken to assess the seroprevalence and risk factors associated with porcine cysticercosis in the rural district of Morropon , Peru . Pigs aged between 2 and 60 months were assessed by the Enzyme-linked Immunoelectrotransfer blot ( EITB ) assay to determine their serological status against porcine cysticercosis in a cross-sectional study . A total of 1 , 153 pigs were sampled . Porcine seroprevalence was 45 . 19% ( 42 . 31–48 . 06 ) . The information about the animals and households was analyzed and risk factors associated with seroprevalence were determined by a multivariate logistic regression analysis . In the porcine population , the risk of being seropositive increased by 7% with every month of age ( OR 1 . 07 , 95% CI 1 . 05–1 . 09 ) , and by 148% for pigs living in East Morropon ( OR 2 . 48 , 95% CI 1 . 82–3 . 37 ) . Whereas , the presence of latrines in a household decreased the risk of being seropositive by 49% ( OR 0 . 51; 95% CI 0 . 39–0 . 67 ) . Sex and rearing system did not represent either risk or protective factors associated with the seroprevalence of porcine cysticercosis . The findings of this study could be used for further development of control programs that might focus on similar population groups within rural communities of developing countries where cysticercosis is endemic .
Neurocysticercosis is a disease that affects humans mainly in developing countries , causing serious morbidity and mortality [1] . T . solium infection in pigs causes production losses to farmers because infected meat has reduced value or may be condemned at slaughterhouses . In rural areas infected pig carcasses can be sold avoiding the legitimate commercial distribution [2] . Epilepsy caused by neurocysticercosis in humans incurs many economic and social costs . It affects workers within highly productive age groups reducing work productivity [3] . Stigmatization arises as a serious problem for the farmers/villagers with neurocysticercosis since they are relegated and do not have the benefit of being part of the normal community life [4] , [5] . In Peru , epidemiological studies based on serological surveys using the Enzyme-linked Immunoelectrotransfer blot ( EITB ) have determined variable porcine cysticercosis seroprevalences in the three natural regions the country: coast , highlands and Amazon . The EITB test has been commonly used to determine the epidemiological characteristics of the taeniasis/cysticercosis complex [6] . Studies done in the Peruvian Amazon found seroprevalences of porcine cysticercosis that ranged from 28% to 49% [6] . In the Peruvian Highlands , a region with a high poverty rate , the disease is known to be hyper-endemic with seroprevalences up to 75% [1] . Studies in the Northern Coast of Peru found seroprevalences that ranged from 13% [4] to 30 . 8% [7] . Few studies on the risk factors for porcine or human cysticercosis in Peru have been done [7] , [8] . These studies assessed the factors in the human and pig populations that are associated with the seroprevalence of porcine cysticercosis in rural villages of the Highlands and Coast of Peru . However , some social , economic , geographic and environmental characteristics are specific to particular locations and therefore risk factors may differ from communities located in different regions . A cross-sectional serological survey in pigs was undertaken to determine the seroprevalence of porcine cysticercosis and identify the risk factors for T . solium transmission . The survey was conducted in 14 villages located in the district of Morropon , Piura , Peru using the EITB as the diagnostic test . The particular region investigated was selected at the beginning based on limited , anecdotal knowledge which suggested a high rate of T . solium transmission in the region .
The study complied with the “National Health and Medical Research Council Australian Code of Practice for the Care and Use of Animals for Scientific Purposes” ( 7th edition , 2004 ) ethics standard . The study protocol was approved by the scientific boards at the Veterinary Faculty , the University of Melbourne , Australia and at the Veterinary Faculty , San Marcos National University , Peru . Study permissions were obtained from the Municipality of Morropon , from village leaders and from the pig owners . Due to a high level of illiteracy among villagers , the scientific board at the Veterinary Faculty , San Marcos National University approved the use of oral consent . Oral consent was obtained from household owners prior to them providing answers to the questionnaire and using their animals in the study; consent was recorded through the completion of the questionnaire . Morropon is a province in the department of Piura , Peru . It is located in Northeast Peru , 82 . 3 km from Piura , the closest major city . The district of Morropon is the commercial center of the region and villages are located in the surroundings . The altitude is 131 meters above sea level . The climate is dry and hot from May to December , with heavy rain fall from January to April . The sample size required for the study was obtained through the Sample Size formula for estimation of a proportion for infinite populations [9] . The referential prevalence used was 26% [10] and a 95% confidence level . The minimum number of animals calculated by the sample size formula was 296 . This number of animals was required in order to represent a valid sample of the total population; however , due to the availability of resources and to not affect the villager's compliance by selecting some houses only , it was decided to undertake a census sampling , attempting to include every pig from all the villages in the study . Sera samples for processing were sent to the diagnostic laboratory of the Instituto Nacional de Ciencias Neurológicas ( Lima , Peru ) to perform the EITB test . The EITB test used the same methodology described by Tsang et al . [11] and Gonzalez et al . [12] . The EITB assay for diagnosis of human or porcine cysticercosis identifies as being positive any sample having reactivity with any one of seven lentil-lectin , affinity-purified Taenia solium metacestode glycoprotein antigens ( GP50 , GP42-39 , GP24 , GP21 , GP18 , GP14 and GP13 ) . The sensitivity and specificity of this assay in pigs were reported to be 100% [12] . The sensitivity of this assay in humans is 98% and its specificity reaches 100% [13] . A serological survey of pigs was undertaken in the following villages: Alto Mambluque , Bocanegra , Coca , El Chorro , Faical , Franco , Franco Alto , Franco Bajo , La Bocana , Mambluque , Maray , San Francisco , Talanquera , and Zapotal located in the district of Morropon . These villages were selected based on various characteristics , such as socio-economic , accessibility , recent cases of human or porcine cysticercosis and acceptance and enthusiasm from villagers and village authorities . Pregnant sows and animals younger than 2 months were not included in the survey . All animals were ear-tagged to identify the animals and their blood samples . Animals were also vaccinated against Classical Swine Fever ( CSF ) as an incentive to the livestock owners to participate in the survey . The household owners were interviewed and information about the household , living conditions and pig husbandry practices were recorded . The respondent was the owner of the household ( husband or wife or any adults living in the household ) . Their oral consent was obtained prior to answering the questionnaire and taking blood samples from the pigs . The questionnaire was concurrently conducted while blood samples of pigs were taken . Specific information was recorded for each animal including: identification number , age and sex . Information about the households included: the presence of latrines , the rearing-system used and the village to which households belonged . The data was analyzed anonymously . Each household's data was kept confidential and not shared with any other household . Data was entered on Microsoft Office Excel 2007 datasheets ( Microsoft ) . Statistical calculations were performed using the computer program STATA 10 . 0 ( StataCorp LP , USA ) . Descriptive analyses were based on frequencies and percentages for qualitative variables , and means with their confidence intervals for quantitative variables . Bivariate analyses were performed calculating odds ratios ( OR ) to assess the variables: sex , age , village , rearing system , and presence/absence of a latrine as potential risk factors . For practical and statistical purposes , Morropon villages were assigned into three different areas: East Morropon , West Morropon and Yamango . The two criteria used to divide the area were geographical and road access . Yamango area included Faical , Coca , San Francisco , Mambluque and Alto Mambluque villages . East Morropon included Maray , El Chorro and Bocanegra villages . West Morropon included Zapotal , Franco , Franco Alto , Franco Bajo , Talanquera and La Bocana villages . Figure 1 shows a schematic map of the study area . The aggregated effect of the sex , age ( months ) , area , rearing system and presence/absence of a latrine over the binary dependent variable , EITB result , was modeled by a multivariate logistic regression analysis . The multivariate model included variables of epidemiological and biological interest . Adjusted relative risks for pig seropositivity to cysticercosis with the corresponding 95% confidence interval ( 95% CI ) and p values were estimated by a multivariate logistic regression . A p value of less than 0 . 05 was considered to indicate statistical significance .
The number of animals sampled , the total seroprevalence and the seroprevalence per village are shown in Table 1 . A total of 1153 animals were sampled belonging to 306 households . The population sample represented approximately 90% of the total pig population in the study villages . Seropositive animals to the EITB test were found in all 14 villages of study . From the 1153 sampled animals , 521 were positive , giving a seroprevalence of 45 . 19% ( 42 . 31–48 . 06 ) . The seroprevalences per village ranged from 14 . 5% for Franco Alto to 81 . 2% for Bocanegra . When analyzed by area , East Morropon had the highest porcine cysticercosis seroprevalence , 56 . 6% ( 176/311 ) . The West Morropon area had the highest number of sampled pigs , 41 . 7% ( 481/1153 ) , while East Morropon area had the smallest number of sampled animals , 27 . 0% ( 311/1153 ) . The mean age of the population was 8 . 5±8 . 3 months , with ages ranging from 2–60 months . To have a gross overview of the age of the study population , age was arranged as an ordinal variable using ≤8 , 9–16 and >16 months as reference ages ( for the multivariate analysis age was modeled as a continuous variable ) . There was a higher proportion of “young” animals , with pig ≤8 months representing 70 . 8% ( 816/1153 ) of the total sample , pigs between 9–16 months represented 18 . 6% ( 214/1153 ) of the population , and pigs ≥16 months represented 10 . 7% ( 123/1153 ) . There were slightly more female pigs , 55 . 5% ( 640/1153 ) in the population compared to males , 44 . 5% ( 513/1153 ) . The majority of pigs were reared using the free-roaming system , 56 . 3% ( 649/1153 ) in contrast to pigs reared in confined conditions , 43 . 7% ( 504/1153 ) . The seroprevalences in animals reared confined and free-roaming were 44 . 8% ( 226/504 ) and 45 . 5% ( 295/649 ) respectively . From the 306 households that participated in the serological survey , 43 . 1% ( 132/306 ) did not have latrines , and 42 . 5% ( 490/1153 ) of the sampled animals belonged to households without latrines . All interviewed families ( 306 ) knew about the occurrence of porcine cysticercosis in the area of study ( known as “triquina” by the locals ) . However , less than 1% ( 2/306 ) associated the pig habit of coprophagy with the onset of the disease in the animals ( data not shown ) . A summary of the various exposure and biological variables and the seroprevalence ( EITB result ) is presented in Table 2 . A multivariate logistic regression analysis was performed for all variables included in the study with the exception of the variable “village” . Instead of “village” , the variable “area” ( the three areas into which Morropon district was divided ) was included in the logistic regression model . The variable age ( months ) was analyzed as a continuous variable . The odds ratios with confidence intervals as well as the p values of a logistic regression to determine the risk factors for the EITB seropositivity are shown in Table 3 . The analysis determined that the pig's age was a risk factor for reaction against the EITB test in seropositive animals ( adjusted by sex , area , rearing method and presence/absence of latrine ) . The odds of being seropositive increased by 7% with every month of age in our study population ( p<0 . 01 ) . The multivariate analysis also determined that animals belonging to the villages in the East Morropon had a 148% increased risk of being seropositive compared to animals from West Morropon . The presence of latrines in households was a protective factor for the occurrence of being seropositive , finding a 49% decreased risk in houses with latrines versus houses without them ( p<0 . 01 ) . Finally , variables such as sex and rearing system did not represent either risk or protective factors associated with the seroprevalence of porcine cysticercosis .
This study found that the age of a pig and the area where the pig lived increased the risk of being seropositive to T . solium whereas the presence of latrines was found to decrease the risk of being seropositive . The seroprevalence of porcine cysticercosis was found to be 45 . 19% which is similar to the seroprevalence of porcine cysticercosis found in areas where the disease is considered hyper-endemic [1] . The finding that the presence of latrines was a protective factor to decrease the seroprevalence of porcine cysticercosis is not surprising as the use of latrines has been proposed to control cysticercosis worldwide by multiple authors [14] , [15] and has been reported to be a protective factor for the occurrence of the disease [16] , [17] , [18] , [19] . However , in other studies it was not associated with the occurrence of porcine cysticercosis [18] and in some cases it acted as a risk factor [20] . In our study , pigs were seen feeding on human feces near latrines . The access of pigs to human feces has been shown to be a risk factor for porcine cysticercosis [21] . Based on our observations , it appears that the presence or absence of latrines is as important as the knowledge required to use them properly . In Morropon , an increase in the pig's age was a risk factor for being seropositive , which agrees with studies from Cameroon , Mozambique and Mexico [7] , [22] , [23] . However , Ngowi et al . [18] reported that the age was not a risk factor for the occurrence of porcine cysticercosis . It has been described that older animals have a higher chance of accessing human feces since younger animals have a disadvantage when foraging and scavenging [19] . Adult pigs also have a higher frequency of feces consumption compared to piglets [24] . In our study , the seroprevalences observed in young pigs may be also a reflection of the transfer of maternal antibodies [25] . Because of the association between seroprevalence and pig's age in rural communities , the effectiveness of control programs could be affected by the presence of older animals , as they might be reservoirs for the disease . Peruvian rural families commonly use the pig as the equivalent of a savings account [26] . Pigs act as recyclers of waste thrown on the streets and walking paths , including feces . There is evidence that indicates that restraining pigs , which provides varying confinement ( different locations and tethering length ) , may reduce the seroprevalence of porcine cysticercosis in rural areas [16] , [27] . It has been reported elsewhere that using a free-range husbandry system increases the risk of acquiring cysticercosis [21] , [23] . However , in this study the free-range rearing system was not a risk factor for porcine cysticercosis seroprevalence and could be explained by 1 ) the presence of human feces in the pig pens ( due to open-air defecation ) ; 2 ) the contamination of the environment by the presence of chickens in the households where pigs were raised ( Jayashi , personal observations ) since their feeding behavior promotes the spreading of Taenia spp . eggs [28]; and , 3 ) the inadequate construction of the pens that allows pigs to often “escape” their confinement and have access to human waste . The villages in the area of East Morropon area are closer to the district center and have less harvesting areas . These properties had less land and animals and owners could be considered poorer than people living in villages at longer distances from the district center . Under these conditions the source of food is scarce and pigs are forced to scavenge for food and may more frequently ingest human feces , which could explain why pigs living in this area had increase odds for being seropositive . This study had some limitations . Studies that have relevant information about the prevalence of porcine cysticercosis including our study are based on EITB serology rather than definitive post mortem examination [1] , [4] , [6] , [20] . EITB has been widely used for epidemiological studies following Gonzalez's et al . [12] results , in which EITB was determined as being 100% specific and 100% sensitive in pigs . Based on these findings , EITB was considered as the gold standard test to diagnose porcine cysticercosis . However , some evidence shows that EITB serology does not correlate perfectly to necropsy results and in many cases animals are seropositive and necropsy is negative [10] , [29] . It has been suggested that a positive serology result and a negative necropsy result is due to exposure of the animal to T . solium caused by an aborted infection which did not lead to mature cysticerci or detectable lesions [12] . A similar situation is presumed to exist with human serology [30] . Therefore , the risk of animal being seropositive in our study does not necessarily represent the risk of the animal being positive at necropsy . This study describes the risks associated with the disease transmission in an area where porcine cysticercosis is highly prevalent . It expands the information available regarding the epidemiology of porcine cysticercosis in rural farming systems . The information obtained in this study was used for making the decision to proceed with a field vaccination trial in pigs to prevent cysticercosis in rural Peru . Nevertheless , this knowledge may also be used for further development of control programs that might focus in particular population groups within rural communities of developing countries where porcine cysticercosis and neurocysticercosis are endemic . | Taenia solium causes taeniasis in humans and cysticercosis in humans and pigs . In humans the parasite may infect the central nervous system and cause neurocysticercosis . The World Health Organization ( WHO ) estimated that over 50 , 000 deaths per year are due to neurocysticercosis and the disease is also the main cause of acquired epilepsy . Pigs act as intermediate hosts for the parasite's transmission . Porcine cysticercosis causes economic losses to farmers of developing countries , because infected pork has reduced value or may be condemned . Previous studies have identified risk factors for T . solium infection in pigs in various parts of the world; however , findings are contradictory or not consistent . In this study , particular areas in which pigs lived and age ( older pigs were at higher risk ) increased the risk of being seropositive; whereas the use of latrines decreased their risk of being seropositive . The results of this study contribute to epidemiology of porcine cysticercosis in rural areas , which is relevant for establishing effective control programs in rural locations with similar characteristics . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"veterinary",
"epidemiology",
"veterinary",
"science"
] | 2012 | Seroprevalence and Risk Factors for Taenia solium Cysticercosis in Rural Pigs of Northern Peru |
Viral infection activates the transcription factors NF-κB and IRF3 , which contribute to the induction of type I interferons ( IFNs ) and cellular antiviral responses . Protein kinases play a critical role in various signaling pathways by phosphorylating their substrates . Here , we identified dual-specificity tyrosine- ( Y ) -phosphorylation-regulated kinase 2 ( DYRK2 ) as a negative regulator of virus-triggered type I IFN induction . DYRK2 inhibited the virus-triggered induction of type I IFNs and promoted the K48-linked ubiquitination and degradation of TANK-binding kinase 1 ( TBK1 ) in a kinase-activity-dependent manner . We further found that DYRK2 phosphorylated Ser527 of TBK1 , which is essential for the recruitment of NLRP4 and for the E3 ubiquitin ligase DTX4 to degrade TBK1 . These findings suggest that DYRK2 negatively regulates virus-triggered signaling by targeting TBK1 for phosphorylation and priming it for degradation , and these data provide new insights into the molecular mechanisms that dictate the cellular antiviral response .
The innate immune system is the first line of host defense against pathogens [1] . A limited number of germline-encoded pattern-recognition receptors ( PRRs ) , including the Toll-like receptors ( TLRs ) , RIG-I-like receptors ( RLRs ) , NOD-like receptors ( NLRs ) and DNA receptors [1 , 2] , recognize microbial components known as pathogen-associated molecular patterns ( PAMPs ) and trigger a series of signaling events that leads to the induction of type I interferons ( IFNs ) and proinflammatory cytokines [1 , 3] . Type I IFNs play central roles in antiviral responses by eliciting the expression of antiviral genes that inhibit viral replication and induce apoptotic cell death in virally infected cells , rendering the cells resistant to viral infection and activating acquired immunity [4] . All TLRs , except TLR3 , recruit MyD88 and initiate MyD88-dependent signaling to activate NF-κB and MAP kinases to induce proinflammatory cytokines [2] . TLR3 and TLR4 associate with TRIF and initiate TRIF-dependent signaling [5 , 6] . TRIF interacts with TRAF3 and activates TBK1/IKKε , which activates IRF3/IRF7 and initiates the transcription of type I interferons [7–10] . The RLR family has three members , i . e . , RIG-I , MDA5 , and LGP2 , which each contain a DEAD box helicase/ATPase domain , two N-terminal CARDs ( LGP2 lacks the CARD ) and a C-terminal regulatory domain [4] . The C-terminal regulatory domain of the RLRs is required for viral RNA recognition and binding , which induces an ATP-dependent conformational change that enables dimer or oligomer formation and exposes the CARD [11–13] . The CARDs of RIG-I and MDA5 are responsible for transmitting signals to the downstream CARD-containing adaptor VISA ( also known as MAVS , IPS-1 , or Cardif ) through homophilic interactions between the CARDs [14–17] . VISA , which is localized at the outer mitochondrial membrane , forms large aggregates and activates the transcription factors NF-κB and IRF3/IRF7 through the IKK complex and TBK1/IKKε , respectively , resulting in the subsequent transcription of inflammatory cytokines and type I interferons [18] . Several DNA receptors have been identified , including RNA polymerase III , DAI , IFI16 , DDX41 , LSm14A and cGAS . Each of these DNA receptors requires a different adaptor to activate TBK1/IKKε to induce type I interferon expression in response to DNA [19–24] . In summary , PRR-induced expression of type I IFNs requires the key molecule TBK1 to activate the transcription factor IRF3 . Although type I IFNs are important for eliminating invading pathogens , the production of these cytokines needs to be properly regulated to avoid excessive harmful immune responses [25] . Ubiquitination and deubiquitination are the primary mechanisms of the regulation of TBK1 activity . The E3 ubiquitin ligase Nrdp1 and MIBs ( i . e . , MIB1 and MIB2 ) promote TBK1 activation and the transcription of type I interferon by mediating K63-linked polyubiquitination of TBK1 [26 , 27] . NLRP4 recruits the E3 ubiquitin ligase DTX4 to TBK1 to enable Lys48 ( K48 ) -linked polyubiquitination , which leads to TBK1 degradation [28] . TRIP is an E3 ubiquitin ligase that also negatively regulates the cellular levels of TBK1 by directly binding to and promoting the K48-linked polyubiquitination of TBK1 [29] . Furthermore , CYLD removes K63-linked polyubiquitin from TBK1 to downregulate the IFN response [30] . Other mechanisms of the regulation of TBK1 activation include modification by phosphatases , such as SHP2 , SHIP1 or PPM1B [31–33] , and alterations in the function of TBK1-containing complexes , such as SIKE , TAX1BP1 , zinc finger protein A20 , OPTN , NEMO , TRIM11 and RNF11 [34–40] . Although it has been reported that glycogen synthase kinase 3β ( GSK3β ) promotes TBK1 self-association and autophosphorylation to activate IRF3 and induce IFN-b expression , this effect is not dependent on GSK3β kinase activity [41] . How TBK1 activity is negatively regulated , particularly via kinase modulation , remains largely unknown . DYRK2 belongs to an evolutionarily conserved family of dual-specificity tyrosine-phosphorylation-regulated kinases ( DYRKs ) that is part of the CMGC group of protein kinases . DYRK2 contains a conserved kinase domain and an adjacent N-terminal DYRK homology ( DH ) box [42 , 43] . DYRK2 autophosphorylates a critical tyrosine residue in the activation loop and phosphorylates its substrates on serine and threonine residues [42] . Once DYRK2 is fully translated and released from the ribosome , the transitional tyrosine-kinase activity is lost , and DYRK2 subsequently functions only as a serine/threonine kinase [44] . DYRK2 appears to contribute to the regulation of xenobiotic detoxification , glucose metabolism , protein synthesis , key developmental steps , cancer progression and cellular processes via the phosphorylation of hPXR , glycogen synthase , eIF2Bε , tau , CRMP4 , 4E-BP1 , Snail , c-Jun , c-Myc and katanin [45–53] . Moreover , DYRK2 has also been suggested to function in various signaling pathways , including NFAT signaling in the brain and immune system , the Hedgehog signaling pathway , the hypoxia response pathway and p53 activation in response to DNA damage [54–57] . However , the exact role of DYRK2 in the virus-triggered signaling pathway has not been previously studied . In the present study , we identified DYRK2 as a critical negative regulator of virus-triggered type I IFN signaling via the targeting of TBK1 . DYRK2 phosphorylates TBK1 at S527 , which is a priming event for TBK1 ubiquitination and degradation . Our findings provide a molecular mechanism for the downregulation of TBK1 , which is a critical step in virus-triggered type I IFN induction and the cellular antiviral response .
Virus-triggered type I IFN expression is precisely regulated to prevent an excessive immune response . Because phosphorylation plays an important role in cell signaling pathways , we reasoned that virus-triggered IFN pathways may be regulated by certain kinases . To further investigate the regulation of the virus-triggered induction of type I IFNs , we screened a protein kinase cDNA library containing 347 independent expression clones using IFN-β reporter assays in 293 cells . These assays identified DYRK2 as an inhibitor of Sendai virus ( SeV ) -induced activation of the IFN-β promoter ( Fig 1A ) . Further validation experiments indicated that DYRK2 inhibited the SeV-induced activation of the IFN-β promoter and the ISRE , a conserved enhancer motif that is recognized by activated IRF3 , in dose-dependent manners in both 293 cells ( Fig 1B ) and HeLa cells ( S1A and S1B Fig ) . However , DYRK2 overexpression did not inhibit the SeV-triggered activations of an NF-κB reporter in 293 cells ( Fig 1B ) or HeLa cells ( S1C Fig ) . Similar results were observed in HeLa cells that were exposed to herpes simplex virus-1 ( HSV-1 ) ( S1D , S1E and S1F Fig ) . As shown in Fig 1C , the secreted IFN-β was markedly inhibited by the overexpression of DYRK2 in 293 cells . These results suggested that DYRK2 inhibited the virus-induced activation of IRF3 . A previous study determined that DYRK2 belongs to a serine/threonine protein kinase family [58] . Thus , we examined whether the kinase activity of DYRK2 was required for the virus-triggered induction of type I IFNs . As shown in Fig 1D , the kinase-dead mutant of DYRK2 ( DYRK2-MT ) nearly completely lost the ability to inhibit SeV-induced IFN-β activation compared to its wild-type ( DYRK2-WT ) counterpart . A similar conclusion was obtained upon the evaluating the SeV-triggered activation of ISRE ( Fig 1D ) . These results suggested that kinase activity was required for DYRK2 to suppress the SeV-induced activation of ISRE and the IFN-β promoter in 293 cells . RT-PCR experiments revealed that the overexpression of DYRK2-WT but not DYRK2-MT robustly inhibited the SeV-induced gene expression of endogenous IFNB1 and RANTES in 293 cells ( Fig 1E ) . Previous studies have shown that the induction of type I IFNs require the coordinated and cooperative actions of the transcription factors IRF3 and NF-κB [25] . The effects of DYRK2 on the virus-induced activation of IRF3 indicated that the overexpression of DYRK2-WT but not that of DYRK2-MT inhibited SeV-induced IRF3 dimerization ( Fig 1F ) , which is a hallmark of IRF3 activation . These results suggested that DYRK2 suppressed the virus-triggered activation of IRF3 and the transcription of the IFNB1 gene and that these effects were dependent on DYRK2 kinase activity . We next examined whether endogenous DYRK2 was required for virus-induced signaling in physiological conditions . To accomplish this goal , we constructed three DYRK2 small hairpin ( sh ) RNA expressing plasmids ( #1 , #2 and #3 ) that targeted different sites within the DYRK2 mRNA . Transient transfection and endogenous expression experiments indicated that shDYRK2 #2 and #3 efficiently downregulated DYRK2 expression at the protein level as suggested by immunoblot analysis ( Fig 2A ) . Next , we observed the effects of DYRK2 RNAi on the activation of type I interferon . Reporter assays revealed that compared with the control group , DYRK2 knockdown enhanced the activations of the IFN-β promoter and ISRE in 293 cells ( Fig 2B ) and HeLa cells ( S2A , S2B , S2D and S2E Fig ) in response to SeV and HSV-1 . Consistently , DYRK2 knockdown did not affect the virus-induced activations of NF-κB in 293 cells ( Fig 2B ) or HeLa cells ( S2C and S2F Fig ) . To further assess the effects of DYRK2 RNAi on the endogenous gene expressions of IFNB1 and RANTES , we knocked down DYRK2 in 293 cells and then infected the cells with or without SeV . SeV infection resulted in increased mRNA expressions of IFNB1 and RANTES in the cells that were transfected with shDYRK2-expressing plasmid as compared with the control transfected cells ( Fig 2D ) . Because the effect of shDYRK2 #2 elicited the greatest results of the three RNAi sequences ( see Fig 2A , 2B and 2D ) , the #2 plasmid was used for all of the following experiments . The induction of IFNB1 gene transcription requires the activation of the transcription factor IRF3 . We next determined whether DYRK2 RNAi affected the virus-induced activation of IRF3 . DYRK2 knockdown clearly potentiated SeV-induced IRF3 dimerization ( Fig 2C ) . qPCR experiments further confirmed that IFNB1 and RANTES mRNA expression were induced by SeV; VSV and HSV-1 were markedly higher in the DYRK2 RNAi-transduced stable THP-1 cells ( Fig 2E ) . Compared with the control groups , the secreted IFN-β proteins were markedly higher in the DYRK2 knockdown THP-1 cells following infection with SeV and HSV-1 ( Fig 2F ) . Because DYRK2 negatively regulated the virus-triggered IFN-β expression , we next determined whether DYRK2 was involved in the regulation of the cellular antiviral response . Plaque assays indicated that knockdown of DYRK2 significantly inhibited VSV and HSV-1 replication and further promoted the cytoplasmic poly ( I:C ) -mediated inhibition of VSV and HSV-1 replication ( Fig 2G ) . Together , these results implied that DYRK2 was a physiological inhibitor of virus-triggered IFN-β induction and the cellular antiviral response . As described above , the overexpression and endogenous experiments revealed that DYRK2 was required for and acted as an inhibitor in virus-triggered signaling . Next , we sought to determine the levels at which DYRK2 regulated the virus-induced IRF3 activation pathway . To accomplish this goal , the 293 cells were cotransfected with plasmids encoding DYRK2 and signaling components . DYRK2 dramatically inhibited the RIG-I- , VISA- and TBK1-mediated but not the IKKε- or IRF3-mediated activation of the ISRE ( Fig 3A ) . Conversely , DYRK2 knockdown elicited the opposite results ( Fig 3B ) . These results suggested that DYRK2 may negatively regulate virus-triggered signaling by interacting with TBK1 . To further confirm the interactions between DYRK2 and TBK1 , transient transfection and coimmunoprecipitation experiments were performed , and the data revealed that DYRK2 associated with TBK1 but not with other signaling molecules , including RIG-I , VISA , MITA , IKKε and IRF3 ( Fig 3C ) . Although TBK1 and IKKε have redundant roles in certain circumstances , it appeared that DYRK2 only targeted TBK1 and not IKKε ( Fig 3A , 3B and 3C ) , which indicated that DYRK2 specifically interacted with TBK1 . We also determined whether DYRK2 physically interacted with TBK1 in untransfected cells . We performed endogenous coimmunoprecipitation and immunoblotting analyses with SeV- and MG132-treated cells at various time points . As shown in Fig 3D , DYRK2 did not associate with TBK1 under physiological conditions . However , the interaction between DYRK2 and TBK1 became evident 8 hours after infection , gradually increased between 8 and 12 hours after stimulation , and then completely disappeared . Interestingly , RT-PCR experiments revealed that the IFN-β mRNA expression induced by SeV infection gradually increased , reaching a maximum 16 hours after infection ( Fig 3D ) . These results implied that DYRK2 associated with TBK1 in a viral infection-dependent manner and that this interaction limited virus-induced IFN-β transcription . We next determined how DYRK2 regulated the virus-trigged induction of IFN-β via its interaction with TBK1 . To achieve this goal , we cotransfected the plasmid encoding Flag-tagged TBK1 with increasing amounts of the plasmid encoding HA-tagged DYRK2 into 293 cells , and the cells were then treated with dimethyl sulfoxide ( DMSO ) or the proteasome inhibitor MG132 . With increased DYRK2 expression , TBK1 levels gradually decreased ( Fig 4A ) . In contrast , no change in TBK1 expression was associated with the increased DYRK2 expression in MG132-treated cells ( Fig 4A ) . These results suggested that DYRK2 promoted TBK1 degradation in a dose-dependent manner and that this process was proteasome-dependent . We then determined whether DYRK2 elicited the ubiquitination of TBK1 . Fig 4B shows that DYRK2-WT but not DYRK2-MT caused the ubiquitination and downregulation of TBK1 . As the interactions between DYRK2 and TBK1 were viral infection-dependent under physiological conditions ( Fig 3D ) , we also wanted to determine whether viral infection caused the ubiquitination of endogenous TBK1 . Endogenous TBK1 was ubiquitinated and downregulated after virus infection ( Fig 4C ) . Conversely , DYRK2 knockdown markedly diminished the virus-induced ubiquitination and downregulation of TBK1 ( Fig 4C ) . These results illustrated that DYRK2 targeted TBK1 for ubiquitination and degradation after virus infection . Because proteasomes normally recognize and degrade proteins that have been modified with K48-linked polyubiquitin chains [59] , we also determined whether DYRK2 promoted the K48- or K63-linked ubiquitination of TBK1 . To achieve this goal , plasmids expressing ubiquitin mutants containing a single lysine residue , i . e . , K48 ( ubiquitin-K48 ) or K63 ( ubiquitin-K63 ) , were used . Immunoprecipitation and immunoblot analyses revealed that TBK1 was predominantly modified by K48-linked ubiquitination and less so by K63-linked ubiquitination in cells cotransfected with DYRK2 and TBK1 compared with cells transfected with TBK1 alone ( Fig 4D ) . Collectively , these results demonstrated that DYRK2 specifically induced the K48-linked ubiquitination of TBK1 , which is recognized by and subsequently degraded by the proteasome pathway . DYRK2 contains a canonical kinase domain that is located between a large N-terminal domain ( 149 amino acids ) and a short C-terminal extension ( 66 amino acids ) [43] . To determine which domain of DYRK2 was necessary for TBK1 ubiquitination and degradation , we constructed five DYRK2 deletion mutants ( Fig 5A ) . Coimmunoprecipitation experiments indicated that all of the deletion mutants of DYRK2 associated with TBK1 , except those that only contained the N- or C-terminal domains ( Fig 5B ) , suggesting that the DYRK2 kinase domain directly interacted with TBK1 . We also generated TBK1 deletion mutants containing various combinations of the TBK1 domains for coimmunoprecipitation analyses ( Fig 5C ) . Among these mutants , only the truncated proteins containing the coiled-coil domain interacted with DYRK2 , whereas the mutants that only containing the kinase or/and ubiquitin domains did not interact with DYRK2 ( Fig 5D ) . These results demonstrated that DYRK2 utilized its kinase domain to bind to the coiled-coil domain of TBK1 . As described above , DYRK2 inhibited the virus-induced activation of IRF3 and the transcription of IFN-β and that these effects depended on its kinase activity ( Fig 1 ) . Additionally , DYRK2 interacted with TBK1 through its kinase domain ( Fig 5B ) . Therefore , we hypothesized that DYRK2 regulated the activity of TBK1 via phosphorylation . To confirm this theory , we performed immunoprecipitation assays and determined that the cotransfection of wild-type ( WT ) DYRK2 but not the kinase-dead mutant ( MT ) with TBK1 caused TBK1 to migrate to a position that was associated with an increased molecular weight ( Fig 5E ) . This higher band represented phosphorylated TBK1 because it could be digested into two bands by calf intestine phosphatase ( CIP ) ( Fig 5E ) . One band represented phosphorylated TBK1 , which was incompletely digested by CIP ( higher band ) , and the other band represented CIP-dephosphorylated TBK1 ( lower band ) ( Fig 5E ) . We next sought to determine which sites within TBK1 were phosphorylated and therefore affected TBK1 function . We constructed a series of point mutants of TBK1 at the potential phosphorylation residues that were predicted with the NetPhos program; these point mutant constructs were cotransfected with DYRK2 into 293 cells , and the activations of ISRE were analyzed . As shown in Fig 5F , DYRK2 clearly inhibited the activation of the ISRE that was mediated by all the mutants except the S527A mutant . We then examined the DYRK2 phosphorylation of TBK1S527A; DYRK2 only phosphorylated TBK1 wild-type ( WT ) and not TBK1 ( S527A ) ( Fig 5G ) . To acquire more evidence , phosphomimetic TBK1 S527D and S527E mutants were constructed . As shown in S3A Fig , the TBK1 mutants of S527D and S527E were constitutively ubiquitinated and degraded regardless of the presence of DYRK2 . Reporter assays revealed thatDYRK2 had no effect on the activations of the IFN-β promoter that were mediated by any of the TBK1 mutants ( S3B Fig ) . qPCR results revealed that the expressions of the endogenous IFNB1 , RNATES and ISG15 mRNAs were not inhibited by DYRK2 when the cells were stimulated with the TBK1 mutants ( S3C Fig ) . It should be noted that TBK1 ( S527A ) did not activate the IFN-β promoter or the expression of downstream genes as robustly as did wild-type TBK1 . The reason behind the phenomenon is unclear . It is possible that mutation of Ser527 into Ala disrupt the structure of TBK1 which impairs its function to phosphorylate IRF3 . In the plaque assays , we found that the overexpression of DYRK2 markedly promoted VSV replication in the cells that were transfected with wild type TBK1 and that this promotion effect disappeared in the cells that were transfected with mutant TBK1 ( S3D Fig ) . These results indicated that DYRK2 specifically phosphorylated TBK1 at the Ser527 residue . We showed that DYRK2 not only phosphorylated serine 527 of TBK1 ( Fig 5G ) but also promoted the K48-linked ubiquitination of TBK1 and its subsequent degradation ( Fig 4D ) . Next , we sought to determine how DYRK2 promoted the ubiquitination of TBK1 through phosphorylation . We screened a cDNA array containing 352 expression clones of ubiquitin-related enzymes by cotransfecting TBK1 with each clone into cells overexpressing DYRK2 and found that the E3 ubiquitin ligases DTX4 and DYRK2 synergistically inhibited the TBK1-mediated activation of the ISRE and the IFN-β promoter ( Fig 6A and 6B ) . It has been reported that NLRP4 recruits the E3 ligase DTX4 to TBK1 for K48-linked ubiquitination , which leads to TBK1 degradation [28] . To further confirm the function of DYRK2 in the NLRP4-mediated degradation of TBK1 , we performed an immunoprecipitation analysis . DYRK2 overexpression significantly enhanced the NLRP4-induced K48-linked ubiquitination of TBK1 and its subsequent degradation ( Fig 6C ) . Consistently , in the context of DYRK2 knockdown , NLRP4 did not induce the K48-linked ubiquitination or degradation of TBK1 ( Fig 6D ) . These results indicated that DYRK2 plays a critical role in NLRP4-mediated TBK1 polyubiquitination and degradation . Coimmunoprecipitation experiments revealed that DYRK2 overexpression enhanced the interaction between NLRP4 and TBK1 ( Fig 6E ) and that DYRK2 knockdown abolished this interaction ( Fig 6F ) . Furthermore , the TBK1 S527A mutant did not interact with NLRP4 ( Fig 6E and 6F ) . We previously determined that the Ser527 of TBK1 is phosphorylated by DYRK2 ( Fig 5G ) . Here , the TBK1 S527A mutant could not be ubiquitinated when DYRK2 was overexpressed ( Fig 6G ) , and wild-type TBK1 could not be ubiquitinated when DYRK2 was knocked down ( Fig 6H ) . These results suggested that DYRK2 was necessary for the interaction between NLRP4 and TBK1 and that the phosphorylation of TBK1 at Ser527 by DYRK2 determined whether NLRP4 could interact with TBK1 and elicit subsequent TBK1 ubiquitination and degradation .
The virus-triggered induction of type I interferon is regulated in a spatio-temporal manner by various molecules and distinct mechanisms [18 , 60] . Although it is clear that TBK1 plays an essential role in antiviral responses , the mechanisms by which its activities are regulated are unknown . In this study , we identified DYRK2 as a novel negative regulator of the virus-induced type I interferon induction pathway and determined that the mechanism of action of DYRK2 involved the phosphorylation of Ser527 on activated TBK1 followed by the triggering of K48-linked ubiquitination and degradation mediated by the NLRP4-DTX4 complex . DYRK2 is involved in regulating numerous cellular signal transduction pathways , such as NFAT signaling , Hedgehog signaling and the hypoxia response and p53-activated signaling pathways [54–57] . Our results indicated that DYRK2 was essential for virus-triggered IFN-β induction and the cellular antiviral response and that this process was dependent on its kinase activity . Ectopic expression of DYRK2 inhibited the activations of the ISRE and the IFN-β reporter , the dimerization of IRF3 , and the transcription and secretion of INF-β , whereas the kinase-dead mutant of DYRK2 ( DYRK2-MT ) abolished this inhibitory activity . Conversely , RNAi-mediated knockdown of DYRK2 potentiated the virus-triggered activations of IRF3 , IFNB1 and RANTES gene expressions and the secretion of INF-β and inhibited viral replication . A series of experiments indicated that DYRK2 targeted TBK1 and facilitated TBK1 degradation under physiological conditions . First , the reporter assays revealed that DYRK2 markedly inhibited the RIG-I- , VISA- , and TBK1-mediated but not the IKKε- and IRF3-mediated activations of the ISRE , whereas DYRK2 knockdown had the opposite results . These findings are consistent with the molecular placement of TBK1 within the pathway . Second , DYRK2 specifically bound to TBK1 in mammalian overexpression systems . Third , the interaction between DYRK2 and TBK1 was the strongest at twelve hours after viral infection under physiological conditions; the interaction subsequently gradually disappeared . The kinetics of the disassociation between these two proteins correlated with the termination of IFNB1 gene transcription . Fourth , compared with the MG132-treated control group , DYRK2 promoted TBK1 degradation in a dose-dependent manner . Finally , overexpression experiments revealed that DYRK2 effectively promoted the K48-linked ubiquitination of TBK1 and its subsequent degradation . DYRK2 belongs to the Ser/Thr protein kinase family [61] , implying that DYRK2 might phosphorylate TBK1 . Our results indicated that the kinase domain ( KD ) of DYRK2 interacted with the coiled-coil domain ( CC ) of TBK1 . We further demonstrated that DYRK2 specifically phosphorylated Ser527 of TBK1 by point mutation analysis . DYRK2 requires an arginine at the -2 or -3 position to efficiently phosphorylate its substrates , and replacing the +1 proline with an alanine nearly completely eliminates substrate phosphorylation [62] . Interestingly , the sequence around Ser527 of TBK1 ( RLSP ) coincides with the consensus DYRK2 phosphorylation site . Crosstalk between different types of posttranslational modifications , particularly between phosphorylation and ubiquitination , is an emerging theme in eukaryotic cells [63] . Although previous studies have suggested that NLRP4 recruits the E3 ubiquitin ligase DTX4 to TBK1 for Lys48 ( K48 ) -linked polyubiquitination and the degradation of TBK1 [28] , the initiating event is unclear; in other words , there must be a signal that initiates this sequence . In our study , we proved that DYRK2 overexpression clearly promoted the NLRP4-mediated K48-linked polyubiquitination and degradation of TBK1 , whereas DYRK2 knockdown failed to induce the polyubiquitination and degradation of TBK1 . Furthermore , DYRK2 knockdown eliminated the interaction between NLRP4 and TBK1 , whereas DYRK2 overexpression enhanced this interaction . Furthermore , the TBK1 S527A mutant could not be ubiquitinated in the context of overexpressed DYRK2 , whereas wild-type TBK1 was ubiquitinated . Additionally , wild-type TBK1 could not be ubiquitinated when DYRK2 was knocked down . Together , these data suggested that DYRK2-mediated phosphorylation was a priming event that was required for the NLRP4-mediated K48-linked polyubiquitination and degradation of TBK1 . Based on all of our findings , we propose the following hypothesis to explain how DYRK2 negatively regulates the type I interferon induction pathway . In uninfected cells , TBK1 does not interact with DYRK2 . Once a cell is infected , TBK1 is rapidly activated by upstream signaling , which activates IRF3 and type I interferon expression to fight the invading virus . To prevent excessive harmful immune responses , DYRK2 may be activated by a similar mechanism mediated by ATM [64] and bind to TBK1 via its kinase domain and phosphorylate Ser527 at the appropriate time after virus infection . Only phosphorylated TBK1 is recognized by and binds to NLRP4 . Ultimately , NLRP4 recruits the E3 ubiquitin ligase DTX4 to degrade TBK1 via K48-linked ubiquitination , and signal transduction is terminated . In this regulation process , it remains unclear how phosphorylated TBK1 is specifically recognized by NLRP4 . A previous report indicated that DYRK2 is required for the assembly of the DYRK2-EDVP E3 ligase complex and phosphorylates its substrates to prime them for degradation [53] . The functions of DYRK2 , NLRP4 and DTX4 and whether a DYRK2-EDVP complex functions in the regulation of the activity of TBK1 require further study . In conclusion , our findings revealed that DYRK2 phosphorylates TBK1 at Ser527 and subsequently triggers the ubiquitination and degradation of TBK1 , which provides new insight into the mechanism of the control of excessive cellular antiviral responses .
This study utilized antibodies against TBK1 ( Cell Signaling Technology ) , IRF3 , DYRK2 , ubiquitin ( Santa Cruz Biotechnology ) , Flag , HA , Myc , β-actin ( Sigma-Aldrich ) , and horseradish peroxidase ( HRP ) -conjugated anti-rabbit IgG and anti-mouse IgG ( Thermo Fisher Scientific ) . Mouse antisera against TBK1 and DYRK2 were raised against the respective recombinant human proteins . The human IFN Beta ELISA kits ( Pestka Biomedical Laboratories ) and SYBR qPCR Mix ( TOYOBO ) were purchased from the indicated companies . SeV , VSV , and HSV-1 were prepared as previously described [23] . The ISRE and IFN-b promoter luciferase reporter plasmids and mammalian expression plasmids for HA- or Flag-tagged RIG-I , VISA , MITA , TBK1 , IKKε , and IRF3 have been previously described [65] . The mammalian expression plasmids for HA-tagged Lys-48- and Lys-63-only ubiquitin mutants were made by site-directed mutagenesis [59] . The mammalian expression plasmids for human DYRK2 , NLRP4 and DTX4 were purchased from OriGene . The mammalian expression plasmids for wild-type and mutant Flag- and HA-tagged DYRK2 were constructed with standard molecular biology techniques . TBK1 mutants were provided by Cao-Qi Lei ( Wuhan University ) . The transfection and reporter assays were performed as previously described [66] . The 293 cells were cultured in 24-well plates and transfected with the indicated plasmids on the following day by standard calcium phosphate precipitation . In these experiments , the pRL-TK ( Renilla luciferase ) reporter plasmid was added into each well to normalize the transfection efficiency , and the empty vector plasmid was used to ensure that the same amounts of total DNA were transfected into each well . Twenty hours after transfection , the cells were stimulated with or without SeV for 10 hours in certain experiments; otherwise , the cells were harvested , and the luciferase assays were performed using a dual-specific luciferase assay kit ( Promega ) . Firefly luciferase activity was normalized to Renilla luciferase activity . All of the reporter assays were repeated three times . Total RNA was extracted from cells using TRIzol reagent ( Invitrogen ) following the protocols recommended by the manufacturer . The RNA samples were then treated with Ambion Turbo RNA-free DNase I ( Ambion ) at 37°C for 30 min to remove the residual DNA . cDNA was prepared from 2 μg of RNA using oligo ( dT ) , M-MLV reverse transcriptase ( Promega ) , and RNasin Ribonuclease Inhibitor ( Biostar International ) in a total volume of 25 μl at 37°C for 1 h . cDNA was subjected to semi-quantitative PCR analysis to measure the expressions of IFNB1 , RANTES and GAPDH according to the manufacturer’s instructions . For each qPCR reaction , the samples were mixed with the Thunderbird SYBR qPCR Mix ( TOYOBO ) , and the final primer concentrations were 0 . 3 μM . The amplifications were performed in a qPCR system ( ABI 7300 ) . The conditions included 1 cycle of 95°C for 1 min , followed by 40 cycles of 95°C for 15 s , 60°C for 30 s , and 72°C for 45 s . The relative quantifications of the mRNAs were normalized to GAPDH using the 2 -ΔΔCt method . The following gene-specific primer sequences were utilized for the qPCR: IFNB1 , 5’-TTGTTGAGAACCTCCTGGCT-3’ and 5’-TGACTATGGTCCAGGCACAG-3’; RANTES , 5’-GGCAGCCCTCGCTGTCATCC-3’ and 5’-GCAGCAGGGTGTGGTGTCCG-3’; ISG15 , 5’-CGCAGATCACCCAGAAGATCG-3’ and 5’-TTCGTCGCATTTGTCCACCA-3’ and GAPDH , 5’-GAGTCAACGGATTTGGTCGT-3’ and 5’-GACAAGCTTCCCGTTCTCAG-3’; for Semi-quantitative PCR: IFNB1 , 5’-CTCTCCTGTTGTGCTTCTCCA-3’ and 5’-CTCTGACTATGGTCCAGGCAC-3’; RANTES , 5’-ATGAAGGTCTCCGCGGCACGCCT-3’ and 5’-CTAGCTCATCTCCAAAGAGTTG-3’; GAPDH , 5’-GAGAAGGCTGGGGCTCATTT-3’ and 5’-GTCAAAGGTGGAGGAGTGGG -3’ . The coimmunoprecipitation analyses were performed as previously described [41] . For the transient transfection coimmunoprecipitation experiments , the 293 cells were transfected with the appropriate plasmid . Twenty-four hours after transfection , the cells were harvested and lysed in 1 ml of lysis buffer ( 20 mM Tris , pH 7 . 5 , 150 mM NaCl , 1% Triton , 1 mM EDTA , 10 μg/ml aprotinin , 10 μg/ml leupeptin , and 1 mM phenylmethylsulfonyl fluoride ) . For each immunoprecipitation reaction , 0 . 4 ml of cell lysate was incubated with 0 . 5 μg of the indicated antibody or control IgG and 40 μl of protein G agarose beads ( Santa Cruz Biotechnology , Inc . ) at 4°C . After a 4-hour incubation , the beads were washed three times with 1 ml of lysis buffer containing 0 . 5 M NaCl . The immunoprecipitates and other samples were subjected to SDS-PAGE , transferred onto nitrocellulose membranes and blotted as described previously [67] . For the endogenous coimmunoprecipitation experiments , the 293 cells were treated with or without SeV for the indicated times . The subsequent procedures were performed as described above . To examine the IRF3 dimerization , native PAGE assays were performed as previously described [59] . Briefly , 293 cells were harvested and lysed with ice-cold lysis buffer ( 50 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM PMSF and 0 . 5% NP-40 ) . Next , the cell lysates were diluted with 2× loading buffer ( 125 mM Tris/HCl , pH 6 . 8 , 30% glycerol and 0 . 1% bromophenol blue ) . Finally , the samples were loaded onto 15×1 . 0 mm precast 7 . 5% native gels and separated at 20 mA for 90 minutes by electrophoresis . The subsequent immunoblotting analyses were performed as described above . According to the pSUPER RNAi System manual ( OligoEngine , Inc . ) , double-strand oligonucleotides corresponding to the target gene were cloned into pSUPER vectors . The target sequences for the human DYRK2 gene were the following: #1: 5’-GAGCTCATCAAGAAGAATA-3’; #2: 5’-GGACAGTGCTCACGACACA-3’; and #3: 5’-GGTGCTATCACATCTATAT-3’ . Control or DYRK2-RNAi retroviral plasmids were co-transfected with packaging plasmids ( pGAG-Pol and pVSV-G ) into 293 cells . Twenty-four hours after transfection , the cell culture medium was replaced with new medium without antibiotics , and then the cells were incubated for 24 h . The THP-1 cells were then infected with recombinant virus-containing medium in the presence of polybrene ( 8 g/mL ) and were selected with puromycin ( 0 . 5 g/mL ) for one month before additional experimentation . RNAi-transduced stable THP-1 cells or 293 cells transfected with the indicated plasmids for 20 h were infected with VSV ( MOI = 0 . 1 ) . One hour after infection , the cells were washed with PBS three times , and medium was then added for another 24 h of incubation . The supernatants were diluted by 10−6 to infect Vero cells seeded in 24-well plates . After 1 h infection , 3% methylcellulose was overlaid , and the plates were incubated for 2 days . The overlay was removed , and cells were fixed with 4% paraformaldehyde for 20 min and stained with 1% crystal violet for 30 min . The plaques were counted , averaged , and multiplied by the dilution factor to determine the viral titer as pfu/ml . RNAi-transduced stable THP-1 cells or 293 cells transfected with the indicated plasmids for 20 h were infected with HSV-1 or SeV . Twelve hours after infection , the culture medium was collected for quantitation of the IFN beta with an ELISA kit ( Pestka Biomedical Laboratories ) following the protocols recommended by the manufacturer . The UniProtKB/Swiss-Prot accession numbers ( parentheses ) are indicated for the following proteins mentioned in the text: DYRK2 ( Q92630 ) , TBK1 ( Q9UHD2 ) , NLRP4 ( Q96MN2 ) , and DTX4 ( Q9Y2E6 ) . | In recent years , the mechanisms of innate antiviral immune responses mediated by pattern recognition receptors ( PRRs ) have been heavily investigated . All PRRs require the key molecule TANK-binding kinase 1 ( TBK1 ) to activate the transcription factor IRF3 , which leads to type I interferon induction and the cellular antiviral response . Here , we identified the dual-specificity tyrosine- ( Y ) -phosphorylation-regulated kinase 2 ( DYRK2 ) as a negative regulator of TBK1 . DYRK2 inhibited the virus-triggered induction of type I interferon and promoted K48-linked ubiquitination and the degradation of TBK1 in a manner that depended on its kinase activity . We further found that DYRK2 phosphorylated Ser527 of TBK1 , which is essential for the recruitment of NLRP4 and for the E3 ubiquitin ligase DTX4 to degrade TBK1 . Our findings suggest that DYRK2 plays an important role in innate immune responses to viruses by modulating TBK1 activity and provide important insights into the intricate regulatory mechanisms of the innate immune response against viruses . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | DYRK2 Negatively Regulates Type I Interferon Induction by Promoting TBK1 Degradation via Ser527 Phosphorylation |
The attachment of sister kinetochores by microtubules emanating from opposite spindle poles establishes chromosome bipolar attachment , which generates tension on chromosomes and is essential for sister-chromatid segregation . Syntelic attachment occurs when both sister kinetochores are attached by microtubules from the same spindle pole and this attachment is unable to generate tension on chromosomes , but a reliable method to induce syntelic attachments is not available in budding yeast . The spindle checkpoint can sense the lack of tension on chromosomes as well as detached kinetochores to prevent anaphase onset . In budding yeast Saccharomyces cerevisiae , tension checkpoint proteins Aurora/Ipl1 kinase and centromere-localized Sgo1 are required to sense the absence of tension but are dispensable for the checkpoint response to detached kinetochores . We have found that the loss of function of a motor protein complex Cik1/Kar3 in budding yeast leads to syntelic attachments . Inactivation of either the spindle or tension checkpoint enables premature anaphase entry in cells with dysfunctional Cik1/Kar3 , resulting in co-segregation of sister chromatids . Moreover , the abolished Kar3-kinetochore interaction in cik1 mutants suggests that the Cik1/Kar3 complex mediates chromosome movement along microtubules , which could facilitate bipolar attachment . Therefore , we can induce syntelic attachments in budding yeast by inactivating the Cik1/Kar3 complex , and this approach will be very useful to study the checkpoint response to syntelic attachments .
One of the most important events during the cell cycle is chromosome segregation and errors in this process will lead to chromosome missegregation . To separate sister chromatids into daughter cells , sister kinetochores must be attached to the microtubules emanating from opposite spindle poles in order to establish bipolar attachment . Even though this process is highly regulated , incorrect attachment takes place occasionally . Syntelic attachment occurs when both sister kinetochores are connected to microtubules from the same spindle pole . In monotelic attachment , only one of the sister kinetochores connects to the microtubules from a spindle pole [1] . It is also possible for both sister kinetochores to be detached . These incorrect attachments have to be corrected before anaphase entry , or chromosome missegregation will occur . The kinetochore is a multi-protein complex that connects chromosomes to microtubules . More than 60 kinetochore proteins have been identified in budding yeast . The CBF3 ( centromere binding factor ) complex associates directly with centromeric DNA , while the DASH/Dam1 complex residues at the kinetochore-microtubule interface . As a ten-protein complex including Dam1 and Ask1 , the DASH can form a ring structure around a single microtubule and mediate the kinetochore-microtubule interaction [2] , [3] , [4] , [5] . Ndc80 ( Ndc80 , Nuf2 , Spc24 , Spc25 ) , COMA ( Ctf19-Okp1-Mcm21-Ame1 ) , and MIND ( Mtw1p including Nnf1-Nsl1-Dsn1 ) complexes bridge the gap between centromere-bound CBF3 and microtubule-associated DASH [6] , [7] . Chromosome attachment is monitored by the spindle checkpoint which includes Bub1 , Bub3 , Mad1 , Mad2 , Mad3 , and Mps1 [8] , [9] , [10] , [11] . Detached kinetochores activate the checkpoint by allowing the formation of a Mad2-Mad3/BubR1-Bub3-Cdc20 complex . Because Cdc20 is an essential activator of the anaphase-promoting complex ( APC ) , the binding of Cdc20 by the spindle checkpoint components blocks APCCdc20 activity [12] , [13] . APCCdc20 mediates the ubiquitination and the subsequent degradation of the anaphase inhibitor securin , known as Pds1 in budding yeast [14] . Pds1 protein inhibits anaphase by binding to separase Esp1 and preventing Esp1-dependent cleavage of cohesin , a protein complex that holds sister chromatids together [15] , [16] . Therefore , the activation of the spindle checkpoint prevents anaphase entry by blocking Pds1 degradation , and stabilized Pds1 protein indicates the activation of the spindle checkpoint . Chromosome bipolar attachment generates tension on sister kinetochores . The observation that the application of tension on an improperly attached chromosome in grasshopper cells abolishes the anaphase entry delay directly demonstrates the role of tension in cell cycle regulation [17] . To analyze the response to the absence of tension in yeast cells , tension defects can be induced by the block of DNA synthesis or by the abrogation of sister chromatid cohesion [18] , [19] . In both situations , the lack of tension prevents anaphase entry as indicated by the stabilized Pds1 protein levels . Ipl1 and Sgo1 were found to be required to sense tension defects and prevent anaphase entry , but they are dispensable for cell cycle arrest induced by the disruption of the spindle structure [19] , [20] . In addition to its checkpoint function , Ipl1 kinase also promotes the turnover of kinetochore-microtubule interaction when tension is absent [21] , [22] . Therefore , it is speculated that Ipl1 may activate the checkpoint by generating detached chromosomes when tension is absent . In contrast , Sgo1 does not play a role in destabilizing kinetochore attachment and its checkpoint function remains unclear at the molecular level [22] . As one of the six kinesin-related proteins in budding yeast , Kar3 was identified as being essential for yeast nuclear fusion during mating [23] . Unlike other kinesins , Kar3 protein contains a motor domain at its carboxy terminus that possesses minus-end-directed motility [24] . Recent evidence indicates that Kar3 localizes at the spindle midzone and may also function as an interpolar-microtubule cross-linker to prevent spindle collapse [25] . Moreover , Kar3 protein promotes the poleward transport of chromosomes along astral microtubules [26] , [27] . Two proteins , Cik1 and Vik1 , associate with Kar3 through coiled-coil domains to form Cik1/Kar3 or Vik1/Kar3 heterodimers . Both kar3Δ and cik1Δ mutants show defects in mating , spindle morphogenesis , and chromosome segregation [28] , but their direct role in mitosis remains unclear . We previously showed that cik1Δ and kar3Δ mutants are sensitive to hydroxyurea ( HU ) , a DNA synthesis inhibitor , and these mutants exhibit chromosome bipolar attachment defects after HU treatment [29] . We recently found that cik1Δ and kar3Δ mutants are synthetically lethal with tension checkpoint mutants ipl1-321 and sgo1Δ , indicating a role for Cik1/Kar3 in chromosome segregation . To further study the function of Cik1/Kar3 , we constructed a plasmid PGALCIK1-CC that contains the coiled-coil domain of Cik1 . Our results indicate that overexpression of CIK1-CC can competitively disrupt the Cik1-Kar3 interaction , which allows us to conditionally abolish Cik1/Kar3 function . With this method , we show that dysfunctional Cik1/Kar3 results in significant co-segregation of sister chromatids in the absence of the spindle checkpoint . Strikingly , dysfunctional Cik1/Kar3 also causes co-segregation of sister chromatids in ipl1-321 and sgo1Δ cells . Given the role of Ipl1 and Sgo1 in sensing chromosomes that lack tension , these data suggest that the loss of function of Cik1/Kar3 results in an increased frequency of syntelic attachment . Results with live-cell imaging and cohesin mutants further support this conclusion . Therefore , syntelic attachments can be induced in budding yeast by inactivating Cik1/Kar3 complex and this method will be a very useful tool for studying the response to tension defects .
Our previous study indicates that the Cik1/Kar3 complex facilitates chromosome bipolar attachment after treatment with HU , an inhibitor of DNA synthesis [29] . We also noticed that cik1Δ and kar3Δ mutants exhibited an anaphase entry delay in the absence of HU , suggesting the presence of improper chromosome attachments . Previous work shows that both cik1 and kar3 mutants are synthetically lethal with spindle checkpoint mutants , bub1 , mad1 , mad2 , and mad3 [30] , [31] . Interestingly , we found that cik1Δ and kar3Δ are also synthetically lethal with tension checkpoint mutants sgo1Δ and ipl1-321 . This genetic interaction with tension checkpoint mutants suggests that the Cik1/Kar3 complex may facilitate the establishment of chromosome bipolar attachment that generates tension on chromosomes . To further study the role of Cik1/Kar3 in chromosome bipolar attachment , we need to examine chromosome segregation in cik1Δ and kar3Δ mutants in the absence of the spindle checkpoint , which allows anaphase entry in spite of incorrect chromosome attachments . Because of the synthetic lethality , we have to develop a way to conditionally inactivate the Cik1/Kar3 complex . Kar3 and Cik1 associate with each other through their respective coiled-coil domains [32] , thus overexpression of this domain may competitively disrupt the Cik1-Kar3 interaction . We constructed a plasmid PGALCIK1-CC that contains the coiled-coil domain of CIK1 under control of a galactose inducible promoter and the Cik1-Kar3 interaction in cells overexpressing CIK1-CC was examined . Consistent with a previous report [28] , we detected the interaction between Cik1 and Kar3 in control cells by co-immunoprecipitation . However , the Cik1-Kar3 interaction was completely abolished after CIK1-CC overexpression . Instead , the association of Kar3 with the coiled-coil domain of Cik1 ( Cik1-CC ) was detected , suggesting that the disruption of Cik1-Kar3 interaction by Cik1-CC is in a competitive manner ( Figure 1A ) . Kar3 is able to form a heterodimer with either Cik1 or Vik1 [33] , [34] . We also noticed that Vik1-Kar3 interaction was decreased in cells overexpressing CIK1-CC ( Figure S1 ) . Next we examined the phenotypes of cells overexpressing CIK1-CC and found that these cells grew slowly and were sensitive to HU . In addition , cells overexpressing CIK1-CC failed to grow at 37°C ( Figure 1B ) , which is reminiscent of cik1Δ and kar3Δ mutants [29] . Therefore , we conclude that overexpression of CIK1-CC disrupts Cik1-Kar3 interaction and cells overexpressing CIK1-CC mimic the phenotypes of cik1Δ and kar3Δ mutants . Next we examined the growth of the spindle checkpoint mutant mad1Δ and tension checkpoint mutants ipl1-321 and sgo1Δ after CIK1-CC overexpression . These mutant cells harboring a vector grew well on galactose plates , but the mutant cells with PGALCIK1-CC plasmids failed to grow on galactose plates ( Figure 2A ) . Moreover , the checkpoint mutant cells lost viability shortly after the induction of CIK1-CC ( Figure 2B ) . Surprisingly , ipl1-321 mutants with PGALCIK1-CC plasmids did not grow on galactose plates and lost viability after incubation in galactose medium at the permissive temperature 25°C ( Figure 2A and 2B ) , which may be due to the fact that mutated Ipl1-321 protein shows significantly impaired kinase activity even at 25°C [35] , [36] . We speculate that the slow growth phenotype in cells overexpressing CIK1-CC is attributed to the incorrect chromosome attachments that delay anaphase entry by stabilizing Pds1 . Moreover , this delay likely depends on the spindle checkpoint . To test this idea , we compared the cell cycle progression in wild-type ( WT ) , mad1Δ , sgo1Δ , and ipl1-321 cells after CIK1-CC overexpression . After G1 release into 25°C galactose medium for 200 min , 39% of WT cells with a control vector were large budded , while the percentage of large budded cells increased to 74% in those expressing CIK1-CC ( Figure 2C ) . Consistent with the cell cycle delay , CIK1-CC overexpression also caused Pds1 protein stabilization . Strikingly , the cell cycle delay and Pds1 stabilization were eliminated not only in the spindle checkpoint mutant mad1Δ , but also in tension checkpoint mutants sgo1Δ and ipl1-321 , which only detect lack of tension ( Figure 2D ) . This result suggests that tension defects but not detached chromosomes activate the checkpoint in cells lacking Cik1/Kar3 . After the establishment of bipolar attachment , chromosomes congress to the spindle equator [37] . In budding yeast , the subsequent tension generation on chromosomes results in a transient separation of sister centromeres [38] , [39] . cdc13-1 mutant cells arrest at preanaphase at high temperatures because of the activation of the DNA damage checkpoint and bipolar attachment is believed to be established in these arrested cells [40] , [41] , [42] . To assay the bipolar establishment in cells lacking Cik1/Kar3 , we introduced the PGALCIK1-CC plasmid into cdc13-1 cells with GFP-marked centromeres of chromosome IV ( CEN4-GFP ) and mCherry-labeled microtubules ( TUB1-mCherry ) . The relative localization of CEN4-GFP to the spindle was analyzed after G1 release into galactose medium at 32°C , the restrictive temperature for cdc13-1 . Similar to cik1Δ mutant cells , overexpression of CIK1-CC also causes the formation of a dot-like spindle in some cells . Here , we only counted the cells with a metaphase spindle structure ( >1 . 5 µM ) . After the establishment of bipolar attachment , we speculate that CEN4-GFP will either separate as two dots along the spindle or localize in the middle part of the spindle as a single dot . Nevertheless , the localization of a CEN4-GFP dot at the end of a spindle will suggest defective bipolar attachment . After G1 release for 150 min , only 12% of control cells showed a CEN4-GFP dot at one end of the spindle , but the percentage of cells with the GFP dot at the end of the spindle increased to 51% in those overexpressing CIK1-CC ( Figure 3A ) , indicating that cells lacking Cik1/Kar3 function exhibit impaired chromosome bipolar attachment even when the spindle appears normal . After tension establishment , kinetochores are resolved into two distinct clusters lying between the spindle poles before anaphase entry [43] . To test whether overexpression of CIK1-CC causes the failure of the formation of two kinetochore clusters , we introduced a PGALCIK1-CC plasmid into cdc13-1 strains with TUB1-mCherry and GFP-tagged MTW1 , which encodes a kinetochore protein . After release from G1 into 32°C galactose medium for 150 min , 82% of cdc13-1 cells with a metaphase spindle showed two clearly resolved Mtw1-GFP foci in the absence of Cik1-CC induction . When CIK1-CC was overproduced , however , only 25% of cells exhibited two clear GFP foci among the cells with a normal metaphase spindle and many cells showed scattered GFP signals along the entire spindle ( Figure 3B ) . Together , these results strongly suggest that dysfunctional Cik1/Kar3 leads to chromosome bipolar attachment defects . Although we cannot exclude the possibility that the abnormal spindle in cells lacking Cik1/Kar3 contributes to bipolar attachment defects , our data suggest a spindle-independent role of Cik1/Kar3 in bipolar attachment . To further study the role of Cik1/Kar3 in chromosome bipolar attachment , we examined the chromosome segregation process in synchronized mad1Δ checkpoint mutant cells with CEN4-GFP TUB1-mCherry in the absence of Cik1/Kar3 function . After G1 release , CIK1-CC overexpression caused an obvious cell cycle delay in WT cells as indicated by the higher proportion of large budded cells , but mad1Δ suppressed this delay completely . Among the cells with an elongated spindle after G1 release for 150 min , more than 40% of mad1Δ cells showed CEN4-GFP co-segregation when Cik1-CC was overproduced , where one or two GFP dots were close to only one of the spindle poles . However , in mad1Δ cells with a vector control , no co-segregation of sister chromatids was observed ( Figure 4A ) , indicating that Cik1-CC overproduction causes a kinetochore attachment defects . The co-segregation of chromosome IV in mad1Δ mutant cells overexpressing CIK1-CC could be a consequence of syntelic attachment , monotelic attachment , or chromosome detachment . For a detached chromosome , both sister chromatids will stay in the mother cell after spindle elongation . For a chromosome with monotelic attachment , after anaphase onset the detached chromatid will stay in the mother cell but the attached one will move along with the connected spindle poles to either the mother or the daughter cell . Hence , it is impossible for both sister chromatids to move to the daughter cell together when a chromosome is either detached or with monotelic attachment . For a chromosome with syntelic attachment , however , the sister chromatids will co-segregate to either the mother or the daughter cell . Therefore , co-segregation of sister chromatids to the daughter cell will be an indication of syntelic attachment . We examined the frequency of sister-CEN4-GFP co-segregation into daughter cells in mad1Δ mutants overexpressing CIK1-CC . Mother cells are usually bigger in size and show a shmoo-like morphology because α-factor was used for G1 synchronization . Among the mad1Δ cells that show CEN4-GFP co-segregation after Cik1-CC induction , 45% of them have the GFP signal in the daughter cell ( Figure 4A ) , indicating the presence of syntelic attachment . As this number is close to 50% , the chance of syntelic attachment to either spindle pole is similar . If syntelic attachment in cells lacking Cik1/Kar3 function leads to a cell cycle delay , we expect that this delay depends on the tension checkpoint , because chromosomes with syntelic attachment are not under tension . Thus , we examined the chromosome segregation in ipl1-321 and sgo1Δ cells at 25°C after CIK1-CC overexpression . Strikingly , more than 40% ipl1-321 and sgo1Δ cells with an elongated spindle exhibited co-segregation of sister CEN4-GFPs after G1 release for 150 min , which is similar to mad1Δ checkpoint mutants . Among them , 54% of ipl1-321 and 48% of sgo1Δ cells showed exclusive daughter cell localization of CEN4-GFP signal . In contrast , no co-segregation was observed in the mutant cells with a vector control ( Figure 4A ) . Given the fact that the loss of function of Ipl1 or Sgo1 fails to abolish the cell cycle arrest in response to detached chromosomes [19] , [20] , this result further indicates that dysfunctional Cik1/Kar3 induces syntelic attachment . Since we performed the experiments at 25°C , the data demonstrate that ipl1-321 mutant cells lose tension checkpoint function at the permissive temperature , which is in agreement with the observation that ipl1-321 mutants exhibit reduced kinase activity at the permissive temperature [36] . We also used a live-cell imaging system to follow chromosome segregation in sgo1Δ mutants overexpressing CIK1-CC in order to clarify if some sister chromatids are connected to a single spindle pole . When incubated in galactose medium , the sister CEN4-GFPs migrated along with one single spindle pole and entered the daughter cell as the spindle elongated in the cell shown in Figure 4B and Video S1 , indicating that both sister chromatids of chromosome IV are connected to one spindle pole . During spindle elongation , we observed separation of the two GFP dots at some time points , suggesting the absence of sister chromatid cohesion . Therefore , the sister chromatid co-segregation observed in this representative cell is likely a consequence of syntelic attachment , but not monotelic attachment . Chromosome mis-segregation was not observed in WT cells overexpressing CIK1-CC in this live-cell assay . Residual cohesion may contribute to sister-chromatid co-segregation of a chromosome with monotelic attachment . To further distinguish syntelic from monotelic attachment , we examined sister-chromatid segregation in mcd1-1 cohesin mutants while overexpressing CIK1-CC . When incubated at 37°C , the absence of cohesion in mcd1-1 mutants not only abolishes the connection between sister chromatids , but also allows the spindle to elongate regardless of checkpoint activation [44] . We introduced a vector and a PGALCIK1-CC plasmid into a mcd1-1 CEN5-GFP TUB1-mCherry strain . After G1 release into galactose medium at 37°C for 200 min , 31% of mcd1-1 mutant cells with an elongated spindle exhibited co-segregation of sister CEN5-GFPs when Cik1-CC was induced . In the absence of Cik1-CC expression , however , only 4% mcd1-1 cells with an elongated spindle showed CEN5-GFP co-segregation , presumably because cohesion also contributes to bipolar attachment [45] ( Figure 4C ) . Moreover , the chance of sister-chromatid co-segregation into the mother or the daughter cell is similar . The dramatically increased frequency of sister-chromatid co-segregation in cohesin mutants after Cik1-CC overexpression further demonstrates the presence of syntelic attachment . Together , these data support the conclusion that loss of function of Cik1/Kar3 by overexpressing CIK1-CC causes syntelic attachment , where two sister kinetochores attach to the same spindle pole . Overexpression of the coiled-coil domain of Cik1 may disrupt the function of other proteins with a coiled-coil domain , which could also contribute to syntelic attachments . To exclude this possibility , we examined chromosome segregation in a temperature sensitive kar3-64 mutant in the absence of Sgo1 . The mutated Kar3-64 protein loses its function at high temperature because kar3-64 is synthetically lethal with kip3Δ at 35°C but not at room temperature [46] . WT , sgo1Δ , kar3-64 , and kar3-64 sgo1Δ strains with CEN4-GFP TUB1-mCherry were first arrested in G1 phase and then released into 35°C medium to inactivate Kar3 . The accumulation of large budded cells in kar3-64 mutants indicates the loss of Kar3 function ( Figure 5A ) . Similar to the cells overexpressing CIK1-CC , the cell cycle delay in kar3-64 mutant was abolished by sgo1Δ . We also found that about 40% of kar3-64 sgo1Δ double mutant cells with an elongated spindle showed CEN4-GFP co-segregation after release for 120 and 150 min at 35°C ( Figure 5B ) , which is comparable to sgo1Δ cells overexpressing CIK1-CC . The majority of kar3-64 sgo1Δ mutant cells exited mitosis after release for 180 min . At this time point , 38% of the G1 cells were either absent for CEN4-GFP signal or showed two CEN4-GFP dots , suggesting the gain or loss of chromosome IV after mitosis ( Figure 5C ) . It is also possible that some G1 cells have two CEN4-GFP dots but they are too close to be distinguished by microscopy . Consistently , only 18% of kar3-64 sgo1Δ mutants were viable after G1 release for 180 min , but 95% of WT and sgo1Δ cells as well as 61% of kar3-64 cells were viable ( Figure 5D ) . kar3-64 cells exhibited partial viability loss presumably due to the inability to recover from mitotic arrest . These results validate the conclusion that the loss of function of Kar3 causes syntelic attachment . The bipolar attachment defects in kar3 mutants or in cells overexpressing CIK1-CC could be a result of dysfunctional Cik1/Kar3 or Vik1/Kar3 , because overexpression of CIK1-CC also partially disrupts Vik1-Kar3 interactions ( Figure S1 ) . Moreover , previous observation that vik1Δ mutant is synthetically lethal with ipl1-321 indicates a possible role of Vik1 in chromosome segregation [45] . To test whether dysfunctional Vik1 also contributes to syntelic attachment , we examined the establishment of bipolar attachment in cdc13-1 vik1Δ cells . Like cdc13-1 single mutant , more than 80% of cdc13-1 vik1Δ cells showed either separated CEN4-GFP dots or one GFP dot at the center region of the spindle after G1 release for 90 min ( Figure S2 ) . We further examined the segregation of sister chromatids in vik1Δ mad1Δ double mutant cells and no mis-segregation was observed . We also crossed vik1Δ with ipl1-321 and sgo1Δ . Surprisingly , we obtained vik1Δ ipl1-321 and vik1Δ sgo1Δ double mutants and these mutants did not show co-segregation of sister chromatids ( Figure S3 ) . Therefore , vik1Δ mutants exhibit distinct phenotypes from cik1Δ . It is likely that only the Cik1/Kar3 complex is required for the establishment of chromosome bipolar attachment . Cik1 and Vik1 are the two Kar3 partners in budding yeast and Cik1/Kar3 localizes along the length of the spindle , and probably at interpolar microtubule plus ends [25] , [33] . Kar3 was also found to bind to the kinetochore to promote its transport along astral microtubules towards spindle poles [26] . The similar mitotic defects in cik1 and kar3 mutants suggest that Cik1 likely mediates the association of Kar3 with kinetochores . Based on the genome-wide yeast two-hybrid assay , Kar3 was shown to interact with Nnf1 , a kinetochore protein in the MIND complex , [7] , [47] , [48] . Using a co-immunoprecipitation ( co-IP ) approach we found that Nnf1-myc was able to pull down Kar3-HA , and vice versa , confirming the Kar3-kinetochore interaction in vivo , although it remains inconclusive whether this Kar3-Nnf1 interaction is direct ( Figure 6A ) . Then , we examined Kar3-Nnf1 interaction in the absence of either Cik1 or Vik1 . As shown in Figure 6B , deletion of CIK1 but not VIK1 abolished this interaction completely , suggesting that the Kar3-kinetochore interaction is dependent on Cik1 . Interestingly , this interaction was obviously increased in vik1Δ mutant cells , presumably because more Kar3 protein is available for the binding to kinetochores . Consistently , chromatin immunoprecipitation ( ChIP ) data shows diminished Kar3-centromere association in cik1Δ cells ( Figure 6C ) . If Cik1 mediates Kar3-Nnf1 interaction , the overexpression of the coiled-coil domain of Cik1 should disrupt this interaction , because CIK1-CC overexpression disrupts Cik1-Kar3 interaction ( Figure 1A ) . Indeed , Kar3-HA failed to pull down Nnf1-myc in cells overexpressing CIK1-CC ( Figure 6D ) . Data from the Sorger lab suggest that Kar3 may associate with detached kinetochores [43] . Moreover , Kar3 is essential for the lateral sliding of chromosomes towards spindle poles during S-phase [27] . Therefore , the association of Kar3 with kinetochores might be cell cycle regulated . To test this possibility , we compared Kar3-Nnf1 interaction in different cell cycle stages . Kar3 interacted with Nnf1 in both HU- and nocodazole-arrested cells ( Figure 6E ) , when bipolar attachment has not established yet . cdc13-1 mutant cells arrest at preanaphase with established bipolar attachment [40] . Interestingly , the Kar3-Nnf1 interaction was not detected in cdc13-1 mutant cells after 2 hr incubation at 32°C ( Figure 6F ) . Similarly , Kar3 did not associate with Nnf1 in cdc15-2-arrested telophase cells . The decreased Kar3-Nnf1 interaction in cdc13-1 or cdc15-2 mutant cells could be due to the degradation of Cik1 [49] , thus the Cik1 protein levels were examined in WT , cdc13-1 , and cdc15-2 mutant cells incubated at permissive or no-permissive temperatures . It is clear that the mutant cells exhibit Cik1 protein levels comparable to WT cells when incubated at high temperatures ( Figure 6G ) . The results suggest that the Cik1/Kar3 complex associates with kinetochores before the establishment of bipolar attachment . This association might be essential for chromosome transport as well as the achievement of bipolar attachment , but lack of this mechanism will contribute to syntelic attachment . The DASH kinetochore complex contains 10 protein subunits including Dam1 and Ask1 . Unlike other kinetochore proteins , the association of the DASH complex with kinetochores occurs only after kinetochore-microtubule interaction [2] . Interestingly , kar3Δ has been shown to be synthetically lethal with dam1-1 mutant [50] . We found that overexpression of CIK1-CC caused lethality to ask1-2 and ask1-3 mutants ( Figure 7A ) . One possibility is that the Cik1/Kar3 complex promotes bipolar attachment by inducing the DASH-kinetochore interaction . Therefore , we performed ChIP assays to examine DASH-centromere interaction in synchronous cell cultures . cdc13-1 mutants were used to arrest cells at preanaphase , when DASH complexes have already been loaded onto centromeres [2] . Interestingly , cdc13-1 cik1Δ cells exhibited reduced Ask1-centromere interaction in G1 phase as well as after G1 release for 90 min ( Figure 7B ) . In order to determine whether the Ask1 binding defect is a result of impaired kinetochore integrity , we also compared the centromere binding of another kinetochore protein Nnf1 . In contrast to Ask1 , the association of Nnf1 with centromeric DNA was similar in synchronous cdc13-1 and cdc13-1 cik1Δ cells either before or after G1 release ( Figure 7C ) , indicating that the core kinetochore structure is intact . These results suggest that the Cik1/Kar3 complex facilitates the association of the DASH complex with the core kinetochore proteins . It is our future interest to determine if decreased DASH-kinetochore interaction is the cause or a consequence of syntelic attachments .
In budding yeast , Kar3 is the only kinesin with minus-end-directed motor activity and the interaction with its partners , Cik1 and Vik1 , is essential for its motor activity . Our data clearly demonstrate the presence of syntelic attachments in cells lacking Cik1/Kar3 function based on the following observations: first , in the absence of the spindle checkpoint , the loss of function of Cik1/Kar3 induced significant sister-chromatid co-segregation into the daughter cell . Moreover , a dysfunctional tension checkpoint is sufficient to enable anaphase entry as well as co-segregation of sister chromatids in cells lacking Cik1/Kar3 function . Because it is well established that cells with tensionless chromosomes require the tension checkpoint for cell cycle arrest , this result indicates the presence of chromosomes with syntelic attachment in cells lacking functional Cik1/Kar3 . Furthermore , our live-cell imaging data show the migration of both sister chromatids along with one spindle pole during anaphase in sgo1Δ cells overexpressing CIK1-CC . As recent evidence indicates that residual sister chromatid cohesion remains during early anaphase [51] , one can argue that the sister chromatid co-segregation is a consequence of monotelic attachment , where only one of the sister kinetochores connects to the spindle pole but the other co-migrates with the sister because of the residual cohesion . Our data suggest that this scenario is unlikely , because we detected separated CEN4-GFP dots during anaphase in a sgo1Δ cell overexpressing CIK1-CC , indicating the absence of cohesion . In addition , we found that dysfunctional Cik1/Kar3 also induces sister chromatid co-segregation in cohesin mutant cells . Therefore , we conclude that inactivation of the Cik1/Kar3 complex induces syntelic attachment . In cells overexpressing the coiled-coil domain of CIK1 , we observed delayed Pds1 degradation , indicating the activation of the spindle checkpoint . However , either ipl1-321 or sgo1Δ abolished this delay completely . In addition , both ipl1-321 and sgo1Δ mutants show normal timing of spindle elongation when CIK1-CC is overexpressed , resulting in a high frequency of sister-chromatid co-segregation that is comparable to mad1Δ mutants . Previous data indicate that ipl1 mutants , but not sgo1 , generate detached chromosomes when these chromosomes are not under tension [22] , thus it is speculated that Ipl1 activates the spindle checkpoint by generating detached chromosomes when tension is absent . However , the complete loss of checkpoint function in sgo1 mutant cells overexpressing CIK1-CC suggests that the generation of detached chromosomes is not necessary to activate the spindle checkpoint in the presence of syntelic attachments . Interestingly , we found that ipl1-321 mutants show checkpoint defect even when grown at 25°C . Therefore , the decreased kinase activity in ipl1-321 mutants at the permissive temperature may be unable to execute the checkpoint function , but is sufficient to support normal chromosome segregation . Although our data indicate that cells lacking functional Cik1/Kar3 show syntelic attachments , the molecular function of this motor complex in chromosome bipolar attachment remains elusive . One possible explanation is that the Kar3-dependent poleward chromosome movement facilitates chromosome bipolar attachment . After the initial chromosome capture , Cik1/Kar3-mediated sliding of this chromosome along spindle microtubules will orient sister kinetochores so that the detached kinetochore will face the opposite spindle pole , thereby facilitating bipolar attachment . In agreement with this possibility , we found that Cik1 mediates the association of Kar3 with kinetochores and this association only occurs before chromosome bipolar attachment . In mammals and flies , kinetochore dynein mediates the poleward chromosome movement , which may also facilitate the correct orientation of sister kinetochores through a similar mechanism [52] , [53] . Another possibility is that the abnormal spindle structure in cik1 and kar3 mutant cells may contribute to the high frequency of syntelic attachments . At 37°C , cik1Δ and kar3Δ mutants arrest with a dot-like spindle structure and many mutant cells show spindle defects even when incubated at room temperature [23] , [29] , [33] . We examined chromosome bipolar attachment in cells arrested at preanaphase and found that the disruption of Cik1/Kar3 function by overexpressing CIK1-CC increases the chance of co-localization of CEN4-GFP with one spindle pole in cells with a metaphase spindle that appears normal . This result suggests that the bipolar attachment defect in cells lacking Cik1/Kar3 function could be independent of the spindle defect . To separate the spindle and kinetochore functions of Cik1/Kar3 , we need to identify the kinetochore protein that directly binds to the Cik1/Kar3 complex and define the domain responsible for this interaction . Mutation of this domain may selectively disrupt the kinetochore function of Cik1/Kar3 but maintain its spindle function . In mammalian cells , syntelic attachment can be induced by treatment with a small molecule monastrol , which inhibits the activity of a kinesin Eg5 [54] , [55] . Moreover , the treatment of mammalian cells with a low-dose of taxol will prevent tension generation on chromosomes [56] . In budding yeast , two methods have been widely used to introduce a tension defects for the study of the tension sensing mechanism . One is to completely block DNA replication where the absence of sister chromatids prevents tension generation , while another method is to abolish sister chromatid cohesion by using temperature sensitive mcd1/scc1 mutants or by expressing MCD1 from a galactose inducible promoter [19] , [20] . The disadvantage of these methods is that Pds1 protein level is the only marker to monitor the checkpoint activity . Because the lack of sister-chromatid cohesion allows the spindle to elongate regardless of checkpoint activation , we cannot use spindle elongation to monitor anaphase entry . Moreover it is difficult to analyze the role of the tension checkpoint in faithful chromosome segregation . Another disadvantage is that these methods will kill the cells after the induction of tension defects , thus it is impossible to perform a genetic screen for additional genes that are required for survival in the presence of tension defects . Here we report a new approach to induce syntelic attachments by inactivating the Cik1/Kar3 motor complex , which prevents tension generation on chromosomes but maintains intact kinetochore attachment . We have demonstrated that overexpression of the coiled-coil domain of Cik1 from a GAL promoter disrupts Cik1-Kar3 interaction , which allows us to conditionally induce syntelic attachment by growing cells with PGALCIK1-CC plasmid in galactose medium . This approach will be a critical tool to study the response to tension defects in budding yeast .
The strains used in this study are derivatives of W303 and listed in Table S1 . Gene deletions and epitope tagging were performed by using a PCR-based protocol [57] . The PGALCIK1-CC plasmid was constructed by inserting the CIK1 coiled-coil fragment into a CEN-TRP-GAL-myc vector . To arrest yeast cells in G1 phase , 5 µg/ml α-factor was added into mid-log phase cells grown in YPD or in TRP dropout medium containing 2% raffinose at 25°C for 2 . 5 hr . G1-arrested cells were centrifuged and washed twice with water to release into YPD at 32°C for cdc13-1 arrest or TRP dropout medium containing 2% galactose at 25°C for CIK1-CC overexpression . To block the next cell cycle , 15 µg/ml α-factor was added when majority of the cells were budded . Hydroxyurea was purchased from ACROS Organics and the final concentration was 100 mM for HU plates . The yeast protein samples were separated and detected as described previously [58] . Protein samples were prepared using an alkaline method and were resolved by 10% SDS- PAGE . Primary antibodies ( anti-myc and anti-HA ) were purchased from Covance ( Madison , WI ) , and anti-Pgk1 antibody was from Molecular Probes ( Eugene , OR ) . The HRP-conjugated secondary antibody was purchased from Jackson ImmunoResearch ( West Grove , PA ) . Cells were collected and fixed with 3 . 7% formaldehyde for 15 min at room temperature . The cells were washed once with 1×PBS ( pH7 . 2 ) and then resuspended in 1×PBS buffer to examine fluorescence signals with a microscope ( Zeiss Axioplan 2 ) . Cell cultures were collected and washed once with water . After being resuspended in RIPA buffer ( 25 mM Tris PH7 . 5 , 10 mM EDTA , 150 mM NaCl and 0 . 05% Tween-20 ) supplied with protease inhibitors , cells were homogenized with a bead-beater . The resulting cell extracts were incubated with primary antibody overnight at 4°C . The cell extracts were then incubated with protein-A conjugated agarose beads ( Santa Cruz Biotechnology ) , which was pre-incubated with BSA at 4°C . After incubation for 1 hr , the beads were collected by centrifugation and washed with RIPA buffer for three times . Equal volume RIPA and protein loading buffer were added and the protein samples were boiled for 5 min for Western blot analysis . The ChIP assay was performed as described previously [59] . For live-cell microscopy , we used a concave glass slide as a culture chamber , which was filled with 2% agarose dissolved in galactose medium . The agarose pad was solidified for 5 min at room temperature before use . Cells were first arrested in G1 phase in raffinose medium . After release into galactose medium for 2 hr , 1 . 5 µl concentrated cells were laid on the top of the agarose pad , which was then sealed with a piece of cover glass . Live-cell microscopy was carried out on a DeltaVision imaging system equipped with an environmental chamber ( Applied Precision , Inc . ) . All live-cell images were acquired at 25°C with a 100× ( NA = 1 . 41 ) objective lens on an Olympus ix71 microscope . A total of 8 z-stacks were collected at each time point and each optical section was 0 . 5 µm thick . Exposure time for each optical section was set between 60 and 100 ms and the time-lapse interval was set at 2 min . Projected images were used for display . | Chromosome bipolar attachment occurs when sister chromatids are attached by microtubules emanating from opposite spindle poles and is essential for faithful sister-chromatid segregation . Chromosomes are under tension once bipolar attachment is established . The absence of tension is sensed by the tension checkpoint that prevents chromosome segregation . The attachment of sister chromatids by microtubules from the same spindle pole generates syntelic attachment , which fails to generate tension on chromosomes . However , a reliable method to induce syntelic attachment is not available . Our findings indicate that the inactivation of the motor complex , Cik1/Kar3 , results in chromosomes with syntelic attachment in budding yeast . In the absence of the tension checkpoint , yeast cells with dysfunctional Cik1/Kar3 enter anaphase , resulting in co-segregation of sister chromatids . Therefore , with this method we can experimentally induce syntelic attachment in yeast and investigate how cells respond to this incorrect attachment . | [
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] | 2012 | Loss of Function of the Cik1/Kar3 Motor Complex Results in Chromosomes with Syntelic Attachment That Are Sensed by the Tension Checkpoint |
Cyanobacteria are the only model circadian clock system in which a circadian oscillator can be reconstituted in vitro . The underlying circadian mechanism appears to comprise two subcomponents: a post-translational oscillator ( PTO ) and a transcriptional/translational feedback loop ( TTFL ) . The PTO and TTFL have been hypothesized to operate as dual oscillator systems in cyanobacteria . However , we find that they have a definite hierarchical interdependency—the PTO is the core pacemaker while the TTFL is a slave oscillator that quickly damps when the PTO stops . By analysis of overexpression experiments and mutant clock proteins , we find that the circadian system is dependent upon the PTO and that suppression of the PTO leads to damped TTFL-based oscillations whose temperature compensation is not stable under different metabolic conditions . Mathematical modeling indicates that the experimental data are compatible with a core PTO driving the TTFL; the combined PTO/TTFL system is resilient to noise . Moreover , the modeling indicates a mechanism by which the TTFL can feed into the PTO such that new synthesis of clock proteins can phase-shift or entrain the core PTO pacemaker . This prediction was experimentally tested and confirmed by entraining the in vivo circadian system with cycles of new clock protein synthesis that modulate the phosphorylation status of the clock proteins in the PTO . In cyanobacteria , the PTO is the self-sustained core pacemaker that can operate independently of the TTFL , but the TTFL damps when the phosphorylation status of the PTO is clamped . However , the TTFL can provide entraining input into the PTO . This study is the first to our knowledge to experimentally and theoretically investigate the dynamics of a circadian clock in which a PTO is coupled to a TTFL . These results have important implications for eukaryotic clock systems in that they can explain how a TTFL could appear to be a core circadian clockwork when in fact the true pacemaker is an embedded biochemical oscillator .
The mechanism of circadian ( daily ) clocks in eukaryotes is generally thought to be based upon autoregulatory transcriptional/translational feedback loops ( TTFLs ) [1] , [2] . When the essential components of the circadian clock in the prokaryotic cyanobacterium Synechococcus elongatus were identified [3] as the proteins KaiA , KaiB , and KaiC , the initial interpretation that the core of this prokaryotic clockwork might also be a TTFL was based on the same kind of evidence that supports TTFL oscillators in eukaryotes , namely: ( i ) rhythms of abundance for mRNAs and proteins encoded by “clock genes , ” ( ii ) feedback of clock proteins on their gene's transcription , and ( iii ) phase setting by experimental expression of clock proteins [3]–[5] . However , later studies found data that were inconsistent with a core TTFL oscillator in cyanobacteria; e . g . , global inhibition of transcription and translation had little effect upon the circadian rhythm of KaiC phosphorylation [6] , and the promoters driving kaiBC gene expression could be replaced with non-specific heterologous promoters without disturbing the circadian rhythm [7] , [8] . Moreover , prolonged treatments of cyanobacterial cells with the protein synthesis inhibitor chloramphenicol did not perturb the phase of the circadian system after return to normal conditions [4] , [9] . Then in 2005 came the astonishing discovery that the phosphorylation status of KaiC in vitro continued to cycle when the three Kai proteins were combined in a test tube with ATP [10] . This in vitro rhythm persists with a circa-24 h period for at least 10 d and is temperature compensated [10] , [11] , which indicates that a circadian temperature compensation mechanism is also encoded in the characteristics of the three Kai proteins and the nature of their interactions . In vivo , this three-protein biochemical oscillator operates as a post-translational oscillator ( PTO ) [6] , [10] . Clearly , a TTFL is not necessary for the circadian rhythm of KaiC phosphorylation . The PTO manifests itself in vitro as three different rhythms that are probably interrelated . The first is the originally observed rhythm of KaiC phosphorylation [10] . The second is a rhythm of formation of complexes among KaiA , KaiB , and KaiC [12] , [13] , and the third is a rhythm of ATP hydrolysis [14] . At this time , it is not clear which of these rhythms is the most fundamental to the PTO mechanism or whether they are all so tightly intermeshed as to be inseparable . Some or all of these rhythmic processes may also be involved in the control of outputs through interactions with other proteins such as SasA and/or RpaA [15] . Moreover , while each of these three rhythms can be measured in vitro , only the KaiC phosphorylation rhythm can be monitored in vivo ( as a rhythmic shift of KaiC mobility on immunoblots ) . Therefore , in this paper , the phosphorylation rhythm will be taken as the indicator of the PTO in vivo . Since the kaiABC gene cluster is essential for rhythms in vivo and the rhythm of KaiC phosphorylation could run without a TTFL in vitro [10] and in vivo [4] , [6] , those results implied that the KaiABC oscillator was the self-sustained core pacemaker and that transcription and translation was involved only in the output [6] , [10] . More recently , however , Kitayama and coworkers suggested that “transcription- and translation-based oscillations in KaiC abundance are also important for circadian rhythm generation in cyanobacteria” [16] . First , those authors reported that over-expression of KaiA resulting in constitutively hyper-phosphorylated KaiC ( a “clamp” of KaiC phosphorylation status ) allows circadian rhythms of gene expression as monitored by promoter-driven luciferase reporters in vivo . Moreover , mutants of KaiC that mimicked constitutive hyperphosphorylation or hypophosphorylation allowed rhythms in vivo . The key phospho-regulatory sites on KaiC are S431 and T432 [17] , [18]; Kitayama and coworkers reported that substitution of glutamate on those two residues ( KaiCEE ) created a constitutively hyper-phosphorylated KaiC mutant strain that “showed a dampened but clear rhythm with a period of 48 h” [16] . Because cyanobacterial cells apparently exhibited oscillations when the KaiABC oscillator was inactivated by clamping the phosphorylation status of KaiC , these two experimental approaches led Kitayama and coworkers to conclude that “transcription-translation oscillates even in the absence of the KaiC phosphorylation cycle and that this oscillation could persist regardless of the phosphorylation state and kinase activity of KaiC” [16] . These results therefore opened the possibility that the KaiABC oscillator ( PTO ) was not an obligatory core oscillator in cyanobacteria . We have extended the experiments of Kitayama and coworkers , and our results lead us to different interpretations , namely that the TTFL is a damped slave oscillator while the PTO is the core pacemaker . Our results indicate that repression of the KaiC phosphorylation rhythm by KaiA overexpression strictly correlates with the suppression of the larger circadian system . We find that the rhythms generated by cells expressing KaiCEE are clearly damped and of long period . Moreover , the damped rhythms exhibited by KaiCEE are not compensated for metabolic activity and therefore cannot be considered as a bona fide circadian phenomenon . These results direct us towards a model of the entire system that explains how the core pacemaker can be a PTO while having input from a TTFL . The implications of this organization extend beyond the cyanobacterial case and encourage a re-evaluation of the evidence for a core TTFL in eukaryotic circadian clocks .
Compared with the rhythm of KaiC abundance that could be the result of a TTFL involving KaiC expression [4] , [6] , the KaiC phosphorylation rhythm is the most reproducible molecular rhythm that can be measured in vivo under a range of conditions in S . elongatus . In both LL and DD , KaiC phosphorylation is robustly rhythmic , despite the fact that KaiC abundance is rhythmic in LL but not in DD [6] . Figure 1 shows that KaiC abundance is rhythmic in LL as noted before [4] with a concomitant rhythm of KaiC phosphorylation ( Figure 1A , 1B; see Figure S1A and S1B for representative immunoblots ) . However , in a light/dark cycle of 12 h light , 12 h dark ( = LD 12:12 ) , there is not a clear daily rhythm of KaiC abundance , while the KaiC phosphorylation rhythm remains robust with a phase relationship that is similar to that in LL ( Figures 1C , 1D , S1 ) . The abundances of KaiA and KaiB can also be arhythmic in LD 12:12 ( representative example shown in Figure S2 ) . Note that LD 12:12 is more relevant to the organism in nature than LL , and yet there is no reproducible KaiC abundance rhythm that would be expected to result from a TTFL . The result of Figure 1C is initially inexplicable since global transcription is strongly dependent upon light in S . elongatus [6] , and therefore a rhythm of KaiC abundance would be expected . However , we discovered that KaiC degradation is also strongly light-dependent; in darkness , KaiC degradation is inhibited ( Figure S3 ) . Therefore , synthesis and degradation of KaiC is counterbalanced in the light phase of LD , while KaiC is neither synthesized [6] nor degraded ( Figure S3 ) in the dark phase of LD; consequently , KaiC abundance remains nearly constant in LD ( Figure 1C ) . On the other hand , in LL the synthesis of KaiC is rhythmic but degradation continues in the subjective night , leading to a rhythm of KaiC abundance in LL [19] . To test whether the KaiC phosphorylation rhythm was disrupted by metabolic noise , we altered the environmental conditions to LD 2:2 ( 2 h light followed by 2 h darkness ) . Because S . elongatus is an obligate photoautotroph that is absolutely dependent upon photosynthesis for energy , a high frequency light/dark cycle will have major effects on intracellular photosynthesis , redox status , and metabolism . The 4 h cycle of LD 2:2 allowed the persistence of a circa-24 h rhythm of luminescence as a reporter of circadian gene expression ( Figure S1C ) . Under these conditions , there is a noisy and possibly rhythmic pattern of KaiC abundance while there is a robust and clear rhythm of KaiC phosphorylation ( Figure 1E and 1F ) . Therefore , it is the KaiC phosphorylation rhythm ( an indicator of the PTO ) that is the most reproducible rhythm under a wide range of in vivo conditions ( LL , LD 12:12 , and LD 2:2 ) , not the KaiC abundance rhythm that could be a direct consequence of a TTFL oscillator . As described in the Introduction , Kitayama and coworkers [16] concluded that “transcription-translation oscillates even in the absence of the KaiC phosphorylation cycle and that this oscillation can persist regardless of the phosphorylation state and kinase activity of KaiC” [16] . One of the main lines of evidence that they marshaled to support their conclusions was that constitutive hyper-phosphorylation of KaiC—either by over-expression of KaiA or by mutation of KaiC—allowed the in vivo system to operate relatively normally . In the in vitro system , a higher proportion of KaiA causes KaiC hyperphosphorylation and a suppression of the in vitro oscillation ( Figure S4 ) , so it is reasonable to hypothesize that KaiA overexpression in vivo will have the same effect . However , when we express KaiA in vivo , we find either different results and/or interpret the data from a different perspective than the report of Kitayama et al . [16] . First , Kitayama and coworkers over-expressed KaiA under the control of an IPTG-inducible construct and found concentrations of IPTG that apparently suppressed the KaiC phosphorylation rhythm but allowed the rhythm of gene expression ( as monitored by a luminescence reporter ) to continue [16] . We used the same inducible construct to express KaiA and find in contrast a clear correlation between the suppression of the KaiC phosphorylation rhythm by KaiC hyperphosphorylation and the rhythm of gene expression , as depicted in Figure 2 . In response to increasing concentrations of the inducer IPTG , KaiC becomes progressively more hyperphosphorylated ( insets to panels A–G of Figure 2 ) , and the luminescence pattern damps to arhythmicity . When the suppression of the phosphorylation rhythm is quantified and normalized ( Figure 2I ) , it correlates precisely with the suppression of the luminescence rhythm ( Figure 2H ) that reflects the global rhythm of promoter activity ( Figure 2J ) . This precise correlation strongly supports the interpretation that repression of the PTO ( as gauged by the KaiC phosphorylation rhythm ) leads inevitably to the suppression of the emergent global rhythm of gene expression ( Figure 2J ) . The basis of the discrepancy between our results and those of Kitayama and coworkers is not clear , but it might be explained as a population phenomenon—perhaps there are a few cells in the population with a higher resistance to the IPTG induction that continue to oscillate their KaiC phosphorylation and luminescence in the experiments of Kitayama and coworkers; this could lead to an apparent suppression of KaiC phosphorylation in the population as measured by the relatively insensitive immunoblotting technique , whereas the few rhythmic cells confer a weak , low amplitude rhythm of luminescence ( which is a very sensitive gauge ) . Moreover , simulations with the model that we introduce below indicate that even a very low amplitude rhythm of KaiC phosphorylation in individual cells ( that might be undetectable by immunoblotting ) can result in a measurable oscillation of transcriptional activity ( Figure S5 ) . A second method by which Kitayama and coworkers achieved constitutive hyper-phosphorylation of KaiC was by mutation of the critical phospho-sites on KaiC ( S431 and T432 ) to glutamate residues , thereby creating KaiCEE ( i . e . , S431E and T432E ) . KaiCEE is a phosphomimetic of hyper-phosphorylated KaiC that cannot have its phosphorylation status regulated further since sites 431 and 432 are now blocked by the glutamate residues [16] . When the endogenous kaiC gene is replaced with a mutated gene encoding KaiCEE , there are long-period rhythms of luminescence in vivo at 30°C [16] . We can replicate the results of Kitayama and colleagues with KaiCEE , but we interpret those results differently . Our results may be seen most clearly by contrasting Figure 3A with Figure 3B . The KaiCWT strain oscillates robustly for at least 6–12 cycles in LL at 30°C with a period of ∼25 . 4 h ( there is a slight damping over time due to growth/density of cells and depletion of medium , Figure S6A ) . Conversely , with the KaiCEE strain , not only are the periods much longer ( average >50 h ) and more variable than for KaiCWT at 30°C , but more crucially the rhythms damp significantly over time ( Figures 3B , 4 , and S6B ) . This damping is a consistent feature of the rhythms with the KaiCEE strain at 30°C , and it is also evident in the data of Kitayama and coworkers [16] . Even more interesting , temperature compensation of the KaiCEE strain is not stable under different metabolic conditions . In healthy cultures on agar , colonies of wild-type and KaiCEE strains show temperature compensated rhythms ( Figure 3E versus 3F , 3I ) ; when the Q10 of these data are calculated ( as in [20] ) , the Q10 for cultures on agar was 1 . 08 for wild-type cells and 1 . 02 for KaiCEE cells ( however , the variability of periods among KaiCEE strains is significantly larger than for wild-type strains , Figure 3I ) . Conversely , for healthy cultures in liquid medium ( “planktonic cultures” ) , a temperature compensation defect is obvious in the KaiCEE strain ( Figure 3G versus 3H , 3I ) . In particular , planktonic wild-type cells show practically the same period at 24°C and 30°C ( Figure 3G , 3I ) with a Q10 = 1 . 08 . However , planktonic KaiCEE cells show dramatically different periods at 24°C versus 30°C ( Figure 3H , 3I; Q10 = 2 . 02 ) that are drastically more variable at 24°C than for wild-type cells ( compare Figure 3G with 3H , and S . D . bars in Figure 3I ) . Metabolic conditions for bacteria in liquid culture are different from those on agar , and therefore temperature compensation is dependent on metabolic conditions in the KaiCEE strain , but not in wild-type cells . Kitayama and coworkers also showed data for a strain expressing constitutively non-phosphorylated KaiC , named K294H , but we find the rhythms of this strain to be highly unstable with respect to period , phase , and amplitude as shown in Figure S7 and will therefore not be considered further here . Moreover , the number of cycles exhibited by KaiCEE strains before they damp out is a function of the number of environmental LD cycles experienced by the cells prior to release into LL ( Figures 4 and S8 ) . In these experiments , we grew cells expressing either KaiCWT or KaiCEE in LL , then gave them 1–2 cycles of LD with either 12 or 24 h dark intervals , followed by a final release to LL and the monitoring of luminescence . For cultures expressing KaiCWT , there were consistent and robust rhythms in the final LL that damped at a slow rate ( Figures 4 and S8 ) . On the other hand , cells expressing KaiCEE exhibited obvious damping that was a function of the number of prior cycles of LD—two cycles of LD promoted longer-lasting oscillations than one cycle of LD; this difference between rhythms expressed by KaiCWT versus KaiCEE strains was most obvious with the LD 24:24 conditions ( Figure 4E and 4F; compare Figure 4B versus 4D and Figure S8F versus Figure S8H ) . These data imply that the rhythms expressed by the KaiCEE strain are driven by a damped oscillator whose persistence can be cumulatively stimulated by increasing the number of cycles of driving stimuli . Building upon our previous stochastic model of the PTO [13] to simulate the larger circadian system that includes transcription and translation , we constructed a mathematical model where the core PTO oscillator is coupled to a “slave” TTFL oscillator in which the KaiB•KaiC complex nonlinearly suppresses transcription of the kaiBC gene ( see Methods and Text S1 for a complete model description ) . This negative feedback is based on generic TTFL repression adapted from a Drosophila clock model [21] . To understand how a damped , long-period oscillation might persist in the KaiCEE strain , it is only necessary to consider the TTFL portion of the model . Briefly , starting with low quantities of KaiB•KaiC , repression of kaiBC transcription is low and KaiB and KaiCEE protein abundances increase ( kaiB and kaiC genes are adjacent and transcribed as a dicistronic mRNA [3] ) . KaiB associates with KaiCEE and the level of KaiB•KaiCEE complex increases; this constitutively “hyper-phosphorylated” KaiB•KaiCEE complex acts non-linearly to repress further transcription of kaiBC mRNA . As KaiB and KaiCEE protein abundances reach their peak , degradation takes over the dynamics in LL such that KaiB•KaiCEE levels then drop , relieving the suppression of kaiBC and typically resulting in oscillatory dynamics . However , as the model shows , because the phosphorylation status of KaiCEE cannot be altered , the transcription and translation loop does not show sustained oscillations ( i . e . , kaiBC mRNA exhibits damped oscillatory dynamics in LL , Figure 3D ) . On the other hand , inclusion of a PTO with the TTFL for KaiCWT generates robust and consistent oscillations ( Figure 3C ) . In addition , the period of the damped KaiCEE cycles are significantly longer than those of the sustained KaiCWT cycles ( compare Figure 3C with 3D ) . Therefore , a generic TTFL model can accurately reproduce the damped , long-period oscillation of cells expressing KaiCEE . We therefore conclude that the data with KaiCEE ( Figures 3B , 4 , ref . [16] ) can be faithfully interpreted in terms of a damped “slave” TTFL oscillator in which a self-sustained PTO pacemaker is embedded . We used the same TTFL repression function from the KaiCEE model described in the previous section to simulate the in vivo oscillator with KaiCWT consisting of a PTO and TTFL in LL , DD , or LD conditions . The resulting simulations compare changes in protein , mRNA , and phosphorylation levels with experimental data . Briefly ( and in simplified form ) , the PTO portion of the model consists of the following cycle ( not including the dissociation kinetics ) :where “Ci” = KaiC hexamers with “i” number of phosphates , “A” = KaiA , “B” = KaiB , “A•C0” is the complex between KaiA and unphosphorylated KaiC , and so on . The exchange of KaiC monomers among the hexamers synchronizes the phosphorylation status within the population of KaiC molecules [11] , . In our ordinary differential equation ( ODE ) model , initially unphosphorylated KaiC binds KaiA and proceeds approximately in a sequence through the phosphorylation states C1 , C2 , … C12 . Hyper-phosphorylated KaiC associates with KaiB , and the KaiB•KaiC complex sequesters KaiA . This sequestration nullifies the stimulatory effect of KaiA and the system dephosphorylates . Synchronization of the phosphorylation status among the KaiC hexamers in the population by KaiC monomer exchange results in sustained oscillations that do not dampen . On the other hand , in the absence of synchronization , this “cyclic” ODE system shows damped oscillations , as we had previously observed with an explicit stochastic matrix model of hexamer interactions [13] . The complete ODE model that comprises the PTO and TTFL includes the association/dissociation kinetics for each state of KaiC with KaiA and KaiB , as well as KaiC auto-phosphorylation and auto-dephosphorylation kinetics . We have not included a more complicated site-dependent ( S431 and T432 ) model of KaiC phosphorylation as experimentally observed [22] , [23] , resorting instead to a simpler yet effective description of net KaiC phosphorylation level within the population ( this PTO model using net KaiC phosphorylation levels and complex association/dissociation kinetics implicitly includes the effects of site-dependence in a phenomenological manner ) . We modeled the TTFL using a generic nonlinear repression term ( as in the KaiCEE case described in the previous section ) and a light-dependent protein degradation term ( based on the data of Figure S3 ) . For the KaiCWT strain , the PTO shows robust sustained circadian oscillations in net phosphorylation level ( Figure 5A ) for a variety of parameter choices . Inclusion of a TTFL without any specific parameter “tuning” results in a circadian oscillation in KaiB/KaiC protein abundances , kaiBC mRNA levels , and KaiC phosphorylation status in LL ( Figure 5B ) . In DD when transcription/translation and protein degradation is turned off ( Figure S3 and [6] ) , there is a circadian KaiC phosphorylation rhythm due solely to the PTO ( Figure 5A ) . Therefore , this model that combines a core PTO plus a damped “slave” TTFL oscillator can accurately reproduce the sustained circa-24 h rhythms of protein/mRNA abundances and KaiC phosphorylation in the KaiCWT strain , while the TTFL portion alone can create damped , long-period oscillations of kaiBC mRNA as observed in cells expressing KaiCEE ( Figures 3C , 3D , 5A , and 5B ) . As further evidence that the PTO can function as the core circadian pacemaker in the larger system , we included noisy fluctuations in the concentration of unphosphorylated KaiC in the PTO/TTFL model . For the PTO alone , both ( i ) the stochastic matrix model for small molecular numbers ( unpublished data ) and ( ii ) the ODE model allow robust circadian oscillations in the presence of noisy fluctuations of unphosphorylated KaiC levels ( Figure 5C ) . When the TTFL is included , the simulated system continues to show robust circadian oscillations in the phosphorylation rhythm and circadian dynamics for the protein abundance even though the same external noise fluctuations were introduced as in the PTO simulation ( Figure 5D ) . These modeling data are supported by experimental data in the LD 2:2 cycle—the 4 h light/dark cycle drives a 4 h modulation of photosynthesis and metabolism , leading to a noisy KaiC abundance pattern ( Figure 1E ) ; nevertheless , KaiC phospho-status exhibits a clean circadian rhythm ( Figure 1F ) as does the luminescence indicator of transcriptional activity ( Figure S1C ) . These simulations of noisy KaiC abundance examine typical fluctuations that may occur in cellular components due to intrinsic noise in transcriptional and translational processes , cell division , and external random perturbations on the clock components . We also simulated the effect of non-random perturbations of abundance on the system both for the PTO alone and including the TTFL . For example , experimental manipulation of KaiC abundance as pulsative increases in KaiC levels has been reported to reset the phase of the circadian system in vivo [3] , [4] . To examine the effects of non-random KaiC perturbations , we first introduced a sinusoidally driven rhythm of KaiC protein abundance to determine how the PTO would respond . The PTO's phosphorylation rhythm was begun in different initial phases relative to the driving KaiC sinusoidal abundance ( Figure 5E ) . After a few “sorting out” cycles , the PTO system shows “phase locking” to the abundance rhythm in a specific phase relationship . The intuitive reason for this effect is that the KaiC phosphorylation rhythm is optimal only when synthesis of unphosphorylated KaiC occurs in phases near the trough of the phosphorylation rhythm ( see below ) . When the TTFL is included , the effect of driving the KaiC abundance externally results in an analogous phase-locking effect ( Figures 5F , 6A ) . One experimental test of the model's prediction can be provided by kaiBC transcription and translation under the control of an oppositely phased promoter—the purF promoter [24] . The cyanobacterial clock system under directed anti-phase expression of the kai genes was reported to have phase relationships that are practically the same as wild-type [24] , and we have confirmed those results ( unpublished data ) . Those results support the model's prediction of “locking in” to the preferred phase relationship between the KaiC phosphorylation rhythm and synthesis of unphosphorylated KaiC . Another experimental test of the model's prediction is to experimentally create a cycle of new KaiC synthesis ( of necessarily unphosphorylated KaiC ) in vivo that begins in different initial phase relationships to the rhythm of KaiC phosphorylation . As shown in Figure 6A ( and Figure 5F ) , these varying phase relationships should resolve after a few cycles into a single steady-state phase relationship between new KaiC synthesis and the KaiC phosphorylation rhythm . Using a strain with additional KaiC expression driven by an IPTG-inducible promoter ( trcp ) at an ectopic site in the chromosome , we experimentally created a 24 h cycle ( 12:12 ) of new KaiC synthesis within a physiological range of KaiC abundance by administering two cycles of 5 µM IPTG ( 12 h IPTG , 12 h no-IPTG , 12 h IPTG , then a free-run without IPTG; Figure S9A ) . This concentration of IPTG will increase KaiC levels by 40%–50% over basal values and it concomitantly decreases the phosphorylation status of KaiC ( Figure S9B–D ) . Four cultures that had been phased into four distinct phases ( 0 , 6 , 12 , and 18 h apart ) were treated with this IPTG:no-IPTG cycle . As shown in Figure 6B , we obtained the clear result that two cycles of new KaiC synthesis caused a locking of the four cultures to a single synchronous phase . Of particular significance is that the phase relationship of these synchronized luminescence rhythms to the cycle of new KaiC synthesis was as predicted by the model . Therefore , the core biochemical PTO is not totally insensitive to changes in levels of unphosphorylated KaiC , but it can be entrained by cycles of KaiC that result from the damped TTFL .
This investigation is the first to our knowledge to investigate the dynamics of a circadian clock in which a PTO is coupled to a damped TTFL . Our data strongly support the interpretation that when the cyanobacterial PTO is suppressed , the emergent circadian rhythms , including the TTFL and global gene expression , are concomitantly suppressed . Moreover , when the PTO is suppressed—either by KaiA-overexpression or by mutation of KaiC—the remaining TTFL shows clear characteristics of a damped oscillator that is effectively a “slave” of the self-sustained PTO . As shown in Figure 7 , our model proposes that the PTO is embedded within a TTFL with the KaiB•KaiC complex repressing transcriptional activity through control of chromosomal topology [25] and/or transcriptional factors such as RpaA [15] . This pervasive transcriptional activity regulates global gene expression but also rhythmically regulates new synthesis of KaiA , KaiB , and KaiC , thus completing a transcription and translation loop ( Figure 7 ) . Despite the fact that the PTO can oscillate independently of the TTFL in vitro and in vivo [6] , [10] , the PTO is not totally independent from the TTFL in vivo . The data and simulations depicted in Figure 5C and 5D imply that stochastic changes in KaiC abundance will not eliminate the circadian dynamics for physiological perturbations . However , experimental manipulations of KaiC abundance as pulsative increases in KaiC levels have been reported to reset the phase of the circadian system in vivo [3] , [4] . How can these disparate conclusions be reconciled ? A likely answer is that because the newly synthesized KaiC monomers/hexamers are necessarily unphosphorylated , the phase at which they are added into the PTO is critical . If they are added at a phase when most of the KaiC hexamers are phosphorylated , then the newly synthesized proteins will monomer-exchange into the existing population of KaiC hexamers [11]–[13] and alter the phospho-status of the KaiC population , potentially disrupting the PTO . On the other hand , if the newly synthesized and unphosphorylated KaiC proteins are added at a phase when the KaiC population is largely unphosphorylated , then the PTO will not be disrupted and will even be potentially reinforced . Therefore , regular cycles of new KaiC synthesis can entrain . On the other hand , if the KaiC abundance fluctuates randomly with a relatively low amplitude , the system is resilient and its circadian nature dominates . This interpretation is supported by both the experimental data and by the modeling simulations . When KaiC abundance is constant , its phosphorylation status oscillates with a circadian period ( experimental data in [6]; simulations: Figure 5A ) . When KaiC abundance is allowed ( or forced ) to oscillate , then the PTO locks into a preferred phase relationship to the KaiC synthesis rhythm ( resulting from the TTFL ) such that newly synthesized KaiC is introduced at phases of the PTO when KaiC is relatively hypo-phosphorylated ( experimental data: Figure 6B; simulations: Figures 5E , 5F , and 6A ) . When the synthesis of KaiC is driven at an unusual phase by an antiphase promoter , the PTO and TTFL nevertheless lock-in at the preferred phase relationship ( experimental data in [24] and our unpublished results; simulations: Figure 5F ) . What about entrainment to the environmental cycles ? Because transcription and translation proceed in the daytime but are turned off in darkness in photoautrophic S . elongatus cells [6] , there is a daily rhythm of synthesis of the Kai proteins in LD ( for the case in LL , see [19] ) . Therefore , it is reasonable to suppose that daily rhythms of total Kai protein abundance could be an entraining stimulus . However , we did not find such a rhythm of total KaiC abundance in LD 12:12 ( Figure 1C ) . At the same time , we found that degradation of KaiC is minimal in darkness ( Figure S3 ) , leading to the interpretation that KaiC synthesis and degradation proceed during the illuminated day phase but essentially counterbalance each other so that there is not a major change in the net KaiC abundance during the day . Then at night , KaiC's degradation is minimal ( Figure S3 ) and its transcription and translation is turned off [6] , resulting in a practically constant level of KaiC abundance over the LD 12:12 cycle ( Figure 1C ) . Therefore , it seems unlikely that changes in Kai protein abundance per se in an LD cycle could provide an entrainment mechanism . However , even though total KaiC abundance does not oscillate in LD 12:12 , new synthesis of KaiC does oscillate in LD ( on in the day , off at night ) . Because new synthesis of KaiC provides unphosphorylated protein and could thereby affect the ratio of hyper- to hypo-phosphorylated KaiC ( Figure S9D ) , this stimulus could contribute to the transduction pathway for entrainment . As shown in Figures 5E , 5F , and 6A , simulations predict such a phasing effect , and our experimental test of this prediction by providing a low amplitude rhythm of new KaiC synthesis supports this hypothesis ( Figure 6B ) . In this sense , the TTFL could play a role in both input and output pathways of the PTO pacemaker , as has been suggested for the mammalian clock [26] . A system composed of a biochemical pacemaker embedded within a damped transcription/translation loop has important implications . Biochemical reactions that involve small numbers of molecules are intrinsically noisy , being dominated by large concentration fluctuations [27] , [28] . In general , the number of transcription factor molecules in a prokaryotic cell is small and this could lead to a high intrinsic noise [29] . On the other hand , a PTO that is rooted in the phosphorylation status of thousands of molecules would be expected to be robust in the face of noise . In the case of KaiC , the current estimate is that there are approximately 10 , 000 KaiC monomers per cell [30] . The model of the PTO supports the hypothesis that the KaiABC PTO is resilient to noise . Figure 5C shows a simulation of noise in the PTO model by introducing fluctuations in the abundance of KaiC in a population of hexamers , and Figure 5D shows the same influence in the combined PTO/TTFL model . Despite the noisy KaiC abundance fluctuations , the circadian rhythm of KaiC phospho-status oscillates consistently , whether the PTO is considered separately ( Figure 5C ) or within the larger TTFL system ( Figure 5D ) . These modeling data are supported by experimental data in the LD 2:2 cycle—the 4 h LD cycle drives an ultradian modulation of metabolism , leading to a noisy KaiC abundance pattern ( Figure 1E ) ; nevertheless , KaiC phospho-status exhibits a clean circadian rhythm ( Figure 1F ) . Not only is the circadian system resilient within the cell , but it is robust among a population of cells . The experimental observation of reproducible rhythms among non-communicating cyanobacterial cells in populations [31] implies that the ODE model is applicable to modeling the mean population behavior of cells , with the noise reflecting population variance in LD ( Figure 5D ) . Resilience of the daily timekeeper is particularly important for cells that must keep accurate track of time in the face of cell division , when a TTFL might become perturbed because the ratio of DNA to transcriptional factors can change during replication and when DNA can become less accessible during chromosomal condensation in preparation for division . In bacterial oscillators that were experimentally designed to be strict TTFLs , cell division clearly disrupts the phasing and/or period of these synthetic clocks [32] . Cell division or chromosomal events should not , however , perturb a strictly biochemical oscillator , as observed in S . elongatus [31] , [33] . Therefore , evolution appears to have selected in cyanobacteria a core biochemical pacemaker that regulates a TTFL that in turn regulates global DNA topology and gene expression . Early evidence for a TTFL as the core pacemaker in the cyanobacterial system came from numerous studies that showed the same phenomena which has been used to support a TTFL model in eukaryotes , namely: ( 1 ) “clock genes” deemed to be essential based on knockout studies [3] , ( 2 ) rhythmic abundances of mRNAs and proteins encoded by clock genes [3] , [4] , [6] , ( 3 ) autoregulatory negative feedback of clock proteins on their gene's transcription [3] , [7] , and ( 4 ) phase setting by pulsatile expression of clock genes [3] , [4] . Eukaryotic circadian genes have no detectable homology to kaiABC sequences , so if there is an evolutionary relationship between the bacterial and eukaryotic systems , it is so diverged as to be undetectable by genetic sequence comparisons . But how about the possibility of convergent evolution to a fundamentally similar biochemical mechanism ? Could self-sustained biochemical core oscillators underlie eukaryotic clocks ? It might seem implausible that independent origins for clocks would converge upon an essentially similar core PTO made more robust by an overlying TTFL . However , the advantages that accrue to the cyanobacterial system by having a post-translational mechanism at its core are also relevant to eukaryotic clocks [5] . For example , individual mammalian fibroblasts express cell-autonomous , self-sustained circadian oscillations of gene expression that are largely unperturbed by cell division [34] , [35] in a fashion reminiscent of cyanobacteria [31] , [33] . In contrast , synthetic TTFL oscillators constructed in mammalian cells ( CHO cells ) only display reproducible oscillations when the cells are arrested in G1 phase of the cell cycle by cultivating the cells at 30°C ( [36] , Dr . Marcel Tigges , personal communication ) . Could the imperturbability of circadian clocks even when buffeted by the gusts of metabolic changes provoked by cell division provide an evolutionary driving force for clock mechanisms in disparate organisms to converge on a relatively similar core mechanism ? Perhaps . Recent results from the mammalian circadian clock do not easily fit into the original TTFL formulation . For example , mammalian clocks are surprisingly resilient to large changes in transcriptional rate [37] , but tight regulation of transcriptional rate would be expected to be necessary if it is a state parameter in a TTFL clock . Also , the mammalian clock is resilient to clamping the level of some of the mammalian clock proteins whose cycling had been thought to be essential [26] , [38] . Moreover , recent results have led to a greater appreciation of the role of small signaling molecules in the mammalian clock [39] . Finally , classical experiments in eukaryotic algae have shown that persisting circadian rhythms are possible in enucleated cells ( in Acetabularia [40] , [41] ) or under translational control in the absence of transcription ( in Gonyaulax [42] ) . Are Acetabularia and Gonyaulax anomalous cases , or are they relevant indicators of the underlying capabilities of the eukaryotic clockwork ? Our growing appreciation of the cyanobacterial system combined with results from eukaryotic clocks that are inconsistent with a sustained TTFL pacemaker embolden such speculations [26] , [39] , [43] . At the least , the studies on prokaryotic cyanobacteria lead to more rigorous criteria for distinguishing whether a TTFL is at the core of eukaryotic clocks .
The cyanobacterium Synechococcus elongatus PCC 7942 was transformed with a luciferase reporter of either the psbAI promoter ( psbAIp::luxAB ) or the kaiB promoter ( kaiBCp::luxAB ) . Luminescence rhythms from the psbAIp::luxAB or kaiBCp::luxAB reporters are approximately equivalent in phase and intensity . For experimental expression of KaiA or KaiC , the kaiA or kaiC genes were fused to an IPTG-derepressible heterologous trc promoter ( trcp ) to make trcp::kaiA or trcp::kaiC expression strains ( in neutral site II , NSII ) . The KaiCEE strain has the wild-type kaiC gene ( kaiCWT ) replaced with a double mutant KaiCS431E/T432E [20] . For measurement of KaiC degradation rate , a kaiC-null strain was transformed with trcp::kaiCWT in NS II [7] . All strains were grown in BG-11 medium and experiments were performed at 30°C except as indicated in Figure 3 . For assay of in vivo rhythms of psbAI or kaiBC promoter activity , luminescence emitted by the psbAIp::luxAB or kaiBCp::luxAB reporter was assayed as previously described from liquid cultures [4] or from colonies on agar [7] . Cells were grown in constant light ( LL; cool-white fluorescence at 40–50 µE/m2s ) , given 1∼2 light:dark cycles ( e . g . LD 12:12 ) to synchronize the cells in the population , and finally released into LL for assay of free-running luminescence rhythms . Analysis of damping was performed with the LumiCycle data analysis program ( Actimetrics , Evanston , IL , courtesy of Dr . David Ferster ) as described in Text S1 . Damping rate ( d ) is the number of days required for the amplitude of the rhythm to decrease to 1/e ( ≈36 . 79% ) of the starting value . For the experiments of Figure 1 , S . elongatus cells were harvested every 4 h for the LL and LD12:12 conditions and every 1 h for the LD2:2 conditions . Total protein was extracted and equal amounts of proteins were loaded into each well for SDS-PAGE and immunoblotting [4] . KaiC protein abundance was determined on 15% SDS-PAGE gels ( to obtain a single KaiC band ) , whereas KaiC phosphorylation was determined on 10% SDS-PAGE gels ( to separate the various KaiC phosphoforms ) . For assay of KaiC phosphorylation rhythms in vitro , Kai proteins from S . elongatus were expressed in Escherichia coli and purified as described previously [13] . In vitro reactions were carried out at 30°C using standard Kai protein concentrations: 50 ng/µl KaiA , 50 ng/µl KaiB , and 200 ng/µl KaiC as described previously [13] . Cells harboring the trcp::kaiC expression construct were inoculated onto nitrocellulose membranes that were placed on the surface of BG-11 agar plates . After 5 d of growth in LL , these cultured membrane plates were divided into four groups and entrained with two LD12:12 cycles ( Figure S9A ) . There were a total of 4 different phasings of the LD12:12 cycles ( Φ1 , Φ2 , Φ3 , and Φ4 ) that were different from each other by 6 h ( i . e . , starting at laboratory clock time 00:00 , 06:00 , 12:00 , and 18:00 ) . After the final dark interval was completed for the last group of plates ( Φ4 ) , the cultures were transferred back and forth between pre-warmed fresh BG-11 agar plates containing 0 or 5 µM IPTG for two IPTG cycles as follows: 12 h IPTG ( = 5 µM IPTG ) followed by 12 h no-IPTG ( = 0 µM IPTG ) followed by 12 h IPTG ( = 5 µM IPTG ) , as depicted in Figure S9A . This protocol created an experimentally controlled 12 h:12 h cycle of new KaiC synthesis ( the parallel control cultures were transferred every 12 h among no-IPTG plates ) . We determined that a concentration of 5 µM IPTG increases KaiC abundance within cells by only ∼40%–50% above basal levels ( Figure S9C ) . After the final IPTG cycle , measurement of the luminescence rhythms in the cultures from each of these phases was performed in LL as described above . The model was implemented in Fortran ( G77 ) using a fourth order Runge-Kutta routine for solving the coupled set of ODEs . The TTFL portion of the combined PTO/TTFL model is modified from the model of Goldbeter [21] . See Text S1 for model description and the parameters used in the figures . | Many organisms from bacteria to humans have evolved circadian mechanisms for regulating biological processes on a daily time scale . In cyanobacteria , a minimal system for such cyclical regulation can be reconstituted in vitro from three proteins , called KaiA , KaiB , and KaiC . This three-protein oscillator is believed to regulate the cyclical activities in vivo through a post-translational mechanism that involves rhythmic phosphorylation of KaiC . Although this post-translational oscillator ( PTO ) is sufficient for generating rhythms in vitro , the cyanobacterial circadian system in vivo also includes a transcriptional/translational feedback loop ( TTFL ) . The precise roles of the PTO and the TTFL and their interdependence in forming the complete clock system in vivo are unclear . By manipulating wild-type and mutant clock protein expression in vivo , we here show that the cyanobacterial circadian system is dependent upon the biochemical oscillator provided by the PTO and that suppression of the PTO leads to a residual damped ( slave ) oscillation that results from the TTFL . Mathematical modeling shows that the experimental data are compatible with a mechanism in which the PTO acts as a pacemaker to drive the activity of the TTFL . Moreover , our analyses suggest a mechanism by which the TTFL can feed back into the PTO such that new synthesis of the Kai proteins entrains the core PTO pacemaker . Therefore , the PTO and TTFL appear to have a definite hierarchical interdependency: the PTO is a self-sustained core pacemaker that can oscillate independently of the TTFL , but the TTFL is a slave oscillator that damps when the phosphorylation status of KaiC in the PTO is clamped . The core circadian pacemaker in eukaryotes is thought to be a TTFL , but our results with cyanobacteria have important implications for eukaryotic clock systems in that they can explain how a TTFL could appear to be the core clock when in fact the true pacemaker is an embedded biochemical oscillator . | [
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... | 2010 | Coupling of a Core Post-Translational Pacemaker to a Slave Transcription/Translation Feedback Loop in a Circadian System |
HTLV-1/2 infection can cause severe and disabling diseases in children and adults . The aim of the study was to estimate the prevalence of HTLV-1/2 infection in pregnant women living in the metropolitan area of Rio de Janeiro . 1 , 204 pregnant women were tested upon hospital admission for delivery in two public hospitals in the cities of Rio de Janeiro and Mesquita , between November , 2012 and April , 2013 . The samples were screened by chemiluminescent microparticle immunoassay ( CMIA ) and reactive ones were confirmed by Western blot ( WB ) . Epi-info software was used for building the database and performing the statistical analysis . Eight patients had confirmed HTLV-1/2 infection ( 7 HTLV-1 , one HTLV-2 ) , equivalent to a prevalence rate of 0 . 66% . Two further reactive screening tests had negative Western blot results and therefore were considered negative in the statistical analysis . All HTLV-1/2-positive patients were born in Rio de Janeiro , most were non-Caucasian ( 87 . 5% ) , in a stable relationship ( 62 . 5% ) , had at least ten years of formal education ( 62 . 5% ) and a monthly family income of up to US$600 . 00 ( 87 . 5% ) . There was only one case of coinfection with syphilis and none with HIV . The mean age of the infected women was 28 . 4 ( SD = 6 . 3 ) years and of the seronegative ones was 24 . 8 ( SD = 6 . 5 ) ( p = 0 . 10 ) . The median number of pregnancies were 3 . 0 and 1 . 0 ( p = 0 . 06 ) and the median number of sexual partners were 3 . 5 and 3 . 0 ( p = 0 . 33 ) in the seropositive and negative groups , respectively . There were no statistically significant differences between the groups . A significant prevalence of HTLV-1/2 was found in our population . The socio-epidemiological profile of carrier mothers was similar to the controls . Such findings expose the need for a public health policy of routine HTLV-1/2 screening in antenatal care , since counselling and preventive measures are the only strategies currently available to interrupt the chain of transmission and the future development of HTLV-1/2-related diseases .
Human T-lymphotropic virus ( HTLV ) -1 and HTLV-2 were the first oncogenic human retroviruses identified in the early 1980's [1] . They have a strong tropism for T-lymphocytes and are transmitted mostly via cell-cell infection [2] . HTLV-1/2 infection has a worldwide distribution , with an estimate of up to 15–20 million people affected [3] . Prevalence changes substantially according to geographical area , ethnical and social background and in specific risk groups such as intravenous ( IV ) drug users and sex workers [3] . HTLV-1 is highly prevalent in Sub-Saharan Africa , South-western Japan , Central and South America and in parts of the Middle East and Melanesia [3] . Regardless of the region , seroprevalence increases with age , particularly in women , in view of the excess efficiency of the male-female sexual transmission . HTLV-2 is endemic in several Native American populations and Pygmy tribes in Central Africa and also thrives in IV drug users worldwide , often in co-infection with HIV [4] , [5] . Infection is life long and most of the patients remain asymptomatic , becoming viral reservoirs and keeping the chain of transmission . On the other hand , about 4% of HTLV-1 carriers will develop adult T-cell leukaemia/lymphoma ( ATLL ) , a highly aggressive CD4+ T-cells malignancy . Type 1 virus is also associated with a wide range of inflammatory conditions , from the mild nonspecific dermatitis and uveitis to the disabling HTLV-1-associated myelopathy/Tropical Spastic Paraparesis ( HAM/TSP ) which affects 2–3% of the infected people [6] . HTLV-2 has been associated to hairy cell leukaemia , erithrodermatitis and a growing number of neurologic disorders [4] . Recent researches also suggest that the incidence of HTLV-1/2 linked diseases might be even higher than the literature traditionally reports due to the influence of local factors in their pathogenesis [7] , [8] . There are three main routes of HTLV-1/2 transmission: sexual intercourse , infected whole blood or cell containing blood products and from mother-to-child ( vertical transmission - VT ) . In endemic areas , VT remains the most important mode of transmission , since it occurs in up to 25% of the children who are breastfed by seropositive women [9] . Greater efficacy of VT is associated with the length of breastfeeding , high antibody titers and high proviral load in maternal blood and milk [10]–[12] . Intrauterine/perinatal transmission has been reported to have no significant epidemiological impact on disease burden [13] , [14] , however about 2 . 5% of the children of infected mothers will become HTLV-1/2-positive even in the absence of breastfeeding [7] . Given its continental dimensions , Brazil is thought to be one of the countries with the highest number of HTLV-1/2 carriers in the world . Estimates range widely from 800 , 000 to 2 . 5 million people [5] , [15] , [16] . Such discrepancy in numbers is a result of both the epidemiological characteristic of the infection , which has endemic clusters alongside low prevalence areas , and the gap in knowledge itself , since vast areas of the country remain unstudied . Additionally , most of the research is done on potentially biased populations such as low risk voluntary blood donors or high risk IV drug users and sex clinic attendees . Intermediate seroprevalence rates have been found in pregnant women . Although not devoid of bias , this group has been proposed to represent a more reliable portrait of the general population since their prevalence data are generally able to characterize geographic areas likely to be endemic [3] , [17] . Unfortunately there are no nationwide data on the seroprevalence of HTLV-1/2 in Brazilian pregnant women since screening is not recommended by the Ministry of Health as part of the routine antenatal care . Furthermore , there are no studies published about this prevalence on the State of Rio de Janeiro . Therefore the aim of this study was to estimate the prevalence of HTLV-1/2 infection in pregnant women admitted for delivery in the metropolitan area of Rio de Janeiro .
A total of 1 , 204 pregnant women were recruited upon admission for delivery in two public hospitals in the metropolitan area of Rio de Janeiro , between November 2012 and April 2013 . The institutions involved in the study were ‘Pedro Ernesto’ University Hospital of the Rio de Janeiro State University ( Universidade do Estado do Rio de Janeiro – HUPE/UERJ ) , a reference for high-risk obstetric patients located in the city of Rio de Janeiro , and ‘Hospital da Mãe’ , a state hospital for low and medium-complexity obstetric care , in the city of Mesquita , metropolitan area of Rio de Janeiro . Thus , both low and high-risk obstetric populations were included . Patients who did not wish to take part in the research were excluded , as well as those considered mentally unable to give consent . At the enrolment , a structured questionnaire was applied by trained interviewers to collect epidemiological , social , clinical , gynaecological and obstetric data . HTLV-1/2 positive women were counseled by a multidisciplinary team which offered information about the infection and support . Breastfeeding was contraindicated and formula milk was provided to safeguard the infants' nutrition . All the children of carrier mothers are being prospectively followed by the Paediatrics Department of HUPE . As per protocol of the Brazilian Ministry of Health all patients admitted for delivery have blood taken for HIV and Syphilis tests . At this moment , an extra sample was taken for HTLV-1/2 screening , which was performed by chemiluminescent microparticle immunoassay ( CMIA - Architect rHTLV-I/II , Abbott ) . Cases of reactive serology were confirmed by Western blot ( WB , Inno Lia HTLV-I/II score Biomerieux ) . All tests were carried out at the Clinical Analysis Laboratory of the HUPE/UERJ . The two patients with reactive screening tests and negative WB results were considered false positive and assigned to the negative group in the statistical analysis . This research complies with the ethical precepts established in the Declaration of Helsinki and the CONEP Resolution n° 196/96 and was approved by the Rio de Janeiro State University Research and Ethics Committee ( COEP-UERJ , n . 041/2012 ) . Written informed consent was obtained from all the subjects and from the parents or guardians of the minors who agreed to take part . Anonymity and data confidentiality were guaranteed . The sample size of 1 , 188 patients was calculated considering an expected prevalence of 0 . 5% at a 95% confidence interval . Means , standard deviations , medians , interquartile ranges and percentages were used to describe the results . Medians were used to define the groups when numerical variables were analyzed as categorical ones ( age , family income , number of partners and number of pregnancies ) . Missing data were excluded from the statistical analyses . Fisher Exact and Mann Whitney tests were used to assess the association between HTLV-1/2 infection and possible risk factors ( demographic , social , familial , behavioral and sexual data ) . Logistic regression was performed to evaluate the relation between condom use and HTLV-1/2 occurrence adjusted by age and marital status . The EPI-INFO software version 3 . 5 . 2 was used for building the database and performing the statistical analyses .
Eight of the 1 , 204 patients had confirmed HTLV-1/2 infection ( seven HTLV-1 and one HTLV-2 ) equivalent to a prevalence rate of 0 . 66% . Two further reactive screening tests had negative WB and were therefore considered false positive and excluded from the statistical analysis . In the HTLV-1/2 positive group , all the patients were born on the State of Rio de Janeiro . They were mostly non-Caucasian ( 7/8 - 87 . 5% ) , reported being in a stable relationship ( 6/8 - 62 . 5% ) , had at least ten years of formal education ( 6/8 - 62 . 5% ) and 87 . 5% declared having a monthly family income of up to US$600 . 00 ( approximately two minimum wages ) . No women reported intravenous drug use , there was only one case of co-infection with syphilis and none with HIV . The mean age of the infected pregnant women was 28 . 4 years ( SD = 6 . 3 ) and of the seronegative ones was 24 . 8 ( SD = 6 . 5 ) ( p = 0 . 10 ) . The mean age at first intercourse was approximately 16 years for both groups ( μ = 15 . 9 , SD = 1 . 9 vs . μ = 16 . 2 , SD = 2 . 7 ) . The median number of pregnancies were 3 and 1 ( p = 0 . 06 ) and the median number of sexual partners were 3 . 5 and 3 . 0 ( p = 0 . 33 ) in the seropositive and negative patients , respectively . The comparison between the HTLV-1/2-positive and negative groups found no statistically significant difference for any of the variables assessed: age , ethnicity , marital status , family income , level of education , age at first intercourse , number of partners , number of pregnancies , use of condom or co-infection with syphilis or HIV ( Table 1 ) . After controlling the association between seropositivity and the number of previous pregnancies by marital status and condom use it was observed that women with two or more previous gestations were about three times more likely to be infected ( OR = 3 . 63 ) . However only a borderline statistic significance was found ( p = 0 . 08 ) .
Antenatal groups are considered to better represent the general population than blood donors and IV drug users , being of particular interest for the chain of transmission since most of the vertical transmission can be prevented by avoiding breastfeeding . A review of the literature retrieved no published data on the prevalence of HTLV-1/2 infection in pregnant women in the State of Rio de Janeiro . The Brazilian articles concerning the infection display a significant geographic heterogeneity in prevalence , ranging from 0 . 07% on the State of Minas Gerais [18] to around 1% in the State of Bahia [19]–[21] ( Table 2 ) . Our research found a prevalence of 0 . 66% which would place the metropolitan area of Rio de Janeiro as the second highest seroprevalence in pregnant women in Brazil . This number is 40% higher than it was found in blood donors ( 0 . 47% ) on the same state in a research performed between 1995 and 2000 [22] . However , this comparison is likely to be inaccurate in view of the long time elapsed between the studies . In Europe , HTLV-1/2 prevalence was found to be 5–10 times higher in pregnant women than in blood donors , regardless of the area specific prevalence [17] . A comprehensive Brazilian study performed at the Public Blood Center Network comprising over 6 million blood donations from all the 27 state capitals revealed a striking geographic variability of the HTLV-1/2 infection [22] . It ranged from 0 . 4/1 , 000 in Florianópolis ( Santa Catarina State ) to a rate 25 times higher ( 10 . 0/1 , 000 ) in São Luís ( Maranhão State ) . It's noteworthy that likewise in pregnant women , the Brazilian states with the highest seroprevalence were located on the North and Northeast of the country ( Pará , Pernambuco , Bahia and Maranhão ) while prevalence rates were markedly lower in the main cities of the Southern states , such as Santa Catarina [22] . These findings were corroborated by a more recent research in blood donors of three densely populated state capitals ( Pernambuco , Minas Gerais and São Paulo ) [23] . The regional clustering phenomenon can be explained by the influence of the ethnical characteristics of the population in the prevalence of HTLV-1/2 . The northern area of Brazil has a greater proportion of African and native-descendants than the South . There is strong evidence of endemicity of HTLV-2 among groups who originally inhabited the Amazon [5] . Additionally , there is abundant phylogenetic evidence of the introduction of HTLV-1 strains via the forced migrations from the African continent [20] , [24] which is thought to be the place of origin of human retroviruses and the largest endemic area for HTLV-1 in the world . Nevertheless even in Africa the seroprevalence for HTLV-1/2 in pregnant women can vary widely from 5 . 5% in Nigeria to 0 . 2% in South Africa , with places of intermediate prevalence such as the Republic of Congo ( 0 . 7% - similar to our own ) [3] . A strength of the present study is that all HTLV-1/2-positive subjects were born in the researched area , confirming it as a portrait of the local population . The mean age of positive pregnant women and the fact that they were mostly non-Caucasian are in accordance with the literature [3] , [15] , [19] , [23] , [25] . In the comparison between HTLV-1/2 positive and negative groups none of the variables was statistically significant , which was in line with the two latest studies from Bahia [20] , [21] . Although there were some reports of an inverse association between both length of formal education and family income with HTLV-1/2 infection [7] , [18] , [19] , [23]–[26] these were not universal findings [15] , [21] , [27]–[30] and were not confirmed in our population . The similarity of the epidemiological profile in both of our groups and the lack of clear risk factors seem to point to routine HTLV-1/2 prenatal screening as the most sensible path to follow , as supported by other colleagues [8] , [16] , [18] , [20] , [21] , [25] , [28] , [30] , [31] . Once carrier mothers were identified , avoidance of breastfeeding would be a fundamental tool for the control of mother-to-child transmission . Taking in consideration the number of live births in the metropolitan area of Rio de Janeiro ( 135 , 938 in 2013 ) and the seroprevalence observed in our study , the introduction of routine HTLV-1/2 antenatal screening could avoid up to 900 cases of vertical transmission per year [32] . A limitation of our research was the possibility of selection bias . Patients were recruited at admission for delivery , which could have underestimated the overall prevalence in pregnancy since we might have lost the patients who miscarried and those who eventually had preterm labour or stillbirths . Implications for future research – our finding of such a significant prevalence shows the need for studies on a larger scale , testing all pregnant women at the beginning of antenatal care in order to avoid selection bias and increase the confidence in our results . Sampling a broader population can help to determine the cost-effectiveness of the test and justify its introduction to routine prenatal screening . Following the children of infected mothers in a cohort study could identify cases of vertical transmission and the incidence of HTLV-1/2 related diseases in our population . Universal screening for HTLV-1/2 in blood donors was implemented in Brazil in 1993 with significant impact on this mode of transmission . Additionally , horizontal transmission is thought to have been greatly reduced by successful public policies aiming at the prevention of other sexually transmitted diseases . Since 2002 HIV and syphilis prenatal screening tests are mandatory according to the Brazilian Ministry of Health in order to prevent mother-to-child transmission . HTLV screening was not included in this guideline; in fact it's not even listed as an infectious disease worthy of being targeted by government policies [31] . Therefore VT of HTLV-1/2 remains the only mode of transmission unaddressed by public health policies in our country . Since currently there is no cure , effective treatment or immunization for HTLV-1/2 infection and its complications , more accurate knowledge about its prevalence is helpful in the elaboration of public policies on educational and prophylactic measures to increase awareness and reduce the rates of viral transmission and the incidence of infection-related diseases . | HTLV-1/2 are retroviruses transmitted by blood products , sexual contact and from mother to child , mainly through breastfeeding . The infection has a characteristic geographical distribution with endemic areas often neighbouring very low prevalence areas . Infection is life long and although asymptomatic in most cases , it can cause severe and disabling diseases in children and adults . There is currently no cure , vaccine or effective treatment for HTLV-1/2 infections . Our research is the first to study the prevalence of HTLV-1/2 in pregnant women living in the metropolitan area of Rio de Janeiro , the second largest in Brazil . 1 , 204 pregnant women were tested upon hospital admission for delivery in two public hospitals in the cities of Rio de Janeiro and Mesquita , between November , 2012 and April , 2013 and a significant prevalence of HTLV-1/2 was found ( 0 . 66% ) . The socio-epidemiological profile of carrier mothers was similar to the controls' . Epidemiological knowledge is fundamental for the elaboration of public health policies such as routine HTLV-1/2 screening in antenatal care , since counselling and preventive measures , mainly avoidance of breastfeeding , are the only strategies currently available to interrupt the chain of transmission and the future development of HTLV-1/2-related diseases . | [
"Abstract",
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] | 2014 | Prevalence of HTLV-1/2 in Pregnant Women Living in the Metropolitan Area of Rio de Janeiro |
Interferon-induced BST2/Tetherin prevents budding of vpu-deficient HIV-1 by tethering mature viral particles to the plasma membrane . BST2 also inhibits release of other enveloped viruses including Ebola virus and Kaposi's sarcoma associated herpesvirus ( KSHV ) , indicating that BST2 is a broadly acting antiviral host protein . Unexpectedly however , recovery of human cytomegalovirus ( HCMV ) from supernatants of BST2-expressing human fibroblasts was increased rather than decreased . Furthermore , BST2 seemed to enhance viral entry into cells since more virion proteins were released into BST2-expressing cells and subsequent viral gene expression was elevated . A significant increase in viral entry was also observed upon induction of endogenous BST2 during differentiation of the pro-monocytic cell line THP-1 . Moreover , treatment of primary human monocytes with siRNA to BST2 reduced HCMV infection , suggesting that BST2 facilitates entry of HCMV into cells expressing high levels of BST2 either constitutively or in response to exogenous stimuli . Since BST2 is present in HCMV particles we propose that HCMV entry is enhanced via a reverse-tethering mechanism with BST2 in the viral envelope interacting with BST2 in the target cell membrane . Our data suggest that HCMV not only counteracts the well-established function of BST2 as inhibitor of viral egress but also employs this anti-viral protein to gain entry into BST2-expressing hematopoietic cells , a process that might play a role in hematogenous dissemination of HCMV .
Human cytomegalovirus ( HCMV ) , a β-herpesvirus , maintains a lifelong , asymptomatic infection in immunocompetent hosts but is an opportunistic pathogen in immunocompromised individuals [1] , [2] . HCMV is also the leading infectious cause of congenital birth defects in neonates [3] . Moreover , in post-transplant patients HCMV is capable of causing disseminated disease in most organs and tissue types [4] , [5] , [6] . Thus , HCMV is able to infect a wide range of host cells . However , the host factors required for viral entry into different cell types are incompletely understood . Initially the virus attaches to heparan sulphate proteoglycans , followed by virion surface glycoproteins interacting with their cellular receptors that include integrins and the EGF receptor along with other as yet undefined molecules in cholesterol rich membrane micro-domains [7] . The two known pathways of HCMV entry are fusion with the plasma membrane and endocytosis . The respective pathway used is dependent on the cell type and viral glycoprotein composition [8] , [9] . The role of cellular receptors in each of these processes is largely unknown , and it is likely that yet to be identified cellular proteins will be involved in viral entry processes . BST2 ( Bone marrow stromal cell antigen 2 ) was initially thought to be involved in normal and malignant B cell differentiation since this protein is expressed on bone marrow stromal cells and multiple myeloma cells [10] . However , the murine homologue was later shown to be highly expressed by plasmacytoid dendritic cells suggesting a role in innate immunity . Moreover , it was shown that BST2 is an IFN inducible protein that can act as a ligand to ILT7 , a receptor on dendritic cells that modulates IFN production [11] , [12] . The first indication that BST2 might be involved in the host defense against viruses was implied by our finding that BST2 was downregulated by the immune evasion molecule K5/MIR2 , a transmembrane E3 ubiquitin ligase of Kaposi's sarcoma associated herpesvirus ( KSHV ) that targets multiple host cell immunoreceptors for destruction via ubiquitination [13] , [14] . Subsequently , it was demonstrated that BST2 represented the interferon-induced host cell factor responsible for preventing release of HIV-1 lacking Vpu , [15] , [16] , [17] . Prior to this work , Vpu was known to eliminate CD4 and MHC-I via ubiquitin-mediated processes [18] . Based on this finding , many unrelated enveloped viruses were recently shown to be restricted by BST2 , including the α-retrovirus RSV , the β-retroviruses MPMV and HERV-K , the δ-retrovirus HTLV-1 , the spumaretrovirus PFV , the filoviruses Marburg and Ebola , the arenavirus Lassa , non-human primate retro viruses , and the endogenous β-retroviruses of sheep enJSRV [19] , [20] , [21] , [22] . In addition to K5 and Vpu , several BST2-antagonists were discovered in other viruses , including HIV-2 Env , simian immunodeficiency virus ( SIVmac/smm ) Nef , SIVtan Env , and Ebola GP [17] , [19] , [21] , [23] , [24] . BST2 is a heavily glycosylated , type II transmembrane protein . It has a short cytoplasmic N-terminal region , a transmembrane region , a coiled coil extracellular domain and a C-terminal glycosylphosphatidylinisotol ( GPI ) anchor [25] . This topology of BST2 with a transmembrane domain and a GPI anchor is rather unusual and is shared by only one other protein , an isoform of the prion protein [25] , [26] . BST2 forms intermolecular disulfide bridges with conserved extracellular cysteines in the coiled-coil domain [27] . It was further suggested that BST2 forms a picket fence-like structure having its transmembrane domain located on the periphery and the GPI anchor inside the lipid raft [25] . This flexible structure of BST2 is thought to be important for preventing virions from budding by retaining the transmembrane domain in the cell membrane and the GPI anchor incorporated into the virion during the budding process [28] . Here , we examine whether BST2 restricts HCMV release . We report the surprising observation that increased titers of HCMV are obtained from supernatants of BST2-expressing fibroblasts , a finding that is in stark contrast to observations reported to date for all other viruses implying that HCMV efficiently overcomes any anti-viral function of BST2 . On closer inspection , we found that increased viral release was a downstream effect of BST2 enhancing viral entry and thereby HCMV gene expression and replication . A potential role for BST2-mediated HCMV entry in viral pathogenesis and dissemination is suggested by the finding that BST2 enhances entry of HCMV into monocytes that constitutively express high levels of BST2 . Our data thus suggest that HCMV uses this IFN-induced anti-viral protein to increase infection of monocytic cells , which play a central role in HCMV latency , reactivation and dissemination .
To examine whether the ability of BST2/Tetherin to prevent release of a wide spectrum of enveloped viruses would extend to HCMV , we examined the supernatant of HCMV-infected telomerized human foreskin fibroblast ( THFs ) cells stably expressing an HA-tagged version of BST2 ( THF-HA146 ) ( Fig . 1A ) . Two different strains of HCMV were used: the lab-adapted strain AD169 and the clinical isolate Toledo . BST2-expressing THFs or control THFs ( THF-PCDH ) were infected with an MOI of 3 , and the supernatants were harvested at 72 hours post-infection ( hpi ) . To determine the presence of infectious virus , we added the supernatant to fresh primary human foreskin fibroblasts ( HFFs ) . After 8 h , cells were washed , trypsinized and viral infection was monitored by analysis of viral immediate early gene ( IE1 ) expression in immunoblot . Unexpectedly , we observed higher IE1 levels in HFFs exposed to supernatants from BST2-expressing THF-HA146 cells compared to the supernatant obtained from control THF-PCDH cells ( Fig . 1B ) . This indicated that there was an increased amount of infectious virus particles released in the presence of BST2 , which is contrary to the observation of restricted virus release of other enveloped viruses . To confirm this observation , we determined the amount of infectious virus released into the supernatant by plaque assay . We infected two different HFF-lines expressing BST2 with HA tags at AA positions 110 or 146 ( Fig . 1 A ) . Each of the BST2-expressing HFF-lines , as well as a control-transfected HFF line , was infected with AD169 at an MOI of 3 . The supernatants were collected 72hpi and the concentration of HCMV plaque forming units ( PFU ) was determined by dilution on HFFs . Compared to the control-transfected HFFs , approximately five times more PFU were recovered from each of the BST2 expressing cells ( Fig . 1C ) . Thus , infectious virus released in the supernatant increased in the presence of BST2 irrespective of the position of the HA tag . To determine whether increased presence of virus in the supernatant was due to increased infection of the BST2-expressing fibroblasts or increased infectivity of released virus we knocked down BST2 in THF-HA146 and HFF-HA146 cells using a previously described siRNA [14] prior to infection with AD169-GFP ( MOI = 1 ) . The number of infected cells was monitored by flow-cytometry for GFP . At 24 hpi the cells were harvested and the number of GFP-positive cells was determined . The results showed that inhibiting BST2 expression reduced the number of GFP-positive cells whereas knockdown of the cellular protein GAPDH or control siRNA had no effect ( Fig . 1D ) . Furthermore , BST2 siRNA did not affect the number of GFP-positive cells in control fibroblasts . Thus , BST2 enhanced an early event in the viral life cycle rather than promoting egress or infectivity of released virus . Since these data suggested that early or immediate early events were modulated by BST2 we compared the expression of the viral immediate early gene 1 ( IE1 ) in BST2-expressing THFs to control THFs or HFFs by immunoblot . For control of host cell gene expression and as loading control we included immunoblots for GAPDH . Furthermore , we treated each cell line with control siRNA ( siGLO ) or with siRNA to BST2 prior to infection with the HCMV strain Toledo . As shown in Fig . 1E , there was a significant increase of IE1 protein recovered from BST2-expressing THFs compared to both HFF and THF . In contrast , targeted knockdown of BST2 , but not of GAPDH or control siRNA treated cells , reduced the levels of IE1 expression in the BST2-transfectants but not in the control cells ( Fig . 1E , F ) . Taken together these data demonstrate that BST2 expression increased viral infection of fibroblasts and that this increase of infection was responsible for increased virus production of these cells . Since BST2 is a protein that recycles between the cell surface and intracellular compartments [25] it was conceivable that enhanced infection of BST2-expressing fibroblasts was caused by BST2 enhancing viral entry . Upon fusion of herpes virions with plasma or endosomal membranes , proteins of the viral tegument , a protein-rich compartment layered between the capsid and the envelope , are released into the cytoplasm together with the viral capsid [29] . Therefore , we determined the amount of the major tegument proteins pp71 and pp65 recovered from BST2-expressing or control THFs immediately after virus infection and prior to onset of immediate early gene expression . Additionally , we monitored the amount of viral genomes present in cells by qPCR . THF-PCDH and THF-HA146 cells were infected with AD169 ( MOI = 3 ) for 2 h at 37°C , and then the cells were washed with citric acid buffer ( pH = 3 ) to remove adhered virions from the cell surface . Cells were trypsinized , lysed and analyzed for genomic DNA by qPCR ( Fig . 2A ) or the tegument protein pp71 by immunoblot ( Fig . 2B ) . At 2hpi , BST2-expressing cells contained an increased amount of viral genome copies as well as tegument protein pp71 ( Fig . 2A , 2B ) . In contrast , when BST2 expression was decreased by siRNA in THF-HA146 ( Fig . 2D ) , reduced amounts of genomic DNA ( Fig . 2C ) and tegument protein pp65 ( Fig . 2E ) was recovered . These results are consistent with viral entry being increased in the presence of BST2 . To rule out that increased tegument proteins were due to increased viral gene expression we exposed cells to UV-inactivated AD169 which is still able to attach to and enter cells but unable to express viral genes . Similar to infection with untreated virus , increased amounts of pp71 were recovered from THF-HA146 infected with UV-inactivated virus as compared to THF-PCDH ( Fig . 2B ) , whereas BST2 knockdown decreased recovery of pp65 from THF-HA146 infected with UV-inactivated HCMV ( Fig . 2E ) . HCMV induces interferon ( IFN ) and IFN-stimulated genes ( ISGs ) , but it is also known to interfere with IFN-dependent , JAK/STAT-mediated ISG-induction [30] , [31] . Since BST2 is induced by IFN we were wondering whether HCMV would modulate IFN-independent and IFN-dependent BST2 gene induction during infection . Non-transfected HFFs express low but detectable amounts of BST2 constitutively and BST2 levels can be strongly induced by IFN , as expected ( Fig . 3A ) . Interestingly , induction of BST2 was also observed upon HCMV-infection . Moreover , UV-inactivated HCMV induced BST2 to a much higher level than live HCMV . However , when HFFs were simultaneously treated with IFN and infected with HCMV , HCMV suppressed BST2 expression ( Fig . 3A ) . These results are consistent with the previously reported IRF3-dependent induction of IFN and ISGs by both live and UV-inactivated HCMV [30] whereas IFN-dependent , JAK/STAT-mediated ISG induction is inhibited by live but not UV-inactivated HCMV [32] . This conclusion was further supported when BST2mRNA expression levels were measured by qPCR . Live HCMV induced BST2 mRNA to a lesser degree than UV-inactivated HCMV ( Fig . 4B ) and BST2 mRNA levels were strongly increased upon IFN treatment . Infection with HCMV reduced this induction ( Fig . 4B ) . Thus , although HCMV interferes with IFN-dependent BST2 gene induction , most likely due to its known interference with JAK/STAT signaling , HCMV induces BST2 mRNA and does not seem to interfere with BST2 protein expression at a post-transcriptional level , in marked contrast to HIV-1 and KSHV . This conclusion is also supported by the fact that HCMV does not downregulate transfected BST2 ( Fig . 3C ) . Thus , BST2 either does not affect HCMV egress , or HCMV counteracts BST2 in a way that does not affect BST2 protein levels or surface expression . To determine whether the entry-enhancing effect of BST2 is limited to fibroblasts , we examined HCMV entry into BST2-expressing HEK293M cells . Unlike HFFs , these cells are not permissive for viral replication and therefore could delineate the role of BST2 in the early events of viral entry versus the later events of viral replication . We generated stable 293 M cells expressing BST2 ( Fig . 4A ) and infected these or control cells with HCMV-AD169 . Viral entry and infection was monitored by immunoblot for pp71 and IE1 as described above . As shown in Fig . 4 , compared to 293 M-PCDH control , 293 M-BST2HA cells contained increased amounts of pp71 at 3hpi and IE expression at 24hpi . These results demonstrate that BST2 enhances viral entry independently of cell type and might even contribute to cell tropism . Most somatic cells express low levels of BST2 unless exposed to IFN . However , BST2 is upregulated during the differentiation of cells in the hematopoietic lineage leading to constitutive high level of expression in B cells , T cells , NK cells , and monocyte/macrophages [10] , [11] . Particularly pronounced is the expression of BST2 on plasmacytoid dendritic cells which are major IFN producers [11] . Among these cell types , monocytes are thought to be particularly important for HCMV latency , reactivation and dissemination in vivo [33]–[34] . Therefore , we explored whether endogenous BST2 expressed by monocytes would facilitate entry of HCMV into this cell type . In vitro , HCMV is known to infect differentiated monocytes preferably over monocyte precursors [35] . This preference can be recapitulated in the pro-monocytic cell line THP-1 which can be differentiated into a monocytic cell type by treatment with phorbol meristate acetate ( PMA ) , and PMA-induced differentiation correlates with increased infection by HCMV [36] . Interestingly , surface levels of BST2 drastically increased upon PMA treatment of THP-1 cells ( Fig . 5A , B ) . It was previously shown that PMA-treatment of THP-1 cells increased entry of the lab-strain AD169 [36] although only clinical isolates are able to replicate productively in this cell type [37] . Indeed , we observed increased IE1 expression in PMA treated THP-1 cells compared to untreated cells upon infection with AD169 ( Fig . 5B ) . AD169-infection of THP-1 cells induced endogenous BST-2 ( Fig . 5B ) consistent with the increase of BST-2 observed in AD169-infected fibroblasts ( Fig . 3 ) . These data indicated a correlation between PMA-dependent AD169 infection of THP-1 cells and BST2 levels . To determine whether there was a causative relationship between AD169 entry and BST2 levels we knocked down BST2 mRNA levels with siRNA prior to PMA treatment and HCMV infection . THP-1 cells transfected with control siRNAs or BST2 siRNA were treated with PMA and entry of AD169 was monitored for IE1 expression by immunoblot and IFA . As shown in Fig . 5C , IE1 expression was strongly reduced upon BST2 siRNA treatment , whereas reduction of GAPDH or transfection of control siRNA did not have a significant effect . Interestingly , residual IE1 expression in BST2 siRNA-treated cells correlated with residual BST2 expression presumably due to incomplete knockdown in some cells ( Fig . 5F ) . To determine whether BST2 siRNA also reduced entry of endothelial cell and macrophage-tropic clinical isolates , we infected PMA-induced THP-1 cells with the clinical strain HCMV-TR . Compared to control siRNA treated cells , reduced amounts of viral genome copies were recovered from THP-1 cells treated with BST2-specific siRNAs ( Fig . 5G ) . Taken together these data suggest that induction of endogenous BST2 during monocyte differentiation facilitates HCMV entry . Significant surface levels of BST2 were also observed on primary human monocytes isolated from peripheral blood mononuclear cells ( PBMC ) ( Fig . 6A ) . To determine whether BST2 facilitated HCMV entry into such primary cells , we knocked down BST2 by transfection of BST2-specific siRNA into monocytes and monitored pp65 ( Fig . 6B ) or IE1 expression ( Fig . 6C ) upon infection with HCMV-strain Toledo . Successful knockdown of BST2 was verified by immunoblot ( Fig . 6B , C ) . The experiments were repeated with monocytes from five different donors . We observed a reduced recovery of pp65 ( Fig . 6B ) and reduced expression of IE1 ( Fig . 6C ) in BST2-siRNA treated cells , but not in control siRNA treated cells . Therefore , we conclude that BST2 enhances viral entry into primary human monocytes . Taken together , above experiments suggest that increased BST2 levels enhance HCMV entry and that this facilitated entry might play an important role in HCMV infection of monocytes . What might be the molecular mechanism by which BST2 enhances entry of viral particles into cells ? Given our current knowledge of BST2 as an anti-viral protein that tethers virions to cell membranes , it is conceivable that BST2 increases HCMV entry by a similar tethering mechanism except that here the tether is used to capture HCMV rather than to prevent its release . In the case of HIV-1 it is thought that BST2 needs to be present in both the plasma membrane and the virion and that BST2 prevents budding either by bridging the virion with the membrane or by BST2 forming homo-dimers or –multimers between virion-associated BST2 and BST2 in the host cell membrane . Thus , a prerequisite for the formation of a tether between HCMV and the plasma membrane would be the presence of BST2 in HCMV virions . To determine whether BST2 was present in HCMV particles we fractionated virion preparations via a Nycodenz gradient to separate contaminating cellular membrane fragments and defective viral particles from intact , infectious virions . We tested the concentration of DNA in each of the fractions and detected representative viral and cellular proteins by immunoblot . The DNA concentration was measured with 260/280 nm absorption values and the highest concentration was measured in fractions 9–12 peaking in fraction 11 ( Fig . 7A ) . This correlated with the presence of infectious virus in fractions 8–13 as detected by immunoblot of IE-1 in fibroblasts inoculated with each fraction ( Fig . 7B ) . The same fractions , particularly fraction 11 , also contained the highest concentration of the virion proteins pp65 and pp28 . In contrast , the cellular transmembrane protein CD81 was only found in lower fractions expected to contain contaminating membranes . To detect BST2 the sample was de-glycosylated using PNGaseF and the blot was probed with rabbit polyclonal anti-BST2 . The result showed that BST2 was present in early fractions and reappeared in the fractions 9–11 that contained infectious virus ( Fig . 7C ) . Another cellular protein , GAPDH that was identified by a proteomics study to be present in the virion [38] , showed a similar pattern of distribution like BST2 in the gradient , where it was detected in early fractions and reappeared in the later fractions containing live virus . To independently determine the presence of BST2 in the virion membrane we analyzed virion preparations by immune electron microscopy with BST2-specific antibodies using gold-conjugated secondary antibodies . The virus preparation was not permeabilized so that antibodies would only recognize proteins at the surface of the viral envelope . For control , we used the viral envelope protein gB . As shown in Fig . 7D and E , both gB and BST2 were detected on virions . These observations demonstrate that BST2 is present in the virus particle .
In this study we addressed the question whether the β-herpesvirus HCMV is affected by the innate immune response protein bone marrow stromal cell antigen 2 ( BST2/HM1 . 24/CD317/Tetherin ) , a protein that has been shown previously to restrict the release of a number of unrelated enveloped viruses [19] . Contrary to our expectations , we observed that BST2 is not only incapable of restricting HCMV from egress but it is , in fact , utilized by the virus to gain entry into BST2-expressing cells . Importantly , BST2 facilitated entry of HCMV into hematopoietic cells which are important in the hematogenous dissemination of the virus . In several previously reported instances , the effect of BST2 can only be observed when viral counter mechanisms are inactivated , e . g . due to genetic deletion of Vpu from HIV-1 or by siRNA treatment against K5 of KSHV [14] , [17] . Thus , our observation that BST2 did not inhibit the egress of HCMV could indicate that HCMV developed effective countermeasures . However , while we observed that HCMV counteracted the IFN-dependent induction of BST2 , presumably as a result of interfering with JAK/STAT signaling , we observed that HCMV actually induced BST2 upon infection of fibroblasts . Induction of BST2 is likely due to the viral activation of IRF3 via the DNA sensor ZBP-1 which results in induction of IFN and IFN-independent ISG induction [39] . These observations suggest that HCMV does not eliminate BST2 as reported for HIV-1 or KSHV [14] , [17] . However , we cannot rule out with certainty that HCMV counteracts the antiviral function of BST2 in a more subtle manner during egress . It is also possible that BST2 is unable to prevent HCMV egress due to differences between cytomegaloviral release and that of viruses susceptible to BST2 . In either case , one of the consequences of BST2 upregulation by HCMV seems to be the incorporation of BST2 into the viral envelope . Whether this is a passive incorporation due to the fact that viral envelopes are derived from cellular membranes or due to an active enrichment of BST2 into the viral envelope is currently unknown . Since a previous proteomics study of HCMV virions did not find BST2 in the virion preparations [38] it is possible that BST2 levels vary between virus preparations . Alternatively , the abundance of BST2 was too low to be detected by mass-spectrometry . In contrast to other enveloped viruses , we observed that BST2 increased HCMV infection . This increase was observed consistently with two viral strains in different cell types using different BST2 construct as well as upon induction of endogenous BST2 . Since this increased infection was observed prior to the onset of viral gene expression and even when virus was UV-inactivated , we concluded that BST2 enhanced viral entry . BST2 is highly expressed on monocytes , monocyte-derived macrophages and dendritic cells . Therefore , we propose that BST2 facilitates entry into these cell types , and our data in THP-1 cells and primary monocytes support this . However , the presence of BST2 is not sufficient for HCMV infection of other BST2-expressing cell types such as T cells and B cells , because HCMV replication can be also restricted post-entry ( as observed for AD169 in THP-1 cells ) . Thus , unlike a bona-fide receptor , BST2 is not essential for infection because viral infection also occurs in the absence of BST2 , particularly in fibroblasts . Instead BST2 seems to act as a co-factor , or co-receptor that facilitates but is not essential for viral entry . In the presence of BST2 a larger percentage of cells seem to be infected rather than an increase of viral replication in individual cells . Therefore , it is conceivable that BST2 increases viral dissemination in vivo by gaining increased access to BST2-expressing cells . At present , it is unclear how BST2 increases viral entry . However , based on the known function of BST2 as a viral tether preventing viral budding it is conceivable that a similar but reverse mechanism could operate during entry . Interestingly , a report by David Perez-Caballero et al . [40] , suggests that the unusual topology of BST2 rather than its primary structure is sufficient for binding and restricting viral release . Thus , replacing the N-terminal transmembrane domain , the extracellular coiled-coil motif and the C-terminal GPI anchor with similar domains from unrelated proteins resulted in a virus-inhibitory protein [40] . Based on these observations , it is hypothesized that BST2 either tethers budding viruses by linking the envelope with the membrane through each of its membrane domains or through dimers that form between virion-associated BST2 and BST2 in the cell membrane . Indeed , electron micrograph analysis revealed BST2 in virion junctures and in the membranes of concatenated virions consistent with either model [40] . Based on these models , we hypothesize that similar tethering mechanisms by BST2 enhance entry of HCMV . Either virion-associated BST2 interacts with BST2 at the cell surface or plasma-membrane-associated BST2 inserts one of its membrane domains into the envelope of the incoming virus , e . g . during membrane fusion . In both cases , BST2 at the cell surface will enhance the virion binding and entry of viruses into the host cells and thereby infection by virus . Although BST2 is a self interacting protein , BST2 has also been shown to interact with the cellular protein Immunoglobulin-like transcript 7 ( ILT-7 ) at the surface of plasmacytoid dendritic cells [12] . Thus , another potential mechanism for BST2 facilitated entry could be that HCMV encodes a protein that is expressed in the virion membrane and mimics ILT-7 allowing interaction with BST2 on the surface of the target cell . Interestingly , HCMV expresses UL18 , an MHC-I like protein that interacts with ILT-2 a protein of the same family as ILT-7 [41] . In summary , our data suggest an unprecedented and unexpected role for BST2 in HCMV entry . This observation is in stark contrast to the well-established role of BST2 in prohibiting egress of a number of unrelated viral families , including at least one herpesvirus [14] . In particular , lentiviruses seem to be highly sensitive to inhibition by BST2 as indicated by the observation that primate lentiviruses generally counteract BST2 of their host species , but are susceptible to inhibition by BST2 from other primate species [23] . In contrast , CMV appears to employ BST2 as an entry factor . Since CMVs co-evolve with their hosts , each CMV strain may adapt to the BST2 of the host species . In vivo , BST2-mediated enhancement of HCMV entry may play a particularly important role in macrophages and dendritic cell that express BST2 upon differentiation in response to infection and inflammation . In addition high levels of BST2 expression have been observed on many cancer cell lines [42] which correlates with reports of high levels of CMV antigen in certain tumors [43] . Thus , BST2 may contribute to CMV tissue tropism . Further studies will delineate these possibilities .
Healthy human volunteers who donated blood provided informed written consent before signing research authorization forms that complied with the US Health Insurance Portability and Accountability Act in addition to a medical history questionnaire . These studies were approved by the Institutional Review Board of OHSU . Primary human foreskin fibroblast ( HFF ) cells were obtained from ATCC and cultured in Dulbecco's minimal essential medium ( DMEM ) supplemented with 10% fetal bovine serum , L-Glutamine and 100 units of penicillin/streptomycin in a humidified incubator with 5% CO2 at 37°C . HFF cells used in this study were between passages 8 and 20 . HFFs stably transfected with the human telomerase gene ( THFs ) to extend passage life were obtained from W . Bresnahan ( University of Minnesota ) [44] . THFs were propagated the same way as HFFs . THP-1 cells were cultured in RPMI with L-Glut , 10% FBS and 100 units of penicillin/streptomycin . HCMV strain AD169 was obtained from the ATCC and Toledo from in house stocks . The viruses were propagated in primary HFFs and purified by centrifugation through a 20% sorbitol cushion for 1 h at 22 , 000 rpm in a Beckman SW28 rotor . The titers of virus stocks were determined using endpoint serial dilution assays on primary HFFs . For pure virus preparations the supernatant collected from infected cells was cleared of whole cell by spinning at 1 , 500 rpm , 4°C for 5 min , then the membrane contaminants were removed by two spins at 7 , 500 rpm , 4°C , 15 min followed by sorbitol cushion purification . This partially cleaned virus was subjected to Nycodenz gradient ( 50-5% in TNE buffer - 50 mM Tris [pH 7 . 4] , 100 mM NaCl , and 10 mM EDTA ) . Fractions of the separated gradient were spun at 30 , 000 rpm at 4°C for 1 h followed by a wash with PBS . The isolated fractions were tested for the presence of viral and cellular proteins , DNA concentration and infectivity . Inactivation of HCMV particles using UV irradiation was performed in a Stratalinker for a length of time sufficient to block expression of protein from the HCMV open reading frame UL123 ( IE1 ) and to induce MHC-I expression as result of IFN activation in the treated cells . Histodenz ( Nycodenz ) was purchased from Sigma Aldrich . The antibodies pp65 , gB ( HCMV ) , CD81 and GAPDH were purchased from Santa Cruz biotechnologies . BST2 rabbit polyclonal antibody was obtained through the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH from Drs . Klaus Strebel and Amy Andrew ( https://www . aidsreagent . org/index . cfm ) . Anti-HA antibody was obtained from Covance Inc . , and the IE1 antibody was from Light Diagnostics . Antibody to pp71 was a gift from Dr Thomas Shenk [45] . Human recombinant IFNβ was obtained from PBL Interferon Source , Piscataway , NJ . Human bst-2 cDNA was amplified as an NheI/BamHI fragment by a PCR using the Pfu enzyme ( Stratagene , San Diego , CA ) and inserted into the lentiviral vector pCDH-CMV-MCSEF1-Puro ( System Biosciences , Mountain View , CA ) . BST2 and its mutants with a hemagglutinin ( HA ) tag at position 146 ( BST2-HA146 ) and position 110 ( BST2-HA11 ) were generated by primer-directed mutagenesis using PCR as previously described [14] . Lentiviral supernatants were produced via triple transfection of 293T cells with the pHP-dl-N/A packaging construct , the pHEF-VSVG envelope construct ( both constructs were obtained through the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH , from Lung-Ji Chang ) , and one of the lentiviral clones described above . Transfections were performed using Effectene ( Qiagen , Germantown , MD ) , with a plasmid ratio of 6∶1∶3 ( packaging construct∶envelope construct∶lentiviral clone ) . After 48 h , the supernatants were collected , and lentiviruses were purified through a 0 . 8 µm filter . Stable cell lines expressing the previously described constructs BST2HA146 and BST2HA110 [14] were generated by lentiviral transduction and puromycin selection ( 0 . 3 µg/ml ) in THFs and HFFs . Cells transfected with empty pCDH vector were generated for control . Isolation of human peripheral blood monocytes was performed as previously described [33] . Briefly , blood was drawn by venipuncture and centrifuged through a Ficol Histopaque 1077 gradient ( Sigma-Aldrich , St . Louis , MO ) at 200× g for 30 min at room temperature ( RT ) . Mononuclear cells were collected and washed twice with PBS and 1 mM EDTA to remove platelets at 150× g for 10 min at RT . Monocytes were then layered on top of a 45% and 52 . 5% iso-osmotic Percoll gradient and centrifuged for 30 min at 400× g at RT yielding monocyte population that was more than >90% pure . Cells were washed twice with saline at 150× g for 10 min at RT to remove residual Percoll and suspended in RPMI 1640 ( Cellgro ) supplemented with 10% human serum ( Sigma-Aldrich ) . To monitor virus entry , HCMV was added to cells at an MOI of 3 unless otherwise noted and rocked in an incubator with CO2 at 37°C for 1 h . Then the cells were washed with citric acid buffer ( pH = 3 ) followed by three cold PBS washes and lysed in Laemmli buffer with 10% β-mercaptoethanol . The lysates were analyzed for the expression of tegument proteins pp65 or pp71 using previously described antibodies . For analysis of immediate early gene expression infected cells were harvested 8hpi and lysates subjected to immunoblot . For the time course analysis with AD169-GFP virus , the cells were treated with the virus at an MOI of 2 and analyzed for GFP positive cells over time . SiRNA treatment was performed 2 days prior to cell infection with AD169-GFP at MOI of 1 and analyzed for GFP positive cells 24hpi . THP-1 cells and 293-M cells were infected with HCMV-AD169 at an MOI of 3 and harvested at early times ( 3 h ) to detect tegument proteins or at a later times ( 24 h ) for analysis of IE1 protein expression . PBMC derived monocytes were infected with HCMV Toledo at an MOI of 5 . Relative genome copies were measured in THP-1 cells activated using PMA and infected with HCMV-TR strain after SiRNA treatments . SiRNA against BST2 , GAPDH and SiGlo were obtained from Dharmacon Inc . , Lafayette , CO ( Smart pool ) and negative control siRNAs ( -ve SiRNA ) from Qiagen . HFF cells were treated with Lipofectamine 2000 ( 3 µL/mL; Invitrogen ) and SiRNAs at a concentration between 10–40 nM twice 72 h apart . THP-1 cells were treated with Hyperfect ( 10 µL/mL; Invitrogen ) and SiRNA at 40 nM after 24 h of treatment with PMA ( 1 µg/mL ) . PBMC-derived monocytes were treated with RNAi Max ( 10 µL/mL; Invitrogen ) after adhering to plastic with 50 nM of the SiRNAs for 48 h and infected with HCMV for 4 h or 24 h . The cells were probed for pp65 or IE1 proteins . THP-1 cells were treated with 1 µg/ml of PMA for 24 h followed by transfection with SiBST2 . After three days , the cells were infected with HCMV and washed with PBS after 24 hours followed by fixing with 2% paraformaldehyde and incubating with antibodies for IE1 and BST2 . Slides were fixed a second time in 2% paraformaldehyde after the final antibody treatment and washed three times with PBS . Coverslips were then mounted on slides and covered with Vectashield H-1200 þ DAPI ( Vector Laboratories ) . For flow cytometry , cells were removed from tissue culture dishes with 0 . 05% trypsin-EDTA ( Invitrogen ) , washed with ice-cold PBS , and fixed with 3 . 7% formaldehyde solution . For intracellular staining , cells were permeabilized using perm wash solution ( 1% saponin , 10% NaN3 , 10% fetal calf serum [FCS] , in PBS ) . For surface staining the cells were directly incubated with appropriate primary antibody for 60 min at 4–8°C . The cells were washed with ice-cold PBS and either resuspended in ice-cold PBS or incubated with PE-conjugated anti-mouse secondary antibody ( Dako , http://www . dako . com ) and washed twice again before analyzed using FACSCalibur flow cytometer ( BD Biosciences ) . Total mRNA from cells was isolated and purified using RNeasy ( Qiagen ) . Specific primers for BST2 and β-actin were designed using Primer 3 software ( BST2 primers CCGTCCTGCTCGGCTTT [forward] and CCGCTCAGAACTGATGAGATCA [reverse]; β-actin primers - TCACCCACACTGTGCCCATCTACGA [forward] and GCGGAACCGCTCATTGCCAATGG [reverse] ) . Transcript levels were determined by quantitative real-time reverse transcriptase PCR ( qPCR ) , using SYBR green dye incorporation ( AB Applied Biosystems , Warrington , United Kingdom ) and AmpliTaq Gold DNA polymerase in an ABI Prism 7900HT sequence detection system ( AB Applied Biosystems , Warrington , United Kingdom ) . The comparative threshold cycle method was used to derive the change in gene expression between different treatments , using β-actin as an internal standard . To quantify the number of viral genomes in DNA isolated from infected cells we used a Taqman primer probe set that amplifies the UL81/82 region of the HCMV genome ( Fwd - GAGGTAGGTCGTAGTGCGGC ; Rev- GCTCTCACGCTCGTCATCC ; probe: TGCTGCACGCTCAC with VIC reporter ) . 50 ng of total cellular DNA isolated from infected cells after citric acid wash was used as template . A standard curve was determined using DNA isolated from the input virus . The DNA was amplified on a ABI step one plus and the data were analyzed using step one software v2 . 1 . The samples were prepared in Laemmli buffer with 10% β-mercaptoethanol and separated using 10% SDS-PAGE . The proteins were transferred to polyvinylidene difluoride membrane ( Waters Ltd . , Milford , MA ) and probed with primary antibodies for 1 h at room temperature , followed by horseradish peroxidase-conjugated secondary antibody ( Santa Cruz Antibody Solutions ) . Membranes were washed in 0 . 1% Tween 20 in PBS . The proteins were detected using a SuperSignal West Femto chemiluminescence kit ( Thermo Scientific , Rockford , IL ) . The bands were quantified using Image J software from NCBI ( http://rsbweb . nih . gov/ij/ ) . The images were converted into the binary mode and ratios were derived by comparing viral protein band to the host control protein band ( GAPDH ) and represented as graphs next to the blots . For detecting BST2 in virion sample , it was deglycosylated using PNGaseF ( New England biolabs inc . , ) . Approximately 108 particles were applied to carbon-coated gold 300-mesh grids . The virions were fixed in 4% paraformaldehyde in PBS and grids were washed three times in PBS , blocked in 5% BSA , 2% normal goat serum in PBS ( pH 7 . 4 ) followed by incubation with antibodies to BST2 or HCMV gB for one hour . Then the grids were washed and incubated with secondary 20-nm gold conjugated anti-mouse antibodies ( Ted Pella Inc . , Redding , CA ) diluted 1∶20 in blocking solution . The samples were stained with ammonium molybdate and examined on a Philips EM 300 electron microscope . For statistical analysis gold positive virus particles were counted and presented as percentage of total virus particles . The bar graphs for viral entry assay represent mean ± SD from 3 to 5 replicates for each experiment . Significance was assessed by analysis of variance ( ANOVA ) with secondary Fishers least significant difference ( FLSD ) and Mann Whitney; P values<0 . 05 considered significant . | Human Cytomegalovirus ( HCMV ) persistently infects a large proportion of the human population without causing any symptoms . The establishment and maintenance of HCMV in infected individuals is thought to be facilitated by the ability of HCMV to modulate innate and adaptive immune responses by the host . BST2 , aka Tetherin , was recently shown to be an innate immune response molecule that is induced by the antiviral cytokine interferon . BST2 has been shown to prevent the release of many different viruses , including the human immunodeficiency virus and Ebola virus , from infected cells by tethering the viral envelope to the host cell membrane . Unexpectedly however , we observed that BST2 had the opposite effect on infection by HCMV . Cells expressing BST2 became more susceptible to infection with HCMV . Thus , HCMV seems to use this antiviral protein to gain access to cells that naturally express high levels of BST2 such as macrophages . | [
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] | 2011 | BST2/Tetherin Enhances Entry of Human Cytomegalovirus |
Novel traits play a key role in evolution , but their origins remain poorly understood . Here we address this problem by using experimental evolution to study bacterial innovation in real time . We allowed 380 populations of Pseudomonas aeruginosa to adapt to 95 different carbon sources that challenged bacteria with either evolving novel metabolic traits or optimizing existing traits . Whole genome sequencing of more than 80 clones revealed profound differences in the genetic basis of innovation and optimization . Innovation was associated with the rapid acquisition of mutations in genes involved in transcription and metabolism . Mutations in pre-existing duplicate genes in the P . aeruginosa genome were common during innovation , but not optimization . These duplicate genes may have been acquired by P . aeruginosa due to either spontaneous gene amplification or horizontal gene transfer . High throughput phenotype assays revealed that novelty was associated with increased pleiotropic costs that are likely to constrain innovation . However , mutations in duplicate genes with close homologs in the P . aeruginosa genome were associated with low pleiotropic costs compared to mutations in duplicate genes with distant homologs in the P . aeruginosa genome , suggesting that functional redundancy between duplicates facilitates innovation by buffering pleiotropic costs .
An evolutionary innovation is a new trait that allows organisms to exploit new ecological opportunities . Some popular examples of innovations include flight , flowers or tetrapod limbs [1 , 2] . Innovation has been proposed to arise through a wide variety of genetic mechanisms , including: domain shuffling [3] , changes in regulation of gene expression [4] , gene duplication and subsequent neofunctionalization [5 , 6] , horizontal gene transfer [7 , 8] or gene fusion [9] . Although innovation is usually phenotypically conspicuous , the underlying genetic basis of innovation is often difficult to discern , because the genetic signature of evolutionary innovation erodes as populations and species diverge through time . One way to circumvent this difficulty is to directly study the evolution of innovation in real time using microbial model systems [10 , 11] . The large population size and short generation time of microbes allows for rapid evolution under conditions that can be easily replicated . Samples from evolving populations can be cryogenically preserved in a non-evolving state so that evolved genotypes can be directly compared with their ancestors . Also , bacteria have compact genomes , making it possible to characterize the functional and genetic basis of adaptation [12 , 13] . Recent experiments using this approach have provided detailed examples of the evolution of a number of innovations [14–19] , such as novel metabolic traits [15] and ecological specialization [19] . However , there is a difference between evolving a new trait ( innovation ) and improving an already exiting one ( optimization ) [17] and it remains unclear if evolutionary adaptations that require qualitatively new traits ( innovations ) generally have a different genetic basis than adaptations that require mere fine tuning ( optimization ) of an existing trait . The objective of this study is to determine the genomic mechanisms underpinning evolutionary innovation and optimization using bacterial metabolism as a model system . To achieve this goal , we allowed populations of P . aeruginosa founded by a single clone to evolve in Biolog microtiter plates containing culture medium supplemented with 95 unique carbon sources . Crucially , the ancestral clone produces a clear bimodal pattern of growth on these carbon sources: in some of the carbon sources it grows poorly while in others it grows well . Carbon sources that support little or no growth above the carbon-free control challenge bacteria to evolve novel metabolic traits . These carbon sources can therefore be used to study evolutionary innovation . In contrast , carbon sources that allow the ancestral clone to grow to at least a moderate population density challenge bacteria to improve existing traits . These carbon sources can be used to study the genetic basis of evolutionary optimization . Following 140 generations of evolution we identified carbon sources that populations consistently adapted to . We then isolated clones from populations that evolved in these carbon sources and used whole genome sequencing of more than 80 evolved clones to determine the genetic basis of evolutionary innovation and optimization . To understand the pleiotropic consequences of innovation we used high-throughput phenotypic assays to measure the fitness of the clones evolved in a single carbon source in the 94 remaining substrates of the Biolog plate . This experimental strategy has two main benefits . First , by comparing the mutations and phenotypes observed in clones adapted through innovation and optimization it is possible to test for distinct genomic signatures associated with innovation . Second , by studying the evolution of multiple novel traits , it is possible to make general conclusions about the genetic basis of innovation .
We first assessed the growth of P . aeruginosa PAO1 in the 95 unique carbon sources provided by Biolog microtiter plates . Each well on a Biolog plate contains a common inorganic growth medium that is either supplemented with a unique carbon source ( 95 wells ) , or not supplemented and acts as a negative control ( 1 well ) . The parental PAO1 strain ( ancestral clone hereafter ) showed a clear bimodal pattern of growth in these 95 carbon sources , both in terms of viable cell titre and optical density ( Fig 1A , see Materials and Methods ) . Some carbon sources supported very low levels of growth that were comparable to the growth observed in the negative control well; selection on these substrates challenges P . aeruginosa to evolve new metabolic traits . In contrast , other carbon sources supported good levels of growth; selection on these substrates challenges P . aeruginosa to optimize existing metabolic traits . Although this distinction is intuitive , it is necessary to formally define a threshold between innovation and optimization . To do so we fitted a mixture distribution to the viable cell titre for the 95 carbon sources . We used the point where the two distributions intersected to classify the carbon sources in two groups: innovation ( carbon sources that supported poor growth , similar to the carbon-free control ) and optimization ( carbon sources that supported growth to high population density ) . This classification was also supported by optical density data ( see Materials and Methods ) . We evolved 4 replicate populations founded by the ancestral clone in each of the 95 carbon sources present in the Biolog microtiter plates by serially propagating cultures on 4 replicate Biolog plates for 30 daily serial transfers , which corresponds to approximately 140 generations of bacterial growth . At the end of the evolution experiment , we tested for adaptation on each of the 95 carbon sources by comparing the growth rate of the 4 replicate populations that had evolved on each carbon source to the growth rate of the ancestral clone on the same carbon source . We used growth rates to assess adaptation because they provide a higher resolution than viable cell titre , which can allow for the detection of small differences in the rate of adaptation across substrates . We note , however , that growth rate and viable cell titre measures strongly correlate ( r = 0 . 887 , P < 0 . 001 ) . Given that evolutionary innovation involves the origin of novel phenotypes , whereas optimization involves the refinement of existing phenotypes , optimization should evolve more readily than innovation . Consistent with this expectation , the proportion of populations that evolved increased growth rate was significantly lower on carbon sources that challenged bacteria to innovate as opposed to optimize existing traits ( 51 . 50% vs . 63 . 89% , P = 0 . 01 , One-tailed Fisher's exact test ) . Moreover , the fraction of carbon sources where all 4 replicate populations evolved increased growth rate was almost 50% lower on carbon sources that challenged bacteria with evolutionary innovation as opposed to optimization ( Fig 1B; P = 0 . 042 , One-tailed Fisher's exact test ) . To understand the genetic basis of adaptation , we sequenced the genome of 4 independently evolved clones from carbon sources where all 4 replicate populations evolved increased growth rates . Our rationale for this sequencing strategy is as follows . Parallel increases in growth rate suggest that selection was very effective on these substrates , increasing the probability that clones from these substrates carry potential beneficial mutations . Second , by sequencing multiple clones that evolved on the same substrate it is possible to identify genes that show parallel molecular evolution . Parallelism is common in bacterial populations , and it provides a simple way to identify genes that contribute to adaptation [19–22] . Specifically , we sequenced the genomes of 84 clones from carbon sources that challenged bacteria to both innovate ( 8 carbon sources , 32 clones ) and optimize existing traits ( 13 carbon source , 52 clones ) . The ancestral clone produces a clearly bimodal distribution of growth on these 21 carbon sources , with an approximately 10-fold difference in mean viable cell density between carbon sources where innovation as opposed to just optimization occurred ( S1 Fig ) . We identified 143 unique mutations in the genomes of the 84 sequenced clones , amounting to a mere 1 . 70 mutations per clone on average ( S1 Data ) . These were all mutations that accumulated in the course of the experiment and that were not present in the ancestral clone . Most of the mutations that we identified were SNPs ( 74% ) , but we also detected short indels ( 8% ) , large deletions ( 12% ) , and duplications ( 4% ) . The proportions of these types of mutations did not differ between clones that had adapted through innovation and optimization ( S2 Fig and S1 Table; P = 0 . 213 , Pearson’s X2 test ) . Although populations of P . aeruginosa sometimes evolve elevated mutation rates during cystic fibrosis infections [19] and during long-term selection experiments [23 , 24] , we did not find any hypermutator strains with mutations in genes involved in DNA replication and repair , such as the methyl-directed mismatch repair pathway . Several lines of evidence suggest that most of the SNPs that we detected were beneficial mutations . First , the vast majority ( 97/106 ) of point mutations we detected were non-synonymous ( S1 Table ) . We only detected three synonymous mutations and two affected a gene where parallel synonymous evolution occurred , suggesting that these were beneficial synonymous mutations [25] . Thus , our estimate of the rate of substitution of non-synonymous mutations to putatively neutral synonymous mutations is 97/1 . Second , the number of mutations per clone was approximately 40% higher in clones that had to adapt through innovation ( 2 . 1 mutations per clone ) as opposed to clones that adapted through optimization ( 1 . 53 mutations per clone ) ( S3 Fig; P = 0 . 034 , two-sample one-tailed Kolmogorov-Smirnov test ) . Given that the number of generations of evolution was highly similar across carbon sources ( see Materials and Methods ) , this difference in the number of genetic changes is consistent with the idea that populations that had to adapt through innovation were exposed to stronger selection . This difference is particularly striking , given that populations that had to adapt through innovation were associated with a small population size , which should reduce the rate of fixation of beneficial mutations . Finally , parallel molecular evolution was very common: 65 . 73% of the mutations occurred in genes that were mutated in more than one clone ( S1 Data ) , which is significantly greater than the amount of parallel evolution expected due to chance alone ( permutation test , P < 0 . 001 ) . Gene-level parallel evolution tended to occur between replicate clones that evolved on the same carbon source , and genes that were only mutated in clones from an individual carbon source accounted for 75% of the parallelism that we observed . Interestingly we found parallel evolution in all 4 replicate clones that evolved on 5 carbon sources ( L-alanyl-glycine , glycyl-l-glutamic acid , L-serine , D , L- α glycerol phosphate , and glycerol ) , involving 24 mutations . In every case , parallel evolution on these substrates involved transcriptional regulators . Recent work in the closely related bacterium P . fluorescens suggests that parallel evolution by mutations in transcriptional regulators is common because it provides an efficient mechanism to translate genetic variation into phenotypic variation [26] . This may explain why parallel regulatory evolution was very common on some substrates . Rigorous test of this idea is outside the scope of this paper and it would require further experimental work , as in [26] . We also observed higher-order parallel evolution involving different genes that act in the same operon . Parallelism by definition becomes more common as the scale at which it is measured increases; for example , parallelism is necessarily more common when it is measured at the level of genes than at the level of nucleotides . However , it is difficult to objectively measure parallelism above the level of the gene at a genome-wide scale , especially given the large number of genes of unknown function in the P . aeruginosa genome , and we therefore , focused our analysis of parallelism at the level of genes . Like many free-living bacteria , the genome of P . aeruginosa is made up almost entirely of protein coding sequences ( 89 . 4% coding DNA ) . Because innovation involves the origin of novel phenotypes , it is reasonable to expect that innovation should be associated with more radical changes to proteins . The vast majority of mutations that we observed were non-synonymous substitutions in protein coding regions ( S1 Table ) , but the relative frequency of radical amino acid substitutions did not differ significantly ( Z-test; P = 0 . 84 ) between evolutionary innovation ( n = 21; 53 . 8% ) and optimization ( n = 28; 56 . 8% ) . Short insertions and deletions ( indels ) that introduce frameshifts can also produce radical changes to proteins . However , we only found 6 indels that introduced frameshifts , making it impossible to test for a difference in the frequency of indels observed under innovation ( n = 4 ) and optimization ( n = 2 ) . In summary , innovation and optimization did not leave distinct signatures on the structure of proteins in our experiments . To gain further insights into the mechanistic basis of adaptation , we compared the functional roles of genes carrying mutations in clones evolved through innovation and optimization . Changes in the regulation of gene expression have been proposed to play an important role in evolutionary innovation [16 , 27 , 28] . Mutations in regulatory genes were common , and in many cases these mutations could be clearly linked to metabolic traits that were under selection ( S2 Table ) . For example , adaptation to L-serine repeatedly evolved by non-synonymous mutations in a transcription factor ( PA2449 ) that regulates the expression of genes involved in serine metabolism [29]; similarly , acquiring the ability to metabolize L-Alanyl-Glycine repeatedly evolved by mutations in pdsR , a repressor of a di-peptide and amino acid transport operon . We found that the proportion of mutations in genes involved in transcription was greater in clones from populations that had to adapt through innovation as opposed to optimization , supporting the idea that altered gene expression is an important feature of innovation ( Fig 2 , S3 Table; P = 0 . 036 , One-tailed Fisher’s Exact Test ) . Intergenic mutations also have the potential to change gene expression , for example by altering transcription factor binding sites [16] . For example , evolution in both L-aspartic acid and L-glutamic acid resulted in parallel substitutions in the promoter region of a P . aeruginosa homolog ( PA5479 ) of a Bacillus subtilis L-aspartate and L-glutamate transporter [30] . Similarly , one clone evolved in D-Serine and one clone evolved in Glycerol have , respectively , a SNP upstream D-Amino acid dehydrogenase ( PA5304 ) and a SNP upstream a glyceraldehyde-3-phosphate dehydrogenase ( PA2323 ) . However , intergenic sequences make up only 10 . 6% of the P . aeruginosa genome , suggesting limited potential for adaptation by regulatory mutations in non-coding sequences . Consistent with this idea , we detected only a very small number of intergenic mutations in clones evolved through innovation ( n = 4 ) and optimization ( n = 6 ) , making it impossible to rigorously test the role of intergenic mutations in innovation . It is difficult to make a priori predictions regarding associations between other functional categories of genes and innovation , but we found that innovation was also preferentially associated with mutations in metabolic genes ( Fig 2 , S3 Table; P<0 . 01 , One-tailed Fisher’s exact test ) , whereas optimization was associated with mutations in genes involved in cell processes and signalling ( Fig 2; P<0 . 01 , One-tailed Fisher’s exact test ) . Recent work in experimental evolution has focused on understanding the detailed molecular mechanisms by which individual beneficial mutations increase fitness ( e . g . :[15 , 18 , 25 , 31 , 32] ) , and this work has made an important contribution to a broader functional synthesis in evolutionary biology [33] . We found that 46% of all mutations occurred in genes that were only mutated on a single carbon source and 83 . 6% of the mutated genes were only mutated in one carbon source , suggesting that substrate-specific adaptation was a key driver of evolution in this experiment . In many cases , mutations in these genes can be putatively linked to the metabolism of the carbon source that populations evolved on , and this was particularly the case for genes involved in transcription and metabolism and among clones that had to adapt through innovation ( S2 Table ) . At the same time , we also found mutations in a small fraction of genes ( 16 . 4% ) across multiple carbon sources . As an extreme example we found a gene ( PA1561 ) involved in aerotaxis , mutated 16 times across 10 substrates , suggesting that mutations in this gene represent a general adaptation to Biolog plates . Unfortunately , it is impossible to precisely measure the substrate specificity of the mutations that we detected without carrying allelic replacement experiments to generate strains carrying single mutations . In S2 Table we provide a list of the mutations that occurred on each carbon source and their putative role . However , rigorously determining the biochemical basis of the fitness advantages conferred by individual mutations is outside the scope of this article , as our goal is to understand the genetic mechanisms of evolutionary innovation , and not the biochemical basis of novel metabolic pathways . Moreover , achieving a detailed functional understanding of adaptation in this system would be incredibly challenging given the diversity of selective pressures that we imposed and the diversity of mutations that we observed . Gene duplication is a major source of evolutionary innovation [6 , 34] , and some elegant studies show that it can facilitate adaptation in bacterial populations [35 , 36] . We detected six cases of de novo gene duplication . Every case involved parallel duplications , suggesting that duplication was adaptive . Strikingly , all four clones that adapted through innovation on glycyl-L-glutamic acid evolved independent duplications of a 5 . 6 Kb region that contains an operon ( PA4496-PA4500 ) involved in di-peptide and amino acid transport [37] ( S4A Fig ) . Using information on the frequency of SNPs in the sequenced clones , we were able to re-construct the evolutionary history of these duplications . Adaptation to glycyl-L-glutamic acid evolved via a repeatable two-step process . The first is a missense or nonsense mutation in the repressor of the operon , psdR ( PA4499 ) . The second is a tandem duplication of the operon , most likely as a result of homologous recombination between the flanking sequences of the operon ( S4B Fig ) . The inactivation of the repressor plus the duplication of the operon probably results in increased expression of this operon . We were able to infer the chronology of this adaptation because all reads supported the novel mutations in the pdsR gene , as we would expect if duplication followed mutation . This multi-step process of potentiating mutations that alter the regulation of an operon , followed by adaptive gene amplification , is very similar to a previously described mechanism for the evolution of citrate utilization in Escherichia coli [15] . We also found large ( > 300 genes , >5% of genome ) duplications in two of the four clones that adapted through optimization on hydroxyl-L-proline . These duplications overlapped in a large region comprising most of their genes ( 262 genes , ≈288 Kb ) . This overlap suggests that the duplications were adaptive , but their large scale makes it difficult to infer exactly why . Overall , the limited incidence of duplication in clones that adapted through either innovation ( n = 4 ) or optimization ( n = 2 ) suggests that de novo duplication is not frequently involved in metabolic innovation . This result is consistent with recent work showing that de novo duplication makes only a minor contribution to adaptation to gene loss in E . coli [16] and yeast [38] . In addition to the origin of novel duplicate genes , the divergence of already existing duplicate genes in the P . aeruginosa genome can also play a key role in evolutionary innovation [39] . To test its importance in our experiment , we classified P . aeruginosa genes into duplicates and singletons using a clustering method based on Blast similarity searches . Sequence similarity in bacterial genomes can arise as a consequence of gene duplication of existing genes in the genome , which produces paralogs that are similar as a result of shared ancestry . Alternatively , bacteria can acquire new genes that are similar to existing genes in the genome by horizontal gene transfer . In practice it is very difficult to distinguish between these two mechanisms for the origin of novel genes , and Lerat and colleagues have proposed the term synologs to describe homologous genes in bacterial genomes [40] . We found that clones that adapted through innovation acquired more mutations in existing duplicate genes than expected due to chance alone based on the frequency of duplicate genes in the P . aeruginosa genome ( Fig 3 , P<0 . 01 , Pearson’s X2 test ) . In contrast , the frequency of mutations in duplicate genes in clones that adapted through optimization was indistinguishable from the frequency of duplicates in the P . aeruginosa genome ( S4 Table; P>0 . 05 , Pearson’s X2 test ) . We repeated this analysis using a broad range of similarity cut-offs to identify duplicate genes ( see Materials and Methods ) . Our results remained robust , we consistently detected an enrichment of mutations in duplicate genes in clones that adapted through innovation , irrespective of the cut-offs used to identify duplicates ( S5 Fig and S4 Table ) . This result suggests that the divergence of existing duplicates plays an important role in the ability to evolve novel metabolic phenotypes . We re-emphasize , however , that this analysis does not distinguish between duplicate genes that arose due to horizontal gene transfer and spontaneous duplication . What constrains the evolution of metabolic innovations that could allow P . aeruginosa to expand its ecological niche ? One possible answer is that fitness costs associated with novel metabolic traits may impose a trade-off that limits metabolic innovation [41–43] . To test this hypothesis , we measured the growth of 2 of the sequenced clones from each carbon source across the 94 alternative carbon sources present on a Biolog plate , and we compared it to the growth of the ancestral clone on each carbon source . We did a total of 4750 growth assays ( S6 Fig ) and we established conservative criteria to infer positive or negative pleiotropy . Because our evolved clones carried only a small number of beneficial mutations ( 1 . 70 mutations per clone on average ) , we can be confident that altered growth on alternative carbon sources reflects the pleiotropic side-effects of beneficial mutations . However , we cannot entirely rule out the possibility that some clones carried conditionally neutral mutations that spread by hitch-hiking with beneficial mutations , but the scarce number of synonymous mutations suggests that conditionally neutral mutations are infrequent . Adaptation was associated with pleiotropic costs , because evolved clones showed reduced growth on an average of 14 . 76 carbon sources that could be used by the ancestral clone . However , the pleiotropic cost of innovation was 70% greater than the pleiotropic cost of optimization , which is consistent with the idea that pleiotropy constrains innovation ( Fig 4 , S5 Table; P<0 . 01 , Pearson’s X2 test ) . The precise mechanistic causes of negative pleiotropy are difficult to determine [44] without measuring the effects of the individual mutations that contributed to adaptation in our system . However , the association between evolutionary innovation and mutations in regulatory and metabolic genes suggests that mutations in both of these categories of genes are likely candidates to explain negative pleiotropy . While these observations show that both innovation and optimization have costs , not all pleiotropy may be negative . Surprisingly , we found that positive pleiotropy—where an evolved population shows increased growth on one or more alternative carbon sources—was just as common as negative pleiotropy . The frequency of positive pleiotropy did not differ between clones that adapted through innovation and optimization ( Fig 4 , S5 Table; P = 0 . 61 , Pearson's X2 test ) . Clones that adapted through innovation were enriched in mutations in duplicated genes and paid higher pleiotropic effects than clones that adapted through optimization . This observation is counter-intuitive , because we would expect that mutations in existing gene duplicates should be associated with low pleiotropic costs , given that the other copy of the duplicate may provide functional backup for the mutated copy . To explore this counter-intuitive observation further , we compared the pleiotropic costs expressed by clones carrying mutations in duplicates genes that have close or distant homologs in the P . aeruginosa genome . This analysis is motivated by the assumption that functional redundancy between duplicate genes decays as they diverge from each other . Interestingly , we found that clones carrying mutations in genes that have close homologs have a lower pleiotropic cost than clones without mutations in genes with close homologs ( Table 1 , S6 Table; P<0 . 01 , Pearson's X2 test ) . In contrast , we see the opposite pattern in distant homologs: The pleiotropic cost of clones with mutations in genes with distant homologs is higher than that of clones without mutations in distant homologs ( Table 1 , S6 Table; P<0 . 01 , Pearson's X2 test ) . Collectively , these results support the idea that redundancy between duplicates minimizes the cost of innovation .
Microbiologists have known for a long time that bacteria can evolve novel metabolic traits in the laboratory [45] , and we have taken advantage of the experimental tractability of microbial metabolism to study evolutionary innovation at a broad scale using high-throughput experimental methods coupled to whole genome re-sequencing . This approach provides the opportunity to study the generality of evolutionary outcomes under a range of selective conditions [16 , 38] . Using this approach , we have shown that there are significant differences in the genomic basis of metabolic innovation and optimization in P . aeruginosa . Opportunistic pathogens , such as P . aeruginosa , encounter a novel niche when they establish long-term infections in human hosts , and altered metabolism plays a role in evolutionary transition to specialization on a pathogenic lifestyle [46] . Understanding the causes of evolutionary innovation may , therefore , contribute to our ability to predict the evolution of host-specialization in pathogenic bacteria . At a functional level , we found that both innovation and optimization are predominantly driven by substitutions in proteins , which is hardly surprising given that the genome of P . aeruginosa is made up of 90% coding DNA . Interestingly , innovation and optimization leave similar signatures in proteins , and we did not find any evidence of an excess of radical substitutions associated with innovation . In contrast , we found profound changes in the functional roles of genes that contributed to innovation and optimization . Specifically , we found that innovation is associated with mutations in transcription regulators and metabolic genes . Changes in the expression of existing metabolic pathways that have a basal or underground ability to metabolize novel compounds and changes in the structure of metabolic enzymes that increase their activity towards novel substrates could be involved in the origin of innovations . Importantly , previous studies have provided detailed examples of how both of these mechanisms can lead to evolutionary innovation in bacteria [15 , 45 , 47–49] . One of the main results of our study is that mutations in pre-existing duplicate genes in the P . aeruginosa genome play an important role in metabolic innovation , but not optimization . It is important to recall that we identified duplicate genes based on sequence similarity , and not necessarily common ancestry . Importantly , this method does not distinguish between duplicates that arise via spontaneous duplication ( paralogs ) and horizontal gene transfer , but irrespective of the origins of the duplicates , duplication is expected to result in genetic and functional redundancy [50] . Why are duplicates so important for innovation ? Our results show that evolving new metabolic traits is associated with pleiotropic costs . This is not surprising given that innovation is associated with mutations in genes involved in transcription and metabolism . Trade-offs between evolving novel metabolic pathways and maintain existing ones may therefore constrain innovation . How can this obstacle be overcome ? Carrying duplicate genes produces redundancy , and this redundancy can potentiate innovation through neo-functionalization [4–6 , 51] . The presence of an extra gene copy with functional overlap increases mutational robustness and this increase facilitates the exploration of novel gene functions while the other copy maintains its ancestral function [52–54] . The importance of gene duplication for mutational robustness is still debated [54–60] . Our results support its importance . We find that mutations in genes that have a close homolog in the genome tend to be associated with lower costs than mutations in duplicate genes that have distant homologs . Therefore , our experiments provide evidences of a link between duplication , robustness , and evolvability in P . aeruginosa . In contrast to eukaryotes , most new genes in γ-proteobacteria , including P . aeruginosa , are acquired by horizontal gene transfer , and not by gene duplication [40] . For example , we identified approximately 10% of genes in the P . aeruginosa genome as being pre-existing duplicates , whereas Lerat and colleagues [40] estimated that only about 1% of genes in γ-proteobacteria genomes arise by duplication . This discrepancy suggests that pre-existing horizontally acquired genes are likely to have played a key role in evolutionary innovation in our experiment . Horizontal gene transfer has mainly been viewed as an important source of evolutionary innovation by providing bacteria with access to a very wide pool of genes that confer novel and important phenotypes , such as antibiotic resistance in pathogenic bacteria [8 , 61 , 62] . Our results suggest that the horizontal acquisition of functionally redundant genes may also play a key role in evolutionary innovation by providing bacteria with increased genetic robustness to mutations that generate novel phenotypes . Although redundant duplicate genes provide a genetic substrate for innovation , it is well established that acquiring new genes as a result of horizontal gene transfer or gene duplication , carries a fitness cost in bacteria [31 , 36 , 63–66] . Owing to this cost , newly acquired genes tend to be lost from bacterial populations unless gene acquisition , per se , is beneficial [6 , 36] or because addiction genes , such as toxin-antitoxin systems , select against the loss of acquired genes [67] . Indeed , fitness costs may explain why we observed so few instances of de novo duplication . Selection against newly acquired redundant genes in the short term may therefore limit the long-term ability of bacteria to evolve novel phenotypes . A key goal for future work will be to understand how this tension between fitness and evolvability arising from gene acquisition is resolved .
In this study we used two bacterial clones , P . aeruginosa PAO1 ( PAO1-wt ) and a P . aeruginosa PAO1 containing the luxCDABE operon ( PAO1-lux ) for luminescence production inserted in a neutral site in the bacterial chromosome ( PAO1::mini-Tn7-pLAC-lux ) [68] . The two clones are genetically identical except for the lux operon . We cultured the strains in Biolog GN2 96-well plates ( Biolog , USA ) , in a final volume of 125 μl of M9 broth ( Fischer Scientific , USA ) per well , at 37°C and without shaking . Biolog GN2 plates contain 95 different carbon sources plus a negative control well . We first cultured bacteria on LB agar plates ( Fischer Scientific , USA ) at 37°C to obtain isolated colonies . We initiated the evolution experiment by inoculating a single colony in each well of a 96 well-plate containing LB broth . Alternating wells contained PAO1-wt and PAO1-lux clones to control for possible cross-contamination occurring during the experiment . We build two plates with two alternative patterns: well A1 containing PAO1-wt ( plate A ) and well A1 containing PAO1-lux ( plate B ) . We incubated the 96 well-plate at 37°C and 225 rpm overnight and we started the experimental evolution with two replicates from each plate ( plate A1 , plate A2 , plate B1 , plate B2 ) . We propagated these four plates independently in Biolog GN2 plates for 30 transfers . Specifically , we transferred 5μl of bacterial culture every day into a fresh Biolog plate with 120 μl M9 broth in each well , allowing approximately 4 . 6 generations per day ( log2 of the dilution factor ) and a total of 140 generations during the experiment . Note that this calculation estimates equal bacterial density after incubation in a given well ( carbon source ) over the entire experiment . An increase in the maximum cell density in a well over the 30 days due to adaptation to the carbon source will slightly increase the total number of generations ( in our experiment , the most extreme example went up to 143 generations ) . These differences are not big enough to significantly affect the mutation supply in the different environments over the experiment . We assessed OD and luminescence every day using a BioTek Synergy H4 plate reader ( Biotek Instruments , UK ) . We incubated the plates at 37°C for 24 hours with no shaking . To assess how well adapted the ancestral strain was to each of the carbon sources in a Biolog GN2 plate , we estimated the number of viable cells in each well after a 16 hour incubation of the parental PAO1 strain . To this end , we first streaked out freezer stocks of wild type ancestors to isolate single colonies , and cultured a single colony in 3 ml LB tubes overnight at 37°C with continuous shaking ( 225 rpm ) . On the next day we diluted 5 μl of the overnight culture into 20 ml of M9 broth , and inoculated a Biolog GN2 plate with 125 μl of the dilute cell suspension per well . We incubated the culture for 16 hours at 37°C without shaking , diluted it ten-fold , and measured cell viability using the BacTiter-Glo Microbial Cell Viability Assay ( Promega , USA ) . We assessed luminescence produced in this assay using a BioTek Synergy H4 plate reader ( BioTek Instruments , UK ) for a total of 16 technical replicates per carbon source . We calculated the final bacterial density in each well with an in-house R script . The numbers of viable cells in the different wells followed a clear bimodal distribution ( Fig 1A ) . We fitted a mixture distribution to the data using the mixtool package in R [69] . We used the point at which the two distributions intersected ( 107 . 84 bacteria/mL ) to classify Biolog carbon sources into two groups , one in which the ancestor grew very poorly ( and required innovation to adapt during the evolution experiment ) , and another where the ancestor grew well ( and required only optimization ) ( Fig 1A , S1 Fig ) . To strengthen our classification we also used OD data obtained before performing the BacTiter-Glo Microbial Cell Viability Assay . We performed a hierarchical clustering analysis using viable cells and OD data ( S7 Table ) using the hclust package in R [70] . Three clear groups clustered together ( S7 Fig ) : carbon sources classified as innovation using the intersection of the two distributions , carbon sources classified as optimization using the distribution intersection and a last group containing three carbon sources that according to the distribution intersection were classified as optimization ( N-acetyl-D Glucosamine , Sebacic Acid and Hydroxy-L-Proline ) . These three carbon sources were clustered together because they have lower OD values than the other carbon sources classified as optimization . We decided to maintain our classification and categorize them as optimization because the BacTiter-Glo Microbial Cell Viability Assay has a better resolution than OD . However , the main results of the paper did not change if we classify these three carbon sources as innovation . To identify which populations had adapted after 30 days of evolution we measured growth curves both for ancestor and final populations at the population level . We used growth rates to determine differences in the fitness of these populations because the growth rate provides a higher level of resolution than final cell density in the population . This higher resolution is needed because we are now comparing the changes in fitness in a given carbon source over the 30 days of experiment , which are subtle ( compared to the differences observed for the parental strain over the 95 different carbon sources ) . To this end , we cultured freezer stocks of both populations in LB broth ( 200 μl/well , 96-well plates ) overnight at 37°C , 225 rpm . The next day we diluted the bacteria 1000-fold in M9 broth , and cultured them overnight in Biolog GN2 plates at 37°C ( 125 μl/well ) . The following day we performed another passage , diluted the culture 1:1000 in fresh Biolog G2 plates using fresh M9 broth ( final volume , 125 μl/well ) , and measured growth rate for 16 hours using a BioTek Synergy H4 plate reader ( BioTek Instruments , UK ) at 37°C with no shaking . We computed the maximum growth rate with the software Gen5 2 . 00 ( BioTek Instruments , UK ) . Then , from the populations in which we observed an increase in growth rate , we isolated a single clone from the freezer stock using LB agar plates ( Fischer Scientific , USA ) and repeated the same protocol to ensure that we found the same behaviour at clone level . We stored frozen stocks of these clones . Subsequently , we sequenced the genomes of selected clones , i . e . , clones evolved in carbon sources that fulfil the following condition: The maximum growth rate for the final population ( and clone ) was higher than that of the ancestral population for all four replicates of populations evolved in that carbon sources . We performed DNA extractions from clones cultured in 3 ml LB broth ( Fischer Scientific , USA ) that had been incubated at 37°C with 225 rpm shaking overnight , using the Qiagen Dneasy Blood and Tissue Kit ( Qiagen , Inc . , Chatworth , California , USA ) and the Promega Wizard Genomic 4 DNA Purification Kit ( Promega , UK ) . We quantified DNA using the QuantiFluor dsDNA system ( Promega , Madison , WI , USA ) following manufacturers' instructions . We conducted library preparation and sequencing ( using HiSeq2000 and 100-bp-paired end reads ) at the Wellcome Trust Centre for Human Genetics , University of Oxford . We sequenced 88 genomes , i . e . , 2 PAO1-lux ancestral strains , 2 PAO1-wt ancestral strains , and 84 evolved clones . We analyzed sequencing data using a pipeline developed in-house , as previously described in San Millan et al . 2014 [71] , and mapped filtered reads to our reference genome , which is P . aeruginosa PAO1 ( NC_002516 . 2 ) with the insertion of the phage RGP42 ( GQ141978 . 1 ) . We analyzed only those mutations that had accumulated during the experiment and that were not present in our ancestral strains at the start of the experiment . Note that each evolved clone was compared to the specific ancestral clone from which it was derived ( i . e . either PAO1-wt or PAO1-lux ) . The reads generated in this work have been deposited in the European Nucleotide Archive database under the accession code PRJEB12874 . To elucidate how frequently adaptation to a specific carbon source affects growth in other carbon sources , we performed an experiment at the level of individual clones , using two evolved clones per carbon source and four wt clones as controls . Specifically , we tested those clones whose growth rate had increased during the experiments ( the same clones that we used for the whole genome sequencing ) . We inoculated each clone that had adapted to a particular carbon source in the 95 carbon sources of a Biolog plate . To this end , we cultured each frozen clone and four wt clones in 3 ml LB tubes overnight at 37°C with continuous shaking ( 225 rpm ) , diluted 5 μl of the overnight culture on the next day into 20 ml of M9 broth , and inoculated a Biolog GN2 plate with 125 μl of the dilute cell suspension per well . We incubated the plate for 16 hours at 37°C without shaking . Subsequently , we diluted each culture 10-fold , and measured cell viability in 384-well black plates , using the BacTiter-Glo Microbial Cell Viability Assay ( Promega , UK ) , following manufacturer's instructions . We assessed luminescence produced in the BacTiter-Glo assay using a FLUOstar OPTIMA plate reader ( BMG Labtech , UK ) . We developed an R script to calculate the number of doublings bacteria experience in each well of the Biolog plate correcting for the number of doublings in the negative control well ( number of effective doublings ) . We used the values obtained from 4 wt replicate controls to calculate the 95% confidence interval of number of effective doublings for each well . Then , to assess the pleiotropic effects that the adaptation to a particular carbon source had in the 94 remaining carbon sources , we checked if the number of doublings in each carbon source for that particular clone fell outside the 95% confidence interval calculated using the wt measurements . If it fell outside the 95% confidence interval we counted it as a positive pleiotropic effect in that specific carbon source if the number of doublings were higher than for the wt or as negative pleiotropic effect if the number of doublings were lower than for the wt . We classified those mutations that involve amino acid replacements as radical if they were associated with a change of polarity group ( polar: C , N , Q , S , T and Y; nonpolar: A , F , G , I , L , M , P , V and M; positively charged: H , K , and R; and negatively charged: D and E ) and as nonradical when the replacement did not imply a change of polarity group . To classify P . aeruginosa PAO1 genes into duplicates and singletons , we used BLASTclust ( ftp://ftp . ncbi . nih . gov/blast/documents/blastclust . html ) . BLASTClust is a stand-alone program used to cluster proteins based on pairwise matches using the BLAST algorithm . We considered as singletons all proteins that formed a cluster whose only member was the protein itself , and as duplicates when the cluster contained more than one protein [72 , 73] . Note that this method does not distinguish between duplicates originated by gene duplication of existing genes in the genome and horizontal gene transfer . To ensure the robustness of the classification we used 10 different cut-offs of minimum length coverage and percentage of identical residues . The results remained robust to the different cut-offs used ( S4 Table ) . Higher values of length coverage and residue identity are more likely to indicate close homology [74] . Moreover , it has been previously suggested that values of 53% coverage and 31% identity are enough to detect homology in duplicates [73 , 75] . For these reasons and also to ensure having enough number of genes classified in each category , we used the cut-off 70 coverage and 50 identity as an indicator of close homology and the cut-off 50 coverage and 40 identity as an indicator of distant homology . We experimentally validated observed duplications after 30 days of evolution in glycyl-L-glutamic acid and hydroxyl-L-proline by PCR-amplifying the edges of the duplication and Sanger sequencing the products to confirm the results ( S8 Table and S4A Fig ) . We assessed statistically if the number of parallel mutations observed in our dataset is higher than the expected by chance . We randomly selected a total of 143 positions in the coding region of the P . aeruginosa PAO1 genome , corresponding to the total number of identified mutations . We then recorded the proportion of loci located in genes with more than one randomly selected position and repeated the procedure 1 , 000 times . As a result , we generated the expected distribution by chance ( mean = 0 . 04 , sd = 0 . 02 ) . We obtained an empirical estimation of the P-value as the proportion of permutations yielding a value more extreme than the observed in our dataset . We performed all statistical analyses and produced all graphics using R [61] . | Novel traits play a key role in evolution by providing organisms with access to new ecological niches . Novelty is often conspicuous at a phenotypic level , but it is difficult to determine its underlying genetic basis . To address this problem , we have studied how the bacterium P . aeruginosa evolves novel metabolic traits , such as the ability to degrade new sugars , in real-time . After 30 days of evolution we sequenced the genomes of bacteria that have evolved novel metabolic traits . We found that mutations mainly affected genes involved in transcription and metabolism . Our main finding is that novelty tends to evolve by mutations in pre-existing duplicated genes in the P . aeruginosa genome . Duplication drives novelty because genetic redundancy provided by duplication allows bacteria to evolve new metabolic functions without compromising existing functions . These findings suggest that past duplication events might be important for future innovations . | [
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"mathematics",
"microbia... | 2016 | The Genomic Basis of Evolutionary Innovation in Pseudomonas aeruginosa |
Pseudomonas protegens is a biocontrol rhizobacterium with a plant-beneficial and an insect pathogenic lifestyle , but it is not understood how the organism switches between the two states . Here , we focus on understanding the function and possible evolution of a molecular sensor that enables P . protegens to detect the insect environment and produce a potent insecticidal toxin specifically during insect infection but not on roots . By using quantitative single cell microscopy and mutant analysis , we provide evidence that the sensor histidine kinase FitF is a key regulator of insecticidal toxin production . Our experimental data and bioinformatic analyses indicate that FitF shares a sensing domain with DctB , a histidine kinase regulating carbon uptake in Proteobacteria . This suggested that FitF has acquired its specificity through domain shuffling from a common ancestor . We constructed a chimeric DctB-FitF protein and showed that it is indeed functional in regulating toxin expression in P . protegens . The shuffling event and subsequent adaptive modifications of the recruited sensor domain were critical for the microorganism to express its potent insect toxin in the observed host-specific manner . Inhibition of the FitF sensor during root colonization could explain the mechanism by which P . protegens differentiates between the plant and insect host . Our study establishes FitF of P . protegens as a prime model for molecular evolution of sensor proteins and bacterial pathogenicity .
Pseudomonas protegens is a beneficial root-associated bacterium of the Pseudomonas fluorescens group that is able to promote the growth of crop plants and to efficiently protect their roots against fungal and oomycete phytopathogens [1] , [2] . P . protegens can also turn into an insect pathogen [3]–[5] . The bacterium produces a potent insecticidal toxin termed Fit ( for P . fluorescens insecticidal toxin ) which is required for its capacity to efficiently kill larvae of important agricultural pest insects upon oral or systemic infection [5] , [6] . The gene encoding the Fit protein toxin is part of an eight-gene cluster which comprises also genes coding for a type I secretion system and three regulatory proteins ( Figure S1 and [6] , [7] ) . Expression of the insecticidal toxin is activated during infection of the insect host , but not on plant roots or in standard laboratory media [7] . We recently demonstrated that toxin expression is tightly controlled by two regulators , named FitG ( an activator ) and FitH ( a repressor ) [7] . The third regulatory protein encoded in the Fit cluster is named FitF and codes for a putative sensor histidine kinase-response regulator hybrid protein . We hypothesize that FitF is responsible for the detection of the host environment and for activating insecticidal toxin production via FitH and FitG specifically upon infection of the insect host ( Figure S1 ) . Sensor proteins enable bacteria to sense the environment they live in and to adapt their behavior accordingly , which is particularly relevant for pathogen-host interactions [8]–[10] . The number of sensor protein types is particularly high in bacteria such as pseudomonads that inhabit diverse and changing environments [11] , [12] . An important category of sensor proteins is that of the two-component regulatory systems , which couple extracellular stimuli to adaptive responses . A typical two-component system consists of a membrane-bound sensor histidine kinase , which perceives a stimulus , and a cytosolic response regulator , which transduces the signal into an output , such as altering specific gene expression . Signal transduction is achieved by phosphotransfer reactions between the sensor kinase and the response regulator . In some cases , like in the so-called phosphorelay system , the sensor histidine kinase is a hybrid response regulator protein undergoing multiple intramolecular phosphotransfer reactions , before finally activating a separate response regulator protein [13] , [14] . Sensor and signal transduction proteins usually show a modular organization of conserved domains [14] , which can be highly variable in their order and topological organization [8] . Not surprisingly , therefore , it has been proposed that the modularity of two-component systems enables rapid evolution and generation of new functional properties . Gene duplication and domain shuffling are considered to be driving mechanisms for the formation of new two-component systems in bacteria [10] , [12] . More than 70% of estimated recently duplicated histidine kinases have input domains different from those of their closest paralogs , suggesting frequent domain shuffling events [10] . It was proposed that by shuffling of the sensor domain recently duplicated histidine kinases gained new sensing specificity and thus might have enabled the bacteria to respond to a broader range of environmental changes [12] . The major goal of our work is to understand the molecular mechanisms that allow P . protegens and related bacteria to survive within and to kill the insect host . Of particular interest for the underlying work was the question as to how insect pathogenicity may have evolved and has been selected for . Because sensory systems are essential for niche adaptation , we felt that an evolutionary analysis of the chemosensory systems enabling insect recognition in P . protegens and in particular of the Fit system would be fundamental to the understanding of host adaptation . Here we thus report the detailed regulation of Fit toxin expression and in particular describe the role of the hybrid sensor kinase protein FitF . We noticed that the periplasmic region of FitF is strikingly similar to the sensor domain of the histidine kinase DctB , which regulates the uptake of C4-dicarboxylates in Proteobacteria [15] . The crystal structures of DctB of Vibrio cholerae and Sinorhizobium meliloti have been solved [16] , [17] and show an inserted repeat of a Per-Arnt-Sim ( PAS ) -like fold ( PASp ) in the periplasmic sensory domain , which was later termed the PhoQ/DcuS/CitA ( PDC ) domain [18] . PAS domains are universally distributed among all kingdoms of life , are the most frequent type of signal sensors in bacteria , can fulfill several functions and can bind chemically diverse small-molecule ligands [9] , [19]–[21] . The membrane distal PASp domain of DctB binds C4-dicarboxylates such as malate , fumarate and succinate [15] . We present several lines of evidence illustrating that the periplasmic sensory domain of FitF evolved from a common ancestor with DctB , enabling P . protegens to survive and switch on toxin expression only in the insect host . By expressing a chimeric DctB-FitF protein in P . protegens and thereby testing the proposed domain shuffling event , we show that the DctB sensor domain is effectively suitable to drive the expression of the insecticidal toxin in a similar way as wild-type FitF . We found that the periplasmic sensor region of FitF possesses an important and conserved peptide motif and demonstrate by site-directed mutagenesis that , as for DctB , it is essential for the function of the histidine kinase . Bioinformatic analyses further support that the specific tandem PASp domain probably served as a sensory module for numerous proteins in P . protegens and other bacterial species , highlighting its importance , mobility and evolutionary plasticity . Our work reveals how the FitF sensor kinase could have evolved into a crucial virulence gene expression regulator , and has contributed to the ability of P . protegens to exploit a new ecological niche by recruiting a functional domain from an ancestor of sensor proteins involved in the regulation of the primary metabolism . In addition , our evolutionary analysis of the Fit regulatory system could provide a unique model system to study the hypothesis of domain shuffling in sensor protein evolution , which so far had been postulated mainly on the basis of bioinformatic analysis of proteins [10] , [22] and construction of artificial chimeric proteins [23]–[25] .
The fit locus ( EU400157 ) of P . protegens comprises three genes ( fitF , fitG , and fitH ) that code for regulatory proteins ( Figure S1 ) . We previously demonstrated that expression of the insecticidal Fit toxin can be activated in strain CHA0 in Lysogeny Broth ( LB ) by overexpression of fitG or deletion of fitH , thus identifying the encoded proteins as an activator and repressor of insect toxin expression , respectively [7] . The third gene fitF , which was predicted to code for a sensor histidine kinase-response regulator hybrid protein ( Figure 1A ) , was hypothesized to function as a detector of the insect environment and a regulator of Fit toxin production [7] . To demonstrate that FitF is necessary for Fit toxin production , we used reporter strains of P . protegens CHA0 in which the full-length fitD gene was translationally fused at its native locus to mcherry by markerless gene replacement [7] . Epifluorescence microscopy confirmed that FitD-mCherry was visibly expressed in P . protegens CHA0 cells during infection of larvae of the greater wax moth Galleria mellonella , but was absent when fitF was inactivated by an in-frame deletion ( Figure 1B ) . Also , the virulence of the CHA0 fitF deletion mutant in a Galleria injection assay was statistically significantly decreased compared to the wild type and was similar to a fitD deletion mutant ( Fit toxin-deficient ) ( Figure 1C ) . These results demonstrate that FitF is essential for the activation of Fit toxin expression by P . protegens CHA0 in the insect host . Although FitD-mCherry was readily expressed during infection of larvae , it was hardly detectable when P . protegens CHA0 was growing in standard bacterial culture media such as LB or Brain Heart Infusion ( BHI ) ( Figure 2A ) . Fit toxin production was strongly induced when the bacteria were grown in Grace's Insect Medium ( GIM ) , with on average 60-fold higher red fluorescence levels of individual cells than in LB . GIM , which is a defined medium rich in amino acids and C4-dicarboxylates , is widely used for insect cell cultures and reflects closely the composition of Lepidopteran hemolymph [26] . In GIM , wild-type bacteria expressed the Fit toxin mostly at the end of exponential growth but no longer produced it in stationary phase ( Figure S2A ) . Compared to LB , FitD-mCherry expression was also significantly higher in M9 minimal medium supplemented with L-malate as sole carbon source , but not in fetal bovine serum or in marine broth , although both media provide conditions similar to insect hemolymph ( Figure 2A ) . Interestingly , FitD-mCherry production was significantly lower in M9 or GIM supplemented with plant root extracts ( Figure 2B ) . Also more than 20% ( v/v ) of LB mixed in with GIM abolished FitD-mCherry expression ( data not shown ) . Altering pH in M9 medium did not impede FitD-mCherry expression ( data not shown ) . Expression levels of the FitD-mCherry fusion protein in GIM were similar in the P . protegens wild type and in a fitH deletion mutant , which constitutively expresses the toxin ( Figure S3 ) . Furthermore , deletion of fitF abolished the expression of FitD-mCherry in GIM ( Figure 2C ) , but could be fully rescued by complementation of the mutant strain by insertion of a single copy of the fitF gene into the chromosome ( Figure 2C ) . Interestingly , the fitF deletion mutant of strain CHA0 could also be fully complemented with the homologue fitF from P . chlororaphis strain PCL1391 ( Figure 2C ) , even though P . chlororaphis FitF is predicted to harbor two cytoplasmic PAS domains instead of one for FitF from P . protegens [4] , [5] , [27] . Results of FitD-mCherry expression were confirmed by assaying the activity of the PfitA promoter , which drives the expression of toxin and type I transporter genes [7] , using a GFP-based transcriptional reporter fusion ( Figure S2B ) . Using a hemolymph-mimicking medium , we were thus able to confirm the essential role of FitF in regulation of insect toxin production in a controlled and reproducible manner in an ex vivo environment . FitF is predicted to possess two transmembrane domains , a periplasmic sensor domain , a cytoplasmic PAS domain , a histidine kinase domain ( comprising a conserved phosphoacceptor domain and an ATPase domain ) , a CheY-homologous receiver domain , and a phosphotransfer domain ( Figure 1A ) . BLAST comparisons with the amino acid sequence of the periplasmic region of FitF ( FitFp ) of P . protegens CHA0 indicated 54% amino acid sequence similarity ( 27% sequence identity ) across the whole length to the double PASp domain of the C4-dicarboxylate sensor DctB ( DctBp ) of V . cholerae ( Figure 3A ) . Phylogenetic analysis further indicated that FitFp homologues from various strains of P . protegens and P . chlororaphis group with DctBp homologues of different proteobacterial species , while the periplasmic regions of DctB-related CitA and DcuS proteins appear to be phylogenetically more distant ( Figure 3B and Table S1 ) . CLANS cluster analysis revealed similar results with FitFp clustering in close proximity to homologs of DctBp and CitA and DcuS clustering further away ( Figure S4 and Table S1 ) . We found a conserved “FRPYF” motif among the FitFp homologues ( Figure 3A ) , which is similar to the previously reported signal molecule-binding “RXYF” motif in DctB homologues and other proteins with double-PASp domains [28] , [29] . Protein threading and modeling approaches predicted a similar secondary and tertiary structure for FitFp as DctBp ( Figure 3C ) . This suggests that the FitFp and DctBp domains share a common ancestor . Concurrently , FitF and DctB display different domain topologies in their cytoplasmic portions , which is in contrast to the similarity in the periplasmic region of the proteins . By using in vivo site-directed mutagenesis , we replaced a number of residues in fitF and fitH and studied the effect on FitD-mCherry expression in P . protegens . Change of Arg141 and of Tyr143 in the RXYF motif of FitF to Ala following the mutagenesis of dctB described by Nan et al . [28] , resulted in almost completely abolished FitD-mCherry production ( Figure 3D ) . In contrast , change of Asp149 to Ala ( used as an internal negative control ) did not alter the expression of the insecticidal toxin . Changing Tyr143 to Phe reduced expression of FitD-mCherry by approximately 45% . Replacement of predicted conserved phosphorylation residues of the histidine kinase and receiver domains in FitF ( H501 and D803 ) and FitH ( D59 ) ( Figure 1A ) by alanine diminished the expression of FitD-mCherry ( Figure 4 ) . Together , these data demonstrate conspicuous structural and functional relatedness between the periplasmic domain of FitF and the sensor domain of DctB , with a conserved peptide motif being crucial for activation of Fit toxin expression . Because of the conspicuous similarity between the FitFp and DctBp domains , we hypothesized that perhaps the actual FitF protein might have been the result of a fusion of an ancestor DctBp domain into a FitF precursor . To simulate the proposed domain shuffling event and to test experimentally whether the sensor module of DctB is effectively suitable to regulate the expression of the Fit toxin , we created an artificial DctBp-FitFc chimera in which the periplasmic domain of DctB of P . protegens CHA0 was fused to the cytoplasmic portion of FitF ( FitFc ) ( Figure 5A ) . Indeed , expression of the DctBp-FitFc chimeric protein in a ΔfitF mutant background of strain CHA0 led to FitD-mCherry production in GIM , but not in LB ( Figure 5B ) . Still , FitD-mCherry expression was significantly higher in GIM in the ΔfitF mutant complemented with wild-type fitF than with the dctB‘-’fitF chimeric gene . Remarkably , however , FitD-mCherry production was activated in CHA0 expressing the DctBp-FitFc chimeric protein when the bacteria were growing on plant roots , while toxin production was completely off in bacteria expressing wild-type FitF ( Figure 5C ) . Furthermore , bacteria with the DctBp-FitFc background produced FitD-mCherry at significantly higher levels in minimal medium with L-malate as sole carbon source than bacteria expressing wild-type FitF ( Figure 5B ) . In a Galleria injection assay the DctBp-FitFc chimera fully complemented the fitF mutant ( Figure 5D ) . These results thus indicate that the DctB sensor domain can replace the FitFp domain of FitF . Yet , this causes a shift in sensor protein sensitivity resulting in a loss of responsiveness in an insect environment and a gain of responsiveness in a root environment . A chimera of the more distantly related PASp sensor domain of CitA and FitFc was functional and even less responsive to the insect mimicking medium than the DctBp-FitFc chimera ( Figure 5 ) . In order to investigate whether toxin production is not only host-dependent but also specific toward certain insect orders , the expression of FitD-mCherry by P . protegens CHA0 was studied in additional insect species . The expression of the Fit toxin was activated in the hemocoel of the African cotton leafworm Spodoptera littoralis ( Lepidoptera ) and the mealworm Tenebrio molitor ( Coleoptera ) ( Figure 6A ) . In contrast to the ΔfitH mutant of strain CHA0 , however , the insecticidal toxin was hardly produced in the phylogenetically distant pea aphid Acyrthosiphon pisum ( Hemiptera ) ( Figure 6A ) . In addition , as already shown for cucumber [7] , no toxin expression was detectable on roots of wheat and tomato ( Figure 6B ) . Moreover , the presence of a phytopathogenic fungus ( Fusarium oxysporum ) on tomato roots did not activate Fit toxin production in the bacteria ( Figure 6B ) . These results suggest that P . protegens CHA0 is capable of expressing its insecticidal toxin in a host-specific manner .
Here we show that the histidine kinase FitF is responsible for activation of Fit toxin expression in P . protegens CHA0 . We deleted fitF in the CHA0 genome and our results show unambiguously that FitF is essential for the induction of Fit toxin expression and for full virulence of the bacterial strain in the insect host ( Figure 1 and Figure 2C ) . We assume that FitF is the primary sensor to signal P . protegens the appropriate conditions to start toxin expression , activating a phosphorelay from the histidine kinase to the receiver and phosphotransfer domain of FitF ( Figure 1A ) . FitF then most likely inactivates FitH via phosphorylation of a conserved aspartate residue , since the substitution of this residue by alanine locked the protein in its repressing state ( Figure 4 ) . Inactivation of FitH might derepress FitG , which subsequently activates transcription of the fitABCDE operon ( Figure S1 ) . The periplasmic region of FitF showed remarkable structural and functional similarity to the sensor domain of DctB ( Figure 3 and Figure 5 ) . In particular , a RXYF motif was found in FitFp and we could show by site-directed mutation analysis that this conserved and known peptide motif is crucial for the activation of Fit toxin expression in P . protegens ( Figure 3D ) . However , these two proteins differ substantially in their domain topologies in the cytoplasmic portion ( Figure 3D ) . This suggested that an ancestor DctBp domain was acquired through shuffling in a precursor FitF . We present experimental and bioinformatic evidence that FitF most likely evolved via a fusion of two genes coding for a histidine kinase-response regulator hybrid protein and a duplicated DctB homolog ( Figure 7 ) . We noticed that DctB and FitF share a high degree of primary sequence identity in the second transmembrane region . It may therefore be possible that the fusion occurred via homologous recombination within the DNA sequence coding for the second transmembrane alpha helix . Despite limited primary sequence conservation between DctBp and FitFp , a constructed DctBp-FitFc chimera was functional and , most interestingly , induced Fit toxin production in P . protegens in the insect medium , although to significantly lower expression levels than wild-type FitF . This strongly suggests that the tandem PASp sensor of DctB is functionally analogous to that of FitF and may have been at the basis of sensor specificity acquisition by FitF . This experiment is limited by the fact that the chimeric protein was constructed using sequences of extant proteins as it is not possible to reconstruct the sensor protein as it was shortly after the proposed domain shuffling event . Protein comparisons further suggested that similar double-PASp domains occur widely among prokaryotes and in a variety of modular proteins ( Figure 3B and Figure S4 ) . Domains homologous to DctBp cannot only be found in histidine kinases but also in cyclic di-GMP modulating proteins ( Figure 3B and Figure S4 ) . PAS domains are known to be the most frequent type of sensor domains in bacteria [9] , [20] . It is thus imaginable that such domains have been frequently interchanged and that such shuffling has been fundamental to evolution of FitF specificity . In contrast to DctB , FitF possesses a cytoplasmic PAS domain as a linker between the sensor and kinase domain ( Figure 1 ) . We noticed that DctB proteins with an inserted PASc domain also occur in certain Acidovorax species . Furthermore , the C4-dicarboxylate sensing DcuS and CitA proteins of Escherichia coli possess a DctB-like PASp sensor domain in the periplasmic portion and a PASc domain as a linker between the sensor and the histidine kinase domain [15] , [30] . These observations further support the notion that an ancestral DctB-like sensor domain served as an adaptable and mobile module for the evolution of diverse proteins , since it can be fused to a variety of other protein domains . This is further supported by our observation that a fusion of the periplasmic sensor domain of CitA to FitFc was functional ( Figure 5 ) . Domain shuffling may require gene duplication and recombination [12] . In this respect , it is interesting to note that like many Pseudomonas species , P . protegens encodes three paralogs of the dctB gene ( Figure 3 ) . The dctB paralogs are functionally different . One of them ( DctB ) is involved in regulation of the uptake of C4-dicarboxylates ( Figure S5 and [31] ) , whereas another ( named MifS ) was reported to be a regulator of biofilm formation in P . aeruginosa [32] . Pseudomonas fulva strain 12-X encodes four dctB paralogs ( GeneBank CP002727 ) , suggesting that duplications of dctB must have occurred frequently and could have been the basis for domain shuffling events in these bacteria . The molecular mechanism of domain shuffling in the bacterial kingdom is still unknown . However , it has been reported that hybrid sensor kinases as is FitF show particularly high levels of DNA polymorphism and fast evolutionary rates [33] . Moreover , they are thought to have mostly evolved by lateral recruitment of individual protein domains [19] . Therefore , not only lineage-specific expansion but also recombination with horizontally acquired sequences could have played a role in the evolution of FitF . The sensor protein could have evolved by shuffling of functional domains that originated from different bacterial species . We discovered that Fit toxin expression in P . protegens CHA0 can be highly induced independently of the host organism in an insect hemolymph-mimicking medium ( Figure 2A ) . The physicochemical conditions given by the insect medium are thus sufficient for the observed activation of toxin production during infection of the insect host . Despite extensive testing ( not shown ) , however , we currently do not know the precise chemical structure of the signaling compound ( s ) that trigger FitF activation . The fact that the DctBp-FitFc chimera controlled Fit toxin production similarly to wild-type FitF , suggests that the signal molecule may be similar to C4-dicarboxylates . However , the chimera seemed to respond differentially to changing environmental conditions ( Figure 5B and C ) . In addition and in contrast to DctB [28] , the conservative replacement of the important tyrosine residue Y143 by phenylalanine did not diminish Fit toxin expression in the insect medium ( Figure 3D ) . Moreover , certain cells within the population of bacteria with the DctBp-FitFc chimera expressed the insect toxin on plant roots , which was not the case with bacteria expressing wild-type FitF ( Figure 5C ) . These results indicate that the signal molecules recognized by FitFp are no longer ( only ) C4-dicarboxylates . Molecules that bind to the sensor domain of FitF could be detected when solving the crystal structure of its periplasmic sensor domain in future studies , as it was demonstrated for several proteins with double-PASp sensor domains in the work of Zhang and Hendrickson [29] . Our findings suggest that even though a DctBp domain may have been at the basis of acquisition of FitF sensory capacity , further adaptive mutations occurred after the domain shuffling event , shifting the spectrum of recognized signals to ensure specificity of toxin production toward the insect environment . Indeed , we found indications that the Fit toxin is produced by wild type P . protegens CHA0 in a host-specific manner ( Figure 6 ) . Interestingly , FitD-mCherry expression by P . protegens diminished when induction media were supplemented with plant root extract ( Figure 2B ) . We speculate that this may be the result of a competitive inhibition rather than of absence of inducer compounds , because the rest of the induction medium was kept the same . If FitF could be directly or indirectly inhibited by plant molecules , this would explain the observed loss in toxin expression on roots , and could form a mechanism for host ( plant or insect ) differentiation . Activation of toxin expression in the insect host via FitF would then be the result of absence of inhibiting plant-derived molecules and the simultaneous presence of specific activating signal molecules in insect hemolymph ( Figure 7 ) . Competitive interactions are known from studies on DctB , where it was reported that molecules structurally resembling C4-dicarboxylates ( e . g . malonate ) can bind to the membrane distal PASp domain of DctB but do not lead to an activation of the kinase by conformational change [17] . The possibility of competition between activating and inhibitory molecules for the signal binding pocket of DctB was not discussed so far , but would be an interesting aspect for future research on PAS sensor domains . Alternatively , the observed inhibition of toxin production on roots could be due to repression of FitF by another protein . In the case of DctB it was suggested that the activity of the sensor kinase can be controlled by the transporter DctA directly by protein-protein interaction [15] . The proposed inhibition of FitF could also be mediated indirectly through changes in the metabolism of the bacterium when growing on roots . In summary , the present study provides evidence that a virulence-associated sensor histidine kinase , contributing to control the switch of the pseudomonad between a plant-beneficial and an insect pathogenic lifestyle , evolved by acquisition of a prominent sensory domain from a common ancestor of a protein , which regulates carbon uptake and primary carbon metabolism . This event was crucial for the ability of the microorganism to activate toxin expression in insects in a host-specific manner and thus to the adaptation of this bacterium to the insect environment . To our best knowledge , P . protegens at first is well adapted to the life on plant roots . The microorganism acquired and evolved virulence determinants , such as the fit cluster , and adapted to the insect environment , allowing it to survive within and to kill larvae of certain insect species . Since two-component signal transduction pathways are often involved in sensing and responding to changing environments , they have played a fundamental role in the adaptation of bacteria to a range of ecological niches [12] . P . protegens has the ability to tightly control Fit toxin production in a way that the toxin is only expressed during infection of certain insects but not on plant roots ( Figure 6 and [7] ) . As we show here , FitF thereby plays an important role as a regulatory protein . We recently demonstrated that the Fit toxin is required for full virulence upon oral or systemic infection of insect larvae [5]–[7] . Therefore , the proposed domain shuffling event during the evolution of FitF has significantly contributed to the adaptation of this bacterium to a new niche and thus to the evolution of insect pathogenicity . With the existing molecular techniques , the provided reporter constructs , the possibility to induce the expression of the Fit toxin in vitro in an insect medium , and the current knowledge about the regulation of Fit toxin expression , the Fit regulatory system could serve as a prime example for future studies on domain shuffling and related molecular mechanisms driving the evolution of sensory systems involved in the regulation of bacterial virulence and on the evolution of pathogenesis in general .
All strains and plasmids used in this study are listed in Table S2 . Bacteria were routinely cultured in LB ( LB Broth Miller , BD Difco ) , or in nutrient yeast broth ( NYB ) or on nutrient agar ( NA ) [34] . E . coli cells were grown at 37°C while P . protegens was cultured at 25°C . When appropriate , growth media were supplemented with ampicillin ( 100 µg/ml ) , chloramphenicol ( 10 µg/ml ) , kanamycin ( 25 µg/ml ) , gentamicin ( 10 µg/ml ) , tetracycline ( 25 µg/ml or 125 µg/ml for E . coli and P . protegens , respectively ) , or isopropyl β-D-1-thiogalactopyranoside ( IPTG ) ( 0 . 1 mM ) . For Fit toxin expression studies , the following media were used . LB; Brain Heart Infusion ( BHI ) ( BD Bacto ) ; sterile-filtered Grace's Insect Medium ( GIM ) ( G9771 , with L-glutamine , without sodium bicarbonate , adjusted to pH 5 . 5 with sodium bicarbonate ) ( Sigma-Aldrich ) ; M9 minimal medium ( 50 mM Na2HPO4×2 H2O , 22 mM KH2PO4 , 9 mM NaCl , 19 mM NH4Cl , 2 mM MgSO4 , 0 . 1 mM CaCl2 , 134 µM EDTA , 31 µM FeCl3×6 H2O , 6 . 2 µM ZnCl2 , 760 nM CuCl2×2 H2O , 420 nM CoCl2×2 H2O , 1 . 62 µM H3BO3 , 81 nM MnCl2×4 H2O , pH 7 ) with 10 mM L-malate , except for growth curve assays which were performed with 20 mM L-malate; sterile-filtered Fetal Bovine Serum ( Invitrogen Gibco ) ; and Marine Broth 2216 ( BD Difco ) . Cold root extracts were prepared by adding 4 g/L of washed and cut roots of field-grown wheat to M9 L-malate or GIM . The mixture was aggitated for 30 min at 300 rpm and room temperature and sterilized by using 5 µm and 0 . 45 µm filters . Dose-response assays were performed with LB , GIM and different ratios of LB and GIM . DNA manipulations and PCRs were conducted according to standard protocols [34] . Genomic DNA was extracted using the Promega Wizard Genomic DNA Purification Kit . Plasmid DNA was routinely extracted and purified using the QIAprep Spin Miniprep Kit ( Qiagen ) . Larger scale plasmid preparations were performed with the Genomed JETStar Plasmid Purification Midi Kit . DNA gel extractions were conducted using the MinElute Gel Extraction Kit and the QIAquick Gel Extraction Kit ( Qiagen ) . DNA restriction and modification enzymes were from Promega and were used according to the manufacturer's recommendations . DNA enzyme reaction cleanups were performed using the QIAquick PCR Purification Kit ( Qiagen ) . PCR was routinely conducted using the PrimeSTAR HS high-fidelity DNA polymerase kit ( Takara Bio Inc . ) for molecular cloning and the GoTaq DNA Polymerase kit ( Promega ) for analytic purposes according to the recommendations of the manufacturer . Primers used for this study were obtained from Microsynth AG ( Balgach , Switzerland ) and are listed in Table S3 . DNA sequencing was conducted at GATC Biotech ( Konstanz , Germany ) . Sequences were analyzed using the DNASTAR Lasergene software suite . For the construction of the ΔfitF mutant CHA1154 , a 2982-bp fragment was deleted in-frame in the fitF gene as follows . Using CHA0 DNA as a template , a 722-bp KpnI-EcoRI fragment encompassing the first 42 codons of fitF and the adjacent upstream region was amplified by PCR with primers PfitF1 and PfitF2 ( Table S3 ) . An 884-bp EcoRI-XbaI fragment comprising the last 41 codons of fitF plus downstream region was amplified by PCR using primers PfitF3 and PfitF4 . The fragments obtained were digested with KpnI and EcoRI and with EcoRI and XbaI , respectively , and cloned by triple ligation into pUK21 opened with KpnI and XbaI . The 1 . 6-kb KpnI-XbaI insert in the resulting plasmid was checked by sequencing , excised and cloned into the suicide plasmid pME3087 digested with the same enzymes , giving pME8256 ( Table S2 ) . The constructed replacement vector was then used to delete fitF in P . protegens CHA0 by D-cycloserine counterselection as described before [35] , [36] , resulting in strain CHA1154 ( Table S2 ) . The suicide plasmid pME8217 was used to replace the native fitD with the fitD-mcherry fusion in strain CHA1154 by homologous recombination , generating strain CHA1174 ( Table S2 ) . For insect assays , the strain CHA1174 additionally was marked with a constitutively expressed GFP tag using the Tn7 delivery vector pBKminiTn7-gfp2 , producing CHA1174-gfp2 ( Table S2 ) . For the mutagenesis of the periplasmic region of FitF , a region of fitF of 979 bp length encompassing the site of interest in the centre was amplified by PCR with CHA0 DNA using the primers fitF-mut1-hr-F and fitF-mut1-hr-R ( Table S3 ) . The resulting fragment was digested with EcoRI and BamHI and ligated into the suicide vector pEMG [37] opened with the same enzymes . The insert of the resulting plasmid pME8271 was checked by DNA sequencing . To introduce mutations into the insert sequence of pME8271 to subsequently replace the single amino acid residues R141 , Y143 , and D149 of FitF , primer pairs fitF-R141A-F/fitF-R141A-R , fitF-Y143A-F/fitF-Y143A-R , fitF-Y143F-F/fitF-Y143F-R , and fitF-D149-F/fitF-D149-R ( Table S3 ) , respectively , were used to amplify the vector pME8271 by PCR . The template plasmids used for the PCR were degraded by DpnI for 1 h at 37°C and PCR-amplified vectors were obtained by electroporation of E . coli DH5α λpir cells with purified PCR reaction and selection for kanamycin resistance . The insert sequences of the resulting plasmids were controlled by DNA sequencing . For the replacement of H501 of FitF by alanine , a 489-bp fragment of the upstream region was amplified by PCR with primers fitF-mut2-hr-F and fitF-mut2-R using CHA0 DNA ( Table S3 ) . A 524-bp fragment of the downstream region was amplified by PCR using primers fitF-mut2-F and fitF-mut2-hr-R using CHA0 DNA as template . The two fragments were combined by overlap extension PCR using the primers fitF-mut2-hr-F and fitF-mut2-hr-R , creating a 984-bp KpnI-HindIII fragment . The PCR product was digested by KpnI and HindIII and ligated into the plasmid pUK21 . The insert was checked by sequencing , excised by digestion with KpnI and BamHI and cloned into the suicide plasmid pEMG by ligation . The resulting plasmid pME8265 was then used to create strain CHA5056 ( Table S2 ) . An analogous approach ( leaving out the cloning of the PCR fragment into the plasmid pUK21 ) was used to create the suicide vector for the replacement of D803 of FitF and D59 of FitH by alanine . For FitF ( D803A ) the primers fitF-REC-hr-F , fitF-REC-hr-R , fitF-D803A-F , and fitF-D803A-R were used to construct the suicide plasmid pME8302 and create strain CHA5075 . For FitH ( D59A ) the primers fitH-REC-hr-F , fitH-REC-hr-R , fitH-D59A-F , and fitH-D59A-R were used to construct the suicide plasmid pME8303 and generate strain CHA5084 . Isogenic mutants of P . protegens strain CHA0 were constructed by allelic replacement using the I-SceI system with pEMG . The I-SceI system protocol described by Martinez-Garcia and de Lorenzo [37] was modified for P . protegens for this study . Briefly , the pEMG suicide vector bearing sequences homologous to genomic counterparts was integrated into the chromosome of P . protegens via homologous recombination after delivery by electroporation of competent cells . Bacteria were selected for kanamycin resistance on agar plates and competent cells were transformed with the expression plasmid pSW-2 by electroporation . Bacterial cells were selected for gentamicin resistance on agar plates and grown overnight at 30°C in LB supplemented with 10 µg/ml gentamicin . Ten milliliter of fresh LB was inoculated with 2 ml of overnight culture , supplemented with 2 mM m-toluate and 10 µg/ml gentamicin and incubated for 7 h at 30°C to allow second homologous recombinations to occur . Bacterial cultures were diluted and plated on nutrient agar plates without antibiotics . Isolated colonies were screened for kanamycin sensitivity and mutants were identified by specific PCR and sequencing of the respective genomic region . Deletions of the three dctB homologs in P . protegens CHA0 were performed based on homologous recombinations using the suicide vector pEMG and the I-SceI system . For the construction of suicide vectors for in-frame gene deletions of CHA0 dctB ( PFLCHA0_c03070 ) , dctB2 ( PFLCHA0_c48560 ) and mifS ( PFLCHA0_c47820 ) , upstream and downstream regions of 500–600 bp length flanking the region to be deleted , encompassing the first five codons and the last 7–18 codons of the open reading frames , were amplified by PCR using the primers listed in Table S3 . The resulting BamHI-HindIII fragments were digested with BamHI and HindIII and cloned by triple ligation into pEMG opened with BamHI . Correct insert sequences of the obtained plasmids pME8307 , pME8308 and pME8309 for ΔdctB1 , ΔdctB2 and ΔmifS , respectively , were confirmed by DNA sequencing ( Table S2 ) . The constructed suicide plasmids then served to construct strains CHA5085 , CHA5090 and CHA5089 , respectively , using the I-SceI system ( Table S2 ) . For complementation of the ΔfitF mutant of CHA0 , the fitF genes of strains P . protegens CHA0 and P . chlororaphis PCL1391 were cloned under the control of the Ptac/lacIq promoter and introduced into the unique chromosomal Tn7 attachment site of strain CHA1174 using the mini-Tn7 delivery vector pME9411 as follows . Primers fitF-F-SD-new and fitF-R-HindIII were used to amplify the fitF gene of strain CHA0 by PCR . The 3 . 2-kb EcoRI-HindIII fragment was digested with EcoRI and HindIII and ligated into plasmid pME4510 opened with the same restriction enzymes . After blunt-ending the EcoRI restriction site , the fragment was ligated into pME9411 opened with SmaI and HindIII , to obtain pME8288 , and the correct insertion was confirmed by sequencing . The pME9411 derivative and the Tn7 transposition helper plasmid pUX-BF13 were co-electroporated into competent cells of the recipient strain CHA1174 to create strain CHA5066 ( Table S2 ) . An analogous approach was taken to complement the ΔfitF mutant of CHA0 in trans with fitF of strain PCL1391 [5] . A 1188-bp EcoRI–BamHI fragment ( primers PCL-fitF-F-SD and PCL-fitF-br-R ) , a 1704-bp BamHI–StuI fragment ( primers PCL-fitF-br-F and PCL-fitF-StuI-R ) , and a 957-bp StuI–HindIII fragment ( primers PCL-fitF-StuI-F and PCL-fitF-R ) were amplified by PCR with the indicated primer pairs using chromosomal DNA from strain PCL1391 . The individual fragments were digested with the respective restriction enzymes and ligated individually into plasmid pUK21 opened with the same enzymes . The inserts in the resulting plasmids were checked by sequencing . The insert fragments were excised from the plasmids with the respective enzymes and cloned by quadruple ligation into plasmid pME4510 opened with EcoRI and HindIII . After blunt-ending the EcoRI restriction site , the fragment was ligated into pME9411 opened with SmaI and HindIII , and the correct insertion was confirmed by sequencing . The resulting mini-Tn7-Ptac/lacIq-fitF ( PCL1391 ) delivery plasmid pME8295 then served to generate strain CHA5073 ( Table S2 ) . Primers ME8300-F and ME8300-SpeI-R were used to amplify the lacIq gene and the IPTG-inducible promoter region of the plasmid pME6032 by PCR . The PCR product was purified , digested with NcoI and HindIII , and ligated into the vector pME6182 opened with the same enzymes . The insert in the resulting plasmid pME8300 was checked by DNA sequencing . Primers dctB-F-SpeI and dctB-R-overlap were used to amplify an 879-bp fragment of dctB using genomic DNA from strain CHA0 . Primers fitFc-F and fitF-R-HindIII were used to amplify a 2271-bp fragment of fitF by PCR with CHA0 DNA . The two fragments were combined by overlap extension PCR using the primers dctB-F-SpeI and fitF-R-HindIII , creating a 3 . 3-kb SpeI-HindIII fragment . The PCR product was digested by SpeI and HindIII and ligated into the plasmid pME8300 . The insert of the resulting plasmid pME8317 was checked by DNA sequencing . The Ptac/lacIq-dctB‘-’fitF construct was then integrated into the chromosome of the ΔfitF mutant of CHA1163 ( CHA1174 ) using the mini-Tn7 delivery system , yielding strain CHA5093 ( Table S2 ) . Analogously , the citA‘-’fitF chimera was constructed with primer pairs citA-F-SpeI/citA-R-overlap and fitFc-F2/fitF-R-HindIII using genomic DNA from E . coli K-12 and P . protegens CHA0 , respectively , as a template . The resulting plasmid pME8354 was used to create strain CHA5151 ( Table S2 ) . For assays with transcriptional reporter strains , GFP fluorescence was measured with a BMG FLUOstar Galaxy multidetection microplate reader as detailed previously [7] , [38] . Bacterial strains were grown overnight in 10 ml of LB at 25°C and 180 rpm . Bacterial cells were washed once in 0 . 9% NaCl solution and the optical density at 600 nm was adjusted to 1 , if not otherwise specified . Ten milliliters of the respective medium ( LB , BHI , marine broth , FBS , M9 L-malate , or GIM ) in 50-ml Erlenmeyer flasks was inoculated 1∶100 with the bacterial suspension and incubated for 8 h ( exponential growth phase ) and 24 h ( stationary growth phase ) at 25°C and 180 rpm . Quantification of red fluorescence intensities of single cells by epifluorescence microscopy was performed as described previously [7] . Exposure times were 2 sec for the DsRed channel and 80 msec for the Ph3 channel . The CHA0 wild-type strain was used to correct for autofluorescence of the bacterial cells . Injection assays for virulence determination using last-instar larvae of G . mellonella ( Reptile-food . ch GmbH , Dübendorf , Switzerland ) were performed as described before [7] . For complementation assays , IPTG was added to the inoculi to a final concentration of 1 mM . Reporter strains of P . protegens CHA0 were in injected in and extracted from forth instar larvae of S . littoralis ( Syngenta Crop Protection , Stein , Switzerland ) and last instar larvae of T . molitor ( The Animal House , Zuzwil , Switzerland ) as described before for G . mellonella [7] . A . pisum ( The Animal House ) was infected with reporter strains of P . protegens CHA0 by placing 20 adult individuals in a small Petri dish on leaves of white beans ( Phaseolus vulgaris ) that contained drops of bacterial suspensions ( at a concentration of 108 cfu per ml , 100 µl per dish ) . After three days of incubation at room temperature , adult aphids were shock frozen in liquid nitrogen , surface-sterilized with 70% ethanol for 2 min and hemolymph was extracted by crushing them on microscope slides . Extracted hemolymph was fixed on 1% agarose pads placed on microscope slides and observed by epifluorescence microscopy as described previously [7] . Visualization of Fit toxin expression on tomato ( Solanum lycopersicum cv . Marmande ) and wheat ( Triticum aestivum cv . Arina ) roots was performed as described previously for cucumber [7] . Infection of tomato roots with the crown and root rot pathogen Fusarium oxysporum f . sp . radicis-lycopersici isolate Forl22 was done as detailed elsewhere [39] . Fit toxin expression on cucumber ( Cucumis sativus cv . Chinese Snake ) roots with the DctBp-FitFc chimera was studied as follows . Cucumber seedlings were grown axenically for three days at room temperature in the dark and inoculated with different reporter strains of P . protegens CHA0 by placing them for 30 min in bacterial suspension , which was prepared from an overnight culture in LB by washing them once in saline solution and adjusting the optical density at 600 nm to 1 . The seedlings were then placed into 50-ml tubes ( three plants per tube ) containing 35-ml of 0 . 35% ( w/v ) water agar supplemented with 0 . 1 mM IPTG , 125 µg/ml tetracycline and 10 µg/ml gentamicin if necessary . The tubes were wrapped in aluminum foil for the lower part to protect roots from light and incubated in a growth chamber set to 80% relative humidity for 16 h with light ( 160 µE/m2/s ) at 22°C , followed by an 8-h dark period at 18°C . After incubation for five days , roots were individually removed , cut into smaller pieces and placed into Eppendorf tubes containing 100 µl of saline solution supplemented with 0 . 1% Silwet L-77 for the isolation of the bacteria ( GE Bayer Silicones Sàrl , Switzerland ) . The mixture was vigorously agitated for 2 min and 5 µl were used for epifluorescence microscopy as described above . Quantification of single cell fluorescence was performed by using the GFP ( 2 sec exposure time ) and DsRed ( 2 sec exposure time ) channels . Homologs of the periplasmic domains of P . protegens FitF were identified from the NCBI nonredundant protein sequence database using PSI-BLAST and an E-value cutoff of 1e-12 [40] . Periplasmic regions of membrane-bound proteins were determined by predicting transmembrane regions using DAS [41] and PRED-TMR ( http://athina . biol . uoa . gr/PRED-TMR/input . html ) . Functional domains of proteins were predicted using the NCBI Conserved Domain Search [42] and SMART [43] with default parameters . Multiple sequence alignments including sequences from reference proteins with known functions were performed with MAFFT version 7 ( http://mafft . cbrc . jp/alignment/server ) and phylogenetic analyses were conducted in MEGA5 using the Minimum Evolution method for inferring the evolutionary history [44] . Cluster analyses were performed with CLANS [45] as described earlier [46] using 2D clustering with default parameters . Secondary and tertiary structure predictions of the periplasmic region of FitF were performed using ESyPred3D [47] , I-TASSER [48] , LOMETS [49] , Phyre2 [50] , SABLE ( http://sable . cchmc . org ) , and SWISS-MODEL [51] using default parameters and the crystal structure of the V . cholerae DctB sensor domain ( 3BY9 ) as template if required . Structure models were visualized using the Swiss-PdbViewer version 4 . 0 . 3 ( http://spdbv . vital-it . ch ) . Significant differences between treatments or strains were calculated in R version 2 . 13 . 1 ( http://www . r-project . org ) by one-way or two-way analysis of variance ( ANOVA ) with Tukey's HSD test for post-hoc comparisons . The Log-Rank test of the Survival package of R was used to calculate significant differences in insect toxicity between P . protegens CHA0 and isogenic mutant strains in the Galleria injection assay . | Pseudomonas bacteria are well-known for their capability of adapting to different environments , which enables them to interact with various host organisms . Pseudomonas protegens is a plant-associated biocontrol bacterium with lifestyles that are of interest for agricultural applications , among them one as a competitive root colonizer protecting plants against pathogenic fungi and the other as an insect pathogen invading and killing insect species of importance as pests in agriculture . We recently discovered that P . protegens produces a potent insecticidal toxin only during infection of insects but not when growing on plant roots . Since sensor proteins enable bacteria to sense and respond to changing environments and are important for pathogen-host interactions , we investigated whether a specific sensory protein could explain our observation . We found that this particular protein tightly controls toxin production and during its evolution has recruited a common sensor domain from a regulatory protein involved in control of nutrient uptake . This so-called domain shuffling event was important for the ability of P . protegens to produce its insecticidal toxin only when it infects insects . Our study provides a prime example of how a sensory system can evolve and contribute to the evolution of bacterial pathogenicity . | [
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... | 2014 | Domain Shuffling in a Sensor Protein Contributed to the Evolution of Insect Pathogenicity in Plant-Beneficial Pseudomonas protegens |
Faithful DNA replication and repair requires the activity of cullin 4-based E3 ubiquitin ligases ( CRL4 ) , but the underlying mechanisms remain poorly understood . The budding yeast Cul4 homologue , Rtt101 , in complex with the linker Mms1 and the putative substrate adaptor Mms22 promotes progression of replication forks through damaged DNA . Here we characterized the interactome of Mms22 and found that the Rtt101Mms22 ligase associates with the replisome progression complex during S-phase via the amino-terminal WD40 domain of Ctf4 . Moreover , genetic screening for suppressors of the genotoxic sensitivity of rtt101Δ cells identified a cluster of replication proteins , among them a component of the fork protection complex , Mrc1 . In contrast to rtt101Δ and mms22Δ cells , mrc1Δ rtt101Δ and mrc1Δ mms22Δ double mutants complete DNA replication upon replication stress by facilitating the repair/restart of stalled replication forks using a Rad52-dependent mechanism . Our results suggest that the Rtt101Mms22 E3 ligase does not induce Mrc1 degradation , but specifically counteracts Mrc1’s replicative function , possibly by modulating its interaction with the CMG ( Cdc45-MCM-GINS ) complex at stalled forks .
DNA replication is a process through which cells duplicate their entire genome prior to cell division . To achieve accurate replication , eukaryotes have evolved intricate surveillance systems that allow fine-tuning of the replication machinery . In order to continually provide the replicative polymerase with a single stranded DNA template , replisomes must adapt to chromatin heterogeneities such as aberrant DNA structures , condensed chromatids , transcriptional obstacles and DNA-protein barriers [1] . In Saccharomyces cerevisiae , this adaptation is regulated by proteins such as Mrc1 , Tof1 , Csm3 and Ctf4 , which assemble around the CMG ( Cdc45-MCM-GINS ) DNA helicase at replication forks . These components form the ‘Replisome Progression Complex’ ( RPC ) , a replisome sub-assembly that exists exclusively at replication forks [2] . The RPC functions in coupling DNA polymerases to the CMG helicase [3 , 4] , and in regulating fork progression [5–9] . Moreover , these replisome components also limit mutagenic frequency and prevent unscheduled homologous recombination ( HR ) events at stalled forks [10–12] . Mrc1 possesses two polymerase epsilon ( Pol ε ) binding sites [13] as well as an Mcm6 interaction motif [14] , and is required for checkpoint activation in response to replication stress [15] . Tof1 and Csm3 help to link Mrc1 to fork components [16 , 17] but also have distinct functions not shared with Mrc1 [9] . Conversely , the replication fork progression defect is enhanced in mrc1Δ compared to tof1Δ and csm3Δ mutants [1] , implying that Mrc1 also promotes replication functions independent of Tof1 and Csm3 . Ctf4 , the yeast homologue of human AND1 , bridges the interaction of the primase , DNA polymerase-α , to the CMG helicase [3 , 5 , 18] . Although the coupling of the CMG to leading and lagging strand DNA polymerases preserves genome integrity during unperturbed DNA replication , this mechanism is partially disrupted when forks encounter replication stress . Indeed , significant stretches of ssDNA generated by uncoupling can promote HR-mediated replication re-start via either a template switch or break-induced replication ( BIR ) [19] . Growing evidence implicates Cullin-RING containing E3 ligases ( CRL’s ) in regulating DNA replication and repair [20] . For example , Cdc53/Cul1 in a complex with the F-box adaptor protein Dia2 ( SCFDia2 ) promotes the ubiquitylation of the Mcm7 subunit of the CMG helicase , which triggers Cdc48/p97-dependent disassembly of CMG at the end of DNA replication [21] . In addition , SCFDia2 has been reported to antagonize Mrc1 upon replication stress , possibly by inducing degradation of phosphorylated Mrc1 to recover from checkpoint arrest following repair [22–24] . Cullin4 ( CRL4 ) -based E3 ubiquitin ligases regulate DNA replication and repair both in yeast and mammalian cells , in part by controlling histone dynamics at active replication forks [25] . Rtt101 , the budding yeast analogue of human Cul4 [26] , has been reported to target Spt16 , a subunit of the FACT complex that reorganizes nucleosomes during DNA replication [27] . Moreover , Rtt101 promotes replication fork progression through DNA lesions and natural pause sites [28] . This function is dependent on MMS1 and MMS22 , which encode a DDB1-like linker protein and a putative substrate specific adaptor , respectively [26] . However , the underlying mechanisms and function of the Rtt101-Mms1-Mms22 complex ( termed Rtt101Mms22 ) remain largely elusive . Recent results suggest that the sensitivity of mms1Δ , mms22Δ and rtt101Δ cells to the DNA-damaging drugs CPT and MMS could be rescued by further deleting MRC1 [18 , 29] , possibly by de-regulating late firing origins [29] . Here we show that the Rtt101Mms22 E3 ubiquitin ligase genetically and biochemically interacts with components of the replication fork . We found that the WD40 domain of Ctf4 , a protein required for coupling the CMG complex to the replicative polymerases , recruits Mms22 to active forks during S-phase . Moreover , Mms22 physically associates with Mrc1 and deletion of the MRC1 gene suppresses the defects of rtt101Δ , mms1Δ and mms22Δ cells , including genotoxic sensitivity , prolonged checkpoint activation and reduced HR rates . Importantly , our results suggest that de-repression of late replication origins is not sufficient to bypass the need of Rtt101Mms22 E3-ligase activity . Instead , MRC1 deletion promotes HR-mediated repair of replication forks that have paused in response to replication stress . Based on genetic and biochemical data we propose that the Rtt101Mms22 complex specifically counteracts the replicative , and not the checkpoint , function of Mrc1 , possibly by modulating its interaction with the CMG helicase complex upon fork stalling .
To elucidate the role of the Rtt101Mms22 E3 ubiquitin ligase in DNA replication , we employed an automated SGA approach [30] to screen for genes that , when deleted , would suppress the growth defects of rtt101Δ cells exposed to either the alkylating agent methyl methanesulfonate ( MMS ) or the topoisomerase 1 ( Top1 ) poison camptothecin ( CPT ) [31] ( Figs 1A and S1 ) . The SGA screen was performed in duplicate and only suppressors that appeared in both screens were considered for further analysis . The combined results for MMS and CPT conditions initially identified 63 suppressor genes that were scored by visual inspection as either strong , medium or weak ( S1 Table ) . Only the strong and medium hits were validated by crossing the single deletion mutants to an independent rtt101Δ strain , in which double mutants were derived by manual tetrad dissection . Cells were then spotted in biological duplicate on MMS and CPT containing media as depicted in Fig 1C–1E . Using this workflow , we confirmed a list of 16 genes that when deleted improved the growth of rtt101Δ cells ( Fig 1B ) . Among the most potent suppressors are genes directly involved in DNA replication , including MRC1 , POL32 , RAD27 , TOP1 , SIZ2 and DPB4 . Additional suppressor mutations implicated in other biological processes were also confirmed ( Fig 1B ) , but were not further characterized in this study . Some suppressors such as the lagging strand polymerase subunit Pol32 only restored growth on CPT containing media ( Fig 1B–1D ) , indicating that the screening approach could isolate functional protein sub-clusters . The slow growth phenotype of other Rtt101-based E3 ubiquitin ligase component mutants , including those lacking the linker protein Mms1 ( Fig 1D ) as well as mutants deleted for the putative substrate specific adaptor Mms22 ( Fig 1E ) , were rescued by deleting the identical replication gene cluster that suppressed the rtt101Δ cell phenotype . These data indicate that Rtt101 likely acts as a fully assembled E3 ubiquitin ligase in a process associated with replication stress . Indeed , deletion of MRC1 suppressed the growth defects of cells expressing the neddylation-deficient Rtt101-K791R mutant [32] , implying that loss of MRC1 suppresses the phenotypes correlated with inactivation of Rtt101Mms22 E3 ligase activity ( S2 Fig ) . In contrast , deletion of MRC1 did not rescue the MMS sensitivity of ubc13Δ cells defective for PCNA polyubiquitylation ( S3 Fig ) , indicating that the genetic suppression is specific to the loss of Rtt101Mms22 function and not other ubiquitylation-defective mutants involved in lesion bypass repair [33] . MRC1 and DPB4 are both linked to the putative leading strand polymerase , Pol ε , and deletion of these genes showed suppression on both MMS and CPT containing media , albeit to varying extents . Furthermore , when the deletions of DPB4 and MRC1 were combined in the absence of either RTT101 or MMS22 , we observed that the genetic rescue was not additive ( Fig 1F and 1G ) . Unlike in rtt101Δ or mms1Δ cells , the deletion of POL32 did not rescue the CPT sensitivity of mms22Δ cells ( Fig 1E ) , supporting the notion that Mms22 has additional functions independent of the E3 ligase complex [34] . Taken together , these data demonstrate that deletion of replication genes such as MRC1 and DPB4 can alleviate the growth defects associated with impaired Rtt101Mms22 E3 ubiquitin ligase activity in response to multiple genotoxic agents . The above genetic data suggests that the Rtt101Mms22 complex may directly interact with the replisome . Since specificity of a CRL complex is mainly conferred by the substrate adaptor [35] , we immunoprecipitated Mms22 from S-phase synchronized cells and identified associated proteins by an unbiased mass-spectrometry method referred to as shotgun LC-MS/MS ( Fig 2A ) . As expected , Mms22 co-purified with the E3 ligase subunits Rtt101 , Mms1 and Hrt1 , with the core histones Htb2 , Hhf1 , Hta1 , and Hht1 , as well as with the FACT complex ( Spt16 , Pob3 ) , a nucleosome re-organizer that likely facilitates the interactions between DNA replication and transcription . These findings are consistent with previously published data showing that Rtt101-Mms1 associates with histone H3 [25] and ubiquitylates the Spt16 subunit of FACT [27] . We also detected a cluster of replication factors , including components of the GINS- ( go-ichi-ni-san ) , the fork protection- and the MCM helicase complexes ( Fig 2A , see also S2 Table for a complete list of Mms22-interacting proteins ) . These results strengthen the genetic interaction clusters that were found to suppress the growth defects of rtt101Δ cells , and strongly suggest a function of Rtt101Mms22 at replisomes during S-phase . To better characterize the interaction of Mms22 with replisome components , we immunoprecipitated functional epitope-tagged versions of Mms22 ( PA-Mms22 ) ( Fig 2C ) and the GINS complex ( TAP-Sld5 ) ( Fig 2D ) from cells synchronized in G1 and S-phase with normal or genotoxic stress ( 0 . 03% MMS ) growth conditions ( Fig 2B ) . We observed that Mms22 and Rtt101 interact with all tested components of the replisome progression complex during S-phase ( Fig 2C and 2D ) . Remarkably , Ctf4 and the FACT complex showed affinity for Mms22 in both G1 and S-phase , although both interactions were more prominent during S-phase . The induction of DNA damage through MMS treatment did not alter the observed interactions , suggesting that the Rtt101Mms22 E3 ligase is not specifically recruited to replisomes upon genotoxic insult , but rather constitutively associates with the active replication machinery . We next examined how the Rtt101Mms22 E3 ligase is recruited to active replisomes . Ctf4 was an intriguing candidate as it was previously found to interact with Mms22 [3 , 18 , 36] . Indeed , genetic analysis revealed that the growth defect of ctf4Δ cells on genotoxic drugs was epistatic with RTT101 and even slightly suppressed the sensitivity of mms22Δ cells ( Fig 3A ) , consistent with previous findings [36] . To test whether Ctf4 tethers Mms22 to the replisome , we compared the presence of replisome components in Mms22 purifications prepared from wild-type and ctf4Δ cells ( Fig 3B ) . Notably , Mms22 failed to interact with the replication factors Mcm2 , Cdc45 and Csm3 during S-phase in ctf4Δ cells ( Fig 3B ) , while binding to Spt16 and Pob3 FACT complex components was not perturbed ( Fig 3C ) . Mms22 interacts with Ctf4 both by two-hybrid [3 , 36] and co-immunoprecipitation analysis ( Fig 2A and 2C ) , and requires the amino-terminal WD40 domain of Ctf4 ( Figs 3D and S4; [3] ) . Together , these results suggest that the Rtt101Mms22 E3 ligase is tethered to active replisomes via the Ctf4 scaffold ( Fig 3E ) . To determine if the alleviated growth defect observed in rtt101Δ mrc1Δ strains is phenocopied by the removal of other components of the fork-protection complex ( FPC ) , we tested whether deletion of either TOF1 , CSM3 alone or in combination would also rescue the sensitivity of rtt101Δ or mms22Δ cells to genotoxic agents . Neither the single nor double deletions of CSM3 and TOF1 were able to restore growth of rtt101Δ or mms22Δ cells on media containing MMS ( Figs 4A and S5 ) . These data suggest that Rtt101-Mms22 counteracts a function of Mrc1 at replication forks that is independent of its interactions with Tof1 and Csm3 . Since the replication and checkpoint functions of Mrc1 are genetically discernable , we set out to examine which functions of Mrc1 are causing lethality in the absence of Rtt101Mms22 . We analyzed the genetic interaction between rtt101Δ or mms22Δ mutants and the well-characterized checkpoint-deficient MRC1-AQ allele , coding for a mutant protein in which all Mec1-dependent phospho-serines ( SQ ) are mutated to alanine ( AQ ) [37] . Interestingly , when the checkpoint-defective Mrc1-AQ variant is the only source of Mrc1 in cells lacking the Rt101Mms22 E3 ubiquitin ligase , the lethal phenotype is comparable to rtt101Δ and mms22Δ single mutant cells when exposed to genotoxic stress ( Fig 4B ) , unlike the complete deletion of MRC1 ( Figs 1C , 1E and S5 ) . This result was surprising in light of recent data proposing that alleviation of the checkpoint-mediated late origin repression may rescue the sensitivity of rtt101Δ cells to MMS exposure [29] . To corroborate these results , we tested whether the requirement of Rtt101 to promote growth on MMS containing media could be bypassed by genetically de-repressing late origins in the sld3-37A dbf4-4A background [38] . In contrast to rtt101Δ mrc1Δ double mutants , rtt101Δ sld3-37A dbf4-4A cells were unable to rescue the sensitivity of rtt101Δ cells to MMS ( Fig 4C ) , supporting the notion that Mrc1-dependent inhibition of late origin firing is not sufficient to explain the essential function of the Rtt101Mms22 complex in response to genotoxic stress . Conversely , the MMS-induced lethality of rtt101Δ or mms22Δ cells was suppressed by expressing an Mrc1 C-terminal truncation mutant ( Mrc11-971 ) as the only copy of Mrc1 ( Fig 4D ) . While this mutant has been reported to be checkpoint defective [24] , the C-terminal domain of Mrc1 is known to directly interact with the C-terminal domain of Pol2 [13] and is important for the replication functions of Mrc1 [39] . To identify a bonafide separation-of-function Mrc1 allele , we thus constructed smaller C-terminal truncations of Mrc1 . Strikingly , deletion of only the last C-terminal 18 amino acids ( Mrc11-1078 ) was sufficient to confer a rescue of MMS sensitivity in both rtt101Δ and mms22Δ cells ( Fig 4E , compare bottom two rows ) , while otherwise checkpoint defective cells expressing the Mrc11-1078 mutant protein were able to activate the replication checkpoint when challenged with replication stress ( Fig 4F ) . Together , these results strongly suggest that loss of Mrc1’s checkpoint function is not responsible for the genetic suppression of rtt101Δ or mms22Δ cells and implicates the Mrc1 replicative functions in the observed cell toxicity . Interestingly , the inability of rtt101Δ or mms22Δ cells to extinguish the DNA damage checkpoint following MMS recovery [28] was alleviated in rtt101Δ mrc1Δ or mms22Δ mrc1Δ strains ( Fig 4G ) , and as a consequence rtt101Δ mrc1Δ double mutants proceeded into the next G1 phase four hours post-recovery whereas the rtt101Δ cells remained largely arrested at the G2/M border as expected ( Fig 4H ) . Based on these data we conclude that Rtt101Mms22 specifically counteracts a replicative function of Mrc1 at stalled replisomes , thereby promoting replication fork repair/restart , which leads to an eventual checkpoint termination . Increasing evidence points towards a key HR function during replication fork restart at stalled replisomes [40] . Since Mrc1 is a known suppressor of HR [11] , we hypothesized that its removal may promote restart of damaged replication forks in rtt101Δ and mms22Δ cells by an HR-dependent mechanism . To assess HR rates we used a previously described reporter system [41] that exploits a plasmid as a recombination substrate for both the single-strand invasion and annealing pathways , resulting in CAN1 gene deletion ( S6 Fig ) . In agreement with previous studies , HR levels were abolished in rad52Δ and reduced in mms22Δ strains [42] , whereas mrc1Δ cells exhibited increased recombination rates compared to wild-type controls ( [12] and Fig 5A ) . Interestingly , mms22Δ mrc1Δ double mutants showed a level of HR comparable to mrc1Δ cells , suggesting that in the absence of MMS22 , Mrc1 may block recombination ( Fig 5A ) . In contrast , ctf4Δ mrc1Δ double mutants are inviable ( [3 , 43] , S7 Fig ) , implying that Mrc1 and Ctf4 share a Mms22-independent essential function during DNA replication . The increased HR phenotype seems to be specific to Mrc1 , as another mutant , sgs1Δ , with increased HR levels did not alleviate the genotoxic sensitivity ( S8 Fig ) or the low HR frequency [34] observed in mms22Δ strains . Since the increased HR level correlated positively with the observed genetic suppression in mms22Δ mrc1Δ cells , we tested whether HR was required for this suppression by deleting the RAD52 gene , and thereby rendering cells HR defective . Indeed , the hyper-sensitivity of rtt101Δ rad52Δ and mms1Δ rad52Δ cells to MMS could no longer be rescued upon further deletion of MRC1 ( Figs 5B , top and S9 ) , consistent with the notion that an intact HR machinery is required for suppression . A similar effect was also observed in cells lacking the substrate adaptor Mms22 ( Fig 5B , bottom ) , but in this case a slight mrc1Δ rescue was still observed in the rad52Δ background . These data imply that in contrast to rtt101Δ and mms1Δ , deletion of MRC1 in mms22Δ cells rescues MMS sensitivity , in part , in a RAD52-independent manner . To corroborate these results , we released G1 synchronized cells endogenously expressing mCherry-tagged Rad52 ( Rad52-mCherry ) into media containing MMS and scored the ability of cells to form Rad52 foci , a proxy for active recombination [44] . As expected , the percentage of cells with Rad52 foci decreased in both rtt101Δ and mms22Δ cells , but strikingly , this defect was corrected by further deleting MRC1 ( Fig 5C and 5D ) . Together , these data indicate that HR upregulation induced by the loss of Mrc1 function may play a key role in the restart/repair of defective replication forks in cells lacking Rtt101Mms22 E3 ligase activity . Available evidence suggests that SCFDia2 targets phosphorylated Mrc1 for proteasomal degradation [22 , 24] . To test whether Mrc1 is degraded by a Rtt101Mms22-dependent mechanism , we monitored Mrc1 stability in synchronized cells with MMS–induced replication stress ( Fig 6A ) . Promoter shut-off by glucose addition to cells expressing galactose-inducible 3HA-Mrc1 ( Figs 6A–6C and S10 ) as well as cyclohexamide ( CHX ) chase experiments of endogenously tagged 3HA-Mrc1 ( S10 Fig ) showed that the degradation kinetics of phosphorylated and unphosphorylated Mrc1 in rtt101Δ or mms22Δ cells exposed to MMS were comparable to wild-type controls . These results were corroborated by quantitative mass spectrometry using selected reaction monitoring ( SRM ) , which demonstrated that Mrc1 levels decreased with similar kinetics in wild-type , rtt101Δ and mms22Δ cells ( S10 Fig ) . Surprisingly , deletion of DIA2 had only slight effects on the half-life of Mrc1 , and thus the role of SCFDia2 in regulating Mrc1 stability remains to be further clarified [24 , 45] . Taken together , these data demonstrate that Rtt101Mms22 does not trigger degradation of the Mrc1 protein at stalled replication forks . Tetrad analysis and plating assays revealed that the growth defects caused by loss of Rtt101 and Mms22 together with the loss of Dia2 function were additive , and double mutants were further impaired for growth in the presence and absence of MMS ( Fig 6D and 6E ) . This indicates that the two E3 ligases may function independently and have non-overlapping roles during DNA replication . Interestingly , deletion of MRC1 restored some of the growth defects of mms22Δ dia2Δ and rtt101Δ dia2Δ double mutants as shown by tetrad analysis ( Fig 6D ) as well as spotting assays on MMS containing media ( Fig 6E ) . Conversely , overexpression of Mrc1 resulted in toxicity in rtt101Δ dia2Δ and mms22Δ dia2Δ double mutants even in the absence of exogenous genotoxic stress ( Fig 6F and 6G ) . Together these results indicate that these two CRLs may genetically interact with Mrc1 function but likely by distinct mechanisms . In addition to binding DNA polymerase ε [13] , Mrc1 also interacts with the Mcm6 subunit of the MCM2-7 helicase [14] . In order to disrupt the binding of Mrc1 to Mcm6 , we crossed the mcm6-IL allele [14] into rtt101Δ , mms22Δ and mms1Δ cells deleted for MRC1 , and compared growth of the resulting single , double and triple mutants treated with DNA damaging agents ( Fig 7A ) . Importantly , we observed that similar to deleting MRC1 the presence of mcm6-IL was able to suppress the sensitivity of rtt101Δ , mms22Δ ( albeit weakly ) and mms1Δ cells to MMS ( Fig 7B–7D ) , indicating that disrupting the association of Mrc1 with the CMG helicase is sufficient to restore growth of rtt101Δ , mms1Δ , and in part , mms22Δ mutants exposed to genotoxic stress . Importantly , deletion of MRC1 in rtt101Δ mcm6-IL or mms22Δ mcm6-IL cells did not further improve growth on MMS containing media ( Fig 7B and 7C ) , suggesting that these mutations affect the same molecular process . We did not observe a difference in the amount of chromatin bound Mrc1 when comparing wild-type to rtt101Δ and mms22Δ cells in the absence and presence of MMS , rendering it unlikely that Rtt101Mms22 promotes Mrc1 eviction from chromatin at sites of replication stress ( S11 Fig ) . These genetic and biochemical results suggest that the Rtt101Mms22 E3 ligase counteracts a function of Mrc1 that is linked to the replicative helicase at stalled replication forks , and possibly modulates the interaction of Mrc1 with the MCM helicase complex upon fork stalling ( Fig 7E ) .
The tight association of DNA synthesis with the unwinding activities of the replicative helicase at replisomes is important for faithful DNA replication . Mrc1 represents a plausible candidate to reinforce this association as it physically interacts with both Pol2 as well as the MCM2-7 helicase . Indeed , altering this interaction may lead to exposed stretches of ssDNA , which are vulnerable to nicking and chemical modifications , and if extensive enough , may unleash the replication checkpoint following the association of sufficient replication protein A ( RPA ) molecules . In accordance , in the presence of MMS we observed more extensive Rad53 phosphorylation in the absence of MRC1 ( Fig 4G ) . Previous studies have shown that mrc1Δ cells fail to inhibit late firing origins [6 , 46] , an effect that could be used to bypass the adverse effects of stalled replication forks . Therefore , deletion of MRC1 might conceivably alleviate the DNA damage sensitivity of rtt101Δ and mms22Δ cells by allowing the firing of additional origins and hence promoting the completion of replication [29] . In light of our results , however , this possibility seems unlikely given that the loss of Mrc1’s checkpoint functions fails to rescue the defects associated with rtt101Δ and mms22Δ cells . Moreover , the requirement of the Rtt101Mms22 complex in genotoxic stress conditions could not be bypassed by genetically de-repressing late origins using the sld3-37A dbf4-4A background [38] . Alternatively , our results suggest that Mrc1 deletion , and in turn replisome uncoupling , may promote HR-mediated fork restart at stalled replication forks [11] . In support of this idea , Rtt101 , Mms1 and Mms22 have been demonstrated to stimulate HR , specifically upon exposure to genotoxic agents [42] , and we found that deletion of MRC1 rescued the reduced recombination rates in cells deleted for MMS22 . Indeed , an intact HR machinery is required for rtt101Δ mrc1Δ and to a lesser extent mms22Δ mrc1Δ double mutants to grow in genotoxic stress conditions . Moreover , recombination foci visualized microscopically by Rad52-mCherry were decreased in both rtt101Δ and mms22Δ cells exposed to genotoxic drugs , but were restored by additionally deleting MRC1 . We thus propose that the Rtt101Mms22 E3 ubiquitin ligase promotes HR-mediated repair and restart by counteracting a replicative function of Mrc1 at stalled replication forks . Surprisingly , this role of Mrc1 is not shared with the other subunits of the replisome progression complex Tof1 and Csm3 , although they are thought to help stabilize Mrc1 at replication forks . However , Mrc1 also interacts with replisomes by a Tof1/Csm3-independent mechanism [47] , perhaps through its interactions with Pol2 and Mcm6 . It seems that this Mrc1 pool is sufficient to inhibit HR and may need to be counteracted by the Rtt101Mms22 E3 ligase upon fork stalling ( Fig 7E ) . In contrast to the S-phase checkpoint defective Mrc1-AQ mutant ( Fig 4B ) , expression of a C-terminal truncation mutant of Mrc1 ( Mrc11-971 ) was able to suppress phenotypes linked to both SCFDia2 and Rtt101Mms22 E3 ligases ( [24] , Fig 4D ) . Importantly , we identified a Mrc1 separation-of-function mutant ( Mrc11-1078 ) , which is checkpoint-proficient but likely unable to perform the replicative function of Mrc1 that leads to toxicity when the Rtt101Mms22 E3 ligase is impaired . While the exact mechanism underlying this specific defect remains to be elucidated , we propose that the C-terminus of Mrc1 may directly regulate replisome function by binding to either Pol2 or Mcm6 . This model might also help explain the higher rate of HR ( Fig 5A ) , as mrc1Δ strains leave more unreplicated single stranded DNA stretches at stalled replication forks [11] that require post-replicative HR . Several mechanisms allow cells to restore stalled replication forks , underlining the importance of this process ( reviewed in [19] ) . Based on our genetic and proteomic analysis , we propose that Rtt101Mms22 , presumably via a ubiquitylation event , counteracts a replicative function of Mrc1 in order to promote recombination at stalled DNA replication forks ( Fig 7E ) . Since both the mcm6-IL allele as well as the deletion of MRC1 may affect polymerase and helicase activities ( Fig 7A ) , the Rtt101Mms22 complex may somehow regulate the activity of these factors at stalled forks . Indeed , our genetic analysis revealed that the requirement of the Rtt101Mms22 complex to inhibit the replicative-function of Mrc1 upon genotoxic stress is epistatic to loss of its binding to the MCM helicase , suggesting that the Rtt101Mms22 E3 ligase may directly or indirectly modulate the interaction of Mrc1 with the MCM complex . Although we did not observe a difference in the total amount of Mrc1 associated with chromatin following exposure to MMS ( S11 Fig ) , it remains possible that regulation specifically occurs at a small subset of stalled replication forks . Interestingly , a previous study has reported decreased association of Pol ε and Mrc1 with replication forks in mms1Δ mutants [48] , which may represent a compensatory response . Thus , while we cannot rigorously exclude that Rtt101Mms22 regulates Mrc1 via replisome association , we favor a model by which Rtt101Mms22-dependent ubiquitination of Mrc1 or an unknown substrate leads to uncoupling of the MCM helicase at the stalled replicon , thereby promoting HR-dependent repair and restart of stalled replication forks ( Fig 7E ) . However , we do not fully understand how Rtt101 or Mms22 interact with either the error prone TLS branch of bypass synthesis or with other means of replication fork restart ( e . g . BIR and replication fork regression ) [19] . Since Rad52 is synthetically-sick with Mms22 but not Rtt101 in unchallenged conditions ( Fig 5B ) , it is conceivable that Mms22 may regulate either TLS or replication fork regression , as part of its Rtt101-independent functions . Future studies will be required to test these possibilities .
Plasmids and yeast strains are listed in S3 and S4 Tables , respectively . Standard methods were used for yeast strain construction and molecular biology . Yeast cells were grown in rich medium ( YPD; 1% yeast extract , 2% peptone , 2% glucose ) or synthetic medium ( SD; 0 . 17% yeast nitrogen base , 0 . 5% ammonium sulphate , 2% glucose , amino acids as required ) . Homologous recombination frequencies were measured as described [41] . For spotting assays , the indicated strains were grown overnight at 30°C , and the cultures were diluted to OD600 0 . 5 . Ten-fold serial dilutions were spotted using a pinning head ( 2 μl ) . The plates were incubated at 30°C and imaged using the ChemiDoc Touch Imaging System ( Bio-Rad ) after 2 and 3 days . For cell cycle synchronization , logarithmically growing cells were treated with 1:1000 α-factor solution ( 5 mg/ml + 0 . 1% BSA ) at 24°C for 3 hours . G1 arrest was monitored by flow cytometry and microscopy ( appearance of pear-shaped “shmoo” morphology of at least 95% of cells ) . Cells were then washed three times with YPD at room temperature and S-phase samples were collected 30 minutes after the release into fresh YPD medium . Synthetic Genetic Array ( SGA ) methodology was used as described [30] , with the following modifications: the non-essential heterozygous diploid S . cerevisiae knockout collection ( kindly provided by M . Knop ) was sporulated and crossed to a rtt101::NAT can1::STE2pr-SpHis5 strain ( Y7092 , C . Boone ) . Diploids were selected by repinning on YPD plates containing 100 μg/ml nourseothricin and 250 μg/ml of the kanamycin analogue G418 . After sporulation , haploid double mutants were selected by repinning on MATa selection plates ( SD-his/arg/lys + canavanine + thiolysine ) followed by a repinning on MATa selection plates containing 100 μg/ml nourseothricin and 250 μg/ml G418 . Colonies were then re-pinned onto SD complete , SD + 0 . 01% MMS and SD + 5 μM CPT , and repinned twice onto the same media after 24 h incubation at 30°C . Pictures of the last repinning were taken after 24 h incubation at 30°C . The occurrence of suppressors , i . e . double mutants that showed increased resistance to either MMS or CPT , were scored manually , and validated by tetrad analysis from independent starter strains followed by duplicate spotting assays onto drug containing media . Culture volumes of exponentially growing cells corresponding to 0 . 68 OD units were collected by centrifugation ( 3000 rpm for 5 min at RT ) , resuspended in 1 ml cold 70% ethanol and stored at 4°C . Cells were washed once in 1 ml H2O ( 3000 rpm for 5 min at RT ) , resuspended in 0 . 5 ml 50 mM Tris-HCl ( pH 8 . 0 ) and incubated with 10 μl RNase A ( 10 mg/ml ) for 3 h at 37°C . After centrifugation ( 3000 rpm for 5 min at RT ) cells were resuspended in 0 . 5 ml 50 mM Tris-HCl ( pH 7 . 5 ) containing 1 mg/ml Proteinase K and incubated for 45 min at 50°C . Cells were spun down ( 3000 rpm for 5 min at RT ) and resuspended in 0 . 5 ml 50 mM Tris-HCl ( pH 7 . 5 ) . 100 μl of cells were sonicated five times 15 sec at low intensity using the Bioruptor Twin XD10 . 50 μl of cells were mixed with 1 ml 1 x SYTOX Green ( Life Technologies ) in 50 mM Tris-HCl ( pH 7 . 5 ) to stain DNA . Cells were kept dark and analyzed immediately for DNA content using a BD FACSCanto II flow cytometer using the following filters and settings: FSC and SSC were detected with a 488 nm laser with detector settings of 318 V and 360 V , respectively . SYTOX Green was detected with a 502 nm longpass filter and 530/30 nm bandpass filter at 466 V . 20000 events per sample were analyzed in each run . Data collection and analysis was performed using BD FACSDiva software and FlowJo v10 . 0 . 6 ( Miltenyi Biotec ) software . 2 OD600 units of exponentially growing cells were pelleted at 13’000 rpm for 2 min , and if necessary stored at -20°C . Cell pellets were resuspended in 150 μl of Solution 1 ( 0 . 97 M 2-mercaptoethanol , 1 . 8 M NaOH ) and incubated on ice for 10 min . 150 μl of Solution 2 ( 50% TCA ) were added , cells were incubated 10 min on ice and centrifuged at 13’000 rpm for 2 min at 4°C . Pellets were resuspended in 1 ml acetone , centrifuged at 13’000 rpm for 2 min at 4°C and the pellets resuspended in 100 μl urea buffer ( 120 mM Tris-HCl pH 6 . 8 , 5% glycerol , 8 M urea , 143 mM 2-mercaptoethanol , 8% SDS , bromophenol blue indicator ) . Protein extracts were incubated 5 min at 55°C , centrifuged at 8’000 rpm for 30 sec , separated by SDS-PAGE and transferred onto nitrocellulose . Membranes were blocked with 5% milk and 1% BSA and incubated with appropriate antibodies: Rabbit peroxidase anti-peroxidase ( 1:10000 ) , mouse monoclonal antibody against c-Myc ( 1:3000 ) , HA ( 1:3000 ) , Mcm2 ( 1:2000 ) , Pgk1 ( 1:200000 ) , Rad53 ( 1:16 , EL7 . E1 , gift from M . Foiani ) , mouse polyclonal antibody against Orc6 ( 1:500 , gift from H . Ulrich ) . Replisome antibodies are from sheep polyclonal antiserum: Ctf4 ( 1:2000 ) , Cdc45 ( 1:1000 ) , Mcm6 ( 1:1000 ) , Sld5 ( 1:1000 ) , Psf2 ( 1:250 ) , Psf3 ( 1:3000 ) , Csm3 ( 1:1000 ) , Spt16 ( 1:3000 ) , Pob3 ( 1:3000 ) . Cell harvesting was performed at 3000 rpm ( Multifuge 3 5-R ) for 3 min at RT . Samples were first washed with 20 mM Tris-acetate pH 9 . 0 , then with lysis buffer ( 75 mM ( or 100 mM in Fig 2C and 2D ) Tris-acetate pH 9 . 0 , 50 mM KOAc , 10 mM MgOAc , 2 mM EDTA , 2 mM NaF , 2 mM β-glycerophosphate , 1× Roche protease inhibitor cocktail , 1× sigma inhibitors ) . The cell pellets’ mass was weighted and re-suspended in 3 volumes of lysis buffer . The cell suspension was shock-frozen in liquid nitrogen as “droplets” and stored at -80°C . All cell manipulations and collection were performed at 4°C , if not specified otherwise . Equal weight of “droplets” was grinded with a cryogenic impact mill ( Freezer-mill 6870 Large SamplePrep ) , using 5 min pre-cool followed by 6 cycles of 2 min milling at 12 CP and 2 min cooling down . Cells were thawed for 5 min at RT and 0 . 25 volume of glycerol mix ( 75 mM ( or 100 mM in Fig 2C and 2D ) Tris-acetate pH 9 . 0 , 300 mM KOAc , 50 mM MgOAc , 2 mM EDTA , 0 . 5% NP40 , 1 mM DTT , 2 mM NaF , 2 mM β-glycerophosphate , 1× Roche protease inhibitors , 1× yeast inhibitors ) was added to the lysate . DNA was digested by 800 Units/ml DNA nuclease ( Benzonase Novagen ) at 4°C for 30 min , followed by 30 min centrifugation at 15’000 rpm ( 25’000g ) at 4°C ( Sorvall RC26 Plus , SS-34 rotor ) and 60 min ultracentrifugation at 25’000 rpm ( 100’000g ) at 4°C ( Beckman Coulter Optima LE80K , SW-41 rotor ) to remove insoluble material . From the resulting extract , 50 μl was used as whole cell extract ( WCE ) and the remaining was used for affinity-precipitation . 50 μl of WCE was dissolved in 100 μl 1 . 5×SDS-loading buffer ( 1× buffer: 50 mM Tris-Cl pH 6 . 8 , 100 mM DTT , 2% SDS , 0 . 1% bromophenol blue , 10% glycerol ) and boiled at 95°C for 5 min . 4 μl of sample was used to load on a Bis-Tris acrylamide gel . Washed IgG coupled dynabeads ( M-270 Epoxy; 14302D , Life Technologies ) were added to extracts for immuno-precipitation . Samples were incubated for 2 hours on a rotating platform at 4°C . Beads were washed 4 times at RT with 1 ml wash buffer ( 100 mM Tris-acetate pH 9 . 0 , 100 mM potassium acetate , 10 mM magnesium acetate , 2 mM EDTA ) and protein was eluted with 50 μl of 1× SDS-loading buffer . At the indicated time points , cells expressing Rad52-mCherry ( 0 . 08 OD600 units ) were pelleted at 3’000 rpm for 3 min , resuspended in 300 μl SD-Trp containing 0 . 03% MMS , and then transferred into one chamber of a Nunc Lab-Tek coverglass ( Thermo Fisher Scientific ) coated with 2 mg/ml Concanavalin A ( Sigma Aldrich ) . Images were obtained on a Leica AF7000 widefield microscope using a 63x/1 . 4 oil objective . Brightfield and fluorescent images were taken along the z-axis , and Rad52-mCherry foci counted in all focal planes for at least 400–600 cells per strain ( n = 2 biological replicates ) . G1-arrested cell cultures were split and released into YPD media containing DMSO solvent or 0 . 03% MMS . S-phase cells were collected after 30 min ( untreated ) or 1 hour ( MMS-treated ) at 30°C , stopped with 0 . 1% sodium azide and harvested at RT by centrifugation for 5 min at 4000 rpm . The pellet was resuspended in 1 . 5 ml pre-spheroplasting buffer ( 100 mM PIPES , pH 9 . 4 , 10 mM DTT , 0 . 1% sodium azide ) , pelleted again after 10 min at room temperature , and resuspended in 1 ml spheroplasting buffer ( 50 mM potassium phosphate buffer , pH 7 . 5 , 0 . 6 M sorbitol , 10 mM DTT , 0 . 2 mg/ml zymolyase ( >200 units/mg ) ) . After 1 h incubation at 30°C , spheroplasts were spun down at 4°C for 1 min at 2500 rpm , washed with 1 ml wash buffer ( 100 mM KCl , 50 mM HEPES-KOH , pH 7 . 5 , 2 . 5 mM MgCl2 , 0 . 4 M sorbitol ) and resuspended in 100 μl extraction buffer ( 100 mM KCl , 50 mM HEPES-KOH , pH 7 . 5 , 2 . 5 mM MgCl2 , 1× Roche protease inhibitor cocktail ) . The suspension was split into three aliquots of 50 μl each ( whole cell extract , soluble fraction , chromatin bound fraction ) , cells lysed by adding 0 . 25% Triton X-100 and 5 min incubation on ice , and the cell extract treated with 1 μl of a 1:50 dilution of benzonase ( NEB ) . After 15 min incubation on ice , NuPAGE LDS sample buffer was added , the soluble fraction centrifuged at 4°C for 10 min at 14000 rpm , and the supernatant transferred to a new reaction tube . The chromatin bound fraction was underlayed with 30% sucrose solution and centrifuged at 4°C for 10 min at 14000 rpm . The supernatant was discarded and the pellet resuspended in 50 μl extraction buffer with 0 . 25% Triton X-100 . This was repeated once , the resuspended final pellet treated at 4°C with 1 μl of a 1:50 dilution of benzonase , and the reaction stopped after 15 min by the addition NuPAGE LDS sample buffer . All samples were incubated for 10 min at 70°C , cleared by centrifugation for 10 min at 13000 rpm , and the samples analyzed by immunoblotting using 4–15% pre-cast polyacrylamide gels ( Bio-Rad ) . | Post-translational protein modifications , such as ubiquitylation , are essential for cells to respond to environmental cues . In order to understand how eukaryotes cope with DNA damage , we have investigated a conserved E3 ubiquitin ligase complex required for the resistance to carcinogenic chemicals . This complex , composed of Rtt101 , Mms1 and Mms22 in budding yeast , plays a critical role in regulating the fate of stalled DNA replication . Here , we found that the Rtt101Mms22 E3 ubiquitin ligase complex interacts with the replisome during S-phase , and orchestrates the repair/restart of DNA synthesis after stalling by activating a Rad52-dependent homologous recombination pathway . Our findings indicate that Rtt101Mms22 specifically counteracts the replicative activity of Mrc1 , a subunit of the fork protection complex , possibly by modulating its interaction with the CMG ( Cdc45-MCM-GINS ) helicase complex upon fork stalling . Altogether , our study unravels a functional protein cluster that is essential to understand how eukaryotic cells cope with DNA damage during replication and , thus deepens our knowledge of the biology that underlies carcinogenesis . | [
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"ph... | 2016 | The Replisome-Coupled E3 Ubiquitin Ligase Rtt101Mms22 Counteracts Mrc1 Function to Tolerate Genotoxic Stress |
The HIV-1 envelope glycoprotein ( Env ) composed of the receptor binding domain gp120 and the fusion protein subunit gp41 catalyzes virus entry and is a major target for therapeutic intervention and for neutralizing antibodies . Env interactions with cellular receptors trigger refolding of gp41 , which induces close apposition of viral and cellular membranes leading to membrane fusion . The energy released during refolding is used to overcome the kinetic barrier and drives the fusion reaction . Here , we report the crystal structure at 2 Å resolution of the complete extracellular domain of gp41 lacking the fusion peptide and the cystein-linked loop . Both the fusion peptide proximal region ( FPPR ) and the membrane proximal external region ( MPER ) form helical extensions from the gp41 six-helical bundle core structure . The lack of regular coiled-coil interactions within FPPR and MPER splay this end of the structure apart while positioning the fusion peptide towards the outside of the six-helical bundle and exposing conserved hydrophobic MPER residues . Unexpectedly , the section of the MPER , which is juxtaposed to the transmembrane region ( TMR ) , bends in a 90°-angle sideward positioning three aromatic side chains per monomer for membrane insertion . We calculate that this structural motif might facilitate the generation of membrane curvature on the viral membrane . The presence of FPPR and MPER increases the melting temperature of gp41 significantly in comparison to the core structure of gp41 . Thus , our data indicate that the ordered assembly of FPPR and MPER beyond the core contributes energy to the membrane fusion reaction . Furthermore , we provide the first structural evidence that part of MPER will be membrane inserted within trimeric gp41 . We propose that this framework has important implications for membrane bending on the viral membrane , which is required for fusion and could provide a platform for epitope and lipid bilayer recognition for broadly neutralizing gp41 antibodies .
HIV-1 employs its trimeric env glycoprotein , composed of the receptor binding domain gp120 and the membrane anchored fusion protein subunit gp41 to enter host cells . Gp120 interacts sequentially with its cellular receptors CD4 and coreceptor CCR5 or CXCR4 [1] , which induce a cascade of conformational changes in gp120 and gp41 [2] , [3] . As a consequence the core of gp41 folds into a six helical bundle structure that leads to the apposition of viral and cellular membranes [4] , [5] . Gp41 catalyses membrane fusion and current models suggest that receptor binding leads to the exposure of the gp41 fusion peptide ( FP ) , which interacts with the target cell membrane producing an intermediate , pre-hairpin state bridging two membranes . This pre-hairpin has a relatively long half-life [6] and constitutes the target for inhibitory peptides [7] , [8] , [9] and neutralizing antibodies directed against HR1 [10][11] and MPER [12] , [13] . Potentially at this stage , MPER was hypothesized to be membrane embedded based on the reactivity of broadly neutralizing MPER-specific antibodies [14] , [15] , [16] , [17] , [18] . The pre-hairpin then refolds into the six-helix bundle core structure [4] , [5] and it is this transition that catalyzes membrane fusion [19] . Six-helix bundle core formation is achieved before fusion pore opening [20] . Experimental evidence [6] , [19] , [21] suggest that fusion proceeds via lipidic intermediate states , a membrane stalk , opening of the fusion pore and its expansion [22] . Mutagenesis analyses indicate that both linkers to the membrane anchors , FPPR and MPER , are implicated in fusion [23] , [24] and the TMRs play an important role in fusion pore enlargement [22] , [25] , [26] . The energy released during gp41 refolding is used to overcome the kinetic barrier [3] , [27] , which is underlined by the high thermostability of gp41 core structures [28] , [29] constituting a common feature of viral fusion proteins [30][31][32][26] . Although the free energy liberated during refolding of one trimer might be sufficient for fusion [26] consistent with experimental evidence [33] , other studies imply that cooperativity of several trimers is required [34] . In order to understand the structural basis of MPER and FPPR in the context of gp41 trimers and their potential contribution to stabilize the gp41 post fusion conformation , we have assembled gp41 containing FPPR and MPER ( gp41528–683 ) . Thermostability measurements show that inclusion of FPPR and MPER increases the melting temperature ( Tm ) substantially compared to the gp41 core , suggesting that the gain of free energy can be directly coupled to membrane fusion . The crystal structure of gp41528–683 shows helical refolding of FPPR and part of MPER as well as the potential membrane insertion of MPER adjacent to the TMR . The structure thus indicates for the first time that part of MPER can insert into the viral membrane within trimeric gp41 and supports the hypothesis that a number of neutralizing gp41 antibodies recognize MPER in a membrane environment .
We assembled the extracellular domain of gp41 from two fragments containing residues 528 ( lacking 16 N-terminal gp41 residues including FP ) to 581 ( FPPR-heptad repeat 1 , HR1 ) and residues 629 to 683 ( HR2-MPER ) ( gp41528–683 ) ( Fig . 1A and Fig . S1 ) . Both chains contain N-terminal Flag-tags to produce a soluble and monodisperse complex ( Fig . S2 ) . Circular dichroism analysis reveals a high helical content of ∼90% ( Fig . S3A ) and a melting temperature ( Tm ) of 87 . 6°C ( Fig . 1B ) . In comparison , the core fragment of gp41 composed of HR1 and HR2 [5] ( gp41541–665 ) containing N-terminal Flag-tags shows a Tm of 75 . 1°C ( Fig . 1B ) . Thus FPPR and MPER interact and impart most likely increased trimer stability . Gp41528–683 was crystallized in space group P63 . The structure was solved by molecular replacement and refined to a resolution of 2 Å ( Table 1 ) . The crystal structure composed of residues 531–581 and 629–681 plus 5 N-terminal Flag-tag residues reveals the six helical bundle core [4] , [5] with FPPR and MPER extending in a helical conformation resulting in an 88 Å-long rod-like structure ( Fig . 2A ) . A striking feature of the structure is a ∼90° turn of the MPER chain at Asn 677 which positions the remaining residues including Trp 678 , Trp 680 and Tyr 681 perpendicular to the rod ( Fig . 2B ) . Two disordered C-terminal residues must connect gp41 into the TMR in the membrane ( Fig . S4 ) . As a consequence , Trp 678 , Trp 680 and Tyr 681 are exposed towards the membrane and well positioned to insert their side chains into the bilayer ( Fig . S4 ) . In order to calculate the membrane curvature generated by a shallow embedding of these MPER residues into the outer leaflet of a bilayer , we used a model for membrane bending by hydrophobic insertions [35] . This suggests that one gp41 chain produces local curvature of ∼0 . 65 nm−1; thus a gp41 trimer might stabilize a membrane cylinder of about 15 nm diameter , which would facilitate fusion considerably [36] . Both FPPR and MPER extend HR1 and HR2 as continuous helices , but neither extension shows the regular knobs into holes packing reminiscent of classical coiled coils . Instead the FPPR region splays the inner core apart starting from Leu 545 ( a position ) ( Fig . 3A ) . The distance between Arg 579 residues at the HR1 C-terminus is 12 . 5 Å while the one at the extreme N-terminus opens up to 22 . 7 Å ( between Gly residues 531 ) . As a consequence HR1 heptad positions are too far apart for interaction ( Fig . 3A ) . The FPPR-MPER region is only stabilized by few hydrophobic contacts between adjacent chains , including interactions of Gly 531- Leu679 , Ala 533- Trp 670 , Met 535-Ile 675/Asn 671 , Thr 536/Leu 537 - Trp 666 and one hydrogen bond between the carbonyl of Ala 533 and NE1 of Trp 670 ( Fig . 3B ) . At position of MPER residue Asn 676 , the N-terminus of FPPR-HR1 points towards the outside of the rod ( Fig . 2A ) facilitating fusion peptide ( residues 512–530 ) membrane interaction or further refolding of FP with MPER and possibly TMR . Another striking feature of the structure is the solvent exposure of a stretch of hydrophobic MPER residues ( Trp 666 , Leu 669 , Trp670 , Trp 672 , Phe 673 ) that generate a hydrophobic surface patch ( Fig . S5 ) . Since the crystals were grown at a high MPD concentration , we tested the effect of MPD on the structure in solution . MPD does not change the overall helical content of gp41528–683 , which is ∼90% in the absence and presence of high MPD concentrations ( Fig . S3A ) . However , MPD reduced the Tm of gp41528–683 to 82 . 2°C ( 5% MPD ) and 74 . 7°C ( 10% MPD ) as well as that of the gp41541–665 core ( Fig . S3B ) . Therefore , we cannot exclude the possibility that MPD might have destabilized the rod resulting in the ‘open’ structure ( Fig . 3A ) and FPPR and MPER might pack tighter in the absence of MPD . The NMR structures of MPER peptides show kinked or straight helical conformations [17] , [37] , which superimpose partly with MPER present in the crystal structure ( Fig . S6A and B ) . Three broadly neutralizing antibodies ( nAb ) target MPER and utilize diverse structural motifs for recognition . NAb 2F5 recognizes a beta-hairpin [15] and Z13e1 binds to a short kinked helix [38] . Both epitopes refold into a straight helix in the gp41 structure ( Fig . 4A and B ) . The epitope of nAb 4E10 is helical [16]; although it is present and exposed in the gp41 crystal structure ( Fig . 4C ) nAb 4E10 does not interact with gp41528–683 ( data not shown ) , due to clashes with the helical conformation of HR2 . However , if we consider only MPER and its membrane orientation and dock the 4E10 structure onto its epitope , nAb 4E10 could present its heavy chain CDR3 loop implicated in bilayer interaction [14] , [18] towards the membrane , lined up with the gp41 membrane embedded residues W678 , W680 and Y681 ( Fig . S7 ) . The comparison of the peptide epitope structures and gp41 corroborate that nAbs 2F5 and Z13e1 block the refolding process of gp41 at early steps . In contrast the 4E10 epitope might be present throughout gp41 refolding from a native conformation as evident by its presence in the late fusion intermediate conformation .
Although the core structure of the HIV-1 fusion protein has been solved [4] , [5] , detailed structural information on the regions linking up to the membrane anchors ( FPPR and MPER ) has been lacking . We crystallized gp41 ( 528–683 ) , which has a similar N- terminal end as a proteolytic fragment of HIV-2 gp41 [39] and N- and C-terminal ends as determined by peptide studies [40] and solved its structure . FPPR and most of MPER extend in a helical fashion from the gp41 core and interact with each other as indicated by peptide studies performed at pH 3 . 2 [40] . Although the interactions are mostly hydrophobic , they are not classical coiled-coil interactions . The TMR-juxtaposed region of MPER positions three aromatic side chains per monomer towards the membrane . We calculate that membrane insertion of these residues could induce membrane curvature in the outer leaflet of the viral lipid bilayer [35] , which would facilitate fusion based on previous studies [36] . Membrane fusion models postulate that fusion proteins induce local bending of both bilayers into “nipples” projecting toward each other to reduce the energy requirement for initial stalk formation [22] , [41] , [42] . Bending on the target-cell side can be stabilized by insertion of the fusion peptide [43] or hydrophobic residues of fusion loops [26] . The present structure suggests that bending on the viral side may be stabilized by membrane-embedded MPER residues . We suggest that MPER membrane insertion may occur early during the conformational transition of gp41 and persist through the process of refolding . Alternatively this segment of MPER may adopt a straight helical conformation [37] in continuity with TMR in the final postfusion conformation . Such a continuous helical structure was observed for the linker sequences that connect the core SNARE complex to its TMRs [44] . The presence of FPPR and MPER splay the “membrane-anchor” end of the rod apart , which may be required to accommodate FP whose chain direction points to the exterior of the structure . The missing part of FP ( residues 512–530 ) could thus contact the membrane and/or interact with the kinked membrane embedded MPER or with a straight helical MPER conformation . Since the thermostability measurements indicate that the MPD crystallization conditions could influence the stability of gp41 in solution , it is possible that FPPR and MPER pack tighter in the absence of MPD . We thus propose that the structure represents a late fusion intermediate state rather than the final postfusion conformation , although the latter possibility cannot be excluded . MPER contains a number of hydrophobic residues , which are conserved in the majority of HIV and SIV isolates , namely Trp666 , Trp672 , Phe673 and Ile675 . Single Ala mutations of these residues do not affect cell-cell fusion but reduce viral infectivity significantly [24] . Interestingly all residues are mostly exposed in the crystal structure and/or contribute to hydrophobic interactions with FPPR . Mutation of FPPR Leu537 , which makes a hydrophobic contact with Trp666 , in combination with mutations of conserved MPER residues Trp666 or Trp672 or Phe673 or Ile675 , reduces virus infectivity further thus confirming the important interplay between FPPR and MPER during fusion [24] . Analysis showed that the defect of mutant Leu537-Trp666 is at the level of lipid mixing [24] . Another study demonstrated that mutations of the five conserved tryptophan residues ( Trp666 , Trp670 , Trp 672 , Trp678 , Trp680 ) alone or in combination or deletion mutants within MPER affect syncytium formation thus supporting the importance of MPER for fusion [45] . Reduction in viral infectivity was also reported for pseudoviruses containing alanine mutations of hydrophobic MPER residues ( Leu 669 , Ile675 , Leu679 ) exposed within MPER in addition to the conserved tryptophan residues [46] . The hydrophobic surface generated by the conserved MPER residues as shown here might induce clustering of several gp41 trimers at the site of fusion although the number of env trimers required for fusion is still debated [33] , [34] . Such a function may be consistent with mutagenesis data showing that single tryptophan exchanges within MPER affect cell-cell fusion , while combinations of tryptophan mutations abrogate cell-cell fusion completely [45] . Thus mutagenesis of multiple tryptophans may reduce the hydrophobicity of the exposed patch sufficiently to affect the clustering function . Six-helix bundle formation leads to fusion pore opening [20] and an intact six-helix bundle is required for its enlargement [47] . Since FPPR and MPER folding most likely follows six-helix bundle formation its hydrophobic patch may further support pore enlargement together with the essential role of TMRs [22] , [25] , [26] . This suggestion is in agreement with data on mutagenesis of all 5 tryptophan residues within MPER; these mutations do not affect fusion pore opening , but inhibit fusion pore expansion [23] . Finally the linker region that connects the SNARE complex with its TMR exposes a similar patch of hydrophobic residues [44] underlining functional similarities between viral fusion protein and SNARE-mediated membrane fusion processes . Fusion proteins utilize the free energy released during their refolding to draw two membranes into close apposition and catalyze membrane fusion [26] . The thermostability measurement of the gp41 core compared to the crystal structure reveals a 12°C increase of the melting temperature , which can translate into an increase in ΔG that can be directly coupled to membrane fusion . Notably , folding of the complete SNARE complex versus the core produces a similar increase in Tm that can convert into energy for fusion [44] . MPER harbors the epitopes of three broadly neutralizing antibodies , 2F5 , Z13e1 and 4E10 . The epitopes of 2F5 and Z13e1 [15] , [38] adopt a straight helical conformation , indicating that both antibodies neutralize by blocking the transition into the trimeric gp41 structure . In contrast the epitope of 4E10 [16] is still present and exposed , although nAb 4E10 does not interact with gp41528–683 due to clashes with the helical conformation of HR2 . NAb 4E10 has a long CDR3 region that does not contact the epitope , but was proposed to interact with the membrane [16] based on its reactivity with lipids [14] . If we consider only the 4E10 epitope and the membrane embedded part of MPER , 4E10 could orient its CDR3 towards the membrane and insert its aromatic residues into the bilayer as required for neutralization [18] . Thus stabilization of a peptide in the conformation of the MPER as present in the crystal structure should prove useful to generate an immunogen capable of inducing 4E10-like antibody responses . Based on the crystal structure we suggest the following extension to our picture of the fusion process . Receptor binding induced conformational changes exposes FP , which interacts and bends the target cell membrane . Concomitantly , TMR and MPER dissociate , potentially from a native MPER coiled-coil structure [48] and a few aromatic MPER residues insert into and bend the outer leaflet of the viral membrane . This then generates the functional epitope for nAb 4E10 . Part of MPER stays membrane associated throughout the folding of the gp41 core that leads to fusion pore opening . Subsequently FPPR and the soluble part of MPER interact , releasing more energy for fusion . Alternatively , we cannot exclude the possibilities that ( i ) membrane insertion of MPER is already present in the native env trimer or ( ii ) that membrane insertion of MPER is not important for the generation of membrane curvature and exerts another role during the fusion process . Finally , although the conformational state of gp41 observed in the crystal structure is no longer targeted by neutralizing antibodies , the development of small molecules targeting the FPPR-MPER conformation could block further gp41 refolding required for membrane fusion .
The gp41 proteins were assembled from different fragments of gp41 ( Fig . S1 ) : FPPR-HR1-HR2-MPER ( Ser528 to Leu581 and Met628 to Lys683; gp41528–683 ) , HR1-HR2 ( Ala541 to Leu581 and Met628 to Lys665; gp41541–665 ) . DNA sequencing and MALDI TOF Mass Spectrometry confirmed all constructs . Fragments of HIV-1 gp41 HXB2 group M subtype B were amplified by standard PCR techniques and cloned either into pETM-MBP-1a ( EMBL , Heidelberg ) , pETM-20 ( thioredoxin fusion , EMBL , Heidelberg ) or pET11 ( His-tag ) . HR1 and HR2 containing constructs were N-terminally fused to the Flag-tag sequence ( ASP-ASP-ASP-ASP-Lys ) to improve solubility ( Fig . S1 ) . Gp41528–683 and gp41541–665 fusion proteins were expressed in E . coli strain Rosetta 3 ( DE3 ) ( Strategene ) . Cells were grown to an OD600 nm of 0 . 7 and induced with 1 mM IPTG at 37°C . After 2 hours cells were harvested by centrifugation , resuspended in buffer A ( 0 . 02 M Tris pH 8 . 0 , 0 . 1 M NaCl ) and pellets of HR1 and HR2 expressing bacteria were mixed before lysis . Notably , bacteria expressing HR2 were used in excess over HR1 expressing bacteria . The soluble fraction was loaded onto an amylose column ( NEB ) and eluted in buffer A with 0 . 01 M maltose . In order to remove fusion proteins , constructs were digested o . n . at 4 C° with TEV ( Tobbacco Etch Protease ) and the uncleaved material was removed by Ni2+ chromatography . Further purification was achieved by anionic exchange chromatography in buffer A . A final purification step included size exclusion chromatography on a superdex 200 column in buffer A . Crystals of gp41528–683 were obtained by the vapor diffusion method in hanging drops mixing equal volumes of purified complex and reservoir solution ( 0 . 1 M citric acid pH 6 , 60% MPD ( v/v ) ) . Crystals were improved by macroseeding; briefly crystals grown in the initial conditions ( 0 . 1 M citric acid pH 6 , 60% MPD ( v/v ) ) were transferred into a new drop equilibrated with 0 . 1 M citric acid pH 6 , 56% MPD ( v/v ) , 1 . 5% glycerol ( v/v ) . Before data collection , crystals were flash frozen at 100 K using the same reservoir solution supplemented with 10% of glycerol ( v/v ) . A dataset was collected at the ESRF beam line ID14-EH4 at 100 K . The images were indexed with MOSFLM [49] and scaled with SCALA [50] , [51] . The crystals were twinned and analysis with phenix . xtriage [52] revealed space group P63 with twin fractions of 0 . 45 ( Britton ) and 0 . 47 ( H test and Maximum likelihood test ) and an associated twin law of h , -h-k , -l . The cell parameters are a = b = 57 . 42 Å , c = 182 . 76 Å , α = β = 90° , γ = 120° . The structure was solved by molecular replacement using the program Phaser [53] and the model of the gp41 core ( PDB ID: 1AIK ) by applying the twin law of h , -h-k , -l on the data , revealing 3 molecules in the asymmetric unit . The model was built manually with COOT [54] and refined with the program Phenix [52] . The final structure has an Rfactor of 0 . 177 and Rfree of 0 . 217 and good stereochemistry ( Table 1 ) . The most complete monomer contains gp41 residues 531–581 and gp41 residues 629–681 plus 5 N-terminal residues ( 624-DDDDK-628 derived from the Flag/enterokinase cleavage site sequence ) ; this monomer was used to reconstruct the trimer by applying crystallographic symmetry . The second monomer contains residues 538–581 and 629–672 plus 5 N-terminal residues ( residues 624-DDDDK-628 ) ; the third monomer contains residues 542–580 and 629–665 plus 5 N-terminal residues ( residues 623-MDDDDK-628 ) . All molecular graphics figures were generated with Pymol ( http://www . pymol . org ) . Coordinates and structure factors have been deposited in the protein data bank with accession number 2×7r . CD measurements were performed using a JASCO Spectropolarimeter equipped with a thermoelectric temperature controller . Spectra of each protein were recorded at 20°C in 1 nm steps from 190 to 260 nm in buffer A or buffer A supplemented with MPD as indicated . Spectra were recorded at 222 nm using a bandwidth of 4 nm and averaging time of 4 sec per step . For thermal denaturation experiments , the ellipticity was recorded at 222 nm with 1°C steps from 20° to 100°C with an increment of 80°C h−1 and an averaging time of 30 s/step . Since the unfolding of gp41528–683 was not reversible , two more spectra were recorded with increments of 40°C h−1 and 120°C h−1 , which resulted in comparable Tms , indicating that the system was in equilibrium . For data analysis , spectra were corrected for the baseline ( recorded with buffer ) and the raw ellipticity values were converted to mean residue ellipticity . Thermal melting ( Tm ) points were calculated with a Boltzmann sigmoid fit using the program OriginLab . The effective shape of a membrane embedding domain consisting of the gp41 hydrophobic residues was approximated by a short cylindrical rod of 16 Å in length and 7 Å in diameter , shallowly inserted up to a 5 Å depth into the outer membrane monolayer ( the insertion volume constituting 468 . 9 Å3 ) . According to the previously developed model of membrane bending by hydrophobic insertions [35] the effective spontaneous curvature of such an insertion equals . The overall membrane curvature generated by the insertions is proportional to their area fraction in the membrane plane whose maximal value is limited by a dense packing of the proteins on the membrane surface . For a gp41 trimer the area of each of the three inserted side chains is 16 Å×7 Å = 112 Å2 , while the total area of the trimer projection on the membrane plane is determined by the dimensions of the ectodomains and constitutes , approximately , 800 Å2 . Taking into account these numbers , we obtain that a maximal area fraction of the gp41 hydrophobic insertions is which results in a total radius of curvature . | HIV-1 employs its envelope glycoprotein complex ( Env ) composed of gp120 and gp41 to catalyze cell entry . Both Env subunits undergo conformational changes triggered by the gp120-mediated interactions with cellular receptors . Notably , gp41 refolds into a core six-helical bundle structure which is central to the fusion process . Here we report the structural basis for the folding of the linker regions connecting to the membrane anchors of gp41 , namely to the transmembrane region ( MPER ) and to the fusion peptide ( FPPR ) . Our structural analysis shows helical assemblies of FPPR and MPER which increase the melting temperature of gp41 and position the fusion peptide towards the outside of the six-helix bundle structure at this stage of gp41 refolding . It suggests that part of MPER must be inserted into the viral membrane , which would induce membrane curvature as postulated to be required for the fusion reaction . Thus our findings shed new light on the refolding of gp41 , which contributes energy to the fusion reaction and reveals for the first time the structural principles of MPER membrane interaction within trimeric gp41 . We propose that the structure presents a late fusion intermediate state that provides a new framework for fusion inhibitor development and MPER immunogen design . | [
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The brain is highly energy consuming , therefore is under strong selective pressure to achieve cost-efficiency in both cortical connectivities and activities . However , cost-efficiency as a design principle for cortical activities has been rarely studied . Especially it is not clear how cost-efficiency is related to ubiquitously observed multi-scale properties: irregular firing , oscillations and neuronal avalanches . Here we demonstrate that these prominent properties can be simultaneously observed in a generic , biologically plausible neural circuit model that captures excitation-inhibition balance and realistic dynamics of synaptic conductance . Their co-emergence achieves minimal energy cost as well as maximal energy efficiency on information capacity , when neuronal firing are coordinated and shaped by moderate synchrony to reduce otherwise redundant spikes , and the dynamical clusterings are maintained in the form of neuronal avalanches . Such cost-efficient neural dynamics can be employed as a foundation for further efficient information processing under energy constraint .
Complex spatiotemporal patterns are ubiquitously observed in spontaneous cortical activities in vitro and in vivo , with prominent features at multiple scales: irregular individual firing [1–3] , synchronized oscillations [4–6] and neuronal avalanches [7–10] . Specially , neuronal avalanches form spatiotemporal clusters of synchronized activities interrupted by periods of silence , yet individual neurons discharge spikes in a rather random way , which is close to a Poisson process [10] . The sizes of spiking clusters in neuronal avalanches follow a power-law distribution , suggesting that such activities are generated by a scale-invariant dynamics , as the system poised at a critical state [11 , 12] . Therefore , self-organized criticality has been considered as an overriding organizing mechanism for the cortical activities at different scales [13–15] . These multi-scale cortical activities are believed to have different implications in information processing . Firstly , irregular firing can be robustly generated in large-size networks , in which excitatory and inhibitory currents to each neuron are dynamically balanced , as a result to increase the accuracy and speed of information relay in terms of firing rate [2] . Secondly , synchronous oscillations are thought to be crucial for neural integration , cognition , and behavior [4–6] . Abnormally strong synchrony can indicate dysfunction of the underlying cortical network , e . g . , excessive synchrony during epileptic seizures [16] and Parkinson’s disease [17] , while abnormally weak synchrony can be associated with disorders such as schizophrenia [18] and autism [19] . Finally , neuronal avalanches have been demonstrated to optimize the response range of stimulus intensities [20 , 21] , the amount of information that can be stored and transferred [7 , 22] , the variability of spontaneous synchrony [23] to allow flexible switching between states , and the information representation in an adaptive sensory neuronal network [15 , 24] . Consequently , complex spatiotemporal patterns are significant on numerous aspects of neural information processing . However , cortical activities should be constrained by its restricted energy budget . Actually , the human brain consumes 20% of the body’s energy despite constituting only 2% of the body’s mass . Thus , optimal brain functioning requires careful balancing of the brain’s energy budget . Nonetheless , the brain is remarkably energy-efficient when compared to the computer CPU [25] , since neurons fire sparsely and the majority of them are at quiescent state for any given time [26] . Cost-efficiency is therefore supposed to be an important organizing principle for cortical connectivities and activities , and should be reflected in the above-mentioned features of cortical activities . This concept has been extraordinarily successful in explaining brain structure , including the scaling between white and gray matters across species [27] , the spatial placement of the neural components [28 , 29] with wiring length minimization and the features of brain connectome by a trade-off with functional values [30 , 31] . It has also been employed to well explain optimal behavioral patterns [32] . Therefore , it is highly desirable to investigate whether the principle of cost-efficiency is reflected in the ubiquitously observed features in cortical activities . To assess the impact of these dynamical features on energy consumption and information processing , we employ a generic but biologically plausible neural circuit to compare various dynamical modes with different synchrony degrees . Since cortical energy usage is dominated by the generation and propagation of action potentials and synaptic transmission , the energy cost is generally proportional to the mean firing rate and can be roughly estimated by the spike rate . On the other hand , information processing and transmission is limited by the repertoire of different activated configurations available to the population , whose extent can be quantified by the entropy H , also known as information capacity [33 , 34] . H is important because it defines an upper limit on various aspects of information processing , e . g . , a population with low entropy will present a bottleneck for information transmission in the cortex . Therefore , following Ref . [35] , the energy efficiency here is introduced as information capacity per energy unit . In this way , our results show that irregular firing , synchronized oscillations and neural avalanches can be observed simultaneously in the regime of moderate synchrony , while their co-emergence indeed robustly achieves maximal energy efficiency and minimal spike rate in comparison to the other synchrony regimes . The superior efficiency at moderate synchrony is attributed to the dynamical mechanism for coordinating and shaping individual firing to reduce otherwise redundant spikes . Thus , co-emergence of the experimentally observed multi-scale cortical activities achieves cost-efficiency in terms of information capacity .
Here we consider a generic model of neuronal networks with basic biological characteristics: excitation-inhibition ( E-I ) balance , conductance-based synaptic currents and realistic synaptic dynamics . The model was proposed in [36] to study the emergence of gamma oscillations from sparse firing of neurons . We simulate large random networks of E-I spiking neurons with E-I ratio γ = 4: 1 and interconnection probability C = 0 . 2 , sketched in Fig 1A . Besides , each neuron receives some independent external excitatory projections , which represent input from other neural circuits or external stimuli . Neuronal spiking dynamics is described by the integrate-and-fire ( IF ) model with refractory period and leaky current ( an example in Fig 1B ) , while conductance-based synaptic currents are used to model the synaptic transmission from presynaptic neurons to postsynaptic neurons ( details in Methods ) . When a presynaptic spike arrives , the unitary conductance change is modelled as a bi-exponential function with conduction delay time τl , rise time τr and decay time τd ( see Fig 1C ) . Moreover , the synaptic strengths are chosen to realize an E-I balanced state , in which neurons fire irregularly [2 , 37] ( details in Methods ) . The synaptic decay times were found important to determine the frequencies of the oscillations [36] . In this study , we explore the parameter space of E-I synaptic decay times ( τd_e , τd_i ) to investigate various dynamical modes and to study whether cost-efficiency on the aspect of information capacity can be achieved . The above model has been previously shown to generate sparsely synchronized oscillations , which consist of irregular and sparse individual spikes but synchronized oscillating population activities [36] . Two different underlying dynamical mechanisms have been discovered: E-I loop for gamma oscillations ( 30 ∼ 80 Hz ) or inhibition-inhibition ( I-I ) loop for sharp-wave ripples ( ∼200 Hz ) , which happens at two different parameter regions of ( τd_e , τd_i ) , where excitatory currents or inhibitory currents dominate the fast dynamics , respectively [36] . Here we focus on the former one with a continuous transition from asynchronous states to synchronized states induced by the E-I loop for gamma oscillations . In Fig 2 , we show three examples with different synchrony degrees ( synchrony defined in Methods ) : For the first case , the individual neuron fires spikes irregularly , due to the incoming E-I balanced currents with their mean cancelled and large fluctuations left ( Fig 2A ) , while the population activity is asynchronous ( Fig 2B ) [37] . Secondly , individual spiking is driven by commonly modulated E-I conductance ( Fig 2C ) , due to the moderately synchronized population activity ( Fig 2D ) . The resulting currents to each neuron are tightly coupled with a little time lag of inhibition behind excitation , closely resembling the observation in in vivo intracellular recordings [38] . Finally , the fast dynamics is dominated by the excitatory currents ( Fig 2E ) and the population activity is highly synchronized by strong E-I loop ( Fig 2F ) . And each neuron is driven by the feedback currents with large E-I time lag , which allow neurons to fire once or even more spikes in each lag window ( Fig 2E ) . Therefore , with different parameters ( τd_e , τd_i ) , different cortical activities at both neuron and population levels can be simultaneously generated in this model . In summary , by decreasing τd_e and increasing τd_i , stronger and stronger synchrony can be induced in the population activities as shown in Fig 3A , while individual spikes are still irregular as shown in Fig 3B . That is because , faster excitation and slower inhibition lead to the formation of a stronger E-I delayed-feedback loop [6 , 36 , 39] . As a result , increasing synchrony will induce the emergence of collective oscillations , where the maximal power shifts to nonzero frequencies in the power spectra of the population activities as shown in Fig 3C and 3D , and the maximal power also increases with the synchrony degree as shown in Fig 3E . On the other hand , increasing either synchrony or τd_i slows down the population rhythm ( Fig 3D ) . Actually , after one bump of excitatory and inhibitory activities , another round cannot be initiated until the residual inhibitory conductance decays to low enough values to be conquered by the external excitatory inputs ( Fig 2C and 2E ) . Therefore , large τd_i ( ∼10 ms ) will limit the rhythm of collective oscillations into gamma band ( 30 ∼ 80 Hz ) ( Fig 3D ) , which is thought to be important for sensory processing , motor activity , and cognitive functions [5] . Moreover , synchronized oscillations with different synchrony degrees temporally split population activities into random clusters , presenting subcritical , critical , or supercritical avalanche dynamics . Here the avalanches are characterized following the spike-based avalanche analysis in vivo of pyramidal neurons [10] ( details in Methods ) . The subcritical dynamics has an exponentially decaying avalanche size distribution; the supercritical one has much more chance of large-size avalanches , while the critical dynamics shows a power-law avalanche size distribution . The avalanche size distributions for the above three examples are plotted in Fig 3F . We can find that the moderately synchronized case is critical , while the asynchronous one is subcritical and the highly synchronized one is supercritical , which is also supported by the distributions of spike number and duration in each avalanche , and waiting time between two consecutive avalanches ( see S1 Fig ) . The criticality at the moderately synchronized states can also be indicated by the exponentially-modulated and small-amplitude sinusoidal autocorrelation of the excitatory population activity , as shown in Fig 2D , bottom inset . To more quantitatively characterize the criticality , we introduce here a distance D of the avalanche size distribution from the best-fitted power-law function , with respect to the average avalanche size ( details in Methods ) . From Fig 3G , we find that neuronal avalanche , as the critical dynamics , coincides with the moderately synchronized states , while the subcritical dynamics occurs in the asynchronous region and the supercritical one in the highly synchronized region . Besides , the moderately synchronized states also correspond to the oscillation onset ( also indicated in Fig 2D , bottom inset ) , where the oscillation power goes through a clear transition from low to high as shown in Fig 3H . Therefore , neuronal avalanches and gamma oscillations emerge jointly , which is consistent with in vivo observations [8 , 40] . Specifically , neuronal avalanches are achieved by aggregating different groups of neurons into clusters at different time instants and sparsely synchronized oscillations emerge when the clusters are organized with typical time-scales . Furthermore , moderate synchrony will feedback to shape individual spikes to be irregularly tonic with coefficients of variation ( CV ) close to 1 as shown in Fig 3H for the excitatory population , where CV is defined as the standard deviation over the mean of inter-spike intervals ( ISIs ) ( details in Methods ) . Distributions of CV separately for excitatory and inhibitory neurons in the various dynamical states with different synchrony degree are also given in S2 Fig for various parameter sets ( τd_e , τd_i ) . The distribution profiles in the critical states with moderate synchrony are consistent with those of experimental data in various cortex areas , as shown in [41 , 42] . As shown in Fig 3B , CV is larger than 1 in the whole asynchronous region , indicating burst in the spikes of individual neurons , that is , several spikes in a short interval followed by a long period of silence . And CV is larger at larger τd_e and τd_i . The generation of burst is due to the effects of both conductance-based currents and slow synaptic conductance . Firstly , large bumps of conductance inputs to each neuron drastically reduce neuronal effective membrane time constant , so that neurons response promptly to positive currents and fire spikes more frequently [43 , 44] . Secondly , the slow synaptic dynamics will induce long time-scale autocorrelation of net currents [45 , 46] , whose fluctuations drive the postsynaptic neurons to generate grouped spikes with short ISIs in between ( Fig 2A and S3 Fig ) . As a result , larger synaptic decay time introduces longer excursion of current fluctuations and induces more bursts in individual activities as shown in Fig 3B [46] . On the other hand , in the highly synchronized region , the currents can also drive neurons to show burst activities ( S3 Fig ) , because of the large E-I time lag in currents . However , in the moderately synchronized region , bursts are reduced by the instantaneously correlated and moderately modulated E-I currents ( Figs 2C , 3H and S3 Fig ) . That is because the current fluctuations are smoothed out by the modulation and neuronal integration is limited in the little E-I time lag of rising phase ( Fig 2C ) . To examine whether cost-efficiency can be achieved on the aspect of information capacity , here we first introduce the definition of the population spike pattern and its corresponding energy cost and efficiency , in analogy to the work by Levy and Baxter [35] . The population spike pattern is defined within a time window Δτ in two scenarios: Binary scenario , each neuron has just two states , spiking or non-spiking; Analog scenario , neuron’s state is represented by its spike count . Assume that a resting neuron consumes r unit of energy within Δτ , due to its leaky current , and a spike costs one extra unit of energy . In this way , 1/r measures the relative energy constraint level on the spike pattern ( details in Methods ) . If we consider a population with n neurons , which fire m spikes on average in each time window Δτ , the generated pattern can be described by its activity level ρ = m/n , energy cost E = nr + m and energy efficiency η = H/E , where the entropy H measures the abundance of different activated configurations available to the population , representing its information capacity ( detailed formulation in Methods ) . Here we just consider excitatory neurons in the pattern , so the activity level can also be given as ρ = vE Δτ , where vE is the mean firing rate of excitatory neurons . Actually , with the given activity level ρ , the theoretical upper-bound efficiency has been derived by Levy and Baxter [35] , based on the maximal entropy principle [47] ( a unified derivation also presented in Methods: Energy efficiency optimization ) . That is , for each given ρ , the optimal efficiency ηopt is written as η opt ( ρ ) = f ( ρ ) ( ρ + r ) , in binary scenario , ( 1 ) η opt ( ρ ) = f ( ρ / ( 1 + ρ ) ) ( ρ + r ) / ( 1 + ρ ) , in analog scenario , ( 2 ) where f ( ρ ) = −ρlog2 ρ − ( 1 − ρ ) log2 ( 1 − ρ ) represents the Shannon’s entropy of a binary event with probability ρ ( more details discussed in Methods: Energy efficiency optimization ) . Note that the optimal efficiency is independent of the number of neurons n but dependent on the parameter r . As shown in Fig 4 , increasing r from 0 to ∞ will shift the value ρm for the maximal ηopt from ρm = 0 to ρm = 0 . 5 in binary scenario or from ρm = 0 to ρm = 1 in analog scenario . Therefore , a pattern with a lower firing rate vE does not always imply a higher energy efficiency ηopt . Generally , the spatiotemporal spike patterns should be discretized by both spatial and temporal resolutions . The former can be naturally set by one neuron , while the latter needs a typical time scale . From the viewpoint of population coding , a pattern is reasonable to include the co-activated neurons within this typical time window , which therefore should be determined by the time scale of cross correlations between neurons [48] . In our simulation , spike series of different neurons are coincident within 20 ms for most parameter pairs ( τd_e , τd_i ) as shown in Fig 5A , thus the spike patterns can be splited into bins with Δτ = 20 as shown in Fig 5B . Such a time scale is also biologically plausible in neural circuit , e . g . , reading out the patterns by downstream neurons through the synaptic current time of a few milliseconds and the membrane time of 10 ∼ 20 ms , and learning by spike-timing dependent plasticity ( STDP ) [49] with precision of spike timing < 20 ∼ 30 ms . Actually , the time window can not be larger , otherwise the spike pattern tends to involve more than one spikes for each neuron , which is not energy efficient as discussed in Methods: Energy efficiency optimization . We have also checked smaller bin sizes for the spike patterns , and found that decreasing Δτ will weaken the advantage of the critical regime in terms of energy efficiency , as shown in S5 Fig . Therefore , we select Δτ = 20 ms as the proper time window to split the spike trains into patterns . From our simulations , as shown in Fig 6A and 6B , one can find that the firing rate vE is minimal and energy efficiency ηsim is maximal in the parameter region for critical dynamics , where irregular firing , synchronized oscillations and neuronal avalanches emerge altogether . Actually , the inhibitory firing rate vI is also minimal in this region , as shown in S4 Fig . Therefore , the spike patterns of cortical activities with moderate synchrony , where the prominent multi-scale dynamical features emerge together , can achieve cost-efficiency on the aspect of information capacity . What is more , such cost-efficiency is robust in both binary and analog scenarios as shown in Fig 6C and 6D ( more data in S6 Fig , upper panel ) , as long as the parameter r is in the empirical range ( 0 . 005 ∼ 0 . 1 ) [50–52] . These results are significant because theoretically the optimal energy efficiency ηopt is not always achieved in the pattern with the lowest firing rate as indicated in Eqs ( 1 and 2 ) and Fig 4 . The minimal firing rate vE in the moderately synchronized states , as shown in Fig 6C and 6D , can be ascribed to the reduction of burst activities through the specific feedback currents . In the asynchronous states , one can find that bursts in the spike trains with intermittent periods of long silence , which is indicated in S3 Fig , make vE slightly larger than that in the critical region . While strong synchrony also drives neurons to show burst activities and much enhances the firing rate vE ( S3 Fig ) , moderate synchrony is just enough to reduce the bursts , which will be generated by fluctuating and balanced currents in the asynchronous states , but avoids to induce burst , when each neuron receives the currents with just a little E-I time lag in the currents ( Fig 2C; S3 Fig ) . Therefore , the moderate synchrony can both coordinate and shape individual spikes to reduce the bursts and render the firing rate vE to be minimal in this critical region , as shown in Fig 6C and 6D . Besides , in the critical region , slower population rhythm at larger τd_i further lowers vE , as indicated in Fig 6A . Such reduction of burst activities also makes the critical dynamics with moderate synchrony to achieve maximal energy efficiency ηsim robustly , as shown in Fig 6C and 6D . As analyzed in Methods: Energy efficiency optimization , the upper bound ηopt can only be achieved when neurons are active independently with an identical probability , so the energy efficiency ηsim in our simulations is reduced from the corresponding upper bound ηopt by two main sources of correlations—the temporal correlation due to burst and synchronization among neurons . Actually , as shown in S7 Fig from the simulation , increasing CV decreases the energy efficiency in the asynchronous states ( synchrony degree < 0 . 1 ) for both binary and analog scenarios and various r . Specifically , in the binary scenario with r = 0 , the effect of burst on reducing energy efficiency can be isolated by eliminating the redundant spikes , because just one spike fired by each neuron contributes to the simulated entropy Hsim in each time window Δτ . If these redundant spikes were not taken into consideration in the energy cost , then the energy efficiency can boost from ηsim = Hsim/m to Bηsim = Hsim/mn , with B = m/mn denoting the burst level and mn representing the average number of spiking neurons in each time window Δτ . Thus the reduction of energy efficiency due to the burst can be given as R B = H sim / m n - H sim / m = ( B - 1 ) η sim . ( 3 ) Except for the burst , the other temporal correlation in individual spiking series seems to be ignorable , which can be inferred from the dependence of the probability p0 of empty patterns on the number of spiking neurons mn at various sample size n , as shown in Fig 7 . The dependence is fitted well with the ideal case of sparse patterns ( mn ≪ n ) p 0 = ( 1 - m n / n ) n ≈ e - m n , ( 4 ) where neurons fire spikes in a random way . Thus , in the asynchronous states , neurons seem to be active in a random way except for the burst activities . Therefore , the remaining gap between Bηsim and ηopt can be approximately ascribed to the synchronization , given as R S = η opt ( m n / n ) - B η sim , ( 5 ) yielding the total reduction of energy efficiency as η opt ( m / n ) - η sim ( m / n ) ≈ η opt ( m n / n ) - H sim / m = R B + R S . ( 6 ) As shown in Fig 3H , red , burst activities can be shaped by moderate synchrony in our simulations , and thus there is a trade-off of their contributions to the reduction of energy efficiency , as shown in Fig 8A for the case r = 0 . Interestingly , the total reduction ηopt − ηsim ( or RB + RS ) is just right minimized in the critical region , as shown in Fig 8B for both binary and analog cases . Actually , in the later case , burst activities also limit the available configurations , whose effect is similar to that by synchronization , although different spike counts within Δτ represent different patterns and spikes are not redundant any more . Such mechanism is robust to minimize the total reduction R and then to maximize the simulated energy efficiency ηsim for any r chosen from the empirical range ( 0 . 005 ∼ 0 . 1 ) , even though larger r shifts the maximum of ηopt to larger ρ , as indicated by the solid lines in Fig 8C , or to the corresponding subcritical and supercritical regions in the parameter space ( τd_e , τd_i ) , as shown in S6 Fig bottom panel . Thus , the energy efficiency reduction ηopt − ηsim keeps minimal in the critical region in both binary and analog scenarios , as shown in Fig 8D , and the simulated energy efficiency ηsim perserved maximal in the critical region pretty well for r ranging from 0 . 005 to 0 . 1 , as shown in S6 Fig top panel . Therefore , the critical dynamics can robustly achieve a maximal energy efficiency ηsim . Furthermore , the spike patterns generated by the critical dynamics are sparse . Specifically , the minimal firing rate vE reaches around 3 Hz ( Fig 6A ) , against 30 ∼ 80 Hz of the primary population rhythm ( Fig 3E ) , indicating that a single pyramidal cell fires only once in every 10 ∼ 20 population cycles , which is consistent with the experimental observation [53] . This implies that the activity level in each configuration is low ( ρ = ΔτvE ∼ 0 . 06 ) , suggesting that such spatiotemporal spike patterns can be reconciled with the ‘sparse coding’ scheme [26 , 54 , 55] , where a small proportion of neurons fire at any one time and a few spikes can be distributed among a large number of neurons in many different ways . Interestingly , despite being very sparse , the critical dynamics still frequently generates readable configurations with large number of neurons simultaneously activated . Actually , as shown in Fig 9 , the frequency of large number of activating neurons , which is comparable to the experimental observation [48] , is 2-order larger than that of the asynchronous case . Therefore , such critical dynamics not only achieves cost-efficiency on the aspect of information capacity , but also is feasible for information processing .
Neuronal avalanche has been widely studied in models , such as the random branching model [7] , the excitatory neuronal network model with short-term synaptic plasticity [13] and the E-I balanced Ising model [56] . However , most previous work treats neuronal avalache as the state with a critical activity level , therefore the critical states generated in these models are always asynchronous without displaying oscillations . On the other hand , synchronized oscillations are mainly investigated by the interplay between excitatory and inhibitory population , emphasizing the role of inhibitory neurons [38 , 57] . Such model can reconcile irregular individual firing and synchronized population oscillations . To this end , we suggest here the critical synchronization to account for the co-emergence of these salient dynamical features . That is , E-I balance and suitable E-I synaptic dynamics induce E-I population oscillations , which moderately modulate the feedback currents with a little E-I time lag , and drive neurons to fire irregularly and continuously in the form of avalanches . Unfortunately , we should point out that the analytical understanding of neuronal response to such correlated and modulated E-I inputs is highly challenging , partly because of the complicate interaction between multiple time scales of synaptic filters and the high-conductance membrane [44] . Therefore , how all of these features can be simultaneously reconciled at this critical state is still unsolved analytically . Nonetheless , the co-occurrence of moderate synchrony and critical states is not only to occur in the model here , but also can be found in a broad class of network models , e . g . , one recent example in Ref . [58] and a current-based neuronal network model with simulation results shown in S8 Fig . Here the critical states with neuronal avalanches are considered as the onset of population oscillations , and Fig 3 ( A ) , 3 ( E ) and 3 ( H ) show that the moderate synchronous state occurs at the oscillation onset . This scenario has been employed to analyze the population frequency close to the critical points via linear stability analysis , when the model here was first introduced by Brunel and Wang [36] . What is more , the corresponding normal form at such critical points was also derived in by Brunel and Hakim [57 , 59] for different models , with the generic underlying dynamical mechanisms of I-I loop or E-I loop , and different synaptic or voltage integrative ( current-based or conductance-based model ) mechanisms . Note however , that the critical states in the current-based model can occur in the states with a relatively wide range of synchrony , not only the moderately synchronized states but also some state with rather weak synchronization ( Synchrony measured in 1-ms window can be low to the value ∼0 . 01 , see S8 Fig . ( F ) ) . The underlying mechanism can be attributed to the effective time scale τeff of membrane potential integration . In the current-based model , τeff is equal to the membrane time constant τE ( discussion focusing on excitatory neurons , but it is the same for inhibitory neurons ) , while in the conductance-based model , τ e f f = τ E / ( 1 + G i E E + G i E I ) , which is dynamical with dependence on the total incoming conductance , and can be reduced to 1 ∼ 2 ms in the so-called high-conductance states [43 , 44 , 60] . Therefore , in the conductance-based model , the population activity should be coordinated into a group with strictly moderate 1-ms pairwise synchrony to support the critical dynamics with neuronal avalanches . However , in the current-based model , due to much larger and constant τeff , the population oscillation may induce the cross-correlation between neuronal spiking in larger time lags , which is suitable to organize the population activity in order to support critical dynamics with neuronal avalanches , even though synchronization in the 1-ms window can be rather weak . Therefore , such critical dynamics can occur in the states with a wide range of synchrony . As discussed above , it is still hard to analyze neuronal response to such correlated , balanced and modulated E-I inputs . We hope our work will stimulate more theoretical analysis on the intricate relationships among these properties . Actually , I-I loop can also generate sparsely synchronized oscillations , and we have also simulated the transition due to the I-I loop . It is found from the simulation that the firing rate change in this transition is abrupt , which is in some sense like the subcritical Hopf bifurcation in terms of the macroscopic state . This is totally different from the one due to the E-I loop , which can be described as a supercritical Hopf bifurcation , where the oscillation amplitude increases gradually from 0 and we can find a large parameter regime for the critical states due to the effects of finite system size and external noise . Therefore , there is little critical regime for the transition due to the I-I loop . It is not clear now why they are so different , because the reduced equation derived by Brunel and Hakim [57] has shown that the dynamics of the population averaged firing rate goes through a supercritical Hopf bifurcation in a simplified and purely inhibitory neuronal network . Thus , it is not clear which property of our model makes the transition due to the I-I loop as a subcritical Hopf bifurcation , which is often accompanied by a hysteresis . It is also unclear how to investigate such kind of hysteresis in the neuronal network dynamics and what is its functional role in the cortex . This topic will be our further work in future . On the other hand , previous studies in both experiments and theoretical models have shown that mutual information or entropy measures has a maximum at criticality or avalanche dynamics [7 , 22 , 61] . However , all of those studies consider the scenario of the critical point in a transition from a quiescent state to a fully activated state in a driven system [7 , 22 , 61] . As discussed above , we are here considering the transition from an asynchronous state to a highly synchronized state . Thus , our results do not contradict with previous facts . Furthermore , our results are not only to extend the existing understanding , but to start from the basic idea of the first fundamental principle ? cost-efficiency , and demonstrates that there exists one biological plausible neuronal network model which can accomplish this principle under the constraint of commonly observed multi-scale dynamical features of cortical activities . Therefore , the novelty of our results is completely not mitigated by the existing facts . Different from the usual view that entropy measures show a maximum at critical points or avalanche dynamics , here we also study the nontrivial change of the firing rate and study the energy efficiency as the ratio of the entropy over the energy ( linearly dependent on the firing rate ) . Different from the common view that the firing rate increases monotonically during the transition , here the firing rate and firing patterns have a nontrivial trade-off since moderate synchrony can coordinate and shape irregular individual activities to simultaneously minimize firing rate ( by reducing the redundant spikes ) and reduce the entropy under the corresponding rate ( due to synchronization ) , and it is the trade-off in the critical regime that robustly maximize the energy efficiency . Such a trade-off shown in Fig 8 , to our best knowledge , has not been discovered yet . Besides , the previous experimental observation is obtained from the electrodes’ signals , like LFPs [7 , 22] . So our results are expected to be further tested in the experiments with neuronal resolutions . The E-I balanced network has been shown as an efficient candidate for rate coding and information transmission [2 , 62] . Such rate sensitivity also provides a dynamical basis for orientation selectivity without the need of neural maps [63] . Moreover , the sensitivity of neuronal response to weakly correlated inputs can surprisingly induce highly nontrivial patterns [64 , 65] . On the other hand , activity in gamma frequencies is thought to play a major role in the propagation of information across cortical areas [66–69] . Synchronous spiking during gamma activity is supposed to allow these neurons to efficiently cooperate in the recruitment for their postsynaptic targets , thereby facilitating the transmission of information , and also regulate the efficiency , thereby contributing to the merger , or ? binding ? , of information originating from distinct regions . And such information transmission during gamma oscillations depends on the precise timing of the oscillation . However , even within a specific cortical location , the instantaneous frequency of gamma oscillations changes from one moment to the next , and this ongoing modulation in oscillation frequency ( or phase ) affects the precise timing of neuronal spiking with that cortical location , thereby altering the efficacy with which information is transmitted to downstream regions . In consistent with previous in vivo observations [70] , cycle-to-cycle fluctuations in the oscillation amplitude reflect underlying fluctuations of both excitatory and inhibitory synaptic currents , yet excitation and inhibition remain balanced during each oscillation cycle . What is more , such fluctuation can be maximized at the critical states , as reflected in the dynamical properties of neuronal avalanches . Thus , the instantaneous E-I balance in the critical dynamics may translate ongoing fluctuation of oscillation amplitudes into the variability of inter-event interval or oscillation phase [70] . Therefore , co-emergence of these salient cortical activities may provide a dynamical substrate for signal transmission with high flexibility and capacity , while neuronal spikes are sparse and irregular . Finally , the temporal correlation of spikes is crucial for spike-timing dependent plasticity ( STDP ) , which is a solid biophysical substrate for learning [71] . STDP can also feedback to drive the network into the critical state with moderate synchrony , which is at the border between synchronization and desynchronization [72] . Thus , a recurrent network endowed with STDP could self-organize into the critical dynamics , and then provide the dynamical foundation for efficient learning . On the other hand , the oscillation frequency could also make impact in the learning process . Of special interest are the beta/gamma-band ( 13 ∼ 30/30 ∼ 80 Hz ) oscillations , where two avalanches are separated by a few tens of milliseconds ( 15 ∼ 80 ms ) . As a result , synapses within the same cluster will be altered significantly by STDP , while the synapses crossing two different clusters are slightly modified . Thus a network endowed with STDP could evolve into modules with stronger connections within a cluster and relatively weaker connections between clusters , providing a potential substrate for memorizing each signal in each cluster . In our further work , preliminary numerical simulations indicate that such cost-efficient critical states are robust in 2-dimensional lattices , whose connection probability decay exponentially with distance . If the neural circuits are geometrically constrained and the wiring is required to be economical , a good candidate for the realistic network structure is the hierarchical module , featured by dense , short-range connections and sparse , long-range connections [30 , 31] . Our previous work has shown that such connection topology can increase the range of parameters for critical dynamics and therefore supports its robustness , because the module renders the activities hard to spread beyond the local modules to the whole network [73 , 74] . In this way , the geometrical constraint is likely to further shape the spike patterns . Therefore , it is significant in the future to study the cost-efficiency on both cortical connectivities and activities .
The model studied here was introduced in [36] , whose biological basis and related discussions can be dated back to the work by Amit and Brunel [75 , 76] . While the model did not consider all the anatomical and neurobiological details , it captures essential features in neuronal spiking , synaptic dynamics and network coupling , as detailed in the following realistic properties: In particular , we model large recurrent networks with excitatory ( Exc ) and inhibitory ( Inh ) neurons ( N = 2500 , NE: NI = 4: 1 ) , randomly connected with a given connection probability C = 0 . 2 . Each neuron receives on average KE excitatory and KI inhibitory synaptic inputs from other neurons within the network , and also KO excitatory synaptic inputs from outside , mimicking connections within the same cortical area and inputs from other areas in the cortex ( KO = KE = 400 , KI = 100 ) , respectively . The external synaptic inputs are modelled as uncorrelated Poisson-type spike trains , with input rate fex = 2 . 5 Hz for each connection . Both excitatory and inhibitory neurons are simplified as leaky integrate-and-fire neurons . The dynamics of sub-threshold membrane potential VE ( VI ) for excitatory ( inhibitory ) neurons are described as τ k d V i k d t = V L − V i k + G i k E ( t ) ( E E − V i k ) + G i k I ( t ) ( E I − V i k ) , ( 7 ) G i k E ( t ) = τ k ( ∑ j ∈ ∂ O i ∑ n g k O + ∑ j ∈ ∂ E i ∑ n g k E ) s E ( t − t j n ) , ( 8 ) G i k I ( t ) = τ k ∑ j ∈ ∂ I i ∑ n g k I s I ( t − t j n ) , ( 9 ) where i = 1 , … , NE , and k = E , I . Here gEO , gIO , gEE , gEI , gIE , gII denote the synaptic strengths of conductance for external input to Exc , external input to Inh , Exc to Exc , Inh to Exc , Exc to Inh and Inh to Inh . Their values are set to satisfy the balanced condition [2 , 77] , e . g . , gEO = 0 . 05 , gIO = 0 . 08 , gEE = 0 . 04 , gIE = 0 . 08 , gEI = 0 . 6 , gII = 0 . 96 , in units of the resting membrane conductance gL = 10 nS . EE ( EI ) is the reversal potential for excitatory ( inhibitory ) synaptic currents , with EE = 0 mV , EI = −70 mV . One corresponding current-based neuronal network model is also employed to investigate the multi-scale activities , whose results are summarized in S8 Fig . The model is similar to the conductance-based model , only with the last two V i k for both excitatory and inhibitory synaptic currents in Eq ( 7 ) replaced by the averaged potentials 〈V〉 , which is set to be 〈V〉 = −60 mV for all cases . Though the modification of the model appears small , but the dynamical features of the current-based and conductance-based models can be quite different , because the latter model has an intrinsic dynamics of the so-called effective time scale for membrane potential integration , which depends on the total incoming conductance [43 , 44 , 60] . The membrane time constants are set as τE = 20 ms , τI = 10 ms , and the leaky potential is VL = −70 mV . When the membrane potential reaches the spike threshold θ = −50 mV , a spike is emitted , the membrane potential is reset to −60 mV , and synaptic integration is halted for 2 ms ( 1 ms ) for excitatory ( inhibitory ) neurons , mimicking the refractory period in real neurons . ∂O i , ∂E i , ∂I i denote the set of incoming external , excitatory , inhibitory neighbors , respectively . sE ( t − tjn ) , sI ( t − tjn ) are the time courses of synaptic conductance induced by the nth presynaptic spike coming at tjn from jth excitatory or inhibitory incoming connection , respectively . They are described as a delayed difference of exponentials with three parameters: latency τl , rise time τr , and decay time τd . They are given as s k ( t ) = Θ ( t - τ l ) τ d - τ r exp - t - τ l τ d - exp - t - τ l τ r , ( 10 ) where k = E , I and Θ ( t ) is the Heaviside function , with Θ ( t ) = 0 for t ≤ 0 and Θ ( t ) = 1 for t > 0 . For both excitatory and inhibitory synapses , τl = 1 ms and τr = 0 . 5 ms . The decay times τd_e , τd_i for excitatory and inhibitory synapses are employed as parameters around typical values ( 2 ∼ 5 ms for τd_e [78 , 79] , 5 ∼ 15 ms for τd_i [80 , 81] ) for investigating the network dynamical modes . Simulations are done using a finite difference integration scheme based on the second-order Runge-Kutta algorithm with time step dt = 0 . 05 ms [82 , 83] . Each network is simulated for 2000 s with the initial 1 s discarded . Networks are simulated on a cluster of 16 nodes ( 8 processors each node ) running Linux , using custom written codes in C++ . The instantaneous population activity A ( t ) is determined by the number of spikes in the full network per 1-ms bin . The autocorrelation of the population activity in the insets in Fig 2B , 2D and 2F is defined as [62] A C k ( τ ) = 1 ⟨ A k ( t ) ⟩ 2 T ∑ t = 1 T A k ( t + τ ) - ⟨ A k ( t ) ⟩ A k ( t ) - ⟨ A k ( t ) ⟩ , ( 11 ) where k = E , I and 〈Ak ( t ) 〉 is the mean activity of kth population . For each neuron , inter-spike interval ( ISI ) is measured by the time distance of two consecutive spikes , each of which has a precise spiking time . The irregularity of individual spikes is characterized by the coefficients of variation ( CV ) of the ISI distribution , which is the ratio of the standard deviation ( SD ) to the mean of the ISI distribution . CV values close to 0 indicate regular spikes , values near 1 indicate irregular spikes , and values much larger than 1 indicate bursts . For burst activities , the neuron is likely to fire several spikes in a short interval followed by a longer period of silence . The averaged CV over the excitatory population is used to characterize the irregularity of individual activities throughout the population . The spatiotemporal clustering of individual spikes is characterized by the pair-wise spiking synchronization . We adopt the average instantaneous cross-correlation of neuronal spiking time to quantify the degree of synchrony . The pair coherence between neuron i and j is defined as K i j = ∑ k = 1 l B i ( k ) B j ( k ) ∑ k = 1 l B i ( k ) ∑ k = 1 l B j ( k ) , ( 12 ) where Bi ( k ) ( Bj ( k ) ) is the spike train of neuron i ( j ) . Bi ( k ) = 0 or 1 ( k = 1 , … , l ) , represents no spike or one spike generated in the kth 1-ms bin . Kij measures the probability of neuron i and j spiking together within 1-ms bins , and the average over all pairs Kij is taken as the synchrony index . The series of population firing rate with the mean detrended are Fourier transformed to calculate the power spectrum . To estimate the peak frequency , a Gaussian kernel is used to smooth the power spectrum and then to catch the peak frequency and peak power . Following recent observation of spike-based neuronal avalanches in vivo [10] , in which just spikes of pyramidal neurons are taken into consider , we here also define neuronal avalanches using spikes in excitatory population . The window size δt is employed to bin the spike train of the whole excitatory population . An avalanche is defined as a sequence of consecutive non-empty bins , flanked by empty bins . δt ranges from the simulation step size dt to 20 dt ( from 0 . 05 ms to 1 ms ) , and the results are almost the same . Here the avalanche size s is measured as the number of neurons firing in an active period . Due to individual burst activity in some cases , a neuron may fire several spikes in this period . We have also defined the avalanche size as the total number of spikes in this sequence and found there is no qualitative difference in our results . The duration of the avalanches and the waiting time between two consecutive avalanches are also examined . To characterize neuronal avalanches , the distribution P ( s ) of avalanche sizes is first visually inspected and then quantified by the distance from the best-fitted power-law distribution Pfit ( s ) , which is defined as the ratio of the average size difference per avalanche to the average size of the best-fitted power-law distribution , as follows: D = ∑ s = 1 N s | P ( s ) - P fit ( s ) | ∑ s = 1 N | s P fit ( s ) | . ( 13 ) Spike trains of excitatory neurons are binned by windows of Δτ = 20 ms into sequences of spike count ( s = 0 , 1 , … , 10 ) in analog scenario or binary sequences of spiking ( 1 ) and non-spiking ( 0 ) in binary scenario . In binary scenario , in case where there is more than one spike in a bin , we denote it as ‘1’ . Information theoretic quantities such as the entropy depend on the full distribution of states for the population . Estimating these quantities could be difficult , because finite data sets lead to systematic errors . In this work , we perform long time simulations ( 2000 s ) and sample n excitatory neurons’ spike trains to investigate the spike patterns . Here the sampled size is set as n = 40 , and the number of available configurations is very large . We try our best to reduce the statistical variability by taking 100 random samples and averaging the obtained entropy values of each subset of chosen neurons . We denote p0 as the probability of the empty configuration with no spike , and correspondingly pi as the probability of ith unique nontrivial configuration with mi spikes distributed in n sampled neurons during the 2000 s simulation time . Fig 5B presents one schematic example of the binary spike patterns of 10 sampled neurons . Then the information capacity can be defined as the entropy of all these configurations H = - ∑ i p i log 2 p i . ( 14 ) In each time window Δτ , each neuron , spiking or not , costs r energy unit due to the leaky currents and one spike costs one extra unit of energy . Then , the average energy expansion per configuration is given as E = ∑ i m i p i + n r = m + n r , ( 15 ) where m = ∑i mi pi is the average spike count over all configurations . Here , the energy efficiency is defined as the ratio of information capacity to energy cost , as follows η = H / E = H / ( m + n r ) , ( 16 ) with the unit bits/energy . In this way , the spike pattern is constrained by the activity level ρ = m/n , and 1/r measures the relative energy constraint on the spike pattern . If r → ∞ , the spikes expend no extra energy and the energy has no constraint on the spike pattern . If r = 0 , the energy cost of resting neurons can be ignored , then the energy efficiency is simplified as η = H/m , which characterizes how much information one spike can express . Decreasing r increases the energy constraint on the energy efficiency of the spike patterns . Empirically , r cannot be ignored , which ranges from 0 . 005 to 0 . 1 [50–52] . The optimization of energy efficiency provides its theoretical upper bound with given ρ , which can be expressed as η opt ( ρ ) = max { p i } η = - ∑ p i log 2 p i m + n r ( 17 ) with given spike expenditures m = ∑i mi pi and population size n . By introducing Lagrangian multiplier λ and μ to assume p i = e - λ - μ m i , ( 18 ) such optimization subject to the constraint of spike expenditures m = ρn can be solved in both binary and analog scenarios by the principle of maximum entropy [47] . Our results are identical with the previous work in Ref . [35] , where the information capacity is estimated by assuming independent and random neuronal activities , and the binary and analog patterns are dealt from different perspectives: fraction of active neurons in binary scenario and firing frequencies in analog scenario . Actually , both scenarios can be unified in the unique framework of the distribution of spike patterns . Here , we derive strictly the optimal energy efficiency with given activity level ρ = m/n in both scenarios , and summarize the results into the formula , which can be used to discuss the significant effect of the relative resting energy r on the constraint of activity level or neuronal firing rate . | The adult human brain consumes more than 20% of the resting metabolism , despite constituting only 2% of the body’s mass . Most energy is consumed by the cerebral cortex with billions of neurons , mainly to restore ion gradients across membranes for generating and propagating action potentials and synaptic transmission . Even small increases in the average spike rate of cortical neurons could cause the cortex to exceed the energy budget for the whole brain . Consequently , the cortex is likely to be under considerable selective pressure to reduce spike rates but to maintain efficient information processing . Experimentally , cortical activities are ubiquitously observed at multiple scales with prominent features: irregular individual firing , synchronized oscillations and neuronal avalanches . Do these features of cortical activities reflect cost-efficiency on the aspect of information capacity ? We employ a generic but biologically plausible local neural circuit to compare various dynamical modes with different degrees of synchrony . Our simulations show that these features of cortical activities can be observed simultaneously and their co-emergence indeed robustly achieves maximal energy efficiency and minimal energy cost . Our work thus suggests that basic neurobiological and dynamical mechanisms can support the foundation for efficient neural information processing under the energy constraint . | [
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"techno... | 2017 | Co-emergence of multi-scale cortical activities of irregular firing, oscillations and avalanches achieves cost-efficient information capacity |
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